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d782387f81
Rather than buffer up symbols in RAM, do it one by one during compaction. Then use the reader's symbol handling for symbol lookups during the rest of the index write. There is some slowdown in compaction, due to having to look through a file rather than a hash lookup. This is noise to the overall cost of compacting series with thousands of samples though. benchmark old ns/op new ns/op delta BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 539917175 675341565 +25.08% BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 2441815993 2477453524 +1.46% BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 3978543559 3922909687 -1.40% BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 8430219716 8586610007 +1.86% BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 1786424591 1909552782 +6.89% BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 5328998202 6020839950 +12.98% BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 10085059958 11085278690 +9.92% BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 25497010155 27018079806 +5.97% BenchmarkCompactionFromHead/labelnames=1,labelvalues=100000-4 2427391406 2817217987 +16.06% BenchmarkCompactionFromHead/labelnames=10,labelvalues=10000-4 2592965497 2538805050 -2.09% BenchmarkCompactionFromHead/labelnames=100,labelvalues=1000-4 2437388343 2668012858 +9.46% BenchmarkCompactionFromHead/labelnames=1000,labelvalues=100-4 2317095324 2787423966 +20.30% BenchmarkCompactionFromHead/labelnames=10000,labelvalues=10-4 2600239857 2096973860 -19.35% benchmark old allocs new allocs delta BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 500851 470794 -6.00% BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 821527 791451 -3.66% BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 1141562 1111508 -2.63% BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 2141576 2111504 -1.40% BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 871466 841424 -3.45% BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 1941428 1911415 -1.55% BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 3071573 3041510 -0.98% BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 6771648 6741509 -0.45% BenchmarkCompactionFromHead/labelnames=1,labelvalues=100000-4 731493 824888 +12.77% BenchmarkCompactionFromHead/labelnames=10,labelvalues=10000-4 793918 887311 +11.76% BenchmarkCompactionFromHead/labelnames=100,labelvalues=1000-4 811842 905204 +11.50% BenchmarkCompactionFromHead/labelnames=1000,labelvalues=100-4 832244 925081 +11.16% BenchmarkCompactionFromHead/labelnames=10000,labelvalues=10-4 921553 1019162 +10.59% benchmark old bytes new bytes delta BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 40532648 35698276 -11.93% BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 60340216 53409568 -11.49% BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 81087336 72065552 -11.13% BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 142485576 120878544 -15.16% BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 208661368 203831136 -2.31% BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 347345904 340484696 -1.98% BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 585185856 576244648 -1.53% BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 1357641792 1358966528 +0.10% BenchmarkCompactionFromHead/labelnames=1,labelvalues=100000-4 126486664 119666744 -5.39% BenchmarkCompactionFromHead/labelnames=10,labelvalues=10000-4 122323192 115117224 -5.89% BenchmarkCompactionFromHead/labelnames=100,labelvalues=1000-4 126404504 119469864 -5.49% BenchmarkCompactionFromHead/labelnames=1000,labelvalues=100-4 119047832 112230408 -5.73% BenchmarkCompactionFromHead/labelnames=10000,labelvalues=10-4 136576016 116634800 -14.60% Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
80 lines
2.2 KiB
Go
80 lines
2.2 KiB
Go
// Copyright 2017 The Prometheus Authors
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package tsdb
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import (
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"github.com/prometheus/prometheus/pkg/labels"
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"github.com/prometheus/prometheus/tsdb/chunkenc"
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"github.com/prometheus/prometheus/tsdb/chunks"
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"github.com/prometheus/prometheus/tsdb/tombstones"
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)
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type mockIndexWriter struct {
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series []seriesSamples
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}
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func (mockIndexWriter) AddSymbol(sym string) error { return nil }
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func (m *mockIndexWriter) AddSeries(ref uint64, l labels.Labels, chunks ...chunks.Meta) error {
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i := -1
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for j, s := range m.series {
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if !labels.Equal(labels.FromMap(s.lset), l) {
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continue
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}
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i = j
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break
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}
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if i == -1 {
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m.series = append(m.series, seriesSamples{
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lset: l.Map(),
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})
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i = len(m.series) - 1
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}
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var iter chunkenc.Iterator
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for _, chk := range chunks {
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samples := make([]sample, 0, chk.Chunk.NumSamples())
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iter = chk.Chunk.Iterator(iter)
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for iter.Next() {
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s := sample{}
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s.t, s.v = iter.At()
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samples = append(samples, s)
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}
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if err := iter.Err(); err != nil {
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return err
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}
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m.series[i].chunks = append(m.series[i].chunks, samples)
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}
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return nil
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}
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func (mockIndexWriter) WriteLabelIndex(names []string, values []string) error { return nil }
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func (mockIndexWriter) Close() error { return nil }
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type mockBReader struct {
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ir IndexReader
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cr ChunkReader
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mint int64
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maxt int64
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}
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func (r *mockBReader) Index() (IndexReader, error) { return r.ir, nil }
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func (r *mockBReader) Chunks() (ChunkReader, error) { return r.cr, nil }
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func (r *mockBReader) Tombstones() (tombstones.Reader, error) {
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return tombstones.NewMemTombstones(), nil
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}
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func (r *mockBReader) Meta() BlockMeta { return BlockMeta{MinTime: r.mint, MaxTime: r.maxt} }
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