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app/vmselect/netstorage: reduce memory allocations when unpacking time series
Unpack time series with less than 4M samples in the currently running goroutine. Previously a new goroutine was being started for unpacking the samples. This was requiring additional memory allocations.
This commit is contained in:
Aliaksandr Valialkin
parent
c8bd3534cb
commit
158a280822
1 changed files with 79 additions and 43 deletions
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@ -210,7 +210,7 @@ func (rss *Results) RunParallel(qt *querytracer.Tracer, f func(rs *Result, worke
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// (e.g. the number of available CPU cores).
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itemsPerWorker := 1
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if len(rss.packedTimeseries) > gomaxprocs {
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itemsPerWorker = 1 + len(rss.packedTimeseries)/gomaxprocs
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itemsPerWorker = (len(rss.packedTimeseries) + gomaxprocs - 1) / gomaxprocs
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}
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var start int
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var i uint
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@ -367,26 +367,34 @@ func scheduleUnpackWork(workChs []chan *unpackWork, uw *unpackWork) {
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}
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func unpackWorker(ch <-chan *unpackWork) {
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v := tmpBlockPool.Get()
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if v == nil {
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v = &storage.Block{}
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}
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tmpBlock := v.(*storage.Block)
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tmpBlock := getTmpStorageBlock()
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for upw := range ch {
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upw.unpack(tmpBlock)
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}
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tmpBlockPool.Put(v)
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putTmpStorageBlock(tmpBlock)
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}
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var tmpBlockPool sync.Pool
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func getTmpStorageBlock() *storage.Block {
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v := tmpStorageBlockPool.Get()
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if v == nil {
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v = &storage.Block{}
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}
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return v.(*storage.Block)
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}
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func putTmpStorageBlock(sb *storage.Block) {
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tmpStorageBlockPool.Put(sb)
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}
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var tmpStorageBlockPool sync.Pool
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// unpackBatchSize is the maximum number of blocks that may be unpacked at once by a single goroutine.
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//
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// It is better to load a single goroutine for up to one second on a system with many CPU cores
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// It is better to load a single goroutine for up to 100ms on a system with many CPU cores
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// in order to reduce inter-CPU memory ping-pong.
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// A single goroutine can unpack up to 40 millions of rows per second, while a single block contains up to 8K rows.
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// So the batch size should be 40M / 8K = 5K.
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var unpackBatchSize = 5000
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// So the batch size should be 100ms * 40M / 8K = 500.
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var unpackBatchSize = 500
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// Unpack unpacks pts to dst.
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func (pts *packedTimeseries) Unpack(dst *Result, tbfs []*tmpBlocksFile, tr storage.TimeRange) error {
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@ -394,12 +402,58 @@ func (pts *packedTimeseries) Unpack(dst *Result, tbfs []*tmpBlocksFile, tr stora
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if err := dst.MetricName.Unmarshal(bytesutil.ToUnsafeBytes(pts.metricName)); err != nil {
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return fmt.Errorf("cannot unmarshal metricName %q: %w", pts.metricName, err)
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}
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sbh := getSortBlocksHeap()
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var err error
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sbh.sbs, err = pts.unpackTo(sbh.sbs[:0], tbfs, tr)
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if err != nil {
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putSortBlocksHeap(sbh)
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return err
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}
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dedupInterval := storage.GetDedupInterval()
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mergeSortBlocks(dst, sbh, dedupInterval)
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putSortBlocksHeap(sbh)
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return nil
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}
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// Spin up local workers.
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func (pts *packedTimeseries) unpackTo(dst []*sortBlock, tbfs []*tmpBlocksFile, tr storage.TimeRange) ([]*sortBlock, error) {
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addrsLen := len(pts.addrs)
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upwsLen := (addrsLen + unpackBatchSize - 1) / unpackBatchSize
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if upwsLen == 1 {
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// Fast path for common case - unpack all the data in the current goroutine
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upw := getUnpackWork()
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upw.tbfs = tbfs
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for _, addr := range pts.addrs {
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upw.ws = append(upw.ws, unpackWorkItem{
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addr: addr,
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tr: tr,
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})
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}
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pts.addrs = pts.addrs[:0]
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tmpBlock := getTmpStorageBlock()
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upw.unpack(tmpBlock)
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putTmpStorageBlock(tmpBlock)
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if err := <-upw.doneCh; err != nil {
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return dst, err
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}
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samples := 0
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for _, sb := range upw.sbs {
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samples += len(sb.Timestamps)
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if *maxSamplesPerSeries > 0 && samples > *maxSamplesPerSeries {
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return dst, fmt.Errorf("cannot process more than %d samples per series; either increase -search.maxSamplesPerSeries "+
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"or reduce time range for the query", *maxSamplesPerSeries)
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}
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dst = append(dst, sb)
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}
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putUnpackWork(upw)
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return dst, nil
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}
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// Slow path - spin up multiple local workers for parallel data unpacking.
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// Do not use global workers pool, since it increases inter-CPU memory ping-poing,
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// which reduces the scalability on systems with many CPU cores.
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addrsLen := len(pts.addrs)
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workers := addrsLen / unpackBatchSize
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workers := upwsLen
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if workers > gomaxprocs {
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workers = gomaxprocs
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}
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@ -409,11 +463,7 @@ func (pts *packedTimeseries) Unpack(dst *Result, tbfs []*tmpBlocksFile, tr stora
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workChs := make([]chan *unpackWork, workers)
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var workChsWG sync.WaitGroup
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for i := 0; i < workers; i++ {
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// Use unbuffered channel on purpose, since there are high chances
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// that only a single unpackWork is needed to unpack.
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// The unbuffered channel should reduce inter-CPU ping-pong in this case,
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// which should improve the performance in a system with many CPU cores.
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workChs[i] = make(chan *unpackWork)
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workChs[i] = make(chan *unpackWork, 1)
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workChsWG.Add(1)
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go func(workerID int) {
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defer workChsWG.Done()
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@ -422,7 +472,7 @@ func (pts *packedTimeseries) Unpack(dst *Result, tbfs []*tmpBlocksFile, tr stora
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}
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// Feed workers with work
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upws := make([]*unpackWork, 0, 1+addrsLen/unpackBatchSize)
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upws := make([]*unpackWork, 0, upwsLen)
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upw := getUnpackWork()
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upw.tbfs = tbfs
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for _, addr := range pts.addrs {
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@ -441,14 +491,8 @@ func (pts *packedTimeseries) Unpack(dst *Result, tbfs []*tmpBlocksFile, tr stora
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upws = append(upws, upw)
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pts.addrs = pts.addrs[:0]
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// Wait until work is complete
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// Collect the unpacked sortBlock items
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samples := 0
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sbh := getSortBlocksHeap()
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sbs := sbh.sbs
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if n := addrsLen - cap(sbs); n > 0 {
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sbs = append(sbs[:cap(sbs)], make([]*sortBlock, n)...)
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}
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sbs = sbs[:0]
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var firstErr error
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for _, upw := range upws {
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if err := <-upw.doneCh; err != nil && firstErr == nil {
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@ -458,22 +502,20 @@ func (pts *packedTimeseries) Unpack(dst *Result, tbfs []*tmpBlocksFile, tr stora
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if firstErr == nil {
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for _, sb := range upw.sbs {
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samples += len(sb.Timestamps)
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if *maxSamplesPerSeries > 0 && samples > *maxSamplesPerSeries {
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firstErr = fmt.Errorf("cannot process more than %d samples per series; either increase -search.maxSamplesPerSeries "+
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"or reduce time range for the query", *maxSamplesPerSeries)
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break
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}
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dst = append(dst, sb)
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}
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if *maxSamplesPerSeries <= 0 || samples < *maxSamplesPerSeries {
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sbs = append(sbs, upw.sbs...)
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} else {
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firstErr = fmt.Errorf("cannot process more than %d samples per series; either increase -search.maxSamplesPerSeries "+
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"or reduce time range for the query", *maxSamplesPerSeries)
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}
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}
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if firstErr != nil {
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} else {
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for _, sb := range upw.sbs {
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putSortBlock(sb)
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}
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}
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putUnpackWork(upw)
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}
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sbh.sbs = sbs
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// Shut down local workers
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for _, workCh := range workChs {
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@ -481,13 +523,7 @@ func (pts *packedTimeseries) Unpack(dst *Result, tbfs []*tmpBlocksFile, tr stora
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}
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workChsWG.Wait()
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if firstErr != nil {
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return firstErr
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}
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dedupInterval := storage.GetDedupInterval()
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mergeSortBlocks(dst, sbh, dedupInterval)
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putSortBlocksHeap(sbh)
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return nil
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return dst, firstErr
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}
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func getSortBlock() *sortBlock {
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