* deployment: create a separate env for VictoriaLogs
The new environment consists of the following components:
* VictoriaLogs
* fluentbit for collecting logs and sending to VictoriaLogs
* VictoriaMetrics for scraping and storing metrics from fluentbit and VictoriaLogs
* Grafana with VictoriaLogs datasource for monitoring
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The motivation for creating a separate environment is to simplify existing environments
and make it easier to update or modify them in future.
Signed-off-by: hagen1778 <roman@victoriametrics.com>
Prevsiously they were swapped - the first arg should be the label name and the second arg should be label filters
This is a follow-up for e389b7b959e8144fdff5075bf7a5a39b2b0c6dd3
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/5847
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
* vmui: add a time picker to the "Logs Explorer" page #5673
* Update app/vmui/packages/vmui/src/pages/ExploreLogs/hooks/useFetchLogs.ts
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Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
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
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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.
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.
This should improve maintainability of the code related to rollup functions,
since it is located in rollup.go
While at it, properly return empty results from holt_winters(), rate_over_sum(),
sum2_over_time(), geomean_over_time() and distinct_over_time() when there are no real samples
on the selected lookbehind window. Previously the previous sample value was mistakenly
returned from these functions.
