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lib/logstorage: optimize performance for top
pipe when it is applied to a field with millions of unique values
- Use parallel merge of per-CPU shard results. This improves merge performance on multi-CPU systems. - Use topN heap sort of per-shard results. This improves performance when results contain millions of entries.
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@ -16,6 +16,7 @@ according to [these docs](https://docs.victoriametrics.com/victorialogs/quicksta
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## tip
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* FEATURE: add basic [alerting rules](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/deployment/docker/alerts-vlogs.yml) for VictoriaLogs process. See details at [monitoring docs](https://docs.victoriametrics.com/victorialogs/index.html#monitoring).
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* FEATURE: improve [`top` pipe](https://docs.victoriametrics.com/victorialogs/logsql/#top-pipe) performance on multi-CPU hosts when it is applied to [log fields](https://docs.victoriametrics.com/victorialogs/keyconcepts/#data-model) with big number of unique values. For example, `_time:1d | top 5 (user_id)` should be executed much faster now when `user_id` field contains millions of unique values.
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## [v0.36.0](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/tag/v0.36.0-victorialogs)
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@ -1,13 +1,17 @@
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package logstorage
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import (
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"container/heap"
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"fmt"
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"slices"
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"sort"
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"strings"
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"sync"
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"sync/atomic"
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"unsafe"
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"github.com/cespare/xxhash/v2"
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"github.com/VictoriaMetrics/VictoriaMetrics/lib/bytesutil"
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"github.com/VictoriaMetrics/VictoriaMetrics/lib/encoding"
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"github.com/VictoriaMetrics/VictoriaMetrics/lib/logger"
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@ -243,43 +247,24 @@ func (ptp *pipeTopProcessor) flush() error {
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if n := ptp.stateSizeBudget.Load(); n <= 0 {
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return fmt.Errorf("cannot calculate [%s], since it requires more than %dMB of memory", ptp.pt.String(), ptp.maxStateSize/(1<<20))
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}
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// merge state across shards
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shards := ptp.shards
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m := shards[0].getM()
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shards = shards[1:]
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for i := range shards {
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if needStop(ptp.stopCh) {
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limit := ptp.pt.limit
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if limit == 0 {
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return nil
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}
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for k, pHitsSrc := range shards[i].getM() {
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pHits, ok := m[k]
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if !ok {
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m[k] = pHitsSrc
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// merge state across shards in parallel
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var entries []*pipeTopEntry
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if len(ptp.shards) == 1 {
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entries = getTopEntries(ptp.shards[0].getM(), limit, ptp.stopCh)
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} else {
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*pHits += *pHitsSrc
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es, err := ptp.getTopEntriesParallel(limit)
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if err != nil {
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return err
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}
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entries = es
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}
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}
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// select top entries with the biggest number of hits
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entries := make([]pipeTopEntry, 0, len(m))
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for k, pHits := range m {
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entries = append(entries, pipeTopEntry{
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k: k,
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hits: *pHits,
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})
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}
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sort.Slice(entries, func(i, j int) bool {
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a, b := &entries[i], &entries[j]
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if a.hits == b.hits {
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return a.k < b.k
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}
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return a.hits > b.hits
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})
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if uint64(len(entries)) > ptp.pt.limit {
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entries = entries[:ptp.pt.limit]
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if needStop(ptp.stopCh) {
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return nil
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}
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// write result
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@ -373,11 +358,184 @@ func (ptp *pipeTopProcessor) flush() error {
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return nil
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}
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func (ptp *pipeTopProcessor) getTopEntriesParallel(limit uint64) ([]*pipeTopEntry, error) {
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shards := ptp.shards
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shardsLen := len(shards)
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var wg sync.WaitGroup
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perShardMaps := make([][]map[string]*uint64, shardsLen)
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for i := range shards {
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wg.Add(1)
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go func(idx int) {
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defer wg.Done()
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shardMaps := make([]map[string]*uint64, shardsLen)
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for i := range shardMaps {
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shardMaps[i] = make(map[string]*uint64)
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}
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n := int64(0)
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for k, pHitsSrc := range shards[idx].getM() {
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if needStop(ptp.stopCh) {
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return
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}
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h := xxhash.Sum64(bytesutil.ToUnsafeBytes(k))
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m := shardMaps[h%uint64(len(shardMaps))]
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n += updatePipeTopMap(m, k, pHitsSrc)
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if n > stateSizeBudgetChunk {
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if nRemaining := ptp.stateSizeBudget.Add(-n); nRemaining < 0 {
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return
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}
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n = 0
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}
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}
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ptp.stateSizeBudget.Add(-n)
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perShardMaps[idx] = shardMaps
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shards[idx].m = nil
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}(i)
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}
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wg.Wait()
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if needStop(ptp.stopCh) {
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return nil, nil
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}
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if n := ptp.stateSizeBudget.Load(); n < 0 {
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return nil, fmt.Errorf("cannot calculate [%s], since it requires more than %dMB of memory", ptp.pt.String(), ptp.maxStateSize/(1<<20))
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}
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entriess := make([][]*pipeTopEntry, shardsLen)
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for i := range entriess {
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wg.Add(1)
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go func(idx int) {
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defer wg.Done()
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m := perShardMaps[0][idx]
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n := int64(0)
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for _, shardMaps := range perShardMaps[1:] {
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for k, pHitsSrc := range shardMaps[idx] {
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if needStop(ptp.stopCh) {
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return
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}
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n += updatePipeTopMap(m, k, pHitsSrc)
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if n > stateSizeBudgetChunk {
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if nRemaining := ptp.stateSizeBudget.Add(-n); nRemaining < 0 {
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return
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}
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n = 0
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}
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}
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}
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ptp.stateSizeBudget.Add(-n)
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entriess[idx] = getTopEntries(m, ptp.pt.limit, ptp.stopCh)
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}(i)
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}
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wg.Wait()
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if needStop(ptp.stopCh) {
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return nil, nil
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}
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if n := ptp.stateSizeBudget.Load(); n < 0 {
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return nil, fmt.Errorf("cannot calculate [%s], since it requires more than %dMB of memory", ptp.pt.String(), ptp.maxStateSize/(1<<20))
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}
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// merge entriess
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entries := entriess[0]
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for _, es := range entriess[1:] {
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entries = append(entries, es...)
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}
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sort.Slice(entries, func(i, j int) bool {
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return entries[j].less(entries[i])
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})
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if uint64(len(entries)) > limit {
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entries = entries[:limit]
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}
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return entries, nil
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}
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func getTopEntries(m map[string]*uint64, limit uint64, stopCh <-chan struct{}) []*pipeTopEntry {
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if limit == 0 {
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return nil
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}
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var eh topEntriesHeap
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for k, pHits := range m {
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if needStop(stopCh) {
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return nil
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}
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e := pipeTopEntry{
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k: k,
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hits: *pHits,
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}
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if uint64(len(eh)) < limit {
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eCopy := e
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heap.Push(&eh, &eCopy)
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continue
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}
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if eh[0].less(&e) {
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eCopy := e
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eh[0] = &eCopy
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heap.Fix(&eh, 0)
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}
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}
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result := ([]*pipeTopEntry)(eh)
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for len(eh) > 0 {
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x := heap.Pop(&eh)
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result[len(eh)] = x.(*pipeTopEntry)
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}
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return result
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}
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func updatePipeTopMap(m map[string]*uint64, k string, pHitsSrc *uint64) int64 {
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pHitsDst, ok := m[k]
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if ok {
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*pHitsDst += *pHitsSrc
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return 0
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}
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m[k] = pHitsSrc
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return int64(unsafe.Sizeof(k) + unsafe.Sizeof(pHitsSrc))
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}
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type topEntriesHeap []*pipeTopEntry
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func (h *topEntriesHeap) Less(i, j int) bool {
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a := *h
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return a[i].less(a[j])
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}
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func (h *topEntriesHeap) Swap(i, j int) {
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a := *h
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a[i], a[j] = a[j], a[i]
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}
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func (h *topEntriesHeap) Len() int {
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return len(*h)
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}
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func (h *topEntriesHeap) Push(v any) {
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x := v.(*pipeTopEntry)
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*h = append(*h, x)
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}
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func (h *topEntriesHeap) Pop() any {
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a := *h
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x := a[len(a)-1]
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a[len(a)-1] = nil
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*h = a[:len(a)-1]
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return x
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}
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type pipeTopEntry struct {
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k string
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hits uint64
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}
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func (e *pipeTopEntry) less(r *pipeTopEntry) bool {
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if e.hits == r.hits {
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return e.k > r.k
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}
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return e.hits < r.hits
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}
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type pipeTopWriteContext struct {
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ptp *pipeTopProcessor
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rcs []resultColumn
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