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downsample.go
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downsample.go
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package downsample
import (
"math"
"path/filepath"
"sort"
"github.com/improbable-eng/thanos/pkg/block"
"github.com/prometheus/prometheus/pkg/value"
"github.com/prometheus/tsdb/chunkenc"
"os"
"github.com/go-kit/kit/log"
"github.com/improbable-eng/thanos/pkg/runutil"
"github.com/oklog/ulid"
"github.com/pkg/errors"
"github.com/prometheus/tsdb"
"github.com/prometheus/tsdb/chunks"
"github.com/prometheus/tsdb/index"
"github.com/prometheus/tsdb/labels"
)
// Standard downsampling resolution levels in Thanos.
const (
ResLevel0 = int64(0) // raw data
ResLevel1 = int64(5 * 60 * 1000) // 5 minutes in milliseconds
ResLevel2 = int64(60 * 60 * 1000) // 1 hour in milliseconds
)
// Downsample downsamples the given block. It writes a new block into dir and returns its ID.
func Downsample(
logger log.Logger,
origMeta *block.Meta,
b tsdb.BlockReader,
dir string,
resolution int64,
) (id ulid.ULID, err error) {
if origMeta.Thanos.Downsample.Resolution >= resolution {
return id, errors.New("target resolution not lower than existing one")
}
indexr, err := b.Index()
if err != nil {
return id, errors.Wrap(err, "open index reader")
}
defer runutil.CloseWithErrCapture(logger, &err, indexr, "downsample index reader")
chunkr, err := b.Chunks()
if err != nil {
return id, errors.Wrap(err, "open chunk reader")
}
defer runutil.CloseWithErrCapture(logger, &err, chunkr, "downsample chunk reader")
rng := origMeta.MaxTime - origMeta.MinTime
// Write downsampled data in a custom memory block where we have fine-grained control
// over created chunks.
// This is necessary since we need to inject special values at the end of chunks for
// some aggregations.
newb := newMemBlock()
pall, err := indexr.Postings(index.AllPostingsKey())
if err != nil {
return id, errors.Wrap(err, "get all postings list")
}
var (
aggrChunks []*AggrChunk
all []sample
chks []chunks.Meta
)
for pall.Next() {
var lset labels.Labels
chks = chks[:0]
all = all[:0]
aggrChunks = aggrChunks[:0]
// Get series labels and chunks. Downsampled data is sensitive to chunk boundaries
// and we need to preserve them to properly downsample previously downsampled data.
if err := indexr.Series(pall.At(), &lset, &chks); err != nil {
return id, errors.Wrapf(err, "get series %d", pall.At())
}
// While #183 exists, we sanitize the chunks we retrieved from the block
// before retrieving their samples.
for i, c := range chks {
chk, err := chunkr.Chunk(c.Ref)
if err != nil {
return id, errors.Wrapf(err, "get chunk %d", c.Ref)
}
chks[i].Chunk = chk
}
// Raw and already downsampled data need different processing.
if origMeta.Thanos.Downsample.Resolution == 0 {
for _, c := range chks {
if err := expandChunkIterator(c.Chunk.Iterator(), &all); err != nil {
return id, errors.Wrapf(err, "expand chunk %d", c.Ref)
}
}
newb.addSeries(&series{lset: lset, chunks: downsampleRaw(all, resolution)})
continue
}
// Downsample a block that contains aggregate chunks already.
for _, c := range chks {
aggrChunks = append(aggrChunks, c.Chunk.(*AggrChunk))
}
res, err := downsampleAggr(
aggrChunks,
&all,
chks[0].MinTime,
chks[len(chks)-1].MaxTime,
origMeta.Thanos.Downsample.Resolution,
resolution,
)
if err != nil {
return id, errors.Wrap(err, "downsample aggregate block")
}
newb.addSeries(&series{lset: lset, chunks: res})
}
if pall.Err() != nil {
return id, errors.Wrap(pall.Err(), "iterate series set")
}
comp, err := tsdb.NewLeveledCompactor(nil, log.NewNopLogger(), []int64{rng}, NewPool())
if err != nil {
return id, errors.Wrap(err, "create compactor")
}
id, err = comp.Write(dir, newb, origMeta.MinTime, origMeta.MaxTime)
if err != nil {
return id, errors.Wrap(err, "compact head")
}
bdir := filepath.Join(dir, id.String())
var tmeta block.ThanosMeta
tmeta = origMeta.Thanos
tmeta.Source = block.CompactorSource
tmeta.Downsample.Resolution = resolution
_, err = block.InjectThanosMeta(logger, bdir, tmeta, &origMeta.BlockMeta)
if err != nil {
return id, errors.Wrapf(err, "failed to finalize the block %s", bdir)
}
if err = os.Remove(filepath.Join(bdir, "tombstones")); err != nil {
return id, errors.Wrap(err, "remove tombstones")
}
return id, nil
}
// memBlock is an in-memory block that implements a subset of the tsdb.BlockReader interface
// to allow tsdb.LeveledCompactor to persist the data as a block.
type memBlock struct {
// Dummies to implement unused methods.
tsdb.IndexReader
symbols map[string]struct{}
postings []uint64
series []*series
chunks []chunkenc.Chunk
}
func newMemBlock() *memBlock {
return &memBlock{symbols: map[string]struct{}{}}
}
func (b *memBlock) addSeries(s *series) {
sid := uint64(len(b.series))
b.postings = append(b.postings, sid)
b.series = append(b.series, s)
for _, l := range s.lset {
b.symbols[l.Name] = struct{}{}
b.symbols[l.Value] = struct{}{}
}
for i, cm := range s.chunks {
cid := uint64(len(b.chunks))
s.chunks[i].Ref = cid
b.chunks = append(b.chunks, cm.Chunk)
}
}
func (b *memBlock) Postings(name, val string) (index.Postings, error) {
allName, allVal := index.AllPostingsKey()
if name != allName || val != allVal {
return nil, errors.New("unsupported call to Postings()")
}
sort.Slice(b.postings, func(i, j int) bool {
return labels.Compare(b.series[b.postings[i]].lset, b.series[b.postings[j]].lset) < 0
})
return index.NewListPostings(b.postings), nil
}
func (b *memBlock) Series(id uint64, lset *labels.Labels, chks *[]chunks.Meta) error {
if id >= uint64(len(b.series)) {
return errors.Wrapf(tsdb.ErrNotFound, "series with ID %d does not exist", id)
}
s := b.series[id]
*lset = append((*lset)[:0], s.lset...)
*chks = append((*chks)[:0], s.chunks...)
return nil
}
func (b *memBlock) Chunk(id uint64) (chunkenc.Chunk, error) {
if id >= uint64(len(b.chunks)) {
return nil, errors.Wrapf(tsdb.ErrNotFound, "chunk with ID %d does not exist", id)
}
return b.chunks[id], nil
}
func (b *memBlock) Symbols() (map[string]struct{}, error) {
return b.symbols, nil
}
func (b *memBlock) SortedPostings(p index.Postings) index.Postings {
return p
}
func (b *memBlock) Index() (tsdb.IndexReader, error) {
return b, nil
}
func (b *memBlock) Chunks() (tsdb.ChunkReader, error) {
return b, nil
}
func (b *memBlock) Tombstones() (tsdb.TombstoneReader, error) {
return tsdb.EmptyTombstoneReader(), nil
}
func (b *memBlock) Close() error {
return nil
}
// currentWindow returns the end timestamp of the window that t falls into.
func currentWindow(t, r int64) int64 {
// The next timestamp is the next number after s.t that's aligned with window.
// We subtract 1 because block ranges are [from, to) and the last sample would
// go out of bounds otherwise.
return t - (t % r) + r - 1
}
// rangeFullness returns the fraction of how the range [mint, maxt] covered
// with count samples at the given step size.
// It return value is bounded to [0, 1].
func rangeFullness(mint, maxt, step int64, count int) float64 {
f := float64(count) / (float64(maxt-mint) / float64(step))
if f > 1 {
return 1
}
return f
}
// targetChunkCount calculates how many chunks should be produced when downsampling a series.
// It consider the total time range, the number of input sample, the input and output resolution.
func targetChunkCount(mint, maxt, inRes, outRes int64, count int) (x int) {
// We compute how many samples we could produce for the given time range and adjust
// it by how densely the range is actually filled given the number of input samples and their
// resolution.
maxSamples := float64((maxt - mint) / outRes)
expSamples := int(maxSamples*rangeFullness(mint, maxt, inRes, count)) + 1
// Increase the number of target chunks until each chunk will have less than
// 140 samples on average.
for x = 1; expSamples/x > 140; x++ {
}
return x
}
// aggregator collects commulative stats for a stream of values.
type aggregator struct {
total int // total samples processed
count int // samples in current window
sum float64 // value sum of current window
min float64 // min of current window
max float64 // max of current window
counter float64 // total counter state since beginning
resets int // number of counter resests since beginning
last float64 // last added value
}
// reset the stats to start a new aggregation window.
func (a *aggregator) reset() {
a.count = 0
a.sum = 0
a.min = math.MaxFloat64
a.max = -math.MaxFloat64
}
func (a *aggregator) add(v float64) {
if a.total > 0 {
if v < a.last {
// Counter reset, correct the value.
a.counter += v
a.resets++
} else {
// Add delta with last value to the counter.
a.counter += v - a.last
}
} else {
// First sample sets the counter.
a.counter = v
}
a.last = v
a.sum += v
a.count++
a.total++
if v < a.min {
a.min = v
}
if v > a.max {
a.max = v
}
}
// aggrChunkBuilder builds chunks for multiple different aggregates.
type aggrChunkBuilder struct {
mint, maxt int64
isCounter bool
added int
chunks [5]chunkenc.Chunk
apps [5]chunkenc.Appender
}
func newAggrChunkBuilder() *aggrChunkBuilder {
b := &aggrChunkBuilder{
mint: math.MaxInt64,
maxt: math.MinInt64,
}
b.chunks[AggrCount] = chunkenc.NewXORChunk()
b.chunks[AggrSum] = chunkenc.NewXORChunk()
b.chunks[AggrMin] = chunkenc.NewXORChunk()
b.chunks[AggrMax] = chunkenc.NewXORChunk()
b.chunks[AggrCounter] = chunkenc.NewXORChunk()
for i, c := range b.chunks {
if c != nil {
b.apps[i], _ = c.Appender()
}
}
return b
}
func (b *aggrChunkBuilder) add(t int64, aggr *aggregator) {
if t < b.mint {
b.mint = t
}
if t > b.maxt {
b.maxt = t
}
b.apps[AggrSum].Append(t, aggr.sum)
b.apps[AggrMin].Append(t, aggr.min)
b.apps[AggrMax].Append(t, aggr.max)
b.apps[AggrCount].Append(t, float64(aggr.count))
b.apps[AggrCounter].Append(t, aggr.counter)
b.added++
}
func (b *aggrChunkBuilder) finalizeChunk(lastT int64, trueSample float64) {
b.apps[AggrCounter].Append(lastT, trueSample)
}
func (b *aggrChunkBuilder) encode() chunks.Meta {
return chunks.Meta{
MinTime: b.mint,
MaxTime: b.maxt,
Chunk: EncodeAggrChunk(b.chunks),
}
}
// downsampleRaw create a series of aggregation chunks for the given sample data.
func downsampleRaw(data []sample, resolution int64) []chunks.Meta {
if len(data) == 0 {
return nil
}
var (
mint, maxt = data[0].t, data[len(data)-1].t
// We assume a raw resolution of 1 minute. In practice it will often be lower
// but this is sufficient for our heuristic to produce well-sized chunks.
numChunks = targetChunkCount(mint, maxt, 1*60*1000, resolution, len(data))
chks = make([]chunks.Meta, 0, numChunks)
batchSize = (len(data) / numChunks) + 1
)
for len(data) > 0 {
j := batchSize
if j > len(data) {
j = len(data)
}
curW := currentWindow(data[j-1].t, resolution)
// The batch we took might end in the middle of a downsampling window. We additionally grab
// all further samples in the window to keep our samples regular.
for ; j < len(data) && data[j].t <= curW; j++ {
}
ab := newAggrChunkBuilder()
batch := data[:j]
data = data[j:]
lastT := downsampleBatch(batch, resolution, ab.add)
// InjectThanosMeta the chunk's counter aggregate with the last true sample.
ab.finalizeChunk(lastT, batch[len(batch)-1].v)
chks = append(chks, ab.encode())
}
return chks
}
// downsampleBatch aggregates the data over the given resolution and calls add each time
// the end of a resolution was reached.
func downsampleBatch(data []sample, resolution int64, add func(int64, *aggregator)) int64 {
var (
aggr aggregator
nextT = int64(-1)
lastT = data[len(data)-1].t
)
// Fill up one aggregate chunk with up to m samples.
for _, s := range data {
if value.IsStaleNaN(s.v) {
continue
}
if s.t > nextT {
if nextT != -1 {
add(nextT, &aggr)
}
aggr.reset()
nextT = currentWindow(s.t, resolution)
// Limit next timestamp to not go beyond the batch. A subsequent batch
// may overlap in time range otherwise.
// We have aligned batches for raw downsamplings but subsequent downsamples
// are forced to be chunk-boundary aligned and cannot guarantee this.
if nextT > lastT {
nextT = lastT
}
}
aggr.add(s.v)
}
// Add the last sample.
add(nextT, &aggr)
return nextT
}
// downsampleAggr downsamples a sequence of aggregation chunks to the given resolution.
func downsampleAggr(chks []*AggrChunk, buf *[]sample, mint, maxt, inRes, outRes int64) ([]chunks.Meta, error) {
// We downsample aggregates only along chunk boundaries. This is required for counters
// to be downsampled correctly since a chunks' last counter value is the true last value
// of the original series. We need to preserve it even across multiple aggregation iterations.
var numSamples int
for _, c := range chks {
numSamples += c.NumSamples()
}
var (
numChunks = targetChunkCount(mint, maxt, inRes, outRes, numSamples)
res = make([]chunks.Meta, 0, numChunks)
batchSize = len(chks) / numChunks
)
for len(chks) > 0 {
j := batchSize
if j > len(chks) {
j = len(chks)
}
part := chks[:j]
chks = chks[j:]
chk, err := downsampleAggrBatch(part, buf, outRes)
if err != nil {
return nil, err
}
res = append(res, chk)
}
return res, nil
}
// expandChunkIterator reads all samples from the iterater and appends them to buf.
// Stale markers and out of order samples are skipped.
func expandChunkIterator(it chunkenc.Iterator, buf *[]sample) error {
// For safety reasons, we check for each sample that it does not go back in time.
// If it does, we skip it.
lastT := int64(0)
for it.Next() {
t, v := it.At()
if value.IsStaleNaN(v) {
continue
}
if t >= lastT {
*buf = append(*buf, sample{t, v})
lastT = t
}
}
return it.Err()
}
func downsampleAggrBatch(chks []*AggrChunk, buf *[]sample, resolution int64) (chk chunks.Meta, err error) {
ab := &aggrChunkBuilder{}
mint, maxt := int64(math.MaxInt64), int64(math.MinInt64)
// do does a generic aggregation for count, sum, min, and max aggregates.
// Counters need special treatment.
do := func(at AggrType, f func(a *aggregator) float64) error {
*buf = (*buf)[:0]
// Expand all samples for the aggregate type.
for _, chk := range chks {
c, err := chk.Get(at)
if err == ErrAggrNotExist {
continue
} else if err != nil {
return err
}
if err := expandChunkIterator(c.Iterator(), buf); err != nil {
return err
}
}
if len(*buf) == 0 {
return nil
}
ab.chunks[at] = chunkenc.NewXORChunk()
ab.apps[at], _ = ab.chunks[at].Appender()
downsampleBatch(*buf, resolution, func(t int64, a *aggregator) {
if t < mint {
mint = t
} else if t > maxt {
maxt = t
}
ab.apps[at].Append(t, f(a))
})
return nil
}
if err := do(AggrCount, func(a *aggregator) float64 {
return a.sum
}); err != nil {
return chk, err
}
if err = do(AggrSum, func(a *aggregator) float64 {
return a.sum
}); err != nil {
return chk, err
}
if err := do(AggrMin, func(a *aggregator) float64 {
return a.min
}); err != nil {
return chk, err
}
if err := do(AggrMax, func(a *aggregator) float64 {
return a.max
}); err != nil {
return chk, err
}
// Handle counters by reading them properly.
acs := make([]chunkenc.Iterator, 0, len(chks))
for _, achk := range chks {
c, err := achk.Get(AggrCounter)
if err == ErrAggrNotExist {
continue
} else if err != nil {
return chk, err
}
acs = append(acs, c.Iterator())
}
*buf = (*buf)[:0]
it := NewCounterSeriesIterator(acs...)
if err := expandChunkIterator(it, buf); err != nil {
return chk, err
}
if len(*buf) == 0 {
ab.mint = mint
ab.maxt = maxt
return ab.encode(), nil
}
ab.chunks[AggrCounter] = chunkenc.NewXORChunk()
ab.apps[AggrCounter], _ = ab.chunks[AggrCounter].Appender()
lastT := downsampleBatch(*buf, resolution, func(t int64, a *aggregator) {
if t < mint {
mint = t
} else if t > maxt {
maxt = t
}
ab.apps[AggrCounter].Append(t, a.counter)
})
ab.apps[AggrCounter].Append(lastT, it.lastV)
ab.mint = mint
ab.maxt = maxt
return ab.encode(), nil
}
type sample struct {
t int64
v float64
}
type series struct {
lset labels.Labels
chunks []chunks.Meta
}
// CounterSeriesIterator iterates over an ordered sequence of chunks and treats decreasing
// values as counter reset.
// Additionally, it can deal with downsampled counter chunks, which set the last value of a chunk
// to the original last value. The last value can be detected by checking whether the timestamp
// did not increase w.r.t to the previous sample
type CounterSeriesIterator struct {
chks []chunkenc.Iterator
i int // current chunk
total int // total number of processed samples
lastT int64 // timestamp of the last sample
lastV float64 // value of the last sample
totalV float64 // total counter state since beginning of series
}
func NewCounterSeriesIterator(chks ...chunkenc.Iterator) *CounterSeriesIterator {
return &CounterSeriesIterator{chks: chks}
}
func (it *CounterSeriesIterator) Next() bool {
if it.i >= len(it.chks) {
return false
}
if ok := it.chks[it.i].Next(); !ok {
it.i++
// While iterators are ordered, they are not generally guaranteed to be
// non-overlapping. Ensure that the series does not go back in time by seeking at least
// to the next timestamp.
return it.Seek(it.lastT + 1)
}
t, v := it.chks[it.i].At()
if math.IsNaN(v) {
return it.Next()
}
// First sample sets the initial counter state.
if it.total == 0 {
it.total++
it.lastT, it.lastV = t, v
it.totalV = v
return true
}
// If the timestamp increased, it is not the special last sample.
if t > it.lastT {
if v >= it.lastV {
it.totalV += v - it.lastV
} else {
it.totalV += v
}
it.lastT, it.lastV = t, v
it.total++
return true
}
// We hit a sample that indicates what the true last value was. For the
// next chunk we use it to determine whether there was a counter reset between them.
if t == it.lastT {
it.lastV = v
}
// Otherwise the series went back in time and we just keep moving forward.
return it.Next()
}
func (it *CounterSeriesIterator) At() (t int64, v float64) {
return it.lastT, it.totalV
}
func (it *CounterSeriesIterator) Seek(x int64) bool {
for {
ok := it.Next()
if !ok {
return false
}
if t, _ := it.At(); t >= x {
return true
}
}
}
func (it *CounterSeriesIterator) Err() error {
if it.i >= len(it.chks) {
return nil
}
return it.chks[it.i].Err()
}
// AverageChunkIterator emits an artificial series of average samples based in aggregate
// chunks with sum and count aggregates.
type AverageChunkIterator struct {
cntIt chunkenc.Iterator
sumIt chunkenc.Iterator
t int64
v float64
err error
}
func NewAverageChunkIterator(cnt, sum chunkenc.Iterator) *AverageChunkIterator {
return &AverageChunkIterator{cntIt: cnt, sumIt: sum}
}
func (it *AverageChunkIterator) Next() bool {
cok, sok := it.cntIt.Next(), it.sumIt.Next()
if cok != sok {
it.err = errors.New("sum and count iterator not aligned")
return false
}
if !cok {
return false
}
cntT, cntV := it.cntIt.At()
sumT, sumV := it.sumIt.At()
if cntT != sumT {
it.err = errors.New("sum and count timestamps not aligned")
return false
}
it.t, it.v = cntT, sumV/cntV
return true
}
func (it *AverageChunkIterator) At() (int64, float64) {
return it.t, it.v
}
func (it *AverageChunkIterator) Err() error {
if it.cntIt.Err() != nil {
return it.cntIt.Err()
}
if it.sumIt.Err() != nil {
return it.sumIt.Err()
}
return it.err
}