Another batch of documentation improvements
Fix Spelling in:
- Comments in code
- Displayed strings
One change was in a json file used for the anomaly dashboard in docker,
else no other code was changed.
Some Markdown changes, related to standards:
- URLs
- List numbering
- Empty spaces at the end of a line
This reduces memory usage when reading large response bodies because the underlying buffer
doesn't need to be re-allocated during the read of large response body in the buffer.
Also decompress response body under the processScrapedDataConcurrencyLimitCh .
This reduces CPU usage and RAM usage a bit when scraping thousands of targets.
Previously the buffer was increased by 30% after it became 50% full.
For example, if more than 5MB of data is read into 10MB buffer, then its' size
was increased to 13MB, leading to 13MB-5MB = 8MB of waste.
This translates to 8MB/5MB = 160% waste in the worst case.
The updated algorithm increases the buffer by 30% after it becomes ~94% full.
This means that if more than 9.4MB of data is read into 10MB buffer,
then its' size is increased to 13MB, leading to 13MB-9.4MB = 3.6MB of waste.
This translates to 3.6MB / 9.4MB = ~38% waste in the worst case.
This should reduce memory usage when vmagent reads big responses from scrape targets.
While at it, properly append the data to buffer if it already has more than 4KiB of data.
Previously the data over 4KiB in the buffer was lost after ReadFrom call.
This is a follow-up for f28f496a9d
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/6761
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6759
### Describe Your Changes
Fix many spelling errors and some grammar, including misspellings in
filenames.
The change also fixes a typo in metric `vm_mmaped_files` to `vm_mmapped_files`.
While this is a breaking change, this metric isn't used in alerts or dashboards.
So it seems to have low impact on users.
The change also deprecates `cspell` as it is much heavier and less usable.
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Co-authored-by: Andrii Chubatiuk <achubatiuk@victoriametrics.com>
Co-authored-by: Andrii Chubatiuk <andrew.chubatiuk@gmail.com>
This commit adds lib/chunkedbuffer.Buffer - an in-memory chunked buffer
optimized for random access via MustReadAt() function.
It is better than bytesutil.ByteBuffer for storing large volumes of data,
since it stores the data in chunks of a fixed size (4KiB at the moment)
instead of using a contiguous memory region. This has the following benefits over bytesutil.ByteBuffer:
- reduced memory fragmentation
- reduced memory re-allocations when new data is written to the buffer
- reduced memory usage, since the allocated chunks can be re-used
by other Buffer instances after Buffer.Reset() call
Performance tests show up to 2x memory reduction for VictoriaLogs
when ingesting logs with big number of fields (aka wide events) under high speed.
…an 64KB at Reset
This commit reverts
b58e2ab214
as it has negative impacts when ByteBuffer is used for workloads that
always exceed 64KiB size. This significantly slows down affected
components because:
* buffers aren't beign reused;
* growing new buffers to >64KiB is very slow.
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/8501
### Describe Your Changes
Please provide a brief description of the changes you made. Be as
specific as possible to help others understand the purpose and impact of
your modifications.
### Checklist
The following checks are **mandatory**:
- [ ] My change adheres [VictoriaMetrics contributing
guidelines](https://docs.victoriametrics.com/contributing/).
Signed-off-by: hagen1778 <roman@victoriametrics.com>
There is little sense in keeping too big buffers - they just waste RAM and do not reduce
the load on GC too much. So it is better dropping such buffers at Reset instead of keeping them around.
Before, buffer growth was always x2 of its size, which could lead to
excessive memory usage when processing big amount of data.
For example, scraping a target with hundreds of MBs in response could
result into hih memory spikes in vmagent because buffer has to double
its size to fit the response. See
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/6759
The change smoothes out the growth rate, trading higher allocation rate
for lower mem usage at certain conditions.
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Signed-off-by: hagen1778 <roman@victoriametrics.com>
'any' type is supported starting from Go1.18. Let's consistently use it
instead of 'interface{}' type across the code base, since `any` is easier to read than 'interface{}'.
- Make it in a separate goroutine, so it doesn't slow down regular intern() calls.
- Do not lock internStringMap.mutableLock during the cleanup routine, since now
it is called from a single goroutine and reads only the readonly part of the internStringMap.
This should prevent from locking regular intern() calls for new strings during cleanups.
- Add jitter to the cleanup interval in order to prevent from synchornous increase in resource usage
during cleanups.
- Run the cleanup twice per -internStringCacheExpireDuration . This should save 30% CPU time spent
on cleanup comparing to the previous code, which was running the cleanup 3 times per -internStringCacheExpireDuration .
This should reduce GC overhead when tens of millions of strings are interned (for example, during stream deduplication
of millions of active time series).
The limit has been increased from 300 bytes to 500 bytes according to the collected production stats.
This allows reducing CPU usage without significant increase of RAM usage in most practical cases.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3692
Allow users fine-tuning the maximum string length for interning via -internStringMaxLen command-line flag.
This may be used for fine-tuning RAM vs CPU usage for certain workloads.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3692
Previously only up to 100K results were cached.
This could result in sub-optimal performance when more than 100K unique strings were actually used.
For example, when the relabeling rule was applied to a million of unique Graphite metric names
like in the https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3466
This commit should reduce the long-term CPU usage for https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3466
after all the unique Graphite metrics are registered in the FastStringMatcher.Transform() cache.
It is expected that the number of unique strings, which are passed to FastStringMatcher.Match(),
FastStringTransformer.Transform() and to InternString() during the last 5 minutes,
is limited, so the function results fit memory. Otherwise OOM crash can occur.
This should be the case for typical production workloads.
- Return meta-labels for the discovered targets via promutils.Labels
instead of map[string]string. This improves the speed of generating
meta-labels for discovered targets by up to 5x.
- Remove memory allocations in hot paths during ScrapeWork generation.
The ScrapeWork contains scrape settings for a single discovered target.
This improves the service discovery speed by up to 2x.
- show dates in human-readable format, e.g. 2022-05-07, instead of a numeric value
- limit the maximum length of queries and filters shown in trace messages
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
