VictoriaMetrics/lib/streamaggr/streamaggr_timing_test.go
Aliaksandr Valialkin 0d5d46f9db
lib/streamaggr: huge pile of changes
- 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
2024-03-02 03:15:43 +02:00

88 lines
2.0 KiB
Go

package streamaggr
import (
"fmt"
"strings"
"testing"
"github.com/VictoriaMetrics/VictoriaMetrics/lib/prompbmarshal"
)
func BenchmarkAggregatorsPushByJobAvg(b *testing.B) {
for _, output := range []string{
"total",
"total_prometheus",
"increase",
"increase_prometheus",
"count_series",
"count_samples",
"unique_samples",
"sum_samples",
"last",
"min",
"max",
"avg",
"stddev",
"stdvar",
"histogram_bucket",
"quantiles(0, 0.5, 1)",
} {
b.Run(fmt.Sprintf("output=%s", output), func(b *testing.B) {
benchmarkAggregatorsPush(b, output)
})
}
}
func benchmarkAggregatorsPush(b *testing.B, output string) {
config := fmt.Sprintf(`
- match: http_requests_total
interval: 24h
without: [job]
outputs: [%q]
`, output)
pushFunc := func(tss []prompbmarshal.TimeSeries) {}
a, err := newAggregatorsFromData([]byte(config), pushFunc, 0)
if err != nil {
b.Fatalf("unexpected error when initializing aggregators: %s", err)
}
defer a.MustStop()
const loops = 10
b.ReportAllocs()
b.SetBytes(int64(len(benchSeries) * loops))
b.RunParallel(func(pb *testing.PB) {
var matchIdxs []byte
for pb.Next() {
for i := 0; i < loops; i++ {
series := benchSeries
for len(series) > 0 {
chunk := series
if len(chunk) > 1_000 {
chunk = series[:1_000]
series = series[len(chunk):]
} else {
series = nil
}
matchIdxs = a.Push(chunk, matchIdxs)
}
}
}
})
}
func newBenchSeries(seriesCount int) []prompbmarshal.TimeSeries {
a := make([]string, seriesCount)
for j := 0; j < seriesCount; j++ {
s := fmt.Sprintf(`http_requests_total{path="/foo/%d",job="foo",instance="bar",pod="pod-123232312",namespace="kube-foo-bar",node="node-123-3434-443",`+
`some_other_label="foo-bar-baz",environment="prod",label1="value1",label2="value2",label3="value3"} %d`, j, j*1000)
a = append(a, s)
}
metrics := strings.Join(a, "\n")
return mustParsePromMetrics(metrics)
}
const seriesCount = 10_000
var benchSeries = newBenchSeries(seriesCount)