prometheus/tsdb/head.go

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2017-04-10 11:59:45 -07:00
// Copyright 2017 The Prometheus Authors
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
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package tsdb
import (
"fmt"
"io"
"math"
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
"path/filepath"
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"sync"
"time"
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"github.com/go-kit/log"
"github.com/go-kit/log/level"
"github.com/oklog/ulid"
"github.com/pkg/errors"
"github.com/prometheus/client_golang/prometheus"
"go.uber.org/atomic"
"github.com/prometheus/prometheus/config"
"github.com/prometheus/prometheus/pkg/exemplar"
"github.com/prometheus/prometheus/pkg/labels"
"github.com/prometheus/prometheus/storage"
"github.com/prometheus/prometheus/tsdb/chunkenc"
"github.com/prometheus/prometheus/tsdb/chunks"
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
tsdb_errors "github.com/prometheus/prometheus/tsdb/errors"
"github.com/prometheus/prometheus/tsdb/index"
"github.com/prometheus/prometheus/tsdb/record"
"github.com/prometheus/prometheus/tsdb/tombstones"
tsdb: Added ChunkQueryable implementations to db; unified MergeSeriesSets and vertical to single struct. (#7069) * tsdb: Added ChunkQueryable implementations to db; unified compactor, querier and fanout block iterating. Chained to https://github.com/prometheus/prometheus/pull/7059 * NewMerge(Chunk)Querier now takies multiple primaries allowing tsdb DB code to use it. * Added single SeriesEntry / ChunkEntry for all series implementations. * Unified all vertical, and non vertical for compact and querying to single merge series / chunk sets by reusing VerticalSeriesMergeFunc for overlapping algorithm (same logic as before) * Added block (Base/Chunk/)Querier for block querying. We then use populateAndTomb(Base/Chunk/) to iterate over chunks or samples. * Refactored endpoint tests and querier tests to include subtests. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Addressed comments from Brian and Beorn. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fixed snapshot test and added chunk iterator support for DBReadOnly. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fixed race when iterating over Ats first. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fixed tests. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fixed populate block tests. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fixed endpoints test. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fixed test. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Added test & fixed case of head open chunk. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fixed DBReadOnly tests and bug producing 1 sample chunks. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Added cases for partial block overlap for multiple full chunks. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Added extra tests for chunk meta after compaction. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fixed small vertical merge bug and added more tests for that. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com>
2020-07-31 08:03:02 -07:00
"github.com/prometheus/prometheus/tsdb/tsdbutil"
"github.com/prometheus/prometheus/tsdb/wal"
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)
var (
// ErrInvalidSample is returned if an appended sample is not valid and can't
// be ingested.
ErrInvalidSample = errors.New("invalid sample")
// ErrInvalidExemplar is returned if an appended exemplar is not valid and can't
// be ingested.
ErrInvalidExemplar = errors.New("invalid exemplar")
// ErrAppenderClosed is returned if an appender has already be successfully
// rolled back or committed.
ErrAppenderClosed = errors.New("appender closed")
)
// Head handles reads and writes of time series data within a time window.
type Head struct {
chunkRange atomic.Int64
numSeries atomic.Uint64
minTime, maxTime atomic.Int64 // Current min and max of the samples included in the head.
minValidTime atomic.Int64 // Mint allowed to be added to the head. It shouldn't be lower than the maxt of the last persisted block.
lastWALTruncationTime atomic.Int64
lastMemoryTruncationTime atomic.Int64
lastSeriesID atomic.Uint64
metrics *headMetrics
opts *HeadOptions
wal *wal.WAL
exemplarMetrics *ExemplarMetrics
exemplars ExemplarStorage
logger log.Logger
appendPool sync.Pool
exemplarsPool sync.Pool
seriesPool sync.Pool
bytesPool sync.Pool
memChunkPool sync.Pool
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// All series addressable by their ID or hash.
series *stripeSeries
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deletedMtx sync.Mutex
deleted map[uint64]int // Deleted series, and what WAL segment they must be kept until.
postings *index.MemPostings // Postings lists for terms.
tombstones *tombstones.MemTombstones
iso *isolation
cardinalityMutex sync.Mutex
cardinalityCache *index.PostingsStats // Posting stats cache which will expire after 30sec.
lastPostingsStatsCall time.Duration // Last posting stats call (PostingsCardinalityStats()) time for caching.
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
// chunkDiskMapper is used to write and read Head chunks to/from disk.
chunkDiskMapper *chunks.ChunkDiskMapper
chunkSnapshotMtx sync.Mutex
closedMtx sync.Mutex
closed bool
React UI: Add Starting Screen (#8662) * Added walreplay API endpoint Signed-off-by: Levi Harrison <git@leviharrison.dev> * Added starting page to react-ui Signed-off-by: Levi Harrison <git@leviharrison.dev> * Documented the new endpoint Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed typos Signed-off-by: Levi Harrison <git@leviharrison.dev> Co-authored-by: Julius Volz <julius.volz@gmail.com> * Removed logo Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed isResponding to isUnexpected Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed width of progress bar Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed width of progress bar Signed-off-by: Levi Harrison <git@leviharrison.dev> * Added DB stats object Signed-off-by: Levi Harrison <git@leviharrison.dev> * Updated starting page to work with new fields Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (pt. 2) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (pt. 3) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (and also implementing a method this time) (pt. 4) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (and also implementing a method this time) (pt. 5) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed const to let Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (pt. 6) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Remove SetStats method Signed-off-by: Levi Harrison <git@leviharrison.dev> * Added comma Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed api Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed to triple equals Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed data response types Signed-off-by: Levi Harrison <git@leviharrison.dev> * Don't return pointer Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed version Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed interface issue Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed pointer Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed copying lock value error Signed-off-by: Levi Harrison <git@leviharrison.dev> Co-authored-by: Julius Volz <julius.volz@gmail.com>
2021-06-05 07:29:32 -07:00
stats *HeadStats
reg prometheus.Registerer
memTruncationInProcess atomic.Bool
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}
type ExemplarStorage interface {
storage.ExemplarQueryable
AddExemplar(labels.Labels, exemplar.Exemplar) error
ValidateExemplar(labels.Labels, exemplar.Exemplar) error
IterateExemplars(f func(seriesLabels labels.Labels, e exemplar.Exemplar) error) error
}
// HeadOptions are parameters for the Head block.
type HeadOptions struct {
Merge release 2.29 in main (#9196) * PromQL: Fix start and end keywords masking label and metric names This commit fixes an issue with the "at modifier" that introduced two new keywords: `start` and `end`. In grouping options and in metric names, these keywords took precedence over metric or label names, so that those metrics and labels could no longer be referenced. Signed-off-by: Clayton Peters <clayton.peters@man.com> * Add in additional tests for metrics and/or labels called start/end. Signed-off-by: Clayton Peters <clayton.peters@man.com> * *: Cut 2.29.0-rc.0 Signed-off-by: Frederic Branczyk <fbranczyk@gmail.com> * VERSION: bump to 2.29.0-rc.0 Signed-off-by: Frederic Branczyk <fbranczyk@gmail.com> * Remove experimental wording on size-based retention Followup of #9004 Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu> * Fix PR reference in changelog Signed-off-by: George Brighton <george@gebn.co.uk> * Describe EC2 availability zone IDs at most once per refresh (#9142) Signed-off-by: George Brighton <george@gebn.co.uk> * Describe EC2 availability zones at most once per SD load Closes #9142. Signed-off-by: George Brighton <george@gebn.co.uk> * Incorporate feedback Signed-off-by: George Brighton <george@gebn.co.uk> * Integrate feedback Signed-off-by: George Brighton <george@gebn.co.uk> * Add a compatibility note for macOS users. Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu> * *: Cut v2.29.0-rc.1 Signed-off-by: Frederic Branczyk <fbranczyk@gmail.com> * Fix `kuma_sd` targetgroup reporting (#9157) * Bundle all xDS targets into a single group Signed-off-by: austin ce <austin.cawley@gmail.com> * *: cut v2.29.0-rc.2 Signed-off-by: Frederic Branczyk <fbranczyk@gmail.com> * Rename links Signed-off-by: Levi Harrison <git@leviharrison.dev> * bump codemirror-promql to 0.17.0 Signed-off-by: Augustin Husson <husson.augustin@gmail.com> * *: cut v2.29.0 Signed-off-by: Frederic Branczyk <fbranczyk@gmail.com> * tsdb: align atomically accessed int64 (#9192) This prevents a panic in 32-bit archs: https://pkg.go.dev/sync/atomic#pkg-note-BUG Fixed #9190 Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu> * Release 2.29.1 (#9193) Signed-off-by: Julien Pivotto <roidelapluie@inuits.eu> Co-authored-by: Clayton Peters <clayton.peters@man.com> Co-authored-by: Frederic Branczyk <fbranczyk@gmail.com> Co-authored-by: George Brighton <george@gebn.co.uk> Co-authored-by: Austin Cawley-Edwards <austin.cawley@gmail.com> Co-authored-by: Levi Harrison <git@leviharrison.dev> Co-authored-by: Augustin Husson <husson.augustin@gmail.com>
2021-08-12 09:38:06 -07:00
// Runtime reloadable option. At the top of the struct for 32 bit OS:
// https://pkg.go.dev/sync/atomic#pkg-note-BUG
MaxExemplars atomic.Int64
ChunkRange int64
// ChunkDirRoot is the parent directory of the chunks directory.
ChunkDirRoot string
ChunkPool chunkenc.Pool
ChunkWriteBufferSize int
// StripeSize sets the number of entries in the hash map, it must be a power of 2.
// A larger StripeSize will allocate more memory up-front, but will increase performance when handling a large number of series.
// A smaller StripeSize reduces the memory allocated, but can decrease performance with large number of series.
StripeSize int
SeriesCallback SeriesLifecycleCallback
EnableExemplarStorage bool
EnableMemorySnapshotOnShutdown bool
}
func DefaultHeadOptions() *HeadOptions {
return &HeadOptions{
ChunkRange: DefaultBlockDuration,
ChunkDirRoot: "",
ChunkPool: chunkenc.NewPool(),
ChunkWriteBufferSize: chunks.DefaultWriteBufferSize,
StripeSize: DefaultStripeSize,
SeriesCallback: &noopSeriesLifecycleCallback{},
}
}
// SeriesLifecycleCallback specifies a list of callbacks that will be called during a lifecycle of a series.
// It is always a no-op in Prometheus and mainly meant for external users who import TSDB.
// All the callbacks should be safe to be called concurrently.
// It is up to the user to implement soft or hard consistency by making the callbacks
// atomic or non-atomic. Atomic callbacks can cause degradation performance.
type SeriesLifecycleCallback interface {
// PreCreation is called before creating a series to indicate if the series can be created.
// A non nil error means the series should not be created.
PreCreation(labels.Labels) error
// PostCreation is called after creating a series to indicate a creation of series.
PostCreation(labels.Labels)
// PostDeletion is called after deletion of series.
PostDeletion(...labels.Labels)
}
// NewHead opens the head block in dir.
func NewHead(r prometheus.Registerer, l log.Logger, wal *wal.WAL, opts *HeadOptions, stats *HeadStats) (*Head, error) {
var err error
if l == nil {
l = log.NewNopLogger()
}
if opts.ChunkRange < 1 {
return nil, errors.Errorf("invalid chunk range %d", opts.ChunkRange)
}
if opts.SeriesCallback == nil {
opts.SeriesCallback = &noopSeriesLifecycleCallback{}
}
if stats == nil {
stats = NewHeadStats()
}
h := &Head{
wal: wal,
logger: l,
opts: opts,
memChunkPool: sync.Pool{
New: func() interface{} {
return &memChunk{}
},
},
stats: stats,
reg: r,
}
if err := h.resetInMemoryState(); err != nil {
return nil, err
}
h.metrics = newHeadMetrics(h, r)
if opts.ChunkPool == nil {
opts.ChunkPool = chunkenc.NewPool()
}
h.chunkDiskMapper, err = chunks.NewChunkDiskMapper(
mmappedChunksDir(opts.ChunkDirRoot),
opts.ChunkPool,
opts.ChunkWriteBufferSize,
)
if err != nil {
return nil, err
}
return h, nil
}
func (h *Head) resetInMemoryState() error {
var err error
em := NewExemplarMetrics(h.reg)
es, err := NewCircularExemplarStorage(h.opts.MaxExemplars.Load(), em)
if err != nil {
return err
}
h.exemplarMetrics = em
h.exemplars = es
h.series = newStripeSeries(h.opts.StripeSize, h.opts.SeriesCallback)
h.postings = index.NewUnorderedMemPostings()
h.tombstones = tombstones.NewMemTombstones()
h.iso = newIsolation()
h.deleted = map[uint64]int{}
h.chunkRange.Store(h.opts.ChunkRange)
h.minTime.Store(math.MaxInt64)
h.maxTime.Store(math.MinInt64)
h.lastWALTruncationTime.Store(math.MinInt64)
h.lastMemoryTruncationTime.Store(math.MinInt64)
return nil
}
type headMetrics struct {
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
activeAppenders prometheus.Gauge
series prometheus.GaugeFunc
seriesCreated prometheus.Counter
seriesRemoved prometheus.Counter
seriesNotFound prometheus.Counter
chunks prometheus.Gauge
chunksCreated prometheus.Counter
chunksRemoved prometheus.Counter
gcDuration prometheus.Summary
samplesAppended prometheus.Counter
outOfBoundSamples prometheus.Counter
outOfOrderSamples prometheus.Counter
walTruncateDuration prometheus.Summary
walCorruptionsTotal prometheus.Counter
walTotalReplayDuration prometheus.Gauge
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
headTruncateFail prometheus.Counter
headTruncateTotal prometheus.Counter
checkpointDeleteFail prometheus.Counter
checkpointDeleteTotal prometheus.Counter
checkpointCreationFail prometheus.Counter
checkpointCreationTotal prometheus.Counter
mmapChunkCorruptionTotal prometheus.Counter
snapshotReplayErrorTotal prometheus.Counter // Will be either 0 or 1.
}
func newHeadMetrics(h *Head, r prometheus.Registerer) *headMetrics {
m := &headMetrics{
activeAppenders: prometheus.NewGauge(prometheus.GaugeOpts{
Name: "prometheus_tsdb_head_active_appenders",
Help: "Number of currently active appender transactions",
}),
series: prometheus.NewGaugeFunc(prometheus.GaugeOpts{
Name: "prometheus_tsdb_head_series",
Help: "Total number of series in the head block.",
}, func() float64 {
return float64(h.NumSeries())
}),
seriesCreated: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_head_series_created_total",
Help: "Total number of series created in the head",
}),
seriesRemoved: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_head_series_removed_total",
Help: "Total number of series removed in the head",
}),
seriesNotFound: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_head_series_not_found_total",
Help: "Total number of requests for series that were not found.",
}),
chunks: prometheus.NewGauge(prometheus.GaugeOpts{
Name: "prometheus_tsdb_head_chunks",
Help: "Total number of chunks in the head block.",
}),
chunksCreated: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_head_chunks_created_total",
Help: "Total number of chunks created in the head",
}),
chunksRemoved: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_head_chunks_removed_total",
Help: "Total number of chunks removed in the head",
}),
gcDuration: prometheus.NewSummary(prometheus.SummaryOpts{
Name: "prometheus_tsdb_head_gc_duration_seconds",
Help: "Runtime of garbage collection in the head block.",
}),
walTruncateDuration: prometheus.NewSummary(prometheus.SummaryOpts{
Name: "prometheus_tsdb_wal_truncate_duration_seconds",
Help: "Duration of WAL truncation.",
}),
walCorruptionsTotal: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_wal_corruptions_total",
Help: "Total number of WAL corruptions.",
}),
walTotalReplayDuration: prometheus.NewGauge(prometheus.GaugeOpts{
Name: "prometheus_tsdb_data_replay_duration_seconds",
Help: "Time taken to replay the data on disk.",
}),
samplesAppended: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_head_samples_appended_total",
Help: "Total number of appended samples.",
}),
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
outOfBoundSamples: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_out_of_bound_samples_total",
Help: "Total number of out of bound samples ingestion failed attempts.",
}),
outOfOrderSamples: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_out_of_order_samples_total",
Help: "Total number of out of order samples ingestion failed attempts.",
}),
headTruncateFail: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_head_truncations_failed_total",
Help: "Total number of head truncations that failed.",
}),
headTruncateTotal: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_head_truncations_total",
Help: "Total number of head truncations attempted.",
}),
checkpointDeleteFail: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_checkpoint_deletions_failed_total",
Help: "Total number of checkpoint deletions that failed.",
}),
checkpointDeleteTotal: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_checkpoint_deletions_total",
Help: "Total number of checkpoint deletions attempted.",
}),
checkpointCreationFail: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_checkpoint_creations_failed_total",
Help: "Total number of checkpoint creations that failed.",
}),
checkpointCreationTotal: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_checkpoint_creations_total",
Help: "Total number of checkpoint creations attempted.",
}),
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
mmapChunkCorruptionTotal: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_mmap_chunk_corruptions_total",
Help: "Total number of memory-mapped chunk corruptions.",
}),
snapshotReplayErrorTotal: prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_tsdb_snapshot_replay_error_total",
Help: "Total number snapshot replays that failed.",
}),
}
if r != nil {
r.MustRegister(
m.activeAppenders,
m.series,
m.chunks,
m.chunksCreated,
m.chunksRemoved,
m.seriesCreated,
m.seriesRemoved,
m.seriesNotFound,
m.gcDuration,
m.walTruncateDuration,
m.walCorruptionsTotal,
m.walTotalReplayDuration,
m.samplesAppended,
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
m.outOfBoundSamples,
m.outOfOrderSamples,
m.headTruncateFail,
m.headTruncateTotal,
m.checkpointDeleteFail,
m.checkpointDeleteTotal,
m.checkpointCreationFail,
m.checkpointCreationTotal,
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
m.mmapChunkCorruptionTotal,
m.snapshotReplayErrorTotal,
// Metrics bound to functions and not needed in tests
// can be created and registered on the spot.
prometheus.NewGaugeFunc(prometheus.GaugeOpts{
Name: "prometheus_tsdb_head_max_time",
Help: "Maximum timestamp of the head block. The unit is decided by the library consumer.",
}, func() float64 {
return float64(h.MaxTime())
}),
prometheus.NewGaugeFunc(prometheus.GaugeOpts{
Name: "prometheus_tsdb_head_min_time",
Help: "Minimum time bound of the head block. The unit is decided by the library consumer.",
}, func() float64 {
return float64(h.MinTime())
}),
prometheus.NewGaugeFunc(prometheus.GaugeOpts{
Name: "prometheus_tsdb_isolation_low_watermark",
Help: "The lowest TSDB append ID that is still referenced.",
}, func() float64 {
return float64(h.iso.lowWatermark())
}),
prometheus.NewGaugeFunc(prometheus.GaugeOpts{
Name: "prometheus_tsdb_isolation_high_watermark",
Help: "The highest TSDB append ID that has been given out.",
}, func() float64 {
return float64(h.iso.lastAppendID())
}),
)
}
return m
}
func mmappedChunksDir(dir string) string { return filepath.Join(dir, "chunks_head") }
React UI: Add Starting Screen (#8662) * Added walreplay API endpoint Signed-off-by: Levi Harrison <git@leviharrison.dev> * Added starting page to react-ui Signed-off-by: Levi Harrison <git@leviharrison.dev> * Documented the new endpoint Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed typos Signed-off-by: Levi Harrison <git@leviharrison.dev> Co-authored-by: Julius Volz <julius.volz@gmail.com> * Removed logo Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed isResponding to isUnexpected Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed width of progress bar Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed width of progress bar Signed-off-by: Levi Harrison <git@leviharrison.dev> * Added DB stats object Signed-off-by: Levi Harrison <git@leviharrison.dev> * Updated starting page to work with new fields Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (pt. 2) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (pt. 3) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (and also implementing a method this time) (pt. 4) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (and also implementing a method this time) (pt. 5) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed const to let Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (pt. 6) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Remove SetStats method Signed-off-by: Levi Harrison <git@leviharrison.dev> * Added comma Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed api Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed to triple equals Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed data response types Signed-off-by: Levi Harrison <git@leviharrison.dev> * Don't return pointer Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed version Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed interface issue Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed pointer Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed copying lock value error Signed-off-by: Levi Harrison <git@leviharrison.dev> Co-authored-by: Julius Volz <julius.volz@gmail.com>
2021-06-05 07:29:32 -07:00
// HeadStats are the statistics for the head component of the DB.
type HeadStats struct {
WALReplayStatus *WALReplayStatus
}
// NewHeadStats returns a new HeadStats object.
func NewHeadStats() *HeadStats {
return &HeadStats{
WALReplayStatus: &WALReplayStatus{},
2017-10-07 06:55:11 -07:00
}
}
// WALReplayStatus contains status information about the WAL replay.
type WALReplayStatus struct {
sync.RWMutex
Min int
Max int
Current int
}
// GetWALReplayStatus returns the WAL replay status information.
func (s *WALReplayStatus) GetWALReplayStatus() WALReplayStatus {
s.RLock()
defer s.RUnlock()
return WALReplayStatus{
Min: s.Min,
Max: s.Max,
Current: s.Current,
}
}
const cardinalityCacheExpirationTime = time.Duration(30) * time.Second
// Init loads data from the write ahead log and prepares the head for writes.
// It should be called before using an appender so that it
// limits the ingested samples to the head min valid time.
func (h *Head) Init(minValidTime int64) error {
h.minValidTime.Store(minValidTime)
defer h.postings.EnsureOrder()
defer h.gc() // After loading the wal remove the obsolete data from the head.
defer func() {
// Loading of m-mapped chunks and snapshot can make the mint of the Head
// to go below minValidTime.
if h.MinTime() < h.minValidTime.Load() {
h.minTime.Store(h.minValidTime.Load())
}
}()
level.Info(h.logger).Log("msg", "Replaying on-disk memory mappable chunks if any")
start := time.Now()
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
snapIdx, snapOffset := -1, 0
refSeries := make(map[uint64]*memSeries)
if h.opts.EnableMemorySnapshotOnShutdown {
level.Info(h.logger).Log("msg", "Chunk snapshot is enabled, replaying from the snapshot")
var err error
snapIdx, snapOffset, refSeries, err = h.loadChunkSnapshot()
if err != nil {
snapIdx, snapOffset = -1, 0
h.metrics.snapshotReplayErrorTotal.Inc()
level.Error(h.logger).Log("msg", "Failed to load chunk snapshot", "err", err)
// We clear the partially loaded data to replay fresh from the WAL.
if err := h.resetInMemoryState(); err != nil {
return err
}
}
level.Info(h.logger).Log("msg", "Chunk snapshot loading time", "duration", time.Since(start).String())
}
mmapChunkReplayStart := time.Now()
mmappedChunks, err := h.loadMmappedChunks(refSeries)
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
if err != nil {
level.Error(h.logger).Log("msg", "Loading on-disk chunks failed", "err", err)
if _, ok := errors.Cause(err).(*chunks.CorruptionErr); ok {
h.metrics.mmapChunkCorruptionTotal.Inc()
}
// If this fails, data will be recovered from WAL.
// Hence we wont lose any data (given WAL is not corrupt).
mmappedChunks = h.removeCorruptedMmappedChunks(err, refSeries)
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
}
level.Info(h.logger).Log("msg", "On-disk memory mappable chunks replay completed", "duration", time.Since(mmapChunkReplayStart).String())
if h.wal == nil {
level.Info(h.logger).Log("msg", "WAL not found")
return nil
}
level.Info(h.logger).Log("msg", "Replaying WAL, this may take a while")
checkpointReplayStart := time.Now()
// Backfill the checkpoint first if it exists.
dir, startFrom, err := wal.LastCheckpoint(h.wal.Dir())
if err != nil && err != record.ErrNotFound {
return errors.Wrap(err, "find last checkpoint")
}
// Find the last segment.
_, endAt, e := wal.Segments(h.wal.Dir())
if e != nil {
return errors.Wrap(e, "finding WAL segments")
}
h.startWALReplayStatus(startFrom, endAt)
multiRef := map[uint64]uint64{}
if err == nil && startFrom >= snapIdx {
sr, err := wal.NewSegmentsReader(dir)
if err != nil {
return errors.Wrap(err, "open checkpoint")
}
defer func() {
if err := sr.Close(); err != nil {
level.Warn(h.logger).Log("msg", "Error while closing the wal segments reader", "err", err)
}
}()
// A corrupted checkpoint is a hard error for now and requires user
// intervention. There's likely little data that can be recovered anyway.
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
if err := h.loadWAL(wal.NewReader(sr), multiRef, mmappedChunks); err != nil {
return errors.Wrap(err, "backfill checkpoint")
}
h.updateWALReplayStatusRead(startFrom)
startFrom++
level.Info(h.logger).Log("msg", "WAL checkpoint loaded")
}
checkpointReplayDuration := time.Since(checkpointReplayStart)
walReplayStart := time.Now()
React UI: Add Starting Screen (#8662) * Added walreplay API endpoint Signed-off-by: Levi Harrison <git@leviharrison.dev> * Added starting page to react-ui Signed-off-by: Levi Harrison <git@leviharrison.dev> * Documented the new endpoint Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed typos Signed-off-by: Levi Harrison <git@leviharrison.dev> Co-authored-by: Julius Volz <julius.volz@gmail.com> * Removed logo Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed isResponding to isUnexpected Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed width of progress bar Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed width of progress bar Signed-off-by: Levi Harrison <git@leviharrison.dev> * Added DB stats object Signed-off-by: Levi Harrison <git@leviharrison.dev> * Updated starting page to work with new fields Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (pt. 2) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (pt. 3) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (and also implementing a method this time) (pt. 4) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (and also implementing a method this time) (pt. 5) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed const to let Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (pt. 6) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Remove SetStats method Signed-off-by: Levi Harrison <git@leviharrison.dev> * Added comma Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed api Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed to triple equals Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed data response types Signed-off-by: Levi Harrison <git@leviharrison.dev> * Don't return pointer Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed version Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed interface issue Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed pointer Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed copying lock value error Signed-off-by: Levi Harrison <git@leviharrison.dev> Co-authored-by: Julius Volz <julius.volz@gmail.com>
2021-06-05 07:29:32 -07:00
if snapIdx > startFrom {
startFrom = snapIdx
}
// Backfill segments from the most recent checkpoint onwards.
for i := startFrom; i <= endAt; i++ {
s, err := wal.OpenReadSegment(wal.SegmentName(h.wal.Dir(), i))
if err != nil {
return errors.Wrap(err, fmt.Sprintf("open WAL segment: %d", i))
}
offset := 0
if i == snapIdx {
offset = snapOffset
}
sr, err := wal.NewSegmentBufReaderWithOffset(offset, s)
if errors.Cause(err) == io.EOF {
// File does not exist.
continue
}
if err != nil {
return errors.Wrapf(err, "segment reader (offset=%d)", offset)
}
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
err = h.loadWAL(wal.NewReader(sr), multiRef, mmappedChunks)
if err := sr.Close(); err != nil {
level.Warn(h.logger).Log("msg", "Error while closing the wal segments reader", "err", err)
}
if err != nil {
return err
}
level.Info(h.logger).Log("msg", "WAL segment loaded", "segment", i, "maxSegment", endAt)
React UI: Add Starting Screen (#8662) * Added walreplay API endpoint Signed-off-by: Levi Harrison <git@leviharrison.dev> * Added starting page to react-ui Signed-off-by: Levi Harrison <git@leviharrison.dev> * Documented the new endpoint Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed typos Signed-off-by: Levi Harrison <git@leviharrison.dev> Co-authored-by: Julius Volz <julius.volz@gmail.com> * Removed logo Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed isResponding to isUnexpected Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed width of progress bar Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed width of progress bar Signed-off-by: Levi Harrison <git@leviharrison.dev> * Added DB stats object Signed-off-by: Levi Harrison <git@leviharrison.dev> * Updated starting page to work with new fields Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (pt. 2) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (pt. 3) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (and also implementing a method this time) (pt. 4) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (and also implementing a method this time) (pt. 5) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed const to let Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (pt. 6) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Remove SetStats method Signed-off-by: Levi Harrison <git@leviharrison.dev> * Added comma Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed api Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed to triple equals Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed data response types Signed-off-by: Levi Harrison <git@leviharrison.dev> * Don't return pointer Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed version Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed interface issue Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed pointer Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed copying lock value error Signed-off-by: Levi Harrison <git@leviharrison.dev> Co-authored-by: Julius Volz <julius.volz@gmail.com>
2021-06-05 07:29:32 -07:00
h.updateWALReplayStatusRead(i)
2017-10-07 06:55:11 -07:00
}
walReplayDuration := time.Since(start)
h.metrics.walTotalReplayDuration.Set(walReplayDuration.Seconds())
level.Info(h.logger).Log(
"msg", "WAL replay completed",
"checkpoint_replay_duration", checkpointReplayDuration.String(),
"wal_replay_duration", time.Since(walReplayStart).String(),
"total_replay_duration", walReplayDuration.String(),
)
return nil
2017-05-13 09:14:18 -07:00
}
func (h *Head) loadMmappedChunks(refSeries map[uint64]*memSeries) (map[uint64][]*mmappedChunk, error) {
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
mmappedChunks := map[uint64][]*mmappedChunk{}
if err := h.chunkDiskMapper.IterateAllChunks(func(seriesRef, chunkRef uint64, mint, maxt int64, numSamples uint16) error {
if maxt < h.minValidTime.Load() {
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
return nil
}
ms, ok := refSeries[seriesRef]
if !ok {
slice := mmappedChunks[seriesRef]
if len(slice) > 0 && slice[len(slice)-1].maxTime >= mint {
return errors.Errorf("out of sequence m-mapped chunk for series ref %d", seriesRef)
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
}
slice = append(slice, &mmappedChunk{
ref: chunkRef,
minTime: mint,
maxTime: maxt,
numSamples: numSamples,
})
mmappedChunks[seriesRef] = slice
return nil
}
if len(ms.mmappedChunks) > 0 && ms.mmappedChunks[len(ms.mmappedChunks)-1].maxTime >= mint {
return errors.Errorf("out of sequence m-mapped chunk for series ref %d", seriesRef)
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
}
h.metrics.chunks.Inc()
h.metrics.chunksCreated.Inc()
ms.mmappedChunks = append(ms.mmappedChunks, &mmappedChunk{
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
ref: chunkRef,
minTime: mint,
maxTime: maxt,
numSamples: numSamples,
})
h.updateMinMaxTime(mint, maxt)
if ms.headChunk != nil && maxt >= ms.headChunk.minTime {
// The head chunk was completed and was m-mapped after taking the snapshot.
// Hence remove this chunk.
ms.nextAt = 0
ms.headChunk = nil
ms.app = nil
}
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
return nil
}); err != nil {
return nil, errors.Wrap(err, "iterate on on-disk chunks")
}
return mmappedChunks, nil
}
// removeCorruptedMmappedChunks attempts to delete the corrupted mmapped chunks and if it fails, it clears all the previously
// loaded mmapped chunks.
func (h *Head) removeCorruptedMmappedChunks(err error, refSeries map[uint64]*memSeries) map[uint64][]*mmappedChunk {
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
level.Info(h.logger).Log("msg", "Deleting mmapped chunk files")
if err := h.chunkDiskMapper.DeleteCorrupted(err); err != nil {
level.Info(h.logger).Log("msg", "Deletion of mmap chunk files failed, discarding chunk files completely", "err", err)
return map[uint64][]*mmappedChunk{}
}
level.Info(h.logger).Log("msg", "Deletion of mmap chunk files successful, reattempting m-mapping the on-disk chunks")
mmappedChunks, err := h.loadMmappedChunks(refSeries)
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
if err != nil {
level.Error(h.logger).Log("msg", "Loading on-disk chunks failed, discarding chunk files completely", "err", err)
mmappedChunks = map[uint64][]*mmappedChunk{}
}
return mmappedChunks
}
func (h *Head) ApplyConfig(cfg *config.Config) error {
if !h.opts.EnableExemplarStorage {
return nil
}
// Head uses opts.MaxExemplars in combination with opts.EnableExemplarStorage
// to decide if it should pass exemplars along to it's exemplar storage, so we
// need to update opts.MaxExemplars here.
prevSize := h.opts.MaxExemplars.Load()
h.opts.MaxExemplars.Store(cfg.StorageConfig.ExemplarsConfig.MaxExemplars)
if prevSize == h.opts.MaxExemplars.Load() {
return nil
}
migrated := h.exemplars.(*CircularExemplarStorage).Resize(h.opts.MaxExemplars.Load())
level.Info(h.logger).Log("msg", "Exemplar storage resized", "from", prevSize, "to", h.opts.MaxExemplars, "migrated", migrated)
return nil
}
// PostingsCardinalityStats returns top 10 highest cardinality stats By label and value names.
func (h *Head) PostingsCardinalityStats(statsByLabelName string) *index.PostingsStats {
h.cardinalityMutex.Lock()
defer h.cardinalityMutex.Unlock()
currentTime := time.Duration(time.Now().Unix()) * time.Second
seconds := currentTime - h.lastPostingsStatsCall
if seconds > cardinalityCacheExpirationTime {
h.cardinalityCache = nil
}
if h.cardinalityCache != nil {
return h.cardinalityCache
}
h.cardinalityCache = h.postings.Stats(statsByLabelName)
h.lastPostingsStatsCall = time.Duration(time.Now().Unix()) * time.Second
return h.cardinalityCache
}
func (h *Head) updateMinMaxTime(mint, maxt int64) {
for {
lt := h.MinTime()
if mint >= lt {
break
}
if h.minTime.CAS(lt, mint) {
break
}
}
for {
ht := h.MaxTime()
if maxt <= ht {
break
}
if h.maxTime.CAS(ht, maxt) {
break
}
}
}
// SetMinValidTime sets the minimum timestamp the head can ingest.
func (h *Head) SetMinValidTime(minValidTime int64) {
h.minValidTime.Store(minValidTime)
}
// Truncate removes old data before mint from the head and WAL.
func (h *Head) Truncate(mint int64) (err error) {
initialize := h.MinTime() == math.MaxInt64
if err := h.truncateMemory(mint); err != nil {
return err
}
if initialize {
return nil
}
return h.truncateWAL(mint)
}
// OverlapsClosedInterval returns true if the head overlaps [mint, maxt].
func (h *Head) OverlapsClosedInterval(mint, maxt int64) bool {
return h.MinTime() <= maxt && mint <= h.MaxTime()
}
// truncateMemory removes old data before mint from the head.
func (h *Head) truncateMemory(mint int64) (err error) {
h.chunkSnapshotMtx.Lock()
defer h.chunkSnapshotMtx.Unlock()
defer func() {
if err != nil {
h.metrics.headTruncateFail.Inc()
}
}()
initialize := h.MinTime() == math.MaxInt64
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if h.MinTime() >= mint && !initialize {
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return nil
}
// The order of these two Store() should not be changed,
// i.e. truncation time is set before in-process boolean.
h.lastMemoryTruncationTime.Store(mint)
h.memTruncationInProcess.Store(true)
defer h.memTruncationInProcess.Store(false)
// We wait for pending queries to end that overlap with this truncation.
if !initialize {
h.WaitForPendingReadersInTimeRange(h.MinTime(), mint)
}
h.minTime.Store(mint)
h.minValidTime.Store(mint)
// Ensure that max time is at least as high as min time.
for h.MaxTime() < mint {
h.maxTime.CAS(h.MaxTime(), mint)
}
2017-09-06 07:20:37 -07:00
// This was an initial call to Truncate after loading blocks on startup.
// We haven't read back the WAL yet, so do not attempt to truncate it.
if initialize {
return nil
}
h.metrics.headTruncateTotal.Inc()
start := time.Now()
actualMint := h.gc()
level.Info(h.logger).Log("msg", "Head GC completed", "duration", time.Since(start))
h.metrics.gcDuration.Observe(time.Since(start).Seconds())
if actualMint > h.minTime.Load() {
// The actual mint of the Head is higher than the one asked to truncate.
appendableMinValidTime := h.appendableMinValidTime()
if actualMint < appendableMinValidTime {
h.minTime.Store(actualMint)
h.minValidTime.Store(actualMint)
} else {
// The actual min time is in the appendable window.
// So we set the mint to the appendableMinValidTime.
h.minTime.Store(appendableMinValidTime)
h.minValidTime.Store(appendableMinValidTime)
}
}
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
// Truncate the chunk m-mapper.
if err := h.chunkDiskMapper.Truncate(mint); err != nil {
return errors.Wrap(err, "truncate chunks.HeadReadWriter")
}
return nil
}
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
// WaitForPendingReadersInTimeRange waits for queries overlapping with given range to finish querying.
// The query timeout limits the max wait time of this function implicitly.
// The mint is inclusive and maxt is the truncation time hence exclusive.
func (h *Head) WaitForPendingReadersInTimeRange(mint, maxt int64) {
maxt-- // Making it inclusive before checking overlaps.
overlaps := func() bool {
o := false
h.iso.TraverseOpenReads(func(s *isolationState) bool {
if s.mint <= maxt && mint <= s.maxt {
// Overlaps with the truncation range.
o = true
return false
}
return true
})
return o
}
for overlaps() {
time.Sleep(500 * time.Millisecond)
}
}
// IsQuerierCollidingWithTruncation returns if the current querier needs to be closed and if a new querier
// has to be created. In the latter case, the method also returns the new mint to be used for creating the
// new range head and the new querier. This methods helps preventing races with the truncation of in-memory data.
//
// NOTE: The querier should already be taken before calling this.
func (h *Head) IsQuerierCollidingWithTruncation(querierMint, querierMaxt int64) (shouldClose bool, getNew bool, newMint int64) {
if !h.memTruncationInProcess.Load() {
return false, false, 0
}
// Head truncation is in process. It also means that the block that was
// created for this truncation range is also available.
// Check if we took a querier that overlaps with this truncation.
memTruncTime := h.lastMemoryTruncationTime.Load()
if querierMaxt < memTruncTime {
// Head compaction has happened and this time range is being truncated.
// This query doesn't overlap with the Head any longer.
// We should close this querier to avoid races and the data would be
// available with the blocks below.
// Cases:
// 1. |------truncation------|
// |---query---|
// 2. |------truncation------|
// |---query---|
return true, false, 0
}
if querierMint < memTruncTime {
// The truncation time is not same as head mint that we saw above but the
// query still overlaps with the Head.
// The truncation started after we got the querier. So it is not safe
// to use this querier and/or might block truncation. We should get
// a new querier for the new Head range while remaining will be available
// in the blocks below.
// Case:
// |------truncation------|
// |----query----|
// Turns into
// |------truncation------|
// |---qu---|
return true, true, memTruncTime
}
// Other case is this, which is a no-op
// |------truncation------|
// |---query---|
return false, false, 0
}
// truncateWAL removes old data before mint from the WAL.
func (h *Head) truncateWAL(mint int64) error {
h.chunkSnapshotMtx.Lock()
defer h.chunkSnapshotMtx.Unlock()
if h.wal == nil || mint <= h.lastWALTruncationTime.Load() {
return nil
}
start := time.Now()
h.lastWALTruncationTime.Store(mint)
first, last, err := wal.Segments(h.wal.Dir())
if err != nil {
return errors.Wrap(err, "get segment range")
}
// Start a new segment, so low ingestion volume TSDB don't have more WAL than
// needed.
if err := h.wal.NextSegment(); err != nil {
return errors.Wrap(err, "next segment")
}
last-- // Never consider last segment for checkpoint.
if last < 0 {
return nil // no segments yet.
}
// The lower two thirds of segments should contain mostly obsolete samples.
// If we have less than two segments, it's not worth checkpointing yet.
// With the default 2h blocks, this will keeping up to around 3h worth
// of WAL segments.
last = first + (last-first)*2/3
if last <= first {
return nil
}
keep := func(id uint64) bool {
if h.series.getByID(id) != nil {
return true
}
h.deletedMtx.Lock()
_, ok := h.deleted[id]
h.deletedMtx.Unlock()
return ok
}
h.metrics.checkpointCreationTotal.Inc()
if _, err = wal.Checkpoint(h.logger, h.wal, first, last, keep, mint); err != nil {
h.metrics.checkpointCreationFail.Inc()
if _, ok := errors.Cause(err).(*wal.CorruptionErr); ok {
h.metrics.walCorruptionsTotal.Inc()
}
return errors.Wrap(err, "create checkpoint")
}
if err := h.wal.Truncate(last + 1); err != nil {
// If truncating fails, we'll just try again at the next checkpoint.
// Leftover segments will just be ignored in the future if there's a checkpoint
// that supersedes them.
level.Error(h.logger).Log("msg", "truncating segments failed", "err", err)
}
// The checkpoint is written and segments before it is truncated, so we no
// longer need to track deleted series that are before it.
h.deletedMtx.Lock()
for ref, segment := range h.deleted {
if segment < first {
delete(h.deleted, ref)
}
}
h.deletedMtx.Unlock()
h.metrics.checkpointDeleteTotal.Inc()
if err := wal.DeleteCheckpoints(h.wal.Dir(), last); err != nil {
// Leftover old checkpoints do not cause problems down the line beyond
// occupying disk space.
// They will just be ignored since a higher checkpoint exists.
level.Error(h.logger).Log("msg", "delete old checkpoints", "err", err)
h.metrics.checkpointDeleteFail.Inc()
}
h.metrics.walTruncateDuration.Observe(time.Since(start).Seconds())
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level.Info(h.logger).Log("msg", "WAL checkpoint complete",
"first", first, "last", last, "duration", time.Since(start))
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return nil
}
type Stats struct {
NumSeries uint64
MinTime, MaxTime int64
IndexPostingStats *index.PostingsStats
}
// Stats returns important current HEAD statistics. Note that it is expensive to
// calculate these.
func (h *Head) Stats(statsByLabelName string) *Stats {
return &Stats{
NumSeries: h.NumSeries(),
MaxTime: h.MaxTime(),
MinTime: h.MinTime(),
IndexPostingStats: h.PostingsCardinalityStats(statsByLabelName),
}
}
type RangeHead struct {
head *Head
mint, maxt int64
}
// NewRangeHead returns a *RangeHead.
func NewRangeHead(head *Head, mint, maxt int64) *RangeHead {
return &RangeHead{
head: head,
mint: mint,
maxt: maxt,
}
}
func (h *RangeHead) Index() (IndexReader, error) {
return h.head.indexRange(h.mint, h.maxt), nil
}
func (h *RangeHead) Chunks() (ChunkReader, error) {
return h.head.chunksRange(h.mint, h.maxt, h.head.iso.State(h.mint, h.maxt))
}
func (h *RangeHead) Tombstones() (tombstones.Reader, error) {
return h.head.tombstones, nil
}
func (h *RangeHead) MinTime() int64 {
Vertical query merging and compaction (#370) * Vertical series iterator Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Select overlapped blocks first in compactor Plan() Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Added vertical compaction Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Code cleanup and comments Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Fix review comments Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Fix tests Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Add benchmark for compaction Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Perform vertical compaction only when blocks are overlapping. Actions for vertical compaction: * Sorting chunk metas * Calling chunks.MergeOverlappingChunks on the chunks Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Benchmark for vertical compaction * BenchmarkNormalCompaction => BenchmarkCompaction * Moved the benchmark from db_test.go to compact_test.go Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Benchmark for query iterator and seek for non overlapping blocks Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Vertical query merge only for overlapping blocks Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Simplify logging in Compact(...) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Updated CHANGELOG.md Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Calculate overlapping inside populateBlock Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * MinTime and MaxTime for BlockReader. Using this to find overlapping blocks in populateBlock() Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Sort blocks w.r.t. MinTime in reload() Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Log about overlapping in LeveledCompactor.write() instead of returning bool Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Log about overlapping inside LeveledCompactor.populateBlock() Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Fix review comments Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Refactor createBlock to take optional []Series Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * review1 Signed-off-by: Krasi Georgiev <kgeorgie@redhat.com> * Updated CHANGELOG and minor nits Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * nits Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Updated CHANGELOG Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Refactor iterator and seek benchmarks for Querier. Also has as overlapping blocks. Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Additional test case Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * genSeries takes optional labels. Updated BenchmarkQueryIterator and BenchmarkQuerySeek. Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Split genSeries into genSeries and populateSeries Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Check error in benchmark Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Fix review comments Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Warn about overlapping blocks in reload() Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2019-02-14 05:29:41 -08:00
return h.mint
}
// MaxTime returns the max time of actual data fetch-able from the head.
// This controls the chunks time range which is closed [b.MinTime, b.MaxTime].
func (h *RangeHead) MaxTime() int64 {
Vertical query merging and compaction (#370) * Vertical series iterator Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Select overlapped blocks first in compactor Plan() Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Added vertical compaction Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Code cleanup and comments Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Fix review comments Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Fix tests Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Add benchmark for compaction Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Perform vertical compaction only when blocks are overlapping. Actions for vertical compaction: * Sorting chunk metas * Calling chunks.MergeOverlappingChunks on the chunks Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Benchmark for vertical compaction * BenchmarkNormalCompaction => BenchmarkCompaction * Moved the benchmark from db_test.go to compact_test.go Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Benchmark for query iterator and seek for non overlapping blocks Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Vertical query merge only for overlapping blocks Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Simplify logging in Compact(...) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Updated CHANGELOG.md Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Calculate overlapping inside populateBlock Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * MinTime and MaxTime for BlockReader. Using this to find overlapping blocks in populateBlock() Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Sort blocks w.r.t. MinTime in reload() Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Log about overlapping in LeveledCompactor.write() instead of returning bool Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Log about overlapping inside LeveledCompactor.populateBlock() Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Fix review comments Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Refactor createBlock to take optional []Series Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * review1 Signed-off-by: Krasi Georgiev <kgeorgie@redhat.com> * Updated CHANGELOG and minor nits Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * nits Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Updated CHANGELOG Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Refactor iterator and seek benchmarks for Querier. Also has as overlapping blocks. Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Additional test case Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * genSeries takes optional labels. Updated BenchmarkQueryIterator and BenchmarkQuerySeek. Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Split genSeries into genSeries and populateSeries Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Check error in benchmark Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Fix review comments Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in> * Warn about overlapping blocks in reload() Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2019-02-14 05:29:41 -08:00
return h.maxt
}
// BlockMaxTime returns the max time of the potential block created from this head.
// It's different to MaxTime as we need to add +1 millisecond to block maxt because block
// intervals are half-open: [b.MinTime, b.MaxTime). Block intervals are always +1 than the total samples it includes.
func (h *RangeHead) BlockMaxTime() int64 {
return h.MaxTime() + 1
}
func (h *RangeHead) NumSeries() uint64 {
return h.head.NumSeries()
}
func (h *RangeHead) Meta() BlockMeta {
return BlockMeta{
MinTime: h.MinTime(),
MaxTime: h.MaxTime(),
ULID: h.head.Meta().ULID,
Stats: BlockStats{
NumSeries: h.NumSeries(),
},
}
}
// String returns an human readable representation of the range head. It's important to
// keep this function in order to avoid the struct dump when the head is stringified in
// errors or logs.
func (h *RangeHead) String() string {
return fmt.Sprintf("range head (mint: %d, maxt: %d)", h.MinTime(), h.MaxTime())
}
// Delete all samples in the range of [mint, maxt] for series that satisfy the given
// label matchers.
func (h *Head) Delete(mint, maxt int64, ms ...*labels.Matcher) error {
// Do not delete anything beyond the currently valid range.
mint, maxt = clampInterval(mint, maxt, h.MinTime(), h.MaxTime())
ir := h.indexRange(mint, maxt)
p, err := PostingsForMatchers(ir, ms...)
if err != nil {
return errors.Wrap(err, "select series")
}
var stones []tombstones.Stone
for p.Next() {
series := h.series.getByID(p.At())
series.RLock()
2018-02-07 05:43:21 -08:00
t0, t1 := series.minTime(), series.maxTime()
series.RUnlock()
2018-02-07 05:43:21 -08:00
if t0 == math.MinInt64 || t1 == math.MinInt64 {
continue
}
// Delete only until the current values and not beyond.
2018-02-07 05:43:21 -08:00
t0, t1 = clampInterval(mint, maxt, t0, t1)
stones = append(stones, tombstones.Stone{Ref: p.At(), Intervals: tombstones.Intervals{{Mint: t0, Maxt: t1}}})
}
if p.Err() != nil {
return p.Err()
}
if h.wal != nil {
var enc record.Encoder
if err := h.wal.Log(enc.Tombstones(stones, nil)); err != nil {
return err
}
}
for _, s := range stones {
h.tombstones.AddInterval(s.Ref, s.Intervals[0])
}
return nil
}
// gc removes data before the minimum timestamp from the head.
// It returns the actual min times of the chunks present in the Head.
func (h *Head) gc() int64 {
// Only data strictly lower than this timestamp must be deleted.
mint := h.MinTime()
2017-01-19 05:01:38 -08:00
// Drop old chunks and remember series IDs and hashes if they can be
// deleted entirely.
deleted, chunksRemoved, actualMint := h.series.gc(mint)
seriesRemoved := len(deleted)
h.metrics.seriesRemoved.Add(float64(seriesRemoved))
h.metrics.chunksRemoved.Add(float64(chunksRemoved))
h.metrics.chunks.Sub(float64(chunksRemoved))
h.numSeries.Sub(uint64(seriesRemoved))
// Remove deleted series IDs from the postings lists.
h.postings.Delete(deleted)
// Remove tombstones referring to the deleted series.
h.tombstones.DeleteTombstones(deleted)
h.tombstones.TruncateBefore(mint)
if h.wal != nil {
_, last, _ := wal.Segments(h.wal.Dir())
h.deletedMtx.Lock()
// Keep series records until we're past segment 'last'
// because the WAL will still have samples records with
// this ref ID. If we didn't keep these series records then
// on start up when we replay the WAL, or any other code
// that reads the WAL, wouldn't be able to use those
// samples since we would have no labels for that ref ID.
for ref := range deleted {
h.deleted[ref] = last
}
h.deletedMtx.Unlock()
}
return actualMint
}
// Tombstones returns a new reader over the head's tombstones
func (h *Head) Tombstones() (tombstones.Reader, error) {
return h.tombstones, nil
}
// NumSeries returns the number of active series in the head.
func (h *Head) NumSeries() uint64 {
return h.numSeries.Load()
}
// Meta returns meta information about the head.
// The head is dynamic so will return dynamic results.
func (h *Head) Meta() BlockMeta {
var id [16]byte
copy(id[:], "______head______")
return BlockMeta{
MinTime: h.MinTime(),
MaxTime: h.MaxTime(),
ULID: ulid.ULID(id),
Stats: BlockStats{
NumSeries: h.NumSeries(),
},
}
}
// MinTime returns the lowest time bound on visible data in the head.
func (h *Head) MinTime() int64 {
return h.minTime.Load()
}
// MaxTime returns the highest timestamp seen in data of the head.
func (h *Head) MaxTime() int64 {
return h.maxTime.Load()
}
// compactable returns whether the head has a compactable range.
// The head has a compactable range when the head time range is 1.5 times the chunk range.
// The 0.5 acts as a buffer of the appendable window.
func (h *Head) compactable() bool {
return h.MaxTime()-h.MinTime() > h.chunkRange.Load()/2*3
}
// Close flushes the WAL and closes the head.
// It also takes a snapshot of in-memory chunks if enabled.
func (h *Head) Close() error {
h.closedMtx.Lock()
defer h.closedMtx.Unlock()
h.closed = true
errs := tsdb_errors.NewMulti(h.chunkDiskMapper.Close())
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
if h.wal != nil {
errs.Add(h.wal.Close())
}
if errs.Err() == nil && h.opts.EnableMemorySnapshotOnShutdown {
errs.Add(h.performChunkSnapshot())
}
return errs.Err()
}
// String returns an human readable representation of the TSDB head. It's important to
// keep this function in order to avoid the struct dump when the head is stringified in
// errors or logs.
func (h *Head) String() string {
return "head"
}
func (h *Head) getOrCreate(hash uint64, lset labels.Labels) (*memSeries, bool, error) {
// Just using `getOrCreateWithID` below would be semantically sufficient, but we'd create
2017-09-18 03:28:56 -07:00
// a new series on every sample inserted via Add(), which causes allocations
// and makes our series IDs rather random and harder to compress in postings.
s := h.series.getByHash(hash, lset)
if s != nil {
return s, false, nil
2017-09-18 03:28:56 -07:00
}
// Optimistically assume that we are the first one to create the series.
id := h.lastSeriesID.Inc()
return h.getOrCreateWithID(id, hash, lset)
}
func (h *Head) getOrCreateWithID(id, hash uint64, lset labels.Labels) (*memSeries, bool, error) {
s, created, err := h.series.getOrSet(hash, lset, func() *memSeries {
return newMemSeries(lset, id, h.chunkRange.Load(), &h.memChunkPool)
})
if err != nil {
return nil, false, err
}
if !created {
return s, false, nil
}
2017-09-18 03:28:56 -07:00
h.metrics.seriesCreated.Inc()
h.numSeries.Inc()
2017-09-18 03:28:56 -07:00
h.postings.Add(id, lset)
return s, true, nil
}
// seriesHashmap is a simple hashmap for memSeries by their label set. It is built
// on top of a regular hashmap and holds a slice of series to resolve hash collisions.
// Its methods require the hash to be submitted with it to avoid re-computations throughout
// the code.
type seriesHashmap map[uint64][]*memSeries
2016-12-04 04:16:11 -08:00
func (m seriesHashmap) get(hash uint64, lset labels.Labels) *memSeries {
for _, s := range m[hash] {
if labels.Equal(s.lset, lset) {
return s
}
}
return nil
}
func (m seriesHashmap) set(hash uint64, s *memSeries) {
l := m[hash]
for i, prev := range l {
if labels.Equal(prev.lset, s.lset) {
l[i] = s
return
}
}
m[hash] = append(l, s)
}
func (m seriesHashmap) del(hash uint64, lset labels.Labels) {
var rem []*memSeries
for _, s := range m[hash] {
if !labels.Equal(s.lset, lset) {
rem = append(rem, s)
}
}
if len(rem) == 0 {
delete(m, hash)
} else {
m[hash] = rem
}
}
const (
// DefaultStripeSize is the default number of entries to allocate in the stripeSeries hash map.
DefaultStripeSize = 1 << 14
)
// stripeSeries locks modulo ranges of IDs and hashes to reduce lock contention.
// The locks are padded to not be on the same cache line. Filling the padded space
// with the maps was profiled to be slower likely due to the additional pointer
// dereferences.
type stripeSeries struct {
size int
series []map[uint64]*memSeries
hashes []seriesHashmap
locks []stripeLock
seriesLifecycleCallback SeriesLifecycleCallback
}
type stripeLock struct {
sync.RWMutex
// Padding to avoid multiple locks being on the same cache line.
_ [40]byte
}
func newStripeSeries(stripeSize int, seriesCallback SeriesLifecycleCallback) *stripeSeries {
s := &stripeSeries{
size: stripeSize,
series: make([]map[uint64]*memSeries, stripeSize),
hashes: make([]seriesHashmap, stripeSize),
locks: make([]stripeLock, stripeSize),
seriesLifecycleCallback: seriesCallback,
}
for i := range s.series {
s.series[i] = map[uint64]*memSeries{}
}
for i := range s.hashes {
s.hashes[i] = seriesHashmap{}
}
return s
2016-12-04 04:16:11 -08:00
}
// gc garbage collects old chunks that are strictly before mint and removes
// series entirely that have no chunks left.
func (s *stripeSeries) gc(mint int64) (map[uint64]struct{}, int, int64) {
var (
deleted = map[uint64]struct{}{}
deletedForCallback = []labels.Labels{}
rmChunks = 0
actualMint int64 = math.MaxInt64
)
// Run through all series and truncate old chunks. Mark those with no
2017-09-06 07:20:37 -07:00
// chunks left as deleted and store their ID.
for i := 0; i < s.size; i++ {
s.locks[i].Lock()
for hash, all := range s.hashes[i] {
for _, series := range all {
2017-09-07 23:48:19 -07:00
series.Lock()
rmChunks += series.truncateChunksBefore(mint)
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
if len(series.mmappedChunks) > 0 || series.headChunk != nil || series.pendingCommit {
seriesMint := series.minTime()
if seriesMint < actualMint {
actualMint = seriesMint
}
2017-09-07 23:48:19 -07:00
series.Unlock()
continue
}
// The series is gone entirely. We need to keep the series lock
// and make sure we have acquired the stripe locks for hash and ID of the
// series alike.
// If we don't hold them all, there's a very small chance that a series receives
// samples again while we are half-way into deleting it.
j := int(series.ref) & (s.size - 1)
if i != j {
s.locks[j].Lock()
}
deleted[series.ref] = struct{}{}
s.hashes[i].del(hash, series.lset)
delete(s.series[j], series.ref)
deletedForCallback = append(deletedForCallback, series.lset)
if i != j {
s.locks[j].Unlock()
}
2017-09-07 23:48:19 -07:00
series.Unlock()
}
}
s.locks[i].Unlock()
s.seriesLifecycleCallback.PostDeletion(deletedForCallback...)
deletedForCallback = deletedForCallback[:0]
}
if actualMint == math.MaxInt64 {
actualMint = mint
}
return deleted, rmChunks, actualMint
}
func (s *stripeSeries) getByID(id uint64) *memSeries {
i := id & uint64(s.size-1)
s.locks[i].RLock()
series := s.series[i][id]
s.locks[i].RUnlock()
return series
}
func (s *stripeSeries) getByHash(hash uint64, lset labels.Labels) *memSeries {
i := hash & uint64(s.size-1)
s.locks[i].RLock()
series := s.hashes[i].get(hash, lset)
s.locks[i].RUnlock()
return series
}
func (s *stripeSeries) getOrSet(hash uint64, lset labels.Labels, createSeries func() *memSeries) (*memSeries, bool, error) {
// PreCreation is called here to avoid calling it inside the lock.
// It is not necessary to call it just before creating a series,
// rather it gives a 'hint' whether to create a series or not.
preCreationErr := s.seriesLifecycleCallback.PreCreation(lset)
// Create the series, unless the PreCreation() callback as failed.
// If failed, we'll not allow to create a new series anyway.
var series *memSeries
if preCreationErr == nil {
series = createSeries()
}
i := hash & uint64(s.size-1)
s.locks[i].Lock()
if prev := s.hashes[i].get(hash, lset); prev != nil {
2017-09-18 02:23:22 -07:00
s.locks[i].Unlock()
return prev, false, nil
}
if preCreationErr == nil {
s.hashes[i].set(hash, series)
}
s.locks[i].Unlock()
if preCreationErr != nil {
// The callback prevented creation of series.
return nil, false, preCreationErr
}
// Setting the series in the s.hashes marks the creation of series
// as any further calls to this methods would return that series.
s.seriesLifecycleCallback.PostCreation(series.lset)
i = series.ref & uint64(s.size-1)
s.locks[i].Lock()
s.series[i][series.ref] = series
s.locks[i].Unlock()
return series, true, nil
}
type sample struct {
t int64
v float64
}
tsdb: Added ChunkQueryable implementations to db; unified MergeSeriesSets and vertical to single struct. (#7069) * tsdb: Added ChunkQueryable implementations to db; unified compactor, querier and fanout block iterating. Chained to https://github.com/prometheus/prometheus/pull/7059 * NewMerge(Chunk)Querier now takies multiple primaries allowing tsdb DB code to use it. * Added single SeriesEntry / ChunkEntry for all series implementations. * Unified all vertical, and non vertical for compact and querying to single merge series / chunk sets by reusing VerticalSeriesMergeFunc for overlapping algorithm (same logic as before) * Added block (Base/Chunk/)Querier for block querying. We then use populateAndTomb(Base/Chunk/) to iterate over chunks or samples. * Refactored endpoint tests and querier tests to include subtests. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Addressed comments from Brian and Beorn. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fixed snapshot test and added chunk iterator support for DBReadOnly. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fixed race when iterating over Ats first. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fixed tests. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fixed populate block tests. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fixed endpoints test. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fixed test. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Added test & fixed case of head open chunk. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fixed DBReadOnly tests and bug producing 1 sample chunks. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Added cases for partial block overlap for multiple full chunks. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Added extra tests for chunk meta after compaction. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fixed small vertical merge bug and added more tests for that. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com>
2020-07-31 08:03:02 -07:00
func newSample(t int64, v float64) tsdbutil.Sample { return sample{t, v} }
func (s sample) T() int64 { return s.t }
func (s sample) V() float64 { return s.v }
2017-09-07 23:48:19 -07:00
// memSeries is the in-memory representation of a series. None of its methods
// are goroutine safe and it is the caller's responsibility to lock it.
type memSeries struct {
sync.RWMutex
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
ref uint64
lset labels.Labels
mmappedChunks []*mmappedChunk
Optimise WAL loading by removing extra map and caching min-time (#9160) * BenchmarkLoadWAL: close WAL after use So that goroutines are stopped and resources released Signed-off-by: Bryan Boreham <bjboreham@gmail.com> * BenchmarkLoadWAL: make series IDs co-prime with #workers Series are distributed across workers by taking the modulus of the ID with the number of workers, so multiples of 100 are a poor choice. Signed-off-by: Bryan Boreham <bjboreham@gmail.com> * BenchmarkLoadWAL: simulate mmapped chunks Real Prometheus cuts chunks every 120 samples, then skips those samples when re-reading the WAL. Simulate this by creating a single mapped chunk for each series, since the max time is all the reader looks at. Signed-off-by: Bryan Boreham <bjboreham@gmail.com> * Fix comment Signed-off-by: Bryan Boreham <bjboreham@gmail.com> * Remove series map from processWALSamples() The locks that is commented to reduce contention in are now sharded 32,000 ways, so won't be contended. Removing the map saves memory and goes just as fast. Signed-off-by: Bryan Boreham <bjboreham@gmail.com> * loadWAL: Cache the last mmapped chunk time So we can skip calling append() for samples it will reject. Signed-off-by: Bryan Boreham <bjboreham@gmail.com> * Improvements from code review Signed-off-by: Bryan Boreham <bjboreham@gmail.com> * Full stops and capitals on comments Signed-off-by: Bryan Boreham <bjboreham@gmail.com> * Cache max time in both places mmappedChunks is updated Including refactor to extract function `setMMappedChunks`, to reduce code duplication. Signed-off-by: Bryan Boreham <bjboreham@gmail.com> * Update head min/max time when mmapped chunks added This ensures we have the correct values if no WAL samples are added for that series. Note that `mSeries.maxTime()` was always `math.MinInt64` before, since that function doesn't consider mmapped chunks. Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
2021-08-10 02:23:31 -07:00
mmMaxTime int64 // Max time of any mmapped chunk, only used during WAL replay.
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
headChunk *memChunk
chunkRange int64
firstChunkID int
nextAt int64 // Timestamp at which to cut the next chunk.
sampleBuf [4]sample
pendingCommit bool // Whether there are samples waiting to be committed to this series.
app chunkenc.Appender // Current appender for the chunk.
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
memChunkPool *sync.Pool
txs *txRing
}
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
func newMemSeries(lset labels.Labels, id uint64, chunkRange int64, memChunkPool *sync.Pool) *memSeries {
s := &memSeries{
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
lset: lset,
ref: id,
chunkRange: chunkRange,
nextAt: math.MinInt64,
txs: newTxRing(4),
memChunkPool: memChunkPool,
}
return s
}
func (s *memSeries) minTime() int64 {
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
if len(s.mmappedChunks) > 0 {
return s.mmappedChunks[0].minTime
2018-02-07 05:43:21 -08:00
}
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
if s.headChunk != nil {
return s.headChunk.minTime
}
return math.MinInt64
}
func (s *memSeries) maxTime() int64 {
2018-02-07 05:43:21 -08:00
c := s.head()
if c != nil {
return c.maxTime
}
if len(s.mmappedChunks) > 0 {
return s.mmappedChunks[len(s.mmappedChunks)-1].maxTime
2018-02-07 05:43:21 -08:00
}
return math.MinInt64
}
// truncateChunksBefore removes all chunks from the series that
// have no timestamp at or after mint.
// Chunk IDs remain unchanged.
2017-08-30 08:38:25 -07:00
func (s *memSeries) truncateChunksBefore(mint int64) (removed int) {
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
if s.headChunk != nil && s.headChunk.maxTime < mint {
// If head chunk is truncated, we can truncate all mmapped chunks.
removed = 1 + len(s.mmappedChunks)
s.firstChunkID += removed
s.headChunk = nil
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
s.mmappedChunks = nil
return removed
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
}
if len(s.mmappedChunks) > 0 {
for i, c := range s.mmappedChunks {
if c.maxTime >= mint {
break
}
removed = i + 1
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
}
s.mmappedChunks = append(s.mmappedChunks[:0], s.mmappedChunks[removed:]...)
s.firstChunkID += removed
}
return removed
}
// cleanupAppendIDsBelow cleans up older appendIDs. Has to be called after
// acquiring lock.
func (s *memSeries) cleanupAppendIDsBelow(bound uint64) {
s.txs.cleanupAppendIDsBelow(bound)
}
func (s *memSeries) head() *memChunk {
return s.headChunk
}
type memChunk struct {
chunk chunkenc.Chunk
minTime, maxTime int64
}
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
// OverlapsClosedInterval returns true if the chunk overlaps [mint, maxt].
func (mc *memChunk) OverlapsClosedInterval(mint, maxt int64) bool {
return overlapsClosedInterval(mc.minTime, mc.maxTime, mint, maxt)
}
func overlapsClosedInterval(mint1, maxt1, mint2, maxt2 int64) bool {
return mint1 <= maxt2 && mint2 <= maxt1
}
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
type mmappedChunk struct {
ref uint64
numSamples uint16
minTime, maxTime int64
}
// Returns true if the chunk overlaps [mint, maxt].
func (mc *mmappedChunk) OverlapsClosedInterval(mint, maxt int64) bool {
return overlapsClosedInterval(mc.minTime, mc.maxTime, mint, maxt)
M-map full chunks of Head from disk (#6679) When appending to the head and a chunk is full it is flushed to the disk and m-mapped (memory mapped) to free up memory Prom startup now happens in these stages - Iterate the m-maped chunks from disk and keep a map of series reference to its slice of mmapped chunks. - Iterate the WAL as usual. Whenever we create a new series, look for it's mmapped chunks in the map created before and add it to that series. If a head chunk is corrupted the currpted one and all chunks after that are deleted and the data after the corruption is recovered from the existing WAL which means that a corruption in m-mapped files results in NO data loss. [Mmaped chunks format](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/head_chunks.md) - main difference is that the chunk for mmaping now also includes series reference because there is no index for mapping series to chunks. [The block chunks](https://github.com/prometheus/prometheus/blob/master/tsdb/docs/format/chunks.md) are accessed from the index which includes the offsets for the chunks in the chunks file - example - chunks of series ID have offsets 200, 500 etc in the chunk files. In case of mmaped chunks, the offsets are stored in memory and accessed from that. During WAL replay, these offsets are restored by iterating all m-mapped chunks as stated above by matching the series id present in the chunk header and offset of that chunk in that file. **Prombench results** _WAL Replay_ 1h Wal reply time 30% less wal reply time - 4m31 vs 3m36 2h Wal reply time 20% less wal reply time - 8m16 vs 7m _Memory During WAL Replay_ High Churn: 10-15% less RAM - 32gb vs 28gb 20% less RAM after compaction 34gb vs 27gb No Churn: 20-30% less RAM - 23gb vs 18gb 40% less RAM after compaction 32.5gb vs 20gb Screenshots are in [this comment](https://github.com/prometheus/prometheus/pull/6679#issuecomment-621678932) Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
2020-05-06 08:30:00 -07:00
}
type noopSeriesLifecycleCallback struct{}
func (noopSeriesLifecycleCallback) PreCreation(labels.Labels) error { return nil }
func (noopSeriesLifecycleCallback) PostCreation(labels.Labels) {}
func (noopSeriesLifecycleCallback) PostDeletion(...labels.Labels) {}
func (h *Head) Size() int64 {
var walSize int64
if h.wal != nil {
walSize, _ = h.wal.Size()
}
cdmSize, _ := h.chunkDiskMapper.Size()
return walSize + cdmSize
}
func (h *RangeHead) Size() int64 {
return h.head.Size()
}
React UI: Add Starting Screen (#8662) * Added walreplay API endpoint Signed-off-by: Levi Harrison <git@leviharrison.dev> * Added starting page to react-ui Signed-off-by: Levi Harrison <git@leviharrison.dev> * Documented the new endpoint Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed typos Signed-off-by: Levi Harrison <git@leviharrison.dev> Co-authored-by: Julius Volz <julius.volz@gmail.com> * Removed logo Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed isResponding to isUnexpected Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed width of progress bar Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed width of progress bar Signed-off-by: Levi Harrison <git@leviharrison.dev> * Added DB stats object Signed-off-by: Levi Harrison <git@leviharrison.dev> * Updated starting page to work with new fields Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (pt. 2) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (pt. 3) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (and also implementing a method this time) (pt. 4) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (and also implementing a method this time) (pt. 5) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed const to let Signed-off-by: Levi Harrison <git@leviharrison.dev> * Passing nil (pt. 6) Signed-off-by: Levi Harrison <git@leviharrison.dev> * Remove SetStats method Signed-off-by: Levi Harrison <git@leviharrison.dev> * Added comma Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed api Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed to triple equals Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed data response types Signed-off-by: Levi Harrison <git@leviharrison.dev> * Don't return pointer Signed-off-by: Levi Harrison <git@leviharrison.dev> * Changed version Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed interface issue Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed pointer Signed-off-by: Levi Harrison <git@leviharrison.dev> * Fixed copying lock value error Signed-off-by: Levi Harrison <git@leviharrison.dev> Co-authored-by: Julius Volz <julius.volz@gmail.com>
2021-06-05 07:29:32 -07:00
func (h *Head) startWALReplayStatus(startFrom, last int) {
h.stats.WALReplayStatus.Lock()
defer h.stats.WALReplayStatus.Unlock()
h.stats.WALReplayStatus.Min = startFrom
h.stats.WALReplayStatus.Max = last
h.stats.WALReplayStatus.Current = startFrom
}
func (h *Head) updateWALReplayStatusRead(current int) {
h.stats.WALReplayStatus.Lock()
defer h.stats.WALReplayStatus.Unlock()
h.stats.WALReplayStatus.Current = current
}