2023-01-04 07:19:18 +01:00
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package streamaggr
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import (
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"math"
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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 01:42:26 +01:00
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"strings"
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2023-01-04 07:19:18 +01:00
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"sync"
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"github.com/VictoriaMetrics/VictoriaMetrics/lib/fasttime"
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)
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// stddevAggrState calculates output=stddev, e.g. the average value over input samples.
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type stddevAggrState struct {
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m sync.Map
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}
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type stddevStateValue struct {
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mu sync.Mutex
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count float64
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avg float64
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q float64
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deleted bool
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}
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func newStddevAggrState() *stddevAggrState {
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return &stddevAggrState{}
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}
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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 01:42:26 +01:00
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func (as *stddevAggrState) pushSamples(samples []pushSample) {
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for i := range samples {
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s := &samples[i]
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outputKey := getOutputKey(s.key)
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again:
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v, ok := as.m.Load(outputKey)
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if !ok {
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// The entry is missing in the map. Try creating it.
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v = &stddevStateValue{}
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outputKey = strings.Clone(outputKey)
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vNew, loaded := as.m.LoadOrStore(outputKey, v)
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if loaded {
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// Use the entry created by a concurrent goroutine.
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v = vNew
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}
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}
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sv := v.(*stddevStateValue)
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sv.mu.Lock()
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deleted := sv.deleted
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if !deleted {
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// See `Rapid calculation methods` at https://en.wikipedia.org/wiki/Standard_deviation
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sv.count++
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avg := sv.avg + (s.value-sv.avg)/sv.count
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sv.q += (s.value - sv.avg) * (s.value - avg)
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sv.avg = avg
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}
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sv.mu.Unlock()
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if deleted {
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// The entry has been deleted by the concurrent call to appendSeriesForFlush
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// Try obtaining and updating the entry again.
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goto again
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2023-01-04 07:19:18 +01:00
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}
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}
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}
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func (as *stddevAggrState) appendSeriesForFlush(ctx *flushCtx) {
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currentTimeMsec := int64(fasttime.UnixTimestamp()) * 1000
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m := &as.m
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m.Range(func(k, v interface{}) bool {
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// Atomically delete the entry from the map, so new entry is created for the next flush.
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m.Delete(k)
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sv := v.(*stddevStateValue)
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sv.mu.Lock()
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stddev := math.Sqrt(sv.q / sv.count)
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// Mark the entry as deleted, so it won't be updated anymore by concurrent pushSample() calls.
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sv.deleted = true
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sv.mu.Unlock()
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key := k.(string)
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ctx.appendSeries(key, "stddev", currentTimeMsec, stddev)
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return true
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})
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}
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