mirror of
https://github.com/prometheus/prometheus.git
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8ef7dfdeeb
Add a chunk size limit in bytes This creates a hard cap for XOR chunks of 1024 bytes. The limit for histogram chunk is also 1024 bytes, but it is a soft limit as a histogram has a dynamic size, and even a single one could be larger than 1024 bytes. This also avoids cutting new histogram chunks if the existing chunk has fewer than 10 histograms yet. In that way, we are accepting "jumbo chunks" in order to have at least 10 histograms in a chunk, allowing compression to kick in. Signed-off-by: Justin Lei <justin.lei@grafana.com>
458 lines
12 KiB
Go
458 lines
12 KiB
Go
// Copyright 2020 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 storage
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import (
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"fmt"
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"math"
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"sort"
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"github.com/prometheus/prometheus/model/histogram"
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"github.com/prometheus/prometheus/model/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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)
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type SeriesEntry struct {
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Lset labels.Labels
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SampleIteratorFn func(chunkenc.Iterator) chunkenc.Iterator
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}
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func (s *SeriesEntry) Labels() labels.Labels { return s.Lset }
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func (s *SeriesEntry) Iterator(it chunkenc.Iterator) chunkenc.Iterator { return s.SampleIteratorFn(it) }
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type ChunkSeriesEntry struct {
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Lset labels.Labels
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ChunkIteratorFn func(chunks.Iterator) chunks.Iterator
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}
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func (s *ChunkSeriesEntry) Labels() labels.Labels { return s.Lset }
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func (s *ChunkSeriesEntry) Iterator(it chunks.Iterator) chunks.Iterator { return s.ChunkIteratorFn(it) }
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// NewListSeries returns series entry with iterator that allows to iterate over provided samples.
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func NewListSeries(lset labels.Labels, s []chunks.Sample) *SeriesEntry {
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samplesS := Samples(samples(s))
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return &SeriesEntry{
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Lset: lset,
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SampleIteratorFn: func(it chunkenc.Iterator) chunkenc.Iterator {
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if lsi, ok := it.(*listSeriesIterator); ok {
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lsi.Reset(samplesS)
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return lsi
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}
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return NewListSeriesIterator(samplesS)
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},
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}
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}
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// NewListChunkSeriesFromSamples returns chunk series entry that allows to iterate over provided samples.
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// NOTE: It uses inefficient chunks encoding implementation, not caring about chunk size.
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// Use only for testing.
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func NewListChunkSeriesFromSamples(lset labels.Labels, samples ...[]chunks.Sample) *ChunkSeriesEntry {
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chksFromSamples := make([]chunks.Meta, 0, len(samples))
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for _, s := range samples {
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cfs, err := chunks.ChunkFromSamples(s)
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if err != nil {
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return &ChunkSeriesEntry{
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Lset: lset,
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ChunkIteratorFn: func(it chunks.Iterator) chunks.Iterator {
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return errChunksIterator{err: err}
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},
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}
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}
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chksFromSamples = append(chksFromSamples, cfs)
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}
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return &ChunkSeriesEntry{
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Lset: lset,
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ChunkIteratorFn: func(it chunks.Iterator) chunks.Iterator {
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lcsi, existing := it.(*listChunkSeriesIterator)
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var chks []chunks.Meta
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if existing {
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chks = lcsi.chks[:0]
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} else {
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chks = make([]chunks.Meta, 0, len(samples))
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}
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chks = append(chks, chksFromSamples...)
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if existing {
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lcsi.Reset(chks...)
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return lcsi
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}
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return NewListChunkSeriesIterator(chks...)
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},
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}
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}
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type listSeriesIterator struct {
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samples Samples
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idx int
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}
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type samples []chunks.Sample
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func (s samples) Get(i int) chunks.Sample { return s[i] }
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func (s samples) Len() int { return len(s) }
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// Samples interface allows to work on arrays of types that are compatible with chunks.Sample.
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type Samples interface {
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Get(i int) chunks.Sample
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Len() int
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}
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// NewListSeriesIterator returns listSeriesIterator that allows to iterate over provided samples.
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func NewListSeriesIterator(samples Samples) chunkenc.Iterator {
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return &listSeriesIterator{samples: samples, idx: -1}
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}
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func (it *listSeriesIterator) Reset(samples Samples) {
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it.samples = samples
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it.idx = -1
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}
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func (it *listSeriesIterator) At() (int64, float64) {
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s := it.samples.Get(it.idx)
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return s.T(), s.F()
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}
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func (it *listSeriesIterator) AtHistogram() (int64, *histogram.Histogram) {
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s := it.samples.Get(it.idx)
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return s.T(), s.H()
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}
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func (it *listSeriesIterator) AtFloatHistogram() (int64, *histogram.FloatHistogram) {
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s := it.samples.Get(it.idx)
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return s.T(), s.FH()
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}
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func (it *listSeriesIterator) AtT() int64 {
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s := it.samples.Get(it.idx)
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return s.T()
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}
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func (it *listSeriesIterator) Next() chunkenc.ValueType {
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it.idx++
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if it.idx >= it.samples.Len() {
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return chunkenc.ValNone
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}
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return it.samples.Get(it.idx).Type()
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}
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func (it *listSeriesIterator) Seek(t int64) chunkenc.ValueType {
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if it.idx == -1 {
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it.idx = 0
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}
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if it.idx >= it.samples.Len() {
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return chunkenc.ValNone
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}
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// No-op check.
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if s := it.samples.Get(it.idx); s.T() >= t {
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return s.Type()
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}
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// Do binary search between current position and end.
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it.idx += sort.Search(it.samples.Len()-it.idx, func(i int) bool {
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s := it.samples.Get(i + it.idx)
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return s.T() >= t
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})
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if it.idx >= it.samples.Len() {
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return chunkenc.ValNone
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}
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return it.samples.Get(it.idx).Type()
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}
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func (it *listSeriesIterator) Err() error { return nil }
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type listChunkSeriesIterator struct {
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chks []chunks.Meta
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idx int
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}
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// NewListChunkSeriesIterator returns listChunkSeriesIterator that allows to iterate over provided chunks.
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func NewListChunkSeriesIterator(chks ...chunks.Meta) chunks.Iterator {
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return &listChunkSeriesIterator{chks: chks, idx: -1}
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}
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func (it *listChunkSeriesIterator) Reset(chks ...chunks.Meta) {
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it.chks = chks
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it.idx = -1
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}
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func (it *listChunkSeriesIterator) At() chunks.Meta {
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return it.chks[it.idx]
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}
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func (it *listChunkSeriesIterator) Next() bool {
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it.idx++
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return it.idx < len(it.chks)
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}
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func (it *listChunkSeriesIterator) Err() error { return nil }
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type chunkSetToSeriesSet struct {
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ChunkSeriesSet
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iter chunks.Iterator
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chkIterErr error
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sameSeriesChunks []Series
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}
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// NewSeriesSetFromChunkSeriesSet converts ChunkSeriesSet to SeriesSet by decoding chunks one by one.
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func NewSeriesSetFromChunkSeriesSet(chk ChunkSeriesSet) SeriesSet {
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return &chunkSetToSeriesSet{ChunkSeriesSet: chk}
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}
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func (c *chunkSetToSeriesSet) Next() bool {
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if c.Err() != nil || !c.ChunkSeriesSet.Next() {
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return false
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}
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c.iter = c.ChunkSeriesSet.At().Iterator(c.iter)
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c.sameSeriesChunks = nil
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for c.iter.Next() {
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c.sameSeriesChunks = append(
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c.sameSeriesChunks,
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newChunkToSeriesDecoder(c.ChunkSeriesSet.At().Labels(), c.iter.At()),
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)
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}
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if c.iter.Err() != nil {
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c.chkIterErr = c.iter.Err()
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return false
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}
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return true
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}
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func (c *chunkSetToSeriesSet) At() Series {
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// Series composed of same chunks for the same series.
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return ChainedSeriesMerge(c.sameSeriesChunks...)
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}
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func (c *chunkSetToSeriesSet) Err() error {
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if c.chkIterErr != nil {
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return c.chkIterErr
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}
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return c.ChunkSeriesSet.Err()
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}
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func newChunkToSeriesDecoder(labels labels.Labels, chk chunks.Meta) Series {
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return &SeriesEntry{
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Lset: labels,
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SampleIteratorFn: func(it chunkenc.Iterator) chunkenc.Iterator {
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// TODO(bwplotka): Can we provide any chunkenc buffer?
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return chk.Chunk.Iterator(it)
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},
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}
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}
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type seriesSetToChunkSet struct {
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SeriesSet
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}
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// NewSeriesSetToChunkSet converts SeriesSet to ChunkSeriesSet by encoding chunks from samples.
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func NewSeriesSetToChunkSet(chk SeriesSet) ChunkSeriesSet {
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return &seriesSetToChunkSet{SeriesSet: chk}
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}
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func (c *seriesSetToChunkSet) Next() bool {
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if c.Err() != nil || !c.SeriesSet.Next() {
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return false
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}
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return true
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}
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func (c *seriesSetToChunkSet) At() ChunkSeries {
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return NewSeriesToChunkEncoder(c.SeriesSet.At())
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}
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func (c *seriesSetToChunkSet) Err() error {
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return c.SeriesSet.Err()
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}
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type seriesToChunkEncoder struct {
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Series
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}
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const seriesToChunkEncoderSplit = 120
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// NewSeriesToChunkEncoder encodes samples to chunks with 120 samples limit.
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func NewSeriesToChunkEncoder(series Series) ChunkSeries {
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return &seriesToChunkEncoder{series}
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}
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func (s *seriesToChunkEncoder) Iterator(it chunks.Iterator) chunks.Iterator {
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var (
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chk, newChk chunkenc.Chunk
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app chunkenc.Appender
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err error
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recoded bool
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)
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mint := int64(math.MaxInt64)
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maxt := int64(math.MinInt64)
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var chks []chunks.Meta
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lcsi, existing := it.(*listChunkSeriesIterator)
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if existing {
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chks = lcsi.chks[:0]
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}
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i := 0
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seriesIter := s.Series.Iterator(nil)
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lastType := chunkenc.ValNone
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for typ := seriesIter.Next(); typ != chunkenc.ValNone; typ = seriesIter.Next() {
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if typ != lastType || i >= seriesToChunkEncoderSplit {
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// Create a new chunk if the sample type changed or too many samples in the current one.
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chks = appendChunk(chks, mint, maxt, chk)
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chk, err = chunkenc.NewEmptyChunk(typ.ChunkEncoding())
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if err != nil {
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return errChunksIterator{err: err}
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}
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app, err = chk.Appender()
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if err != nil {
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return errChunksIterator{err: err}
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}
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mint = int64(math.MaxInt64)
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// maxt is immediately overwritten below which is why setting it here won't make a difference.
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i = 0
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}
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lastType = typ
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var (
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t int64
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v float64
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h *histogram.Histogram
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fh *histogram.FloatHistogram
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)
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switch typ {
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case chunkenc.ValFloat:
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t, v = seriesIter.At()
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app.Append(t, v)
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case chunkenc.ValHistogram:
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t, h = seriesIter.AtHistogram()
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newChk, recoded, app, err = app.AppendHistogram(nil, t, h, false)
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if err != nil {
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return errChunksIterator{err: err}
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}
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if newChk != nil {
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if !recoded {
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chks = appendChunk(chks, mint, maxt, chk)
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mint = int64(math.MaxInt64)
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// maxt is immediately overwritten below which is why setting it here won't make a difference.
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i = 0
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}
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chk = newChk
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}
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case chunkenc.ValFloatHistogram:
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t, fh = seriesIter.AtFloatHistogram()
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newChk, recoded, app, err = app.AppendFloatHistogram(nil, t, fh, false)
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if err != nil {
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return errChunksIterator{err: err}
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}
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if newChk != nil {
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if !recoded {
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chks = appendChunk(chks, mint, maxt, chk)
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mint = int64(math.MaxInt64)
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// maxt is immediately overwritten below which is why setting it here won't make a difference.
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i = 0
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}
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chk = newChk
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}
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default:
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return errChunksIterator{err: fmt.Errorf("unknown sample type %s", typ.String())}
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}
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maxt = t
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if mint == math.MaxInt64 {
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mint = t
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}
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i++
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}
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if err := seriesIter.Err(); err != nil {
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return errChunksIterator{err: err}
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}
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chks = appendChunk(chks, mint, maxt, chk)
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if existing {
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lcsi.Reset(chks...)
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return lcsi
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}
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return NewListChunkSeriesIterator(chks...)
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}
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func appendChunk(chks []chunks.Meta, mint, maxt int64, chk chunkenc.Chunk) []chunks.Meta {
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if chk != nil {
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chks = append(chks, chunks.Meta{
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MinTime: mint,
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MaxTime: maxt,
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Chunk: chk,
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})
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}
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return chks
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}
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type errChunksIterator struct {
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err error
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}
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func (e errChunksIterator) At() chunks.Meta { return chunks.Meta{} }
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func (e errChunksIterator) Next() bool { return false }
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func (e errChunksIterator) Err() error { return e.err }
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// ExpandSamples iterates over all samples in the iterator, buffering all in slice.
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// Optionally it takes samples constructor, useful when you want to compare sample slices with different
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// sample implementations. if nil, sample type from this package will be used.
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func ExpandSamples(iter chunkenc.Iterator, newSampleFn func(t int64, f float64, h *histogram.Histogram, fh *histogram.FloatHistogram) chunks.Sample) ([]chunks.Sample, error) {
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if newSampleFn == nil {
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newSampleFn = func(t int64, f float64, h *histogram.Histogram, fh *histogram.FloatHistogram) chunks.Sample {
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switch {
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case h != nil:
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return hSample{t, h}
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case fh != nil:
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return fhSample{t, fh}
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default:
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return fSample{t, f}
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}
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}
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}
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var result []chunks.Sample
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for {
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switch iter.Next() {
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case chunkenc.ValNone:
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return result, iter.Err()
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case chunkenc.ValFloat:
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t, f := iter.At()
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// NaNs can't be compared normally, so substitute for another value.
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if math.IsNaN(f) {
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f = -42
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}
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result = append(result, newSampleFn(t, f, nil, nil))
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case chunkenc.ValHistogram:
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t, h := iter.AtHistogram()
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result = append(result, newSampleFn(t, 0, h, nil))
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case chunkenc.ValFloatHistogram:
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t, fh := iter.AtFloatHistogram()
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result = append(result, newSampleFn(t, 0, nil, fh))
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}
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}
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}
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// ExpandChunks iterates over all chunks in the iterator, buffering all in slice.
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func ExpandChunks(iter chunks.Iterator) ([]chunks.Meta, error) {
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var result []chunks.Meta
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for iter.Next() {
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result = append(result, iter.At())
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}
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return result, iter.Err()
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}
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