This creates a new `model` directory and moves all data-model related
packages over there:
exemplar labels relabel rulefmt textparse timestamp value
All the others are more or less utilities and have been moved to `util`:
gate logging modetimevfs pool runtime
Signed-off-by: beorn7 <beorn@grafana.com>
* TSDB: demistify seriesRefs and ChunkRefs
The TSDB package contains many types of series and chunk references,
all shrouded in uint types. Often the same uint value may
actually mean one of different types, in non-obvious ways.
This PR aims to clarify the code and help navigating to relevant docs,
usage, etc much quicker.
Concretely:
* Use appropriately named types and document their semantics and
relations.
* Make multiplexing and demuxing of types explicit
(on the boundaries between concrete implementations and generic
interfaces).
* Casting between different types should be free. None of the changes
should have any impact on how the code runs.
TODO: Implement BlockSeriesRef where appropriate (for a future PR)
Signed-off-by: Dieter Plaetinck <dieter@grafana.com>
* feedback
Signed-off-by: Dieter Plaetinck <dieter@grafana.com>
* agent: demistify seriesRefs and ChunkRefs
Signed-off-by: Dieter Plaetinck <dieter@grafana.com>
* 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>
Rather than buffer up symbols in RAM, do it one by one
during compaction. Then use the reader's symbol handling
for symbol lookups during the rest of the index write.
There is some slowdown in compaction, due to having to look through a file
rather than a hash lookup. This is noise to the overall cost of compacting
series with thousands of samples though.
benchmark old ns/op new ns/op delta
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 539917175 675341565 +25.08%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 2441815993 2477453524 +1.46%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 3978543559 3922909687 -1.40%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 8430219716 8586610007 +1.86%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 1786424591 1909552782 +6.89%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 5328998202 6020839950 +12.98%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 10085059958 11085278690 +9.92%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 25497010155 27018079806 +5.97%
BenchmarkCompactionFromHead/labelnames=1,labelvalues=100000-4 2427391406 2817217987 +16.06%
BenchmarkCompactionFromHead/labelnames=10,labelvalues=10000-4 2592965497 2538805050 -2.09%
BenchmarkCompactionFromHead/labelnames=100,labelvalues=1000-4 2437388343 2668012858 +9.46%
BenchmarkCompactionFromHead/labelnames=1000,labelvalues=100-4 2317095324 2787423966 +20.30%
BenchmarkCompactionFromHead/labelnames=10000,labelvalues=10-4 2600239857 2096973860 -19.35%
benchmark old allocs new allocs delta
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 500851 470794 -6.00%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 821527 791451 -3.66%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 1141562 1111508 -2.63%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 2141576 2111504 -1.40%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 871466 841424 -3.45%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 1941428 1911415 -1.55%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 3071573 3041510 -0.98%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 6771648 6741509 -0.45%
BenchmarkCompactionFromHead/labelnames=1,labelvalues=100000-4 731493 824888 +12.77%
BenchmarkCompactionFromHead/labelnames=10,labelvalues=10000-4 793918 887311 +11.76%
BenchmarkCompactionFromHead/labelnames=100,labelvalues=1000-4 811842 905204 +11.50%
BenchmarkCompactionFromHead/labelnames=1000,labelvalues=100-4 832244 925081 +11.16%
BenchmarkCompactionFromHead/labelnames=10000,labelvalues=10-4 921553 1019162 +10.59%
benchmark old bytes new bytes delta
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 40532648 35698276 -11.93%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 60340216 53409568 -11.49%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 81087336 72065552 -11.13%
BenchmarkCompaction/type=normal,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 142485576 120878544 -15.16%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=101-4 208661368 203831136 -2.31%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=1001-4 347345904 340484696 -1.98%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=2001-4 585185856 576244648 -1.53%
BenchmarkCompaction/type=vertical,blocks=4,series=10000,samplesPerSeriesPerBlock=5001-4 1357641792 1358966528 +0.10%
BenchmarkCompactionFromHead/labelnames=1,labelvalues=100000-4 126486664 119666744 -5.39%
BenchmarkCompactionFromHead/labelnames=10,labelvalues=10000-4 122323192 115117224 -5.89%
BenchmarkCompactionFromHead/labelnames=100,labelvalues=1000-4 126404504 119469864 -5.49%
BenchmarkCompactionFromHead/labelnames=1000,labelvalues=100-4 119047832 112230408 -5.73%
BenchmarkCompactionFromHead/labelnames=10000,labelvalues=10-4 136576016 116634800 -14.60%
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
Rather than building up a 2nd copy of all the posting
tables, construct it from the data we've already written
to disk. This takes more time, but saves memory.
Current benchmark numbers have this as slightly faster, but that's
likely due to the synthetic data not having many label names.
Memory usage is roughly halved for the relevant bits.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
beorn7