VictoriaMetrics/lib/streamaggr/dedup.go

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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 00:42:26 +00:00
package streamaggr
import (
"strings"
"sync"
"unsafe"
"github.com/VictoriaMetrics/VictoriaMetrics/lib/bytesutil"
"github.com/cespare/xxhash/v2"
)
const dedupAggrShardsCount = 128
type dedupAggr struct {
shards []dedupAggrShard
}
type dedupAggrShard struct {
dedupAggrShardNopad
// The padding prevents false sharing on widespread platforms with
// 128 mod (cache line size) = 0 .
_ [128 - unsafe.Sizeof(dedupAggrShardNopad{})%128]byte
}
type dedupAggrShardNopad struct {
mu sync.Mutex
m map[string]*dedupAggrSample
}
type dedupAggrSample struct {
value float64
}
func newDedupAggr() *dedupAggr {
shards := make([]dedupAggrShard, dedupAggrShardsCount)
return &dedupAggr{
shards: shards,
}
}
func (da *dedupAggr) sizeBytes() uint64 {
n := uint64(unsafe.Sizeof(*da))
for i := range da.shards {
n += da.shards[i].sizeBytes()
}
return n
}
func (da *dedupAggr) itemsCount() uint64 {
n := uint64(0)
for i := range da.shards {
n += da.shards[i].itemsCount()
}
return n
}
func (das *dedupAggrShard) sizeBytes() uint64 {
das.mu.Lock()
n := uint64(unsafe.Sizeof(*das))
for k, s := range das.m {
n += uint64(len(k)) + uint64(unsafe.Sizeof(k)+unsafe.Sizeof(s)+unsafe.Sizeof(*s))
}
das.mu.Unlock()
return n
}
func (das *dedupAggrShard) itemsCount() uint64 {
das.mu.Lock()
n := uint64(len(das.m))
das.mu.Unlock()
return n
}
func (da *dedupAggr) pushSamples(samples []pushSample) {
pss := getPerShardSamples()
shards := pss.shards
for _, sample := range samples {
h := xxhash.Sum64(bytesutil.ToUnsafeBytes(sample.key))
idx := h % uint64(len(shards))
shards[idx] = append(shards[idx], sample)
}
for i, shardSamples := range shards {
if len(shardSamples) == 0 {
continue
}
da.shards[i].pushSamples(shardSamples)
}
putPerShardSamples(pss)
}
type dedupFlushCtx struct {
samples []pushSample
}
func (ctx *dedupFlushCtx) reset() {
clear(ctx.samples)
ctx.samples = ctx.samples[:0]
}
func (da *dedupAggr) flush(f func(samples []pushSample)) {
ctx := &dedupFlushCtx{}
shards := da.shards
for i := range shards {
ctx.reset()
shards[i].flush(ctx, f)
}
}
type perShardSamples struct {
shards [][]pushSample
}
func (pss *perShardSamples) reset() {
shards := pss.shards
for i, shardSamples := range shards {
if len(shardSamples) > 0 {
clear(shardSamples)
shards[i] = shardSamples[:0]
}
}
}
func getPerShardSamples() *perShardSamples {
v := perShardSamplesPool.Get()
if v == nil {
return &perShardSamples{
shards: make([][]pushSample, dedupAggrShardsCount),
}
}
return v.(*perShardSamples)
}
func putPerShardSamples(pss *perShardSamples) {
pss.reset()
perShardSamplesPool.Put(pss)
}
var perShardSamplesPool sync.Pool
func (das *dedupAggrShard) pushSamples(samples []pushSample) {
das.mu.Lock()
defer das.mu.Unlock()
m := das.m
if m == nil {
m = make(map[string]*dedupAggrSample, len(samples))
das.m = m
}
for _, sample := range samples {
s, ok := m[sample.key]
if ok {
s.value = sample.value
} else {
key := strings.Clone(sample.key)
m[key] = &dedupAggrSample{
value: sample.value,
}
}
}
}
func (das *dedupAggrShard) flush(ctx *dedupFlushCtx, f func(samples []pushSample)) {
das.mu.Lock()
m := das.m
das.m = nil
das.mu.Unlock()
if len(m) == 0 {
return
}
dstSamples := ctx.samples
for key, s := range m {
dstSamples = append(dstSamples, pushSample{
key: key,
value: s.value,
})
}
ctx.samples = dstSamples
f(dstSamples)
}