Extends Appender.AppendHistogram function to accept the FloatHistogram. TSDB supports appending, querying, WAL replay, for this new type of histogram.
Signed-off-by: Marc Tudurí <marctc@protonmail.com>
Signed-off-by: Ganesh Vernekar <ganeshvern@gmail.com>
Co-authored-by: Ganesh Vernekar <ganeshvern@gmail.com>
Patterned after `Chunk.Iterator()`: pass the old iterator in so it
can be re-used to avoid allocating a new object.
(This commit does not do any re-use; it is just changing all the method
signatures so re-use is possible in later commits.)
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
To avoid building up data in memory, commit and make a new appender
periodically.
The number `commitAfter = 10000` was chosen arbitrarily; testing with
10x more or less gives slightly worse results.
Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
- Pick At... method via return value of Next/Seek.
- Do not clobber returned buckets.
- Add partial FloatHistogram suppert.
Note that the promql package is now _only_ dealing with
FloatHistograms, following the idea that PromQL only knows float
values.
As a byproduct, I have removed the histogramSeries metric. In my
understanding, series can have both float and histogram samples, so
that metric doesn't make sense anymore.
As another byproduct, I have converged the sampleBuf and the
histogramSampleBuf in memSeries into one. The sample type stored in
the sampleBuf has been extended to also contain histograms even before
this commit.
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>
This moves the label lookup into TSDB, whilst still keeping the cached-ref optimisation for repeated Appends.
This makes the API easier to consume and implement. In particular this change is motivated by the scrape-time-aggregation work, which I don't think is possible to implement without it as it needs access to label values.
Signed-off-by: Tom Wilkie <tom.wilkie@gmail.com>
* Callbacks for lifecycle of series in TSDB
Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
* Add more comments
Signed-off-by: Ganesh Vernekar <cs15btech11018@iith.ac.in>
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>
This is part of https://github.com/prometheus/prometheus/pull/5882 that can be done to simplify things.
All todos I added will be fixed in follow up PRs.
* querier.Querier, querier.Appender, querier.SeriesSet, and querier.Series interfaces merged
with storage interface.go. All imports that.
* querier.SeriesIterator replaced by chunkenc.Iterator
* Added chunkenc.Iterator.Seek method and tests for xor implementation (?)
* Since we properly handle SelectParams for Select methods I adjusted min max
based on that. This should help in terms of performance for queries with functions like offset.
* added Seek to deletedIterator and test.
* storage/tsdb was removed as it was only a unnecessary glue with incompatible structs.
No logic was changed, only different source of abstractions, so no need for benchmarks.
Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com>
* Added CreateBlock and CreateHead functions to new file to make it reusable across packages.
Signed-off-by: Dipack P Panjabi <dipack.panjabi@gmail.com>
