/
iter.go
710 lines (611 loc) · 18 KB
/
iter.go
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// Copyright (c) The Thanos Authors.
// Licensed under the Apache License 2.0.
package query
import (
"math"
"sort"
"github.com/pkg/errors"
"github.com/prometheus/prometheus/pkg/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/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 []storepb.Label
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 storepb.CompareLabels(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() ([]storepb.Label, []storepb.AggrChunk) {
return s.series[s.i].Labels, 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 []storepb.Label, chunks []storepb.AggrChunk, mint, maxt int64, aggrs []storepb.Aggr) *chunkSeries {
return &chunkSeries{
lset: storepb.LabelsToPromLabels(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))
}
sit = downsample.NewApplyCounterResetsIterator(its...)
default:
return errSeriesIterator{err: errors.Errorf("unexpected result aggregate type %v", s.aggrs)}
}
return 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 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 }
// boundedSeriesIterator wraps a series iterator and ensures that it only emits
// samples within a fixed time range.
type boundedSeriesIterator struct {
it chunkenc.Iterator
mint, maxt int64
}
func newBoundedSeriesIterator(it chunkenc.Iterator, mint, maxt int64) *boundedSeriesIterator {
return &boundedSeriesIterator{it: it, mint: mint, maxt: maxt}
}
func (it *boundedSeriesIterator) Seek(t int64) (ok bool) {
if t > it.maxt {
return false
}
if t < it.mint {
t = it.mint
}
return it.it.Seek(t)
}
func (it *boundedSeriesIterator) At() (t int64, v float64) {
return it.it.At()
}
func (it *boundedSeriesIterator) Next() bool {
if !it.it.Next() {
return false
}
t, _ := it.it.At()
// Advance the iterator if we are before the valid interval.
if t < it.mint {
if !it.Seek(it.mint) {
return false
}
t, _ = it.it.At()
}
// Once we passed the valid interval, there is no going back.
return t <= it.maxt
}
func (it *boundedSeriesIterator) Err() error {
return it.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 dedupSeriesSet struct {
set storage.SeriesSet
replicaLabels map[string]struct{}
isCounter bool
replicas []storage.Series
lset labels.Labels
peek storage.Series
ok bool
}
func newDedupSeriesSet(set storage.SeriesSet, replicaLabels map[string]struct{}, isCounter bool) storage.SeriesSet {
s := &dedupSeriesSet{set: set, replicaLabels: replicaLabels, isCounter: isCounter}
s.ok = s.set.Next()
if s.ok {
s.peek = s.set.At()
}
return s
}
func (s *dedupSeriesSet) Next() bool {
if !s.ok {
return false
}
// Set the label set we are currently gathering to the peek element
// without the replica label if it exists.
s.lset = s.peekLset()
s.replicas = append(s.replicas[:0], s.peek)
return s.next()
}
// peekLset returns the label set of the current peek element stripped from the
// replica label if it exists.
func (s *dedupSeriesSet) peekLset() labels.Labels {
lset := s.peek.Labels()
if len(s.replicaLabels) == 0 {
return lset
}
// Check how many replica labels are present so that these are removed.
var totalToRemove int
for i := 0; i < len(s.replicaLabels); i++ {
if len(lset)-i == 0 {
break
}
if _, ok := s.replicaLabels[lset[len(lset)-i-1].Name]; ok {
totalToRemove++
}
}
// Strip all present replica labels.
return lset[:len(lset)-totalToRemove]
}
func (s *dedupSeriesSet) next() bool {
// Peek the next series to see whether it's a replica for the current series.
s.ok = s.set.Next()
if !s.ok {
// There's no next series, the current replicas are the last element.
return len(s.replicas) > 0
}
s.peek = s.set.At()
nextLset := s.peekLset()
// If the label set modulo the replica label is equal to the current label set
// look for more replicas, otherwise a series is complete.
if !labels.Equal(s.lset, nextLset) {
return true
}
s.replicas = append(s.replicas, s.peek)
return s.next()
}
func (s *dedupSeriesSet) At() storage.Series {
if len(s.replicas) == 1 {
return seriesWithLabels{Series: s.replicas[0], lset: s.lset}
}
// Clients may store the series, so we must make a copy of the slice before advancing.
repl := make([]storage.Series, len(s.replicas))
copy(repl, s.replicas)
return newDedupSeries(s.lset, repl, s.isCounter)
}
func (s *dedupSeriesSet) Err() error {
return s.set.Err()
}
func (s *dedupSeriesSet) Warnings() storage.Warnings {
return s.set.Warnings()
}
type seriesWithLabels struct {
storage.Series
lset labels.Labels
}
func (s seriesWithLabels) Labels() labels.Labels { return s.lset }
type dedupSeries struct {
lset labels.Labels
replicas []storage.Series
isCounter bool
}
func newDedupSeries(lset labels.Labels, replicas []storage.Series, isCounter bool) *dedupSeries {
return &dedupSeries{lset: lset, isCounter: isCounter, replicas: replicas}
}
func (s *dedupSeries) Labels() labels.Labels {
return s.lset
}
func (s *dedupSeries) Iterator() chunkenc.Iterator {
var it adjustableSeriesIterator
if s.isCounter {
it = &counterErrAdjustSeriesIterator{Iterator: s.replicas[0].Iterator()}
} else {
it = noopAdjustableSeriesIterator{Iterator: s.replicas[0].Iterator()}
}
for _, o := range s.replicas[1:] {
var replicaIter adjustableSeriesIterator
if s.isCounter {
replicaIter = &counterErrAdjustSeriesIterator{Iterator: o.Iterator()}
} else {
replicaIter = noopAdjustableSeriesIterator{Iterator: o.Iterator()}
}
it = newDedupSeriesIterator(it, replicaIter)
}
return it
}
// adjustableSeriesIterator iterates over the data of a time series and allows to adjust current value based on
// given lastValue iterated.
type adjustableSeriesIterator interface {
chunkenc.Iterator
// adjustAtValue allows to adjust value by implementation if needed knowing the last value. This is used by counter
// implementation which can adjust for obsolete counter value.
adjustAtValue(lastValue float64)
}
type noopAdjustableSeriesIterator struct {
chunkenc.Iterator
}
func (it noopAdjustableSeriesIterator) adjustAtValue(float64) {}
// counterErrAdjustSeriesIterator is extendedSeriesIterator used when we deduplicate counter.
// It makes sure we always adjust for the latest seen last counter value for all replicas.
// Let's consider following example:
//
// Replica 1 counter scrapes: 20 30 40 Nan - 0 5
// Replica 2 counter scrapes: 25 35 45 Nan - 2
//
// Now for downsampling purposes we are accounting the resets(rewriting the samples value)
// so our replicas before going to dedup iterator looks like this:
//
// Replica 1 counter total: 20 30 40 - - 40 45
// Replica 2 counter total: 25 35 45 - - 47
//
// Now if at any point we will switch our focus from replica 2 to replica 1 we will experience lower value than previous,
// which will trigger false positive counter reset in PromQL.
//
// We mitigate this by taking allowing invoking AdjustAtValue which adjust the value in case of last value being larger than current at.
// (Counter cannot go down)
//
// This is to mitigate https://github.com/thanos-io/thanos/issues/2401.
// TODO(bwplotka): Find better deduplication algorithm that does not require knowledge if the given
// series is counter or not: https://github.com/thanos-io/thanos/issues/2547.
type counterErrAdjustSeriesIterator struct {
chunkenc.Iterator
errAdjust float64
}
func (it *counterErrAdjustSeriesIterator) adjustAtValue(lastValue float64) {
_, v := it.At()
if lastValue > v {
// This replica has obsolete value (did not see the correct "end" of counter value before app restart). Adjust.
it.errAdjust += lastValue - v
}
}
func (it *counterErrAdjustSeriesIterator) At() (int64, float64) {
t, v := it.Iterator.At()
return t, v + it.errAdjust
}
type dedupSeriesIterator struct {
a, b adjustableSeriesIterator
aok, bok bool
// TODO(bwplotka): Don't base on LastT, but on detected scrape interval. This will allow us to be more
// responsive to gaps: https://github.com/thanos-io/thanos/issues/981, let's do it in next PR.
lastT int64
lastV float64
penA, penB int64
useA bool
}
func newDedupSeriesIterator(a, b adjustableSeriesIterator) *dedupSeriesIterator {
return &dedupSeriesIterator{
a: a,
b: b,
lastT: math.MinInt64,
lastV: float64(math.MinInt64),
aok: a.Next(),
bok: b.Next(),
}
}
func (it *dedupSeriesIterator) Next() bool {
lastValue := it.lastV
lastUseA := it.useA
defer func() {
if it.useA != lastUseA {
// We switched replicas.
// Ensure values are correct bases on value before At.
it.adjustAtValue(lastValue)
}
}()
// Advance both iterators to at least the next highest timestamp plus the potential penalty.
if it.aok {
it.aok = it.a.Seek(it.lastT + 1 + it.penA)
}
if it.bok {
it.bok = it.b.Seek(it.lastT + 1 + it.penB)
}
// Handle basic cases where one iterator is exhausted before the other.
if !it.aok {
it.useA = false
if it.bok {
it.lastT, it.lastV = it.b.At()
it.penB = 0
}
return it.bok
}
if !it.bok {
it.useA = true
it.lastT, it.lastV = it.a.At()
it.penA = 0
return true
}
// General case where both iterators still have data. We pick the one
// with the smaller timestamp.
// The applied penalty potentially already skipped potential samples already
// that would have resulted in exaggerated sampling frequency.
ta, va := it.a.At()
tb, vb := it.b.At()
it.useA = ta <= tb
// For the series we didn't pick, add a penalty twice as high as the delta of the last two
// samples to the next seek against it.
// This ensures that we don't pick a sample too close, which would increase the overall
// sample frequency. It also guards against clock drift and inaccuracies during
// timestamp assignment.
// If we don't know a delta yet, we pick 5000 as a constant, which is based on the knowledge
// that timestamps are in milliseconds and sampling frequencies typically multiple seconds long.
const initialPenalty = 5000
if it.useA {
if it.lastT != math.MinInt64 {
it.penB = 2 * (ta - it.lastT)
} else {
it.penB = initialPenalty
}
it.penA = 0
it.lastT = ta
it.lastV = va
return true
}
if it.lastT != math.MinInt64 {
it.penA = 2 * (tb - it.lastT)
} else {
it.penA = initialPenalty
}
it.penB = 0
it.lastT = tb
it.lastV = vb
return true
}
func (it *dedupSeriesIterator) adjustAtValue(lastValue float64) {
if it.aok {
it.a.adjustAtValue(lastValue)
}
if it.bok {
it.b.adjustAtValue(lastValue)
}
}
func (it *dedupSeriesIterator) Seek(t int64) bool {
// Don't use underlying Seek, but iterate over next to not miss gaps.
for {
ts, _ := it.At()
if ts >= t {
return true
}
if !it.Next() {
return false
}
}
}
func (it *dedupSeriesIterator) At() (int64, float64) {
if it.useA {
return it.a.At()
}
return it.b.At()
}
func (it *dedupSeriesIterator) Err() error {
if it.a.Err() != nil {
return it.a.Err()
}
return it.b.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
}