VictoriaMetrics/lib/mergeset/table.go

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2019-05-22 21:16:55 +00:00
package mergeset
import (
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
2023-03-19 08:36:05 +00:00
"encoding/json"
"errors"
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"fmt"
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
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"math"
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"os"
"path/filepath"
"sort"
"strings"
"sync"
"sync/atomic"
"time"
"unsafe"
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"github.com/VictoriaMetrics/VictoriaMetrics/lib/cgroup"
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"github.com/VictoriaMetrics/VictoriaMetrics/lib/fs"
"github.com/VictoriaMetrics/VictoriaMetrics/lib/logger"
"github.com/VictoriaMetrics/VictoriaMetrics/lib/memory"
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"github.com/VictoriaMetrics/VictoriaMetrics/lib/syncwg"
"github.com/VictoriaMetrics/VictoriaMetrics/lib/timeutil"
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)
// maxInmemoryParts is the maximum number of inmemory parts in the table.
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//
// This limit allows reducing CPU usage under high ingestion rate.
// See https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212
//
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// This number may be reached when the insertion pace outreaches merger pace.
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
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// If this number is reached, then the data ingestion is paused until background
// mergers reduce the number of parts below this number.
const maxInmemoryParts = 30
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// Default number of parts to merge at once.
//
// This number has been obtained empirically - it gives the lowest possible overhead.
// See appendPartsToMerge tests for details.
const defaultPartsToMerge = 15
// maxPartSize is the maximum part size in bytes.
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//
// This number should be limited by the amount of time required to merge parts of this summary size.
// The required time shouldn't exceed a day.
const maxPartSize = 400e9
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// The interval for flushing buffered data to parts, so it becomes visible to search.
const pendingItemsFlushInterval = time.Second
// The interval for guaranteed flush of recently ingested data from memory to on-disk parts so they survive process crash.
var dataFlushInterval = 5 * time.Second
// SetDataFlushInterval sets the interval for guaranteed flush of recently ingested data from memory to disk.
//
// The data can be flushed from memory to disk more frequently if it doesn't fit the memory limit.
//
// This function must be called before initializing the indexdb.
func SetDataFlushInterval(d time.Duration) {
if d < pendingItemsFlushInterval {
// There is no sense in setting dataFlushInterval to values smaller than pendingItemsFlushInterval,
// since pending rows unconditionally remain in memory for up to pendingItemsFlushInterval.
d = pendingItemsFlushInterval
}
dataFlushInterval = d
}
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// maxItemsPerCachedPart is the maximum items per created part by the merge,
// which must be cached in the OS page cache.
//
// Such parts are usually frequently accessed, so it is good to cache their
// contents in OS page cache.
func maxItemsPerCachedPart() uint64 {
mem := memory.Remaining()
// Production data shows that each item occupies ~4 bytes in the compressed part.
// It is expected no more than defaultPartsToMerge/2 parts exist
// in the OS page cache before they are merged into bigger part.
// Halft of the remaining RAM must be left for lib/storage parts,
// so the maxItems is calculated using the below code:
maxItems := uint64(mem) / (4 * defaultPartsToMerge)
if maxItems < 1e6 {
maxItems = 1e6
}
return maxItems
}
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// Table represents mergeset table.
type Table struct {
activeInmemoryMerges atomic.Int64
activeFileMerges atomic.Int64
inmemoryMergesCount atomic.Uint64
fileMergesCount atomic.Uint64
inmemoryItemsMerged atomic.Uint64
fileItemsMerged atomic.Uint64
itemsAdded atomic.Uint64
itemsAddedSizeBytes atomic.Uint64
inmemoryPartsLimitReachedCount atomic.Uint64
mergeIdx atomic.Uint64
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path string
flushCallback func()
needFlushCallbackCall atomic.Bool
prepareBlock PrepareBlockCallback
isReadOnly *atomic.Bool
// rawItems contains recently added items that haven't been converted to parts yet.
//
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// rawItems are converted to inmemoryParts at least every pendingItemsFlushInterval or when rawItems becomes full.
//
// rawItems aren't visible for search due to performance reasons.
rawItems rawItemsShards
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// partsLock protects inmemoryParts and fileParts.
partsLock sync.Mutex
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// inmemoryParts contains inmemory parts, which are visible for search.
inmemoryParts []*partWrapper
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// fileParts contains file-backed parts, which are visible for search.
fileParts []*partWrapper
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// inmemoryPartsLimitCh limits the number of inmemory parts to maxInmemoryParts
// in order to prevent from data ingestion slowdown as described at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212
inmemoryPartsLimitCh chan struct{}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// stopCh is used for notifying all the background workers to stop.
//
// It must be closed under partsLock in order to prevent from calling wg.Add()
// after stopCh is closed.
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stopCh chan struct{}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// wg is used for waiting for all the background workers to stop.
//
// wg.Add() must be called under partsLock after checking whether stopCh isn't closed.
// This should prevent from calling wg.Add() after stopCh is closed and wg.Wait() is called.
wg sync.WaitGroup
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// Use syncwg instead of sync, since Add/Wait may be called from concurrent goroutines.
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
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flushPendingItemsWG syncwg.WaitGroup
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}
type rawItemsShards struct {
flushDeadlineMs atomic.Int64
shardIdx atomic.Uint32
// shards reduce lock contention when adding rows on multi-CPU systems.
shards []rawItemsShard
ibsToFlushLock sync.Mutex
ibsToFlush []*inmemoryBlock
}
// The number of shards for rawItems per table.
//
// Higher number of shards reduces CPU contention and increases the max bandwidth on multi-core systems.
var rawItemsShardsPerTable = func() int {
cpus := cgroup.AvailableCPUs()
multiplier := cpus
if multiplier > 16 {
multiplier = 16
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
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return cpus * multiplier
}()
var maxBlocksPerShard = 256
func (riss *rawItemsShards) init() {
riss.shards = make([]rawItemsShard, rawItemsShardsPerTable)
}
func (riss *rawItemsShards) addItems(tb *Table, items [][]byte) {
shards := riss.shards
shardsLen := uint32(len(shards))
for len(items) > 0 {
n := riss.shardIdx.Add(1)
idx := n % shardsLen
tailItems, ibsToFlush := shards[idx].addItems(items)
riss.addIbsToFlush(tb, ibsToFlush)
items = tailItems
}
}
func (riss *rawItemsShards) addIbsToFlush(tb *Table, ibsToFlush []*inmemoryBlock) {
if len(ibsToFlush) == 0 {
return
}
var ibsToMerge []*inmemoryBlock
riss.ibsToFlushLock.Lock()
if len(riss.ibsToFlush) == 0 {
riss.updateFlushDeadline()
}
riss.ibsToFlush = append(riss.ibsToFlush, ibsToFlush...)
if len(riss.ibsToFlush) >= maxBlocksPerShard*cgroup.AvailableCPUs() {
ibsToMerge = riss.ibsToFlush
riss.ibsToFlush = nil
}
riss.ibsToFlushLock.Unlock()
tb.flushBlocksToInmemoryParts(ibsToMerge, false)
}
func (riss *rawItemsShards) Len() int {
n := 0
for i := range riss.shards {
n += riss.shards[i].Len()
}
return n
}
func (riss *rawItemsShards) updateFlushDeadline() {
riss.flushDeadlineMs.Store(time.Now().Add(pendingItemsFlushInterval).UnixMilli())
}
type rawItemsShardNopad struct {
flushDeadlineMs atomic.Int64
mu sync.Mutex
ibs []*inmemoryBlock
}
type rawItemsShard struct {
rawItemsShardNopad
// The padding prevents false sharing on widespread platforms with
// 128 mod (cache line size) = 0 .
_ [128 - unsafe.Sizeof(rawItemsShardNopad{})%128]byte
}
func (ris *rawItemsShard) Len() int {
ris.mu.Lock()
n := 0
for _, ib := range ris.ibs {
n += len(ib.items)
}
ris.mu.Unlock()
return n
}
func (ris *rawItemsShard) addItems(items [][]byte) ([][]byte, []*inmemoryBlock) {
var ibsToFlush []*inmemoryBlock
var tailItems [][]byte
ris.mu.Lock()
ibs := ris.ibs
if len(ibs) == 0 {
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
ibs = append(ibs, &inmemoryBlock{})
ris.updateFlushDeadline()
ris.ibs = ibs
}
ib := ibs[len(ibs)-1]
for i, item := range items {
if ib.Add(item) {
continue
}
if len(ibs) >= maxBlocksPerShard {
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
ibsToFlush = append(ibsToFlush, ibs...)
ibs = make([]*inmemoryBlock, 0, maxBlocksPerShard)
tailItems = items[i:]
break
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
ib = &inmemoryBlock{}
if ib.Add(item) {
ibs = append(ibs, ib)
continue
}
// Skip too long item
itemPrefix := item
if len(itemPrefix) > 128 {
itemPrefix = itemPrefix[:128]
}
tooLongItemsTotal.Add(1)
tooLongItemLogger.Errorf("skipping adding too long item to indexdb: len(item)=%d; it shouldn't exceed %d bytes; item prefix=%q", len(item), maxInmemoryBlockSize, itemPrefix)
}
ris.ibs = ibs
ris.mu.Unlock()
return tailItems, ibsToFlush
}
func (ris *rawItemsShard) updateFlushDeadline() {
ris.flushDeadlineMs.Store(time.Now().Add(pendingItemsFlushInterval).UnixMilli())
}
var tooLongItemLogger = logger.WithThrottler("tooLongItem", 5*time.Second)
var tooLongItemsTotal atomic.Uint64
2019-05-22 21:16:55 +00:00
type partWrapper struct {
// refCount is the number of references to partWrapper
refCount atomic.Int32
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// mustDrop marks partWrapper for deletion.
// This field should be updated only after partWrapper
// was removed from the list of active parts.
mustDrop atomic.Bool
p *part
mp *inmemoryPart
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isInMerge bool
// The deadline when the in-memory part must be flushed to disk.
flushToDiskDeadline time.Time
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}
func (pw *partWrapper) incRef() {
pw.refCount.Add(1)
2019-05-22 21:16:55 +00:00
}
func (pw *partWrapper) decRef() {
n := pw.refCount.Add(-1)
if n < 0 {
logger.Panicf("BUG: pw.refCount must be bigger than 0; got %d", n)
2019-05-22 21:16:55 +00:00
}
if n > 0 {
return
}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
2023-03-19 08:36:05 +00:00
deletePath := ""
if pw.mp == nil && pw.mustDrop.Load() {
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
2023-03-19 08:36:05 +00:00
deletePath = pw.p.path
}
2019-05-22 21:16:55 +00:00
if pw.mp != nil {
// Do not return pw.mp to pool via putInmemoryPart(),
// since pw.mp size may be too big compared to other entries stored in the pool.
// This may result in increased memory usage because of high fragmentation.
2019-05-22 21:16:55 +00:00
pw.mp = nil
}
pw.p.MustClose()
pw.p = nil
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
2023-03-19 08:36:05 +00:00
if deletePath != "" {
fs.MustRemoveAll(deletePath)
}
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}
// MustOpenTable opens a table on the given path.
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//
// Optional flushCallback is called every time new data batch is flushed
// to the underlying storage and becomes visible to search.
//
// Optional prepareBlock is called during merge before flushing the prepared block
// to persistent storage.
//
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// The table is created if it doesn't exist yet.
func MustOpenTable(path string, flushCallback func(), prepareBlock PrepareBlockCallback, isReadOnly *atomic.Bool) *Table {
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path = filepath.Clean(path)
// Create a directory for the table if it doesn't exist yet.
fs.MustMkdirIfNotExist(path)
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// Open table parts.
pws := mustOpenParts(path)
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tb := &Table{
path: path,
flushCallback: flushCallback,
prepareBlock: prepareBlock,
isReadOnly: isReadOnly,
fileParts: pws,
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
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inmemoryPartsLimitCh: make(chan struct{}, maxInmemoryParts),
stopCh: make(chan struct{}),
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}
tb.mergeIdx.Store(uint64(time.Now().UnixNano()))
tb.rawItems.init()
tb.startBackgroundWorkers()
2019-05-22 21:16:55 +00:00
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
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return tb
}
func (tb *Table) startBackgroundWorkers() {
// Start file parts mergers, so they could start merging unmerged parts if needed.
// There is no need in starting in-memory parts mergers, since there are no in-memory parts yet.
tb.startFilePartsMergers()
tb.startPendingItemsFlusher()
tb.startInmemoryPartsFlusher()
tb.startFlushCallbackWorker()
}
func (tb *Table) startInmemoryPartsMergers() {
tb.partsLock.Lock()
for i := 0; i < cap(inmemoryPartsConcurrencyCh); i++ {
tb.startInmemoryPartsMergerLocked()
}
tb.partsLock.Unlock()
}
func (tb *Table) startInmemoryPartsMergerLocked() {
select {
case <-tb.stopCh:
return
default:
}
tb.wg.Add(1)
go func() {
tb.inmemoryPartsMerger()
tb.wg.Done()
}()
}
func (tb *Table) startFilePartsMergers() {
tb.partsLock.Lock()
for i := 0; i < cap(filePartsConcurrencyCh); i++ {
tb.startFilePartsMergerLocked()
}
tb.partsLock.Unlock()
}
func (tb *Table) startFilePartsMergerLocked() {
select {
case <-tb.stopCh:
return
default:
}
tb.wg.Add(1)
go func() {
tb.filePartsMerger()
tb.wg.Done()
}()
}
func (tb *Table) startPendingItemsFlusher() {
tb.wg.Add(1)
go func() {
tb.pendingItemsFlusher()
tb.wg.Done()
}()
}
func (tb *Table) startInmemoryPartsFlusher() {
tb.wg.Add(1)
go func() {
tb.inmemoryPartsFlusher()
tb.wg.Done()
}()
}
func (tb *Table) startFlushCallbackWorker() {
if tb.flushCallback == nil {
return
}
tb.wg.Add(1)
go func() {
// call flushCallback once per 10 seconds in order to improve the effectiveness of caches,
// which are reset by the flushCallback.
d := timeutil.AddJitterToDuration(time.Second * 10)
tc := time.NewTicker(d)
for {
select {
case <-tb.stopCh:
tb.flushCallback()
tb.wg.Done()
return
case <-tc.C:
if tb.needFlushCallbackCall.CompareAndSwap(true, false) {
tb.flushCallback()
}
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
}
}()
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
var (
inmemoryPartsConcurrencyCh = make(chan struct{}, getInmemoryPartsConcurrency())
filePartsConcurrencyCh = make(chan struct{}, getFilePartsConcurrency())
)
func getInmemoryPartsConcurrency() int {
// The concurrency for processing in-memory parts must equal to the number of CPU cores,
// since these operations are CPU-bound.
return cgroup.AvailableCPUs()
2019-05-22 21:16:55 +00:00
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
func getFilePartsConcurrency() int {
n := cgroup.AvailableCPUs()
if n < 4 {
// Allow at least 4 concurrent workers for file parts on systems
// with less than 4 CPU cores in order to be able to make small file merges
// when big file merges are in progress.
return 4
}
return n
}
2019-05-22 21:16:55 +00:00
// MustClose closes the table.
func (tb *Table) MustClose() {
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// Notify background workers to stop.
// The tb.partsLock is aquired in order to guarantee that tb.wg.Add() isn't called
// after tb.stopCh is closed and tb.wg.Wait() is called below.
tb.partsLock.Lock()
2019-05-22 21:16:55 +00:00
close(tb.stopCh)
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
tb.partsLock.Unlock()
2019-05-22 21:16:55 +00:00
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// Wait for background workers to stop.
tb.wg.Wait()
2019-05-22 21:16:55 +00:00
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
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// Flush the remaining in-memory items to files.
tb.flushInmemoryItemsToFiles()
// Remove references to parts from the tb, so they may be eventually closed after all the searches are done.
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tb.partsLock.Lock()
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
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if n := tb.rawItems.Len(); n > 0 {
logger.Panicf("BUG: raw items must be empty at this stage; got %d items", n)
}
if n := len(tb.inmemoryParts); n > 0 {
logger.Panicf("BUG: in-memory parts must be empty at this stage; got %d parts", n)
}
tb.inmemoryParts = nil
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
fileParts := tb.fileParts
tb.fileParts = nil
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
2019-05-22 21:16:55 +00:00
tb.partsLock.Unlock()
for _, pw := range fileParts {
2019-05-22 21:16:55 +00:00
pw.decRef()
}
}
// Path returns the path to tb on the filesystem.
func (tb *Table) Path() string {
return tb.path
}
// TableMetrics contains essential metrics for the Table.
type TableMetrics struct {
ActiveInmemoryMerges uint64
ActiveFileMerges uint64
InmemoryMergesCount uint64
FileMergesCount uint64
InmemoryItemsMerged uint64
FileItemsMerged uint64
ItemsAdded uint64
ItemsAddedSizeBytes uint64
2019-05-22 21:16:55 +00:00
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
InmemoryPartsLimitReachedCount uint64
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PendingItems uint64
InmemoryPartsCount uint64
FilePartsCount uint64
InmemoryBlocksCount uint64
FileBlocksCount uint64
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InmemoryItemsCount uint64
FileItemsCount uint64
InmemorySizeBytes uint64
FileSizeBytes uint64
2019-05-22 21:16:55 +00:00
DataBlocksCacheSize uint64
DataBlocksCacheSizeBytes uint64
DataBlocksCacheSizeMaxBytes uint64
DataBlocksCacheRequests uint64
DataBlocksCacheMisses uint64
IndexBlocksCacheSize uint64
IndexBlocksCacheSizeBytes uint64
IndexBlocksCacheSizeMaxBytes uint64
IndexBlocksCacheRequests uint64
IndexBlocksCacheMisses uint64
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PartsRefCount uint64
TooLongItemsDroppedTotal uint64
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}
// TotalItemsCount returns the total number of items in the table.
func (tm *TableMetrics) TotalItemsCount() uint64 {
return tm.InmemoryItemsCount + tm.FileItemsCount
}
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// UpdateMetrics updates m with metrics from tb.
func (tb *Table) UpdateMetrics(m *TableMetrics) {
m.ActiveInmemoryMerges += uint64(tb.activeInmemoryMerges.Load())
m.ActiveFileMerges += uint64(tb.activeFileMerges.Load())
m.InmemoryMergesCount += tb.inmemoryMergesCount.Load()
m.FileMergesCount += tb.fileMergesCount.Load()
m.InmemoryItemsMerged += tb.inmemoryItemsMerged.Load()
m.FileItemsMerged += tb.fileItemsMerged.Load()
m.ItemsAdded += tb.itemsAdded.Load()
m.ItemsAddedSizeBytes += tb.itemsAddedSizeBytes.Load()
2019-05-22 21:16:55 +00:00
m.InmemoryPartsLimitReachedCount += tb.inmemoryPartsLimitReachedCount.Load()
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
m.PendingItems += uint64(tb.rawItems.Len())
2019-05-22 21:16:55 +00:00
tb.partsLock.Lock()
m.InmemoryPartsCount += uint64(len(tb.inmemoryParts))
for _, pw := range tb.inmemoryParts {
p := pw.p
m.InmemoryBlocksCount += p.ph.blocksCount
m.InmemoryItemsCount += p.ph.itemsCount
m.InmemorySizeBytes += p.size
m.PartsRefCount += uint64(pw.refCount.Load())
}
2019-05-22 21:16:55 +00:00
m.FilePartsCount += uint64(len(tb.fileParts))
for _, pw := range tb.fileParts {
p := pw.p
m.FileBlocksCount += p.ph.blocksCount
m.FileItemsCount += p.ph.itemsCount
m.FileSizeBytes += p.size
m.PartsRefCount += uint64(pw.refCount.Load())
2019-05-22 21:16:55 +00:00
}
tb.partsLock.Unlock()
m.DataBlocksCacheSize = uint64(ibCache.Len())
m.DataBlocksCacheSizeBytes = uint64(ibCache.SizeBytes())
m.DataBlocksCacheSizeMaxBytes = uint64(ibCache.SizeMaxBytes())
m.DataBlocksCacheRequests = ibCache.Requests()
m.DataBlocksCacheMisses = ibCache.Misses()
m.IndexBlocksCacheSize = uint64(idxbCache.Len())
m.IndexBlocksCacheSizeBytes = uint64(idxbCache.SizeBytes())
m.IndexBlocksCacheSizeMaxBytes = uint64(idxbCache.SizeMaxBytes())
m.IndexBlocksCacheRequests = idxbCache.Requests()
m.IndexBlocksCacheMisses = idxbCache.Misses()
m.TooLongItemsDroppedTotal = tooLongItemsTotal.Load()
2019-05-22 21:16:55 +00:00
}
// AddItems adds the given items to the tb.
//
// The function ignores items with length exceeding maxInmemoryBlockSize.
// It logs the ignored items, so users could notice and fix the issue.
func (tb *Table) AddItems(items [][]byte) {
tb.rawItems.addItems(tb, items)
tb.itemsAdded.Add(uint64(len(items)))
n := 0
for _, item := range items {
n += len(item)
}
tb.itemsAddedSizeBytes.Add(uint64(n))
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}
// getParts appends parts snapshot to dst and returns it.
//
// The appended parts must be released with putParts.
func (tb *Table) getParts(dst []*partWrapper) []*partWrapper {
tb.partsLock.Lock()
for _, pw := range tb.inmemoryParts {
pw.incRef()
}
for _, pw := range tb.fileParts {
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pw.incRef()
}
dst = append(dst, tb.inmemoryParts...)
dst = append(dst, tb.fileParts...)
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tb.partsLock.Unlock()
return dst
}
// putParts releases the given pws obtained via getParts.
func (tb *Table) putParts(pws []*partWrapper) {
for _, pw := range pws {
pw.decRef()
}
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
func (tb *Table) mergeInmemoryPartsToFiles(pws []*partWrapper) error {
pwsLen := len(pws)
var errGlobal error
var errGlobalLock sync.Mutex
wg := getWaitGroup()
for len(pws) > 0 {
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
pwsToMerge, pwsRemaining := getPartsForOptimalMerge(pws)
wg.Add(1)
inmemoryPartsConcurrencyCh <- struct{}{}
go func(pwsChunk []*partWrapper) {
defer func() {
<-inmemoryPartsConcurrencyCh
wg.Done()
}()
if err := tb.mergeParts(pwsChunk, nil, true); err != nil {
// There is no need for errors.Is(err, errForciblyStopped) check here, since stopCh=nil is passed to mergeParts.
errGlobalLock.Lock()
if errGlobal == nil {
errGlobal = err
}
errGlobalLock.Unlock()
}
}(pwsToMerge)
pws = pwsRemaining
}
wg.Wait()
putWaitGroup(wg)
if errGlobal != nil {
return fmt.Errorf("cannot optimally merge %d parts: %w", pwsLen, errGlobal)
}
return nil
}
// DebugFlush makes sure all the recently added data is visible to search.
//
// Note: this function doesn't store all the in-memory data to disk - it just converts
// recently added items to searchable parts, which can be stored either in memory
// (if they are quite small) or to persistent disk.
//
// This function is for debugging and testing purposes only,
// since it may slow down data ingestion when used frequently.
func (tb *Table) DebugFlush() {
tb.flushPendingItems(true)
// Wait for background flushers to finish.
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
tb.flushPendingItemsWG.Wait()
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
func (tb *Table) pendingItemsFlusher() {
// do not add jitter in order to guarantee flush interval
d := pendingItemsFlushInterval
ticker := time.NewTicker(d)
defer ticker.Stop()
2019-05-22 21:16:55 +00:00
for {
select {
case <-tb.stopCh:
return
case <-ticker.C:
tb.flushPendingItems(false)
2019-05-22 21:16:55 +00:00
}
}
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
func (tb *Table) inmemoryPartsFlusher() {
// do not add jitter in order to guarantee flush interval
d := dataFlushInterval
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
ticker := time.NewTicker(d)
defer ticker.Stop()
for {
select {
case <-tb.stopCh:
return
case <-ticker.C:
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
tb.flushInmemoryPartsToFiles(false)
2019-05-22 21:16:55 +00:00
}
}
}
func (tb *Table) flushPendingItems(isFinal bool) {
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
tb.flushPendingItemsWG.Add(1)
tb.rawItems.flush(tb, isFinal)
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
tb.flushPendingItemsWG.Done()
}
2019-05-22 21:16:55 +00:00
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
func (tb *Table) flushInmemoryItemsToFiles() {
tb.flushPendingItems(true)
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
tb.flushInmemoryPartsToFiles(true)
2019-05-22 21:16:55 +00:00
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
func (tb *Table) flushInmemoryPartsToFiles(isFinal bool) {
currentTime := time.Now()
var pws []*partWrapper
tb.partsLock.Lock()
for _, pw := range tb.inmemoryParts {
if !pw.isInMerge && (isFinal || pw.flushToDiskDeadline.Before(currentTime)) {
pw.isInMerge = true
pws = append(pws, pw)
}
}
tb.partsLock.Unlock()
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
if err := tb.mergeInmemoryPartsToFiles(pws); err != nil {
logger.Panicf("FATAL: cannot merge in-memory parts to files: %s", err)
}
}
func (riss *rawItemsShards) flush(tb *Table, isFinal bool) {
var dst []*inmemoryBlock
currentTimeMs := time.Now().UnixMilli()
flushDeadlineMs := riss.flushDeadlineMs.Load()
if isFinal || currentTimeMs >= flushDeadlineMs {
riss.ibsToFlushLock.Lock()
dst = riss.ibsToFlush
riss.ibsToFlush = nil
riss.ibsToFlushLock.Unlock()
}
for i := range riss.shards {
dst = riss.shards[i].appendBlocksToFlush(dst, currentTimeMs, isFinal)
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
tb.flushBlocksToInmemoryParts(dst, isFinal)
}
func (ris *rawItemsShard) appendBlocksToFlush(dst []*inmemoryBlock, currentTimeMs int64, isFinal bool) []*inmemoryBlock {
flushDeadlineMs := ris.flushDeadlineMs.Load()
if !isFinal && currentTimeMs < flushDeadlineMs {
// Fast path - nothing to flush
return dst
2019-05-22 21:16:55 +00:00
}
// Slow path - move ris.ibs to dst
ris.mu.Lock()
ibs := ris.ibs
dst = append(dst, ibs...)
for i := range ibs {
ibs[i] = nil
}
ris.ibs = ibs[:0]
ris.mu.Unlock()
return dst
2019-05-22 21:16:55 +00:00
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
func (tb *Table) flushBlocksToInmemoryParts(ibs []*inmemoryBlock, isFinal bool) {
if len(ibs) == 0 {
return
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// Merge ibs into in-memory parts.
var pwsLock sync.Mutex
pws := make([]*partWrapper, 0, (len(ibs)+defaultPartsToMerge-1)/defaultPartsToMerge)
wg := getWaitGroup()
for len(ibs) > 0 {
2019-05-22 21:16:55 +00:00
n := defaultPartsToMerge
if n > len(ibs) {
n = len(ibs)
2019-05-22 21:16:55 +00:00
}
wg.Add(1)
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
inmemoryPartsConcurrencyCh <- struct{}{}
go func(ibsChunk []*inmemoryBlock) {
defer func() {
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
<-inmemoryPartsConcurrencyCh
wg.Done()
}()
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
if pw := tb.createInmemoryPart(ibsChunk); pw != nil {
pwsLock.Lock()
pws = append(pws, pw)
pwsLock.Unlock()
}
// Clear references to ibsChunk items, so they may be reclaimed faster by Go GC.
for i := range ibsChunk {
ibsChunk[i] = nil
}
}(ibs[:n])
ibs = ibs[n:]
2019-05-22 21:16:55 +00:00
}
wg.Wait()
putWaitGroup(wg)
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// Merge pws into a single in-memory part.
maxPartSize := getMaxInmemoryPartSize()
for len(pws) > 1 {
pws = tb.mustMergeInmemoryParts(pws)
pwsRemaining := pws[:0]
for _, pw := range pws {
if pw.p.size >= maxPartSize {
tb.addToInmemoryParts(pw, isFinal)
} else {
pwsRemaining = append(pwsRemaining, pw)
}
}
pws = pwsRemaining
}
if len(pws) == 1 {
tb.addToInmemoryParts(pws[0], isFinal)
}
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
func (tb *Table) addToInmemoryParts(pw *partWrapper, isFinal bool) {
// Wait until the number of in-memory parts goes below maxInmemoryParts.
// This prevents from excess CPU usage during search in tb under high ingestion rate to tb.
// See https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212
select {
case tb.inmemoryPartsLimitCh <- struct{}{}:
default:
tb.inmemoryPartsLimitReachedCount.Add(1)
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
select {
case tb.inmemoryPartsLimitCh <- struct{}{}:
case <-tb.stopCh:
}
}
tb.partsLock.Lock()
tb.inmemoryParts = append(tb.inmemoryParts, pw)
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
tb.startInmemoryPartsMergerLocked()
tb.partsLock.Unlock()
if tb.flushCallback != nil {
if isFinal {
tb.flushCallback()
} else {
// Use Load in front of CompareAndSwap in order to avoid slow inter-CPU synchronization
// at fast path when needFlushCallbackCall is already set to true.
if !tb.needFlushCallbackCall.Load() {
tb.needFlushCallbackCall.CompareAndSwap(false, true)
}
}
2019-05-22 21:16:55 +00:00
}
}
func getWaitGroup() *sync.WaitGroup {
v := wgPool.Get()
if v == nil {
return &sync.WaitGroup{}
}
return v.(*sync.WaitGroup)
}
func putWaitGroup(wg *sync.WaitGroup) {
wgPool.Put(wg)
}
var wgPool sync.Pool
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
func (tb *Table) mustMergeInmemoryParts(pws []*partWrapper) []*partWrapper {
var pwsResult []*partWrapper
var pwsResultLock sync.Mutex
wg := getWaitGroup()
for len(pws) > 0 {
pwsToMerge, pwsRemaining := getPartsForOptimalMerge(pws)
wg.Add(1)
inmemoryPartsConcurrencyCh <- struct{}{}
go func(pwsChunk []*partWrapper) {
defer func() {
<-inmemoryPartsConcurrencyCh
wg.Done()
}()
pw := tb.mustMergeInmemoryPartsFinal(pwsChunk)
pwsResultLock.Lock()
pwsResult = append(pwsResult, pw)
pwsResultLock.Unlock()
}(pwsToMerge)
pws = pwsRemaining
}
wg.Wait()
putWaitGroup(wg)
return pwsResult
}
func (tb *Table) mustMergeInmemoryPartsFinal(pws []*partWrapper) *partWrapper {
if len(pws) == 0 {
logger.Panicf("BUG: pws must contain at least a single item")
}
if len(pws) == 1 {
// Nothing to merge
return pws[0]
}
bsrs := make([]*blockStreamReader, 0, len(pws))
for _, pw := range pws {
if pw.mp == nil {
logger.Panicf("BUG: unexpected file part")
}
bsr := getBlockStreamReader()
bsr.MustInitFromInmemoryPart(pw.mp)
bsrs = append(bsrs, bsr)
}
flushToDiskDeadline := getFlushToDiskDeadline(pws)
return tb.mustMergeIntoInmemoryPart(bsrs, flushToDiskDeadline)
}
func (tb *Table) createInmemoryPart(ibs []*inmemoryBlock) *partWrapper {
// Prepare blockStreamReaders for source blocks.
bsrs := make([]*blockStreamReader, 0, len(ibs))
for _, ib := range ibs {
2019-05-22 21:16:55 +00:00
if len(ib.items) == 0 {
continue
}
bsr := getBlockStreamReader()
bsr.MustInitFromInmemoryBlock(ib)
bsrs = append(bsrs, bsr)
}
if len(bsrs) == 0 {
return nil
}
flushToDiskDeadline := time.Now().Add(dataFlushInterval)
if len(bsrs) == 1 {
// Nothing to merge. Just return a single inmemory part.
bsr := bsrs[0]
mp := &inmemoryPart{}
mp.Init(&bsr.Block)
putBlockStreamReader(bsr)
return newPartWrapperFromInmemoryPart(mp, flushToDiskDeadline)
2019-05-22 21:16:55 +00:00
}
return tb.mustMergeIntoInmemoryPart(bsrs, flushToDiskDeadline)
}
func (tb *Table) mustMergeIntoInmemoryPart(bsrs []*blockStreamReader, flushToDiskDeadline time.Time) *partWrapper {
2019-05-22 21:16:55 +00:00
// Prepare blockStreamWriter for destination part.
outItemsCount := uint64(0)
for _, bsr := range bsrs {
outItemsCount += bsr.ph.itemsCount
}
compressLevel := getCompressLevel(outItemsCount)
2019-05-22 21:16:55 +00:00
bsw := getBlockStreamWriter()
mpDst := &inmemoryPart{}
bsw.MustInitFromInmemoryPart(mpDst, compressLevel)
2019-05-22 21:16:55 +00:00
// Merge parts.
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// The merge shouldn't be interrupted by stopCh, so use nil stopCh.
ph, err := tb.mergePartsInternal("", bsw, bsrs, partInmemory, nil)
2019-05-22 21:16:55 +00:00
putBlockStreamWriter(bsw)
for _, bsr := range bsrs {
putBlockStreamReader(bsr)
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
if err != nil {
logger.Panicf("FATAL: cannot merge inmemoryBlocks: %s", err)
}
mpDst.ph = *ph
return newPartWrapperFromInmemoryPart(mpDst, flushToDiskDeadline)
}
2019-05-22 21:16:55 +00:00
func newPartWrapperFromInmemoryPart(mp *inmemoryPart, flushToDiskDeadline time.Time) *partWrapper {
p := mp.NewPart()
pw := &partWrapper{
p: p,
mp: mp,
flushToDiskDeadline: flushToDiskDeadline,
2019-05-22 21:16:55 +00:00
}
pw.incRef()
return pw
2019-05-22 21:16:55 +00:00
}
func getMaxInmemoryPartSize() uint64 {
// Allow up to 5% of memory for in-memory parts.
n := uint64(0.05 * float64(memory.Allowed()) / maxInmemoryParts)
if n < 1e6 {
n = 1e6
}
return n
}
func (tb *Table) getMaxFilePartSize() uint64 {
n := fs.MustGetFreeSpace(tb.path)
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// Divide free space by the max number of concurrent merges for file parts.
maxOutBytes := n / uint64(cap(filePartsConcurrencyCh))
if maxOutBytes > maxPartSize {
maxOutBytes = maxPartSize
}
return maxOutBytes
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// NotifyReadWriteMode notifies tb that it may be switched from read-only mode to read-write mode.
func (tb *Table) NotifyReadWriteMode() {
tb.startInmemoryPartsMergers()
tb.startFilePartsMergers()
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
func (tb *Table) inmemoryPartsMerger() {
for {
if tb.isReadOnly.Load() {
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
return
}
maxOutBytes := tb.getMaxFilePartSize()
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
tb.partsLock.Lock()
pws := getPartsToMerge(tb.inmemoryParts, maxOutBytes)
tb.partsLock.Unlock()
2019-05-22 21:16:55 +00:00
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
if len(pws) == 0 {
// Nothing to merge
return
}
2019-05-22 21:16:55 +00:00
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
inmemoryPartsConcurrencyCh <- struct{}{}
err := tb.mergeParts(pws, tb.stopCh, false)
<-inmemoryPartsConcurrencyCh
2019-05-22 21:16:55 +00:00
if err == nil {
// Try merging additional parts.
continue
}
if errors.Is(err, errForciblyStopped) {
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// Nothing to do - finish the merger.
return
2019-05-22 21:16:55 +00:00
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// Unexpected error.
logger.Panicf("FATAL: unrecoverable error when merging inmemory parts in %q: %s", tb.path, err)
}
}
func (tb *Table) filePartsMerger() {
for {
if tb.isReadOnly.Load() {
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
return
2019-05-22 21:16:55 +00:00
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
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maxOutBytes := tb.getMaxFilePartSize()
2019-05-22 21:16:55 +00:00
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
tb.partsLock.Lock()
pws := getPartsToMerge(tb.fileParts, maxOutBytes)
tb.partsLock.Unlock()
if len(pws) == 0 {
// Nothing to merge
return
2019-05-22 21:16:55 +00:00
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
filePartsConcurrencyCh <- struct{}{}
err := tb.mergeParts(pws, tb.stopCh, false)
<-filePartsConcurrencyCh
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
if err == nil {
// Try merging additional parts.
continue
}
if errors.Is(err, errForciblyStopped) {
// The merger has been stopped.
return
}
// Unexpected error.
logger.Panicf("FATAL: unrecoverable error when merging file parts in %q: %s", tb.path, err)
}
}
func assertIsInMerge(pws []*partWrapper) {
for _, pw := range pws {
if !pw.isInMerge {
logger.Panicf("BUG: partWrapper.isInMerge unexpectedly set to false")
}
}
}
func (tb *Table) releasePartsToMerge(pws []*partWrapper) {
tb.partsLock.Lock()
for _, pw := range pws {
if !pw.isInMerge {
logger.Panicf("BUG: missing isInMerge flag on the part %q", pw.p.path)
}
pw.isInMerge = false
}
tb.partsLock.Unlock()
}
// mergeParts merges pws to a single resulting part.
//
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// It is expected that pws contains at least a single part.
//
// Merging is immediately stopped if stopCh is closed.
//
// If isFinal is set, then the resulting part will be stored to disk.
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// If at least a single source part at pws is stored on disk, then the resulting part
// will be stored to disk.
//
// All the parts inside pws must have isInMerge field set to true.
// The isInMerge field inside pws parts is set to false before returning from the function.
func (tb *Table) mergeParts(pws []*partWrapper, stopCh <-chan struct{}, isFinal bool) error {
2019-05-22 21:16:55 +00:00
if len(pws) == 0 {
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
logger.Panicf("BUG: empty pws cannot be passed to mergeParts()")
2019-05-22 21:16:55 +00:00
}
assertIsInMerge(pws)
defer tb.releasePartsToMerge(pws)
2019-05-22 21:16:55 +00:00
startTime := time.Now()
// Initialize destination paths.
dstPartType := getDstPartType(pws, isFinal)
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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mergeIdx := tb.nextMergeIdx()
dstPartPath := ""
if dstPartType == partFile {
dstPartPath = filepath.Join(tb.path, fmt.Sprintf("%016X", mergeIdx))
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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}
if isFinal && len(pws) == 1 && pws[0].mp != nil {
// Fast path: flush a single in-memory part to disk.
mp := pws[0].mp
mp.MustStoreToDisk(dstPartPath)
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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pwNew := tb.openCreatedPart(pws, nil, dstPartPath)
tb.swapSrcWithDstParts(pws, pwNew, dstPartType)
return nil
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}
// Prepare BlockStreamReaders for source parts.
bsrs := mustOpenBlockStreamReaders(pws)
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// Prepare BlockStreamWriter for destination part.
srcSize := uint64(0)
srcItemsCount := uint64(0)
srcBlocksCount := uint64(0)
for _, pw := range pws {
srcSize += pw.p.size
srcItemsCount += pw.p.ph.itemsCount
srcBlocksCount += pw.p.ph.blocksCount
}
compressLevel := getCompressLevel(srcItemsCount)
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bsw := getBlockStreamWriter()
var mpNew *inmemoryPart
if dstPartType == partInmemory {
mpNew = &inmemoryPart{}
bsw.MustInitFromInmemoryPart(mpNew, compressLevel)
} else {
nocache := srcItemsCount > maxItemsPerCachedPart()
bsw.MustInitFromFilePart(dstPartPath, nocache, compressLevel)
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}
// Merge source parts to destination part.
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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ph, err := tb.mergePartsInternal(dstPartPath, bsw, bsrs, dstPartType, stopCh)
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putBlockStreamWriter(bsw)
for _, bsr := range bsrs {
putBlockStreamReader(bsr)
}
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if err != nil {
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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return err
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}
if mpNew != nil {
// Update partHeader for destination inmemory part after the merge.
mpNew.ph = *ph
} else {
// Make sure the created part directory listing is synced.
fs.MustSyncPath(dstPartPath)
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}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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// Atomically swap the source parts with the newly created part.
pwNew := tb.openCreatedPart(pws, mpNew, dstPartPath)
pDst := pwNew.p
dstItemsCount := pDst.ph.itemsCount
dstBlocksCount := pDst.ph.blocksCount
dstSize := pDst.size
tb.swapSrcWithDstParts(pws, pwNew, dstPartType)
d := time.Since(startTime)
if d <= 30*time.Second {
return nil
}
// Log stats for long merges.
durationSecs := d.Seconds()
itemsPerSec := int(float64(srcItemsCount) / durationSecs)
logger.Infof("merged (%d parts, %d items, %d blocks, %d bytes) into (1 part, %d items, %d blocks, %d bytes) in %.3f seconds at %d items/sec to %q",
len(pws), srcItemsCount, srcBlocksCount, srcSize, dstItemsCount, dstBlocksCount, dstSize, durationSecs, itemsPerSec, dstPartPath)
return nil
}
func getFlushToDiskDeadline(pws []*partWrapper) time.Time {
d := time.Now().Add(dataFlushInterval)
for _, pw := range pws {
if pw.mp != nil && pw.flushToDiskDeadline.Before(d) {
d = pw.flushToDiskDeadline
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}
}
return d
}
type partType int
var (
partInmemory = partType(0)
partFile = partType(1)
)
func getDstPartType(pws []*partWrapper, isFinal bool) partType {
dstPartSize := getPartsSize(pws)
if isFinal || dstPartSize > getMaxInmemoryPartSize() {
return partFile
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}
if !areAllInmemoryParts(pws) {
// If at least a single source part is located in file,
// then the destination part must be in file for durability reasons.
return partFile
}
return partInmemory
}
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func mustOpenBlockStreamReaders(pws []*partWrapper) []*blockStreamReader {
bsrs := make([]*blockStreamReader, 0, len(pws))
for _, pw := range pws {
bsr := getBlockStreamReader()
if pw.mp != nil {
bsr.MustInitFromInmemoryPart(pw.mp)
} else {
bsr.MustInitFromFilePart(pw.p.path)
}
bsrs = append(bsrs, bsr)
}
return bsrs
}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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func (tb *Table) mergePartsInternal(dstPartPath string, bsw *blockStreamWriter, bsrs []*blockStreamReader, dstPartType partType, stopCh <-chan struct{}) (*partHeader, error) {
var ph partHeader
var itemsMerged *atomic.Uint64
var mergesCount *atomic.Uint64
var activeMerges *atomic.Int64
switch dstPartType {
case partInmemory:
itemsMerged = &tb.inmemoryItemsMerged
mergesCount = &tb.inmemoryMergesCount
activeMerges = &tb.activeInmemoryMerges
case partFile:
itemsMerged = &tb.fileItemsMerged
mergesCount = &tb.fileMergesCount
activeMerges = &tb.activeFileMerges
default:
logger.Panicf("BUG: unknown partType=%d", dstPartType)
}
activeMerges.Add(1)
err := mergeBlockStreams(&ph, bsw, bsrs, tb.prepareBlock, stopCh, itemsMerged)
activeMerges.Add(-1)
mergesCount.Add(1)
if err != nil {
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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return nil, fmt.Errorf("cannot merge %d parts to %s: %w", len(bsrs), dstPartPath, err)
}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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if dstPartPath != "" {
ph.MustWriteMetadata(dstPartPath)
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}
return &ph, nil
}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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func (tb *Table) openCreatedPart(pws []*partWrapper, mpNew *inmemoryPart, dstPartPath string) *partWrapper {
// Open the created part.
if mpNew != nil {
// Open the created part from memory.
flushToDiskDeadline := getFlushToDiskDeadline(pws)
pwNew := newPartWrapperFromInmemoryPart(mpNew, flushToDiskDeadline)
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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return pwNew
}
// Open the created part from disk.
pNew := mustOpenFilePart(dstPartPath)
pwNew := &partWrapper{
p: pNew,
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}
pwNew.incRef()
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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return pwNew
}
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func areAllInmemoryParts(pws []*partWrapper) bool {
for _, pw := range pws {
if pw.mp == nil {
return false
}
}
return true
}
func (tb *Table) swapSrcWithDstParts(pws []*partWrapper, pwNew *partWrapper, dstPartType partType) {
// Atomically unregister old parts and add new part to tb.
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
m := makeMapFromPartWrappers(pws)
removedInmemoryParts := 0
removedFileParts := 0
2019-05-22 21:16:55 +00:00
tb.partsLock.Lock()
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
2023-03-19 08:36:05 +00:00
tb.inmemoryParts, removedInmemoryParts = removeParts(tb.inmemoryParts, m)
tb.fileParts, removedFileParts = removeParts(tb.fileParts, m)
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
2023-03-19 08:36:05 +00:00
switch dstPartType {
case partInmemory:
tb.inmemoryParts = append(tb.inmemoryParts, pwNew)
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
tb.startInmemoryPartsMergerLocked()
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
2023-03-19 08:36:05 +00:00
case partFile:
tb.fileParts = append(tb.fileParts, pwNew)
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
tb.startFilePartsMergerLocked()
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
2023-03-19 08:36:05 +00:00
default:
logger.Panicf("BUG: unknown partType=%d", dstPartType)
}
// Atomically store the updated list of file-based parts on disk.
// This must be performed under partsLock in order to prevent from races
// when multiple concurrently running goroutines update the list.
if removedFileParts > 0 || dstPartType == partFile {
mustWritePartNames(tb.fileParts, tb.path)
}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
2023-03-19 08:36:05 +00:00
2019-05-22 21:16:55 +00:00
tb.partsLock.Unlock()
// Update inmemoryPartsLimitCh accordingly to the number of the remaining in-memory parts.
for i := 0; i < removedInmemoryParts; i++ {
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
select {
case <-tb.inmemoryPartsLimitCh:
case <-tb.stopCh:
}
}
if dstPartType == partInmemory {
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
select {
case tb.inmemoryPartsLimitCh <- struct{}{}:
case <-tb.stopCh:
}
}
removedParts := removedInmemoryParts + removedFileParts
2019-05-22 21:16:55 +00:00
if removedParts != len(m) {
logger.Panicf("BUG: unexpected number of parts removed; got %d, want %d", removedParts, len(m))
2019-05-22 21:16:55 +00:00
}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
2023-03-19 08:36:05 +00:00
// Mark old parts as must be deleted and decrement reference count,
// so they are eventually closed and deleted.
2019-05-22 21:16:55 +00:00
for _, pw := range pws {
pw.mustDrop.Store(true)
2019-05-22 21:16:55 +00:00
pw.decRef()
}
}
2019-05-22 21:16:55 +00:00
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
func makeMapFromPartWrappers(pws []*partWrapper) map[*partWrapper]struct{} {
m := make(map[*partWrapper]struct{}, len(pws))
for _, pw := range pws {
m[pw] = struct{}{}
}
if len(m) != len(pws) {
logger.Panicf("BUG: %d duplicate parts found in %d source parts", len(pws)-len(m), len(pws))
}
return m
}
func getPartsSize(pws []*partWrapper) uint64 {
n := uint64(0)
for _, pw := range pws {
n += pw.p.size
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}
return n
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}
func getCompressLevel(itemsCount uint64) int {
if itemsCount <= 1<<16 {
// -5 is the minimum supported compression for zstd.
// See https://github.com/facebook/zstd/releases/tag/v1.3.4
return -5
}
if itemsCount <= 1<<17 {
return -4
}
if itemsCount <= 1<<18 {
return -3
}
if itemsCount <= 1<<19 {
return -2
}
if itemsCount <= 1<<20 {
return -1
}
if itemsCount <= 1<<22 {
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return 1
}
if itemsCount <= 1<<25 {
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return 2
}
return 3
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}
func (tb *Table) nextMergeIdx() uint64 {
return tb.mergeIdx.Add(1)
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}
func mustOpenParts(path string) []*partWrapper {
// The path can be missing after restoring from backup, so create it if needed.
fs.MustMkdirIfNotExist(path)
fs.MustRemoveTemporaryDirs(path)
2019-05-22 21:16:55 +00:00
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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// Remove txn and tmp directories, which may be left after the upgrade
// to v1.90.0 and newer versions.
fs.MustRemoveAll(filepath.Join(path, "txn"))
fs.MustRemoveAll(filepath.Join(path, "tmp"))
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partsFile := filepath.Join(path, partsFilename)
partNames := mustReadPartNames(partsFile, path)
2019-05-22 21:16:55 +00:00
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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// Remove dirs missing in partNames. These dirs may be left after unclean shutdown
// or after the update from versions prior to v1.90.0.
des := fs.MustReadDir(path)
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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m := make(map[string]struct{}, len(partNames))
for _, partName := range partNames {
// Make sure the partName exists on disk.
// If it is missing, then manual action from the user is needed,
// since this is unexpected state, which cannot occur under normal operation,
// including unclean shutdown.
partPath := filepath.Join(path, partName)
if !fs.IsPathExist(partPath) {
logger.Panicf("FATAL: part %q is listed in %q, but is missing on disk; "+
"ensure %q contents is not corrupted; remove %q to rebuild its' content from the list of existing parts",
partPath, partsFile, partsFile, partsFile)
}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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m[partName] = struct{}{}
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}
for _, de := range des {
if !fs.IsDirOrSymlink(de) {
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// Skip non-directories.
continue
}
fn := de.Name()
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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if _, ok := m[fn]; !ok {
deletePath := filepath.Join(path, fn)
logger.Infof("deleting %q because it isn't listed in %q; this is the expected case after unclean shutdown", deletePath, partsFile)
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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fs.MustRemoveAll(deletePath)
}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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}
fs.MustSyncPath(path)
// Open parts
var pws []*partWrapper
for _, partName := range partNames {
partPath := filepath.Join(path, partName)
p := mustOpenFilePart(partPath)
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pw := &partWrapper{
p: p,
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}
pw.incRef()
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pws = append(pws, pw)
}
if !fs.IsPathExist(partsFile) {
// Create parts.json file if it doesn't exist yet.
// This should protect from possible carshloops just after the migration from versions below v1.90.0
// See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/4336
mustWritePartNames(pws, path)
}
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return pws
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}
// CreateSnapshotAt creates tb snapshot in the given dstDir.
//
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
2023-03-19 08:36:05 +00:00
// Snapshot is created using linux hard links, so it is usually created very quickly.
//
// The caller is responsible for data removal at dstDir on unsuccessful snapshot creation.
func (tb *Table) CreateSnapshotAt(dstDir string) error {
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logger.Infof("creating Table snapshot of %q...", tb.path)
startTime := time.Now()
var err error
srcDir := tb.path
srcDir, err = filepath.Abs(srcDir)
if err != nil {
return fmt.Errorf("cannot obtain absolute dir for %q: %w", srcDir, err)
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}
dstDir, err = filepath.Abs(dstDir)
if err != nil {
return fmt.Errorf("cannot obtain absolute dir for %q: %w", dstDir, err)
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}
if strings.HasPrefix(dstDir, srcDir+string(filepath.Separator)) {
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return fmt.Errorf("cannot create snapshot %q inside the data dir %q", dstDir, srcDir)
}
// Flush inmemory items to disk.
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
tb.flushInmemoryItemsToFiles()
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fs.MustMkdirFailIfExist(dstDir)
2019-05-22 21:16:55 +00:00
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
2023-03-19 08:36:05 +00:00
pws := tb.getParts(nil)
defer tb.putParts(pws)
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
2023-03-19 08:36:05 +00:00
// Create a file with part names at dstDir
mustWritePartNames(pws, dstDir)
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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// Make hardlinks for pws at dstDir
for _, pw := range pws {
if pw.mp != nil {
// Skip in-memory parts
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continue
}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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srcPartPath := pw.p.path
dstPartPath := filepath.Join(dstDir, filepath.Base(srcPartPath))
fs.MustHardLinkFiles(srcPartPath, dstPartPath)
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}
fs.MustSyncPath(dstDir)
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parentDir := filepath.Dir(dstDir)
fs.MustSyncPath(parentDir)
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logger.Infof("created Table snapshot of %q at %q in %.3f seconds", srcDir, dstDir, time.Since(startTime).Seconds())
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return nil
}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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func mustWritePartNames(pws []*partWrapper, dstDir string) {
partNames := make([]string, 0, len(pws))
for _, pw := range pws {
if pw.mp != nil {
// Skip in-memory parts
continue
}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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partName := filepath.Base(pw.p.path)
partNames = append(partNames, partName)
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}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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sort.Strings(partNames)
data, err := json.Marshal(partNames)
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if err != nil {
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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logger.Panicf("BUG: cannot marshal partNames to JSON: %s", err)
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}
partsFile := filepath.Join(dstDir, partsFilename)
fs.MustWriteAtomic(partsFile, data, true)
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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}
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func mustReadPartNames(partsFile, srcDir string) []string {
if fs.IsPathExist(partsFile) {
data, err := os.ReadFile(partsFile)
if err != nil {
logger.Panicf("FATAL: cannot read %q: %s", partsFile, err)
}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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var partNames []string
if err := json.Unmarshal(data, &partNames); err != nil {
logger.Panicf("FATAL: cannot parse %q: %s", partsFile, err)
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}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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return partNames
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}
// The partsFilename is missing. This is the upgrade from versions previous to v1.90.0.
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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// Read part names from directories under srcDir
des := fs.MustReadDir(srcDir)
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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var partNames []string
for _, de := range des {
if !fs.IsDirOrSymlink(de) {
// Skip non-directories.
continue
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}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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partName := de.Name()
if isSpecialDir(partName) {
// Skip special dirs.
continue
}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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partNames = append(partNames, partName)
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}
all: add Windows build for VictoriaMetrics This commit changes background merge algorithm, so it becomes compatible with Windows file semantics. The previous algorithm for background merge: 1. Merge source parts into a destination part inside tmp directory. 2. Create a file in txn directory with instructions on how to atomically swap source parts with the destination part. 3. Perform instructions from the file. 4. Delete the file with instructions. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since the remaining files with instructions is replayed on the next restart, after that the remaining contents of the tmp directory is deleted. Unfortunately this algorithm doesn't work under Windows because it disallows removing and moving files, which are in use. So the new algorithm for background merge has been implemented: 1. Merge source parts into a destination part inside the partition directory itself. E.g. now the partition directory may contain both complete and incomplete parts. 2. Atomically update the parts.json file with the new list of parts after the merge, e.g. remove the source parts from the list and add the destination part to the list before storing it to parts.json file. 3. Remove the source parts from disk when they are no longer used. This algorithm guarantees that either source parts or destination part is visible in the partition after unclean shutdown at any step above, since incomplete partitions from step 1 or old source parts from step 3 are removed on the next startup by inspecting parts.json file. This algorithm should work under Windows, since it doesn't remove or move files in use. This algorithm has also the following benefits: - It should work better for NFS. - It fits object storage semantics. The new algorithm changes data storage format, so it is impossible to downgrade to the previous versions of VictoriaMetrics after upgrading to this algorithm. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3236 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3821 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/70
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return partNames
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}
// getPartsToMerge returns optimal parts to merge from pws.
//
// The summary size of the returned parts must be smaller than the maxOutBytes.
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
func getPartsToMerge(pws []*partWrapper, maxOutBytes uint64) []*partWrapper {
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pwsRemaining := make([]*partWrapper, 0, len(pws))
for _, pw := range pws {
if !pw.isInMerge {
pwsRemaining = append(pwsRemaining, pw)
}
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
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pwsToMerge := appendPartsToMerge(nil, pwsRemaining, defaultPartsToMerge, maxOutBytes)
for _, pw := range pwsToMerge {
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if pw.isInMerge {
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
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logger.Panicf("BUG: partWrapper.isInMerge unexpectedly set to true")
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}
pw.isInMerge = true
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
return pwsToMerge
}
// getPartsForOptimalMerge returns parts from pws for optimal merge, plus the remaining parts.
//
// the pws items are replaced by nil after the call. This is needed for helping Go GC to reclaim the referenced items.
func getPartsForOptimalMerge(pws []*partWrapper) ([]*partWrapper, []*partWrapper) {
pwsToMerge := appendPartsToMerge(nil, pws, defaultPartsToMerge, math.MaxUint64)
if len(pwsToMerge) == 0 {
return pws, nil
}
m := makeMapFromPartWrappers(pwsToMerge)
pwsRemaining := make([]*partWrapper, 0, len(pws)-len(pwsToMerge))
for _, pw := range pws {
if _, ok := m[pw]; !ok {
pwsRemaining = append(pwsRemaining, pw)
}
}
// Clear references to pws items, so they could be reclaimed faster by Go GC.
for i := range pws {
pws[i] = nil
}
return pwsToMerge, pwsRemaining
2019-05-22 21:16:55 +00:00
}
// minMergeMultiplier is the minimum multiplier for the size of the output part
// compared to the size of the maximum input part for the merge.
//
// Higher value reduces write amplification (disk write IO induced by the merge),
// while increases the number of unmerged parts.
// The 1.7 is good enough for production workloads.
const minMergeMultiplier = 1.7
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
// appendPartsToMerge finds optimal parts to merge from src, appends them to dst and returns the result.
func appendPartsToMerge(dst, src []*partWrapper, maxPartsToMerge int, maxOutBytes uint64) []*partWrapper {
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if len(src) < 2 {
// There is no need in merging zero or one part :)
return dst
}
if maxPartsToMerge < 2 {
logger.Panicf("BUG: maxPartsToMerge cannot be smaller than 2; got %d", maxPartsToMerge)
}
// Filter out too big parts.
// This should reduce N for O(n^2) algorithm below.
maxInPartBytes := uint64(float64(maxOutBytes) / minMergeMultiplier)
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tmp := make([]*partWrapper, 0, len(src))
for _, pw := range src {
if pw.p.size > maxInPartBytes {
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continue
}
tmp = append(tmp, pw)
}
src = tmp
sortPartsForOptimalMerge(src)
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maxSrcParts := maxPartsToMerge
if maxSrcParts > len(src) {
maxSrcParts = len(src)
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}
minSrcParts := (maxSrcParts + 1) / 2
if minSrcParts < 2 {
minSrcParts = 2
}
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// Exhaustive search for parts giving the lowest write amplification when merged.
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var pws []*partWrapper
maxM := float64(0)
for i := minSrcParts; i <= maxSrcParts; i++ {
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for j := 0; j <= len(src)-i; j++ {
a := src[j : j+i]
if a[0].p.size*uint64(len(a)) < a[len(a)-1].p.size {
// Do not merge parts with too big difference in size,
// since this results in unbalanced merges.
continue
}
outBytes := uint64(0)
for _, pw := range a {
outBytes += pw.p.size
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}
if outBytes > maxOutBytes {
// There is no sense in checking the remaining bigger parts.
break
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}
m := float64(outBytes) / float64(a[len(a)-1].p.size)
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if m < maxM {
continue
}
maxM = m
pws = a
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}
}
minM := float64(maxPartsToMerge) / 2
if minM < minMergeMultiplier {
minM = minMergeMultiplier
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}
if maxM < minM {
// There is no sense in merging parts with too small m,
// since this leads to high disk write IO.
2019-05-22 21:16:55 +00:00
return dst
}
return append(dst, pws...)
}
func sortPartsForOptimalMerge(pws []*partWrapper) {
// Sort src parts by size.
sort.Slice(pws, func(i, j int) bool {
return pws[i].p.size < pws[j].p.size
})
}
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
func removeParts(pws []*partWrapper, partsToRemove map[*partWrapper]struct{}) ([]*partWrapper, int) {
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dst := pws[:0]
for _, pw := range pws {
lib/{mergeset,storage}: make background merge more responsive and scalable - Maintain a separate worker pool per each part type (in-memory, file, big and small). Previously a shared pool was used for merging all the part types. A single merge worker could merge parts with mixed types at once. For example, it could merge simultaneously an in-memory part plus a big file part. Such a merge could take hours for big file part. During the duration of this merge the in-memory part was pinned in memory and couldn't be persisted to disk under the configured -inmemoryDataFlushInterval . Another common issue, which could happen when parts with mixed types are merged, is uncontrolled growth of in-memory parts or small parts when all the merge workers were busy with merging big files. Such growth could lead to significant performance degradataion for queries, since every query needs to check ever growing list of parts. This could also slow down the registration of new time series, since VictoriaMetrics searches for the internal series_id in the indexdb for every new time series. The third issue is graceful shutdown duration, which could be very long when a background merge is running on in-memory parts plus big file parts. This merge couldn't be interrupted, since it merges in-memory parts. A separate pool of merge workers per every part type elegantly resolves both issues: - In-memory parts are merged to file-based parts in a timely manner, since the maximum size of in-memory parts is limited. - Long-running merges for big parts do not block merges for in-memory parts and small parts. - Graceful shutdown duration is now limited by the time needed for flushing in-memory parts to files. Merging for file parts is instantly canceled on graceful shutdown now. - Deprecate -smallMergeConcurrency command-line flag, since the new background merge algorithm should automatically self-tune according to the number of available CPU cores. - Deprecate -finalMergeDelay command-line flag, since it wasn't working correctly. It is better to run forced merge when needed - https://docs.victoriametrics.com/#forced-merge - Tune the number of shards for pending rows and items before the data goes to in-memory parts and becomes visible for search. This improves the maximum data ingestion rate and the maximum rate for registration of new time series. This should reduce the duration of data ingestion slowdown in VictoriaMetrics cluster on e.g. re-routing events, when some of vmstorage nodes become temporarily unavailable. - Prevent from possible "sync: WaitGroup misuse" panic on graceful shutdown. This is a follow-up for fa566c68a6ccf7385a05f649aee7e5f5a38afb15 . Thanks @misutoth to for the inspiration at https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3425 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3641 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/648 Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/291
2024-01-26 20:39:49 +00:00
if _, ok := partsToRemove[pw]; !ok {
dst = append(dst, pw)
2019-05-22 21:16:55 +00:00
}
}
for i := len(dst); i < len(pws); i++ {
pws[i] = nil
}
return dst, len(pws) - len(dst)
2019-05-22 21:16:55 +00:00
}
func isSpecialDir(name string) bool {
// Snapshots and cache dirs aren't used anymore.
// Keep them here for backwards compatibility.
return name == "tmp" || name == "txn" || name == "snapshots" || name == "cache" || fs.IsScheduledForRemoval(name)
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}