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https://github.com/VictoriaMetrics/VictoriaMetrics.git
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28a9e92b5e
- 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
88 lines
2.2 KiB
Go
88 lines
2.2 KiB
Go
package streamaggr
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import (
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"fmt"
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"reflect"
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"strings"
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"sync"
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"sync/atomic"
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"testing"
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)
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func TestDedupAggrSerial(t *testing.T) {
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da := newDedupAggr()
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const seriesCount = 100_000
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expectedSamplesMap := make(map[string]pushSample)
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for i := 0; i < 2; i++ {
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samples := make([]pushSample, seriesCount)
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for j := range samples {
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sample := &samples[j]
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sample.key = fmt.Sprintf("key_%d", j)
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sample.value = float64(i + j)
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expectedSamplesMap[sample.key] = *sample
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}
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da.pushSamples(samples)
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}
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if n := da.sizeBytes(); n > 4_200_000 {
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t.Fatalf("too big dedupAggr state before flush: %d bytes; it shouldn't exceed 4_200_000 bytes", n)
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}
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if n := da.itemsCount(); n != seriesCount {
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t.Fatalf("unexpected itemsCount; got %d; want %d", n, seriesCount)
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}
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flushedSamplesMap := make(map[string]pushSample)
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flushSamples := func(samples []pushSample) {
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for _, sample := range samples {
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sample.key = strings.Clone(sample.key)
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flushedSamplesMap[sample.key] = sample
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}
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}
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da.flush(flushSamples)
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if !reflect.DeepEqual(expectedSamplesMap, flushedSamplesMap) {
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t.Fatalf("unexpected samples;\ngot\n%v\nwant\n%v", flushedSamplesMap, expectedSamplesMap)
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}
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if n := da.sizeBytes(); n > 17_000 {
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t.Fatalf("too big dedupAggr state after flush; %d bytes; it shouldn't exceed 17_000 bytes", n)
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}
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if n := da.itemsCount(); n != 0 {
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t.Fatalf("unexpected non-zero itemsCount after flush; got %d", n)
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}
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}
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func TestDedupAggrConcurrent(t *testing.T) {
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const concurrency = 5
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const seriesCount = 10_000
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da := newDedupAggr()
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var samplesFlushed atomic.Int64
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flushSamples := func(samples []pushSample) {
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samplesFlushed.Add(int64(len(samples)))
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}
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var wg sync.WaitGroup
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for i := 0; i < concurrency; i++ {
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wg.Add(1)
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go func() {
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defer wg.Done()
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for i := 0; i < 10; i++ {
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samples := make([]pushSample, seriesCount)
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for j := range samples {
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sample := &samples[j]
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sample.key = fmt.Sprintf("key_%d", j)
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sample.value = float64(i + j)
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}
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da.pushSamples(samples)
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}
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da.flush(flushSamples)
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}()
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}
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wg.Wait()
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if n := samplesFlushed.Load(); n < seriesCount {
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t.Fatalf("too small number of series flushed; got %d; want at least %d", n, seriesCount)
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}
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}
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