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.
Callers of these functions log the returned error and then exit. The returned error already contains the path
to directory, which was failed to be created. So let's just log the error together with the call stack
inside these functions. This leaves the debuggability of the returned error at the same level
while allows simplifying the code at callers' side.
While at it, properly use MustMkdirFailIfExist instead of MustMkdirIfNotExist inside inmemoryPart.MustStoreToDisk().
It is expected that the inmemoryPart.MustStoreToDick() must fail if there is already a directory under the given path.
When WriteFileAndSync fails, then the caller eventually logs the error message
and exits. The error message returned by WriteFileAndSync already contains the path
to the file, which couldn't be created. This information alongside the call stack
is enough for debugging the issue. So just use log.Panicf("FATAL: ...") inside MustWriteAndSync().
This simplifies error handling at caller side a bit.
This is a follow-up after 42bba64aa7
Previously the part directory listing was fsync'ed implicitly inside partHeader.WriteMetadata()
by calling fs.WriteFileAtomically(). Now it must be fsync'ed explicitly.
There is no need in fsync'ing the parent directory, since it is fsync'ed by the caller
when updating parts.json file.
Previously the created part directory listing was fsynced implicitly
when storing metadata.json file in it.
Also remove superflouous fsync for part directory listing,
which was called at blockStreamWriter.MustClose().
After that the metadata.json file is created, so an additional fsync
for the directory contents is needed.
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
lib{mergset,storage}: prevent possible race condition with logging stats for merges
Previously partwrapper could be release by background process and reference for part may be invalid
during logging stats. It will lead to panic at vmstorage
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3897
- Use flag.Duration instead of flagutil.Duration for -snapshotCreateTimeout,
since the flagutil.Duration is intended mostly for big durations, e.g. days, months and years,
while the -snapshotCreateTimeout is usually smaller than one hour.
- Add links to https://docs.victoriametrics.com/#how-to-work-with-snapshots in docs/CHANGELOG.md,
so readers could easily find the corresponding docs when reading the changelog.
- Properly remove all the created directories on unsuccessful attempt to create
snapshot in Storage.CreateSnapshot().
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3551
* lib/{fs,mergeset,storage}: skip `.must-remove.` dirs when creating snapshot (#3858)
* lib/{mergeset,storage}: add timeout configuration for snapshots creation, remove incomplete snapshots from storage
* docs: fix formatting
* app/vmstorage: add metrics to track status of snapshots
* app/vmstorage: use `vm_http_requests_total` metric for snapshot endpoints metrics, rename new flag to make name more clear
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* app/vmstorage: update flag name in docs
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* app/vmstorage: reflect new metrics names change in docs
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
---------
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
Assisted merges are intended to be performed by goroutines, which accept the incoming samples,
in order to limit the data ingestion rate.
The worker, which converts pending samples to parts, shouldn't be penalized by assisted merges,
since this may result in increased number of pending rows as seen at https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3647#issuecomment-1385039142
when the assisted merge takes too much time.
Blocked small merges may result into big number of small parts, which, in turn,
may result in increased CPU and memory usage during queries, since queries need to inspect
all the existing small parts.
The issue has been introduced in 8189770c50
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337
The main purpose of this command-line flag is to increase the lifetime of low-end flash storage
with the limited number of write operations it can perform. Such flash storage is usually
installed on Raspberry PI or similar appliances.
For example, `-inmemoryDataFlushInterval=1h` reduces the frequency of disk write operations
to up to once per hour if the ingested one-hour worth of data fits the limit for in-memory data.
The in-memory data is searchable in the same way as the data stored on disk.
VictoriaMetrics automatically flushes the in-memory data to disk on graceful shutdown via SIGINT signal.
The in-memory data is lost on unclean shutdown (hardware power loss, OOM crash, SIGKILL).
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337
The panic has been introduced in 68f3a02589
While at it, add padding to shard structs in order to avoid false sharing on mordern CPUs
This should improve scalability on systems with many CPU cores
ioutil.ReadAll is deprecated since Go1.16 - see https://tip.golang.org/doc/go1.16#ioutil
VictoriaMetrics requires at least Go1.18, so it is OK to switch from ioutil.ReadAll to io.ReadAll.
This is a follow-up for 02ca2342ab
The ioutil.{Read|Write}File is deprecated since Go1.16 -
see https://tip.golang.org/doc/go1.16#ioutil
VictoriaMetrics needs at least Go1.18, so it is safe to remove ioutil usage
from source code.
This is a follow-up for 02ca2342ab
This metric is equivalent to `vm_available_memory_bytes`, but it has better name,
since the metric is related to a process, not VictoriaMetrics itself.
Leave `vm_available_memory_bytes` for backwards compatibility.
This should improve the performance for items sorting inside inmemoryBlock.MarshalUnsortedData
if they have common prefix.
While at it, improve the performance for inmemoryBlock.updateCommonPrefix for sorted items.
This should improve performance for inmemoryBlock.MarshalSortedData during background merge.
The lifetime of storageBlock is much shorter comparing to the lifetime of inmemoryPart,
so sync.Pool usage should reduce overall memory usage and improve performance
because of better locality of reference when marshaling inmemoryBlock to inmemoryPart.
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2247
There is no need to sort the underlying data according to sorted items there.
This should reduce cpu usage when registering new time series in `indexdb`.
Thanks to @ahfuzhang for the suggestion at https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2245
* optimized code ,because only the first error,so no need var errors []error
* optimized code ,because only the first error,so no need var errors []error
Co-authored-by: lirenzuo <lirenzuo@shein.com>
Previously bytesutil.Resize() was copying the original byte slice contents to a newly allocated slice.
This wasted CPU cycles and memory bandwidth in some places, where the original slice contents wasn't needed
after slize resizing. Switch such places to bytesutil.ResizeNoCopy().
Rename the original bytesutil.Resize() function to bytesutil.ResizeWithCopy() for the sake of improved readability.
Additionally, allocate new slice with `make()` instead of `append()`. This guarantees that the capacity of the allocated slice
exactly matches the requested size. The `append()` could return a slice with bigger capacity as an optimization for further `append()` calls.
This could result in excess memory usage when the returned byte slice was cached (for instance, in lib/blockcache).
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2007
Previously these caches could exceed limits set via `-memory.allowedPercent` and/or `-memory.allowedBytes`,
since limits were set independently per each data part. If the number of data parts was big, then limits could be exceeded,
which could result to out of memory errors.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2007
The vm_cache_size_max_bytes metric can be used for determining caches which reach their capacity via the following query:
vm_cache_size_bytes / vm_cache_size_max_bytes > 0.9
This should reduce memory usage on systems with big number of CPU cores,
since every inmemoryPart object occupies at least 64KB of memory and sync.Pool maintains
a separate pool inmemoryPart objects per each CPU core.
Though the new scheme for the pool worsens per-cpu cache locality, this should be amortized
by big sizes of inmemoryPart objects.
CPU and memory profiles show that the pool capacity for inmemoryBlock objects is too small.
This results in the increased load on memory allocation code in Go runtime.
Increase the pool capacity in order to reduce the load on Go runtime.
This should improve hit ratio for tagFiltersCache when big number of new time series are constantly registered
(aka high churn rate). This, in turn, should reduce CPU usage for queries over such time series.
One minute cache timeout result in slower queries in some production workloads where the interval
between query execution is in the range 1 minute - 2 minutes.
This should reduce memory usage on a system with high number of active time series and a high churn rate.
One minute is enough for caching the blocks needed for repeated queries (e.g. alerting rules, recording rules and dashboard refreshes).
This should reduce memory usage for the pool on systems with big number of CPU cores.
The sync.Pool maintains per-CPU pools, so the total number of objects in the pool
is proportional to the number of available CPU cores. The channel limits the number
of pooled objects by its own capacity. This means smaller number of pooled objects on average.
