Call runtime.Gosched() only when there is a work to steal from other workers.
Simplify the timeseriesWorker() and unpackWroker() code a bit by inlining stealTimeseriesWork() and stealUnpackWork().
This should reduce CPU usage when processing queries on systems with big number of CPU cores.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3966
- Allocate and initialize seriesByWorkerID slice in a single go instead
of initializing every item in the list separately.
This should reduce CPU usage a bit.
- Properly set anti-false sharing padding at timeseriesWithPadding structure
- Document the change at docs/CHANGELOG.md
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3966
* vmselect/promql: refactor `evalRollupNoIncrementalAggregate` to use lock-less approach for parallel workers computation
Locking there is causing issues when running on highly multi-core system as it introduces lock contention during results merge.
New implementation uses lock less approach to store results per workerID and merges final result in the end, this is expected to significantly reduce lock contention and CPU usage for systems with high number of cores.
Related: #3966
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* vmselect/promql: add pooling for `timeseriesWithPadding` to reduce allocations
Related: #3966
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
* vmselect/promql: refactor `evalRollupFuncWithSubquery` to avoid using locks
Uses same approach as `evalRollupNoIncrementalAggregate` to remove locking between workers and reduce lock contention.
Related: #3966
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
---------
Signed-off-by: Zakhar Bessarab <z.bessarab@victoriametrics.com>
This opens the possibility to remove tssLock from evalRollupFuncWithSubquery()
in the follow-up commit from @zekker6 in order to speed up the code
for systems with many CPU cores.
Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3966
* feat: include fonts in the build
* fix: reduce size fonts
* wip
- Document the change at docs/CHANGELOG.md
- Run `make vmui-update`
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
* feat: make the step input field global
* fix: correct get step from url
* fix: set minimumSignificantDigits to 1
* app/vmselect/vmui: `make vmui-update`
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
The per-series timestamps are usually shared among series, so it is unsafe modifying them.
The issue has been appeared after the optimization at 2f3ddd4884
* {lib/server, app/}: use `httpAuth.*` flag as fallback for `*AuthKey` if it is not set
* lib/ingestserver/opentsdbhttp: fix opentdb HTTP handler not respecting `httpAuth.*` flags
* Apply suggestions from code review
Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
Previously the selected time series were split evenly among available CPU cores
for further processing - e.g unpacking the data and applying the given rollup
function to the unpacked data.
Some time series could be processed slower than others.
This could result in uneven work distribution among available CPU cores,
e.g. some CPU cores could complete their work sooner than others.
This could slow down query execution.
The new algorithm allows stealing time series to process from other CPU cores
when all the local work is done. This should reduce the maximum time
needed for query execution (aka tail latency).
The new algorithm should also scale better on systems with many CPU cores,
since every CPU processes locally assigned time series without inter-CPU communications.
The inter-CPU communications are used only when all the local work is finished
and the pending work from other CPUs needs to be stealed.
Unpack time series with less than 400K samples in the currently running goroutine.
Previously a new goroutine was being started for unpacking the samples.
This was requiring additional memory allocations.
