The issue has been introduced in bace9a2501
The improper fix was in the d4c0615dcd ,
since it fixed the issue just by an accident, because Go comiler aligned the rawRowsShards field
by 4-byte boundary inside partition struct.
The proper fix is to use atomic.Int64 field - this guarantees that the access to this field
won't result in unaligned 64-bit atomic operation. See https://github.com/golang/go/issues/50860
and https://github.com/golang/go/issues/19057
Do not convert shard items to part when a shard becomes full. Instead, collect multiple
full shards and then convert them to a searchable part at once. This reduces
the number of searchable parts, which, in turn, should increase query performance,
since queries need to scan smaller number of parts.
Previously the interval between item addition and its conversion to searchable in-memory part
could vary significantly because of too coarse per-second precision. Switch from fasttime.UnixTimestamp()
to time.Now().UnixMilli() for millisecond precision. It is OK to use time.Now() for tracking
the time when buffered items must be converted to searchable in-memory parts, since time.Now()
calls aren't located in hot paths.
Increase the flush interval for converting buffered samples to searchable in-memory parts
from one second to two seconds. This should reduce the number of blocks, which are needed
to be processed during high-frequency alerting queries. This, in turn, should reduce CPU usage.
While at it, hardcode the maximum size of rawRows shard to 8Mb, since this size gives the optimal
data ingestion pefromance according to load tests. This reduces memory usage and CPU usage on systems
with big amounts of RAM under high data ingestion rate.
The pooled rawRowsBlock objects occupies big amounts of memory between flushes,
and the flushes are relatively rare. So it is better to don't use the pool
and to allocate rawRow blocks on demand. This should reduce the average
memory usage between flushes.
The buffer can be quite big under high ingestion rate (e.g. more than 100MB).
This leads to increased memory usage between buffer flushes.
So it is better to re-create the buffer on every flush in order to reduce memory usage
between buffer flushes.
Instead, log a sample of these long items once per 5 seconds into error log,
so users could notice and fix the issue with too long labels or too many labels.
Previously this panic could occur in production when ingesting samples with too long labels.
The 3c246cdf00 added an optimization where the previous metaindexRow
could be saved to disk when the current block header couldn't be added indexBlock because the resulting
indexBlock size became too big. This could result in an empty metaindexRow.firstItem for the next metaindexRow.
There is no sense in storing commonPrefix for blockHeader containing only a single item,
since this only increases blockHeader size without any benefits.
This panic could occur when samples with too long label values are ingested into VictoriaMetrics.
This could result in too long fistItem and commonPrefix values at blockHeader (up to 64kb each).
This may inflate the maximum index block size by 4 * maxIndexBlockSize.
- 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 fa566c68a6 .
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
The maxFileParts usage has been accidentally removed in fa566c68a6
While at it, add Count suffix to *AssistedMerges counter names in order to make them less misleading.
Previously their names were falsely suggesting that these are gauges, which show the number of concurrently
executed assisted merges.
It has been appeared that the registration of new time series slows down linearly
with the number of indexdb parts, since VictoriaMetrics needs to check every indexdb part
when it searches for TSID by newly ingested metric name.
The number of in-memory parts grows when new time series are registered
at high rate. The number of in-memory parts grows faster on systems with big number
of CPU cores, because the mergeset maintains per-CPU buffers with newly added entries
for the indexdb, and every such entry is transformed eventually into a separate in-memory part.
The solution has been suggested in https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212
by @misutoth - to limit the number of in-memory parts with buffered channel.
This solution is implemented in this commit. Additionally, this commit merges per-CPU parts
into a single part before adding it to the list of in-memory parts. This reduces CPU load
when searching for TSID by newly ingested metric name.
The https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5212 recommends setting the limit on the number
of in-memory parts to 100, but my internal testing shows that much lower limit 15 works with the same efficiency
on a system with 16 CPU cores while reducing memory usage for `indexdb/dataBlocks` cache by up to 50%.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5190
This should smooth CPU and RAM usage spikes related to these periodic tasks,
by reducing the probability that multiple concurrent periodic tasks are performed at the same time.
This should reduce tail latency during data ingestion.
This shouldn't slow down data ingestion in the worst case, since assisted merges are spread among
distinct addRows/addItems calls after this change.
* lib/storage/partition: add check to ensure parts exist on disk
If part exists in parts.json but is missing on disk there will be a misleading error similar to "unexpected number of substrings in the part name".
This change forces verification of part existence and throws a correct error in case it is missing on disk.
Such issue can be result of https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5005 or disk corruption.
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* lib/storage/partition: use filepath.Join instead of string concatenation
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* lib/storage/partition: add action points for error message
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* all: add a check for missing part in lib/mergeset and lib/logstorage
---------
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
* lib/storage: creates parts.json on start-up if it not exists.
It fixes migrations from versions below v1.90.0.
Previously parts.json was created only after successful merge.
But if merge was interruped for some reason (OOM or shutdown), parts.json wasn't created and partitions left after interruped merge weren't properly deleted.
Since VM cannot check if it must be removed or not.
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/4336
* Apply suggestions from code review
Co-authored-by: Roman Khavronenko <roman@victoriametrics.com>
* Update lib/storage/partition.go
Co-authored-by: Roman Khavronenko <roman@victoriametrics.com>
---------
Co-authored-by: Roman Khavronenko <roman@victoriametrics.com>
Use fs.MustReadDir() instead of os.ReadDir() across the code in order to reduce the code verbosity.
The fs.MustReadDir() logs the error with the directory name and the call stack on error
before exit. This information should be enough for debugging the cause of the error.
Callers of CreateFlockFile log the returned err and exit.
It is better to log the error inside the MustCreateFlockFile together with the path
to the specified directory and the call stack. This simplifies
the code at the callers' side while leaving the debuggability at the same level.
Callers of InitFromFilePart log the error and exit.
It is better to log the error with the path to the part and the call stack
directly inside the MustInitFromFilePart() function.
This simplifies the code at callers' side while leaving the same level of debuggability.
Callers of this function log the returned error and exit.
It is better logging the error together with the path to the filename
and call stack directly inside the function. This simplifies
the code at callers' side without reducing the level of debuggability
Callers of this function log the returned error and exit.
Let's log the error with the path to the filename and call stack
inside the function. This simplifies the code at callers' side
without reducing the level of debuggability.
Callers of ReadFullData() log the error and then exit.
So let's log the error with the path to the filename and the call stack
inside MustReadData(). This simplifies the code at callers' side,
while leaving the debuggability at the same level.
Callers of this function log the returned error and then exit.
Let's log the error with the call stack inside the function itself.
This simplifies the code at callers' side, while leaving the same
level of debuggability in case of errors.
Callers of this function log the returned error and exit.
So let's just log the error with the given filepath and the call stack
inside the function itself and then exit. This simplifies the code
at callers' place while leaves the same level of debuggability in case of errors.
