The labelsMap struct employs the fact that label indexes are condensed around 0,
so it stores the referred labels in a slice instead of map and uses slice index as label key.
This allows increasing the LabelsCompressor.Decompress performance by up to 3x.
This also reduces the latency of data flush in stream aggregation.
- Reduce memory usage by up to 5x when de-duplicating samples across big number of time series.
- Reduce memory usage by up to 5x when aggregating across big number of output time series.
- Add lib/promutils.LabelsCompressor, which is going to be used by other VictoriaMetrics components
for reducing memory usage for marshaled []prompbmarshal.Label.
- Add `dedup_interval` option at aggregation config, which allows setting individual
deduplication intervals per each aggregation.
- Add `keep_metric_names` option at aggregation config, which allows keeping the original
metric names in the output samples.
- Add `unique_samples` output, which counts the number of unique sample values.
- Add `increase_prometheus` and `total_prometheus` outputs, which ignore the first sample
per each newly encountered time series.
- Use 64-bit hashes instead of marshaled labels as map keys when calculating `count_series` output.
This makes obsolete https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5579
- Expose various metrics, which may help debugging stream aggregation:
- vm_streamaggr_dedup_state_size_bytes - the size of data structures responsible for deduplication
- vm_streamaggr_dedup_state_items_count - the number of items in the deduplication data structures
- vm_streamaggr_labels_compressor_size_bytes - the size of labels compressor data structures
- vm_streamaggr_labels_compressor_items_count - the number of entries in the labels compressor
- vm_streamaggr_flush_duration_seconds - a histogram, which shows the duration of stream aggregation flushes
- vm_streamaggr_dedup_flush_duration_seconds - a histogram, which shows the duration of deduplication flushes
- vm_streamaggr_flush_timeouts_total - counter for timed out stream aggregation flushes,
which took longer than the configured interval
- vm_streamaggr_dedup_flush_timeouts_total - counter for timed out deduplication flushes,
which took longer than the configured dedup_interval
- Actualize docs/stream-aggregation.md
The memory usage reduction increases CPU usage during stream aggregation by up to 30%.
This commit is based on https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5850
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5898
- Document the ability to read OpenTelemetry data from Amazon Firehose at docs/CHANGELOG.md
- Simplify parsing Firehose data. There is no need in trying to optimize the parsing with fastjson
and byte slice tricks, since OpenTelemetry protocol is really slooow because of over-engineering.
It is better to write clear code for better maintanability in the future.
- Move Firehose parser from /lib/protoparser/firehose to lib/protoparser/opentelemetry/firehose,
since it is used only by opentelemetry parser.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5893
This solves two issues:
1. The vm_backups_uploaded_bytes_total metric will grow more smoothly
2. This prevents from int overflow at metrics.Counter.Add() when uploading files bigger than 2GiB
This should simplify code maintenance by gradually converting to atomic.* types instead of calling atomic.* functions
on int and bool types.
See ea9e2b19a5
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
It has been appeared that there are VictoriaMetrics users, who rely on the fact that
VictoriaMetrics components were closing incoming connections to -httpListenAddr every 2 minutes
by default. So let's return back this value by default in order to fix the breaking change
made at d8c1db7953 .
See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1304#issuecomment-1961891450 .
Previously the (date, metricID) entries for dates older than the last 2 days were removed.
This could lead to slow check for the (date, metricID) entry in the indexdb during ingesting historical data (aka backfilling).
The issue has been introduced in 431aa16c8d
This commit returns back limits for these endpoints, which have been removed at 5d66ee88bd ,
since it has been appeared that missing limits result in high CPU usage, while the introduced concurrency limiter
results in failed lightweight requests to these endpoints because of timeout when heavyweight requests are executed.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5055
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.
* app/vmselect: adds milliseconds to the csv export response for rfc3339
* milliseconds is a standard prescion for VictoriaMetrics query request responses
https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5837
* app/victoria-metrics: adds tests for csv export/import
follow-up after 3541a8d0cf96dd4f8563624c4aab6816615d0756
---------
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Co-authored-by: hagen1778 <roman@victoriametrics.com>
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.
