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lib/streamaggr: do not flush dedup shards in parallel
This significantly increases CPU usage on systems with many CPU cores, while doesn't reduce flush latency too much
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parent
946814afee
commit
eb40395a1c
2 changed files with 8 additions and 14 deletions
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@ -113,22 +113,14 @@ func (ctx *dedupFlushCtx) reset() {
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}
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}
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func (da *dedupAggr) flush(f func(samples []pushSample)) {
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func (da *dedupAggr) flush(f func(samples []pushSample)) {
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var wg sync.WaitGroup
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// Do not flush shards in parallel, since this significantly increases CPU usage
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// on systems with many CPU cores, while doesn't improve flush latency too much.
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ctx := getDedupFlushCtx()
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for i := range da.shards {
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for i := range da.shards {
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flushConcurrencyCh <- struct{}{}
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ctx.reset()
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wg.Add(1)
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da.shards[i].flush(ctx, f)
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go func(shard *dedupAggrShard) {
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defer func() {
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<-flushConcurrencyCh
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wg.Done()
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}()
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ctx := getDedupFlushCtx()
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shard.flush(ctx, f)
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putDedupFlushCtx(ctx)
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}(&da.shards[i])
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}
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}
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wg.Wait()
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putDedupFlushCtx(ctx)
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}
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}
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type perShardSamples struct {
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type perShardSamples struct {
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@ -670,7 +670,9 @@ func (a *aggregator) dedupFlush(dedupInterval time.Duration) {
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startTime := time.Now()
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startTime := time.Now()
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flushConcurrencyCh <- struct{}{}
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a.da.flush(a.pushSamples)
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a.da.flush(a.pushSamples)
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<-flushConcurrencyCh
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d := time.Since(startTime)
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d := time.Since(startTime)
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a.dedupFlushDuration.Update(d.Seconds())
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a.dedupFlushDuration.Update(d.Seconds())
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