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iter.go
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// Copyright (c) The Thanos Authors.
// Licensed under the Apache License 2.0.
package query
import (
"sort"
"github.com/pkg/errors"
"github.com/prometheus/prometheus/model/labels"
"github.com/prometheus/prometheus/storage"
"github.com/prometheus/prometheus/tsdb/chunkenc"
"github.com/thanos-io/thanos/pkg/compact/downsample"
"github.com/thanos-io/thanos/pkg/dedup"
"github.com/thanos-io/thanos/pkg/store/storepb"
)
// promSeriesSet implements the SeriesSet interface of the Prometheus storage
// package on top of our storepb SeriesSet.
type promSeriesSet struct {
set storepb.SeriesSet
done bool
mint, maxt int64
aggrs []storepb.Aggr
initiated bool
currLset labels.Labels
currChunks []storepb.AggrChunk
warns storage.Warnings
}
func (s *promSeriesSet) Next() bool {
if !s.initiated {
s.initiated = true
s.done = s.set.Next()
}
if !s.done {
return false
}
// storage.Series are more strict then SeriesSet:
// * It requires storage.Series to iterate over full series.
s.currLset, s.currChunks = s.set.At()
for {
s.done = s.set.Next()
if !s.done {
break
}
nextLset, nextChunks := s.set.At()
if labels.Compare(s.currLset, nextLset) != 0 {
break
}
s.currChunks = append(s.currChunks, nextChunks...)
}
// Samples (so chunks as well) have to be sorted by time.
// TODO(bwplotka): Benchmark if we can do better.
// For example we could iterate in above loop and write our own binary search based insert sort.
// We could also remove duplicates in same loop.
sort.Slice(s.currChunks, func(i, j int) bool {
return s.currChunks[i].MinTime < s.currChunks[j].MinTime
})
// Proxy handles duplicates between different series, let's handle duplicates within single series now as well.
// We don't need to decode those.
s.currChunks = removeExactDuplicates(s.currChunks)
return true
}
// removeExactDuplicates returns chunks without 1:1 duplicates.
// NOTE: input chunks has to be sorted by minTime.
func removeExactDuplicates(chks []storepb.AggrChunk) []storepb.AggrChunk {
if len(chks) <= 1 {
return chks
}
ret := make([]storepb.AggrChunk, 0, len(chks))
ret = append(ret, chks[0])
for _, c := range chks[1:] {
if ret[len(ret)-1].Compare(c) == 0 {
continue
}
ret = append(ret, c)
}
return ret
}
func (s *promSeriesSet) At() storage.Series {
if !s.initiated || s.set.Err() != nil {
return nil
}
return newChunkSeries(s.currLset, s.currChunks, s.mint, s.maxt, s.aggrs)
}
func (s *promSeriesSet) Err() error {
return s.set.Err()
}
func (s *promSeriesSet) Warnings() storage.Warnings {
return s.warns
}
// storeSeriesSet implements a storepb SeriesSet against a list of storepb.Series.
type storeSeriesSet struct {
// TODO(bwplotka): Don't buffer all, we have to buffer single series (to sort and dedup chunks), but nothing more.
series []storepb.Series
i int
}
func newStoreSeriesSet(s []storepb.Series) *storeSeriesSet {
return &storeSeriesSet{series: s, i: -1}
}
func (s *storeSeriesSet) Next() bool {
if s.i >= len(s.series)-1 {
return false
}
s.i++
return true
}
func (storeSeriesSet) Err() error {
return nil
}
func (s storeSeriesSet) At() (labels.Labels, []storepb.AggrChunk) {
return s.series[s.i].PromLabels(), s.series[s.i].Chunks
}
// chunkSeries implements storage.Series for a series on storepb types.
type chunkSeries struct {
lset labels.Labels
chunks []storepb.AggrChunk
mint, maxt int64
aggrs []storepb.Aggr
}
// newChunkSeries allows to iterate over samples for each sorted and non-overlapped chunks.
func newChunkSeries(lset labels.Labels, chunks []storepb.AggrChunk, mint, maxt int64, aggrs []storepb.Aggr) *chunkSeries {
return &chunkSeries{
lset: lset,
chunks: chunks,
mint: mint,
maxt: maxt,
aggrs: aggrs,
}
}
func (s *chunkSeries) Labels() labels.Labels {
return s.lset
}
func (s *chunkSeries) Iterator() chunkenc.Iterator {
var sit chunkenc.Iterator
its := make([]chunkenc.Iterator, 0, len(s.chunks))
if len(s.aggrs) == 1 {
switch s.aggrs[0] {
case storepb.Aggr_COUNT:
for _, c := range s.chunks {
its = append(its, getFirstIterator(c.Count, c.Raw))
}
sit = newChunkSeriesIterator(its)
case storepb.Aggr_SUM:
for _, c := range s.chunks {
its = append(its, getFirstIterator(c.Sum, c.Raw))
}
sit = newChunkSeriesIterator(its)
case storepb.Aggr_MIN:
for _, c := range s.chunks {
its = append(its, getFirstIterator(c.Min, c.Raw))
}
sit = newChunkSeriesIterator(its)
case storepb.Aggr_MAX:
for _, c := range s.chunks {
its = append(its, getFirstIterator(c.Max, c.Raw))
}
sit = newChunkSeriesIterator(its)
case storepb.Aggr_COUNTER:
for _, c := range s.chunks {
its = append(its, getFirstIterator(c.Counter, c.Raw))
}
// TODO(bwplotka): This breaks resets function. See https://github.com/thanos-io/thanos/issues/3644
sit = downsample.NewApplyCounterResetsIterator(its...)
default:
return errSeriesIterator{err: errors.Errorf("unexpected result aggregate type %v", s.aggrs)}
}
return dedup.NewBoundedSeriesIterator(sit, s.mint, s.maxt)
}
if len(s.aggrs) != 2 {
return errSeriesIterator{err: errors.Errorf("unexpected result aggregate type %v", s.aggrs)}
}
switch {
case s.aggrs[0] == storepb.Aggr_SUM && s.aggrs[1] == storepb.Aggr_COUNT,
s.aggrs[0] == storepb.Aggr_COUNT && s.aggrs[1] == storepb.Aggr_SUM:
for _, c := range s.chunks {
if c.Raw != nil {
its = append(its, getFirstIterator(c.Raw))
} else {
sum, cnt := getFirstIterator(c.Sum), getFirstIterator(c.Count)
its = append(its, downsample.NewAverageChunkIterator(cnt, sum))
}
}
sit = newChunkSeriesIterator(its)
default:
return errSeriesIterator{err: errors.Errorf("unexpected result aggregate type %v", s.aggrs)}
}
return dedup.NewBoundedSeriesIterator(sit, s.mint, s.maxt)
}
func getFirstIterator(cs ...*storepb.Chunk) chunkenc.Iterator {
for _, c := range cs {
if c == nil {
continue
}
chk, err := chunkenc.FromData(chunkEncoding(c.Type), c.Data)
if err != nil {
return errSeriesIterator{err}
}
return chk.Iterator(nil)
}
return errSeriesIterator{errors.New("no valid chunk found")}
}
func chunkEncoding(e storepb.Chunk_Encoding) chunkenc.Encoding {
switch e {
case storepb.Chunk_XOR:
return chunkenc.EncXOR
}
return 255 // Invalid.
}
type errSeriesIterator struct {
err error
}
func (errSeriesIterator) Seek(int64) bool { return false }
func (errSeriesIterator) Next() bool { return false }
func (errSeriesIterator) At() (int64, float64) { return 0, 0 }
func (it errSeriesIterator) Err() error { return it.err }
// chunkSeriesIterator implements a series iterator on top
// of a list of time-sorted, non-overlapping chunks.
type chunkSeriesIterator struct {
chunks []chunkenc.Iterator
i int
}
func newChunkSeriesIterator(cs []chunkenc.Iterator) chunkenc.Iterator {
if len(cs) == 0 {
// This should not happen. StoreAPI implementations should not send empty results.
return errSeriesIterator{err: errors.Errorf("store returned an empty result")}
}
return &chunkSeriesIterator{chunks: cs}
}
func (it *chunkSeriesIterator) Seek(t int64) (ok bool) {
// We generally expect the chunks already to be cut down
// to the range we are interested in. There's not much to be gained from
// hopping across chunks so we just call next until we reach t.
for {
ct, _ := it.At()
if ct >= t {
return true
}
if !it.Next() {
return false
}
}
}
func (it *chunkSeriesIterator) At() (t int64, v float64) {
return it.chunks[it.i].At()
}
func (it *chunkSeriesIterator) Next() bool {
lastT, _ := it.At()
if it.chunks[it.i].Next() {
return true
}
if it.Err() != nil {
return false
}
if it.i >= len(it.chunks)-1 {
return false
}
// Chunks are guaranteed to be ordered but not generally guaranteed to not overlap.
// We must ensure to skip any overlapping range between adjacent chunks.
it.i++
return it.Seek(lastT + 1)
}
func (it *chunkSeriesIterator) Err() error {
return it.chunks[it.i].Err()
}
type lazySeriesSet struct {
create func() (s storage.SeriesSet, ok bool)
set storage.SeriesSet
}
func (c *lazySeriesSet) Next() bool {
if c.set != nil {
return c.set.Next()
}
var ok bool
c.set, ok = c.create()
return ok
}
func (c *lazySeriesSet) Err() error {
if c.set != nil {
return c.set.Err()
}
return nil
}
func (c *lazySeriesSet) At() storage.Series {
if c.set != nil {
return c.set.At()
}
return nil
}
func (c *lazySeriesSet) Warnings() storage.Warnings {
if c.set != nil {
return c.set.Warnings()
}
return nil
}