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${ noResults }
8 Commits (0f760f63dd5137ad406e0bc9f08676898e04ac97)
Author | SHA1 | Message | Date |
---|---|---|---|
Bryan Boreham |
134e8dc7af
|
TSDB: Simplify OOO Select by copying the head chunk (#14396)
Instead of carrying around extra fields in `Meta` structs which let us approximate what was in the chunk at the time, take a copy of the chunk. This simplifies lots of code, and lets us correct a couple of tests which were embedding the wrong answer. We can also remove boundedIterator, which was only used to constrain the OOO head chunk. Signed-off-by: Bryan Boreham <bjboreham@gmail.com> |
5 months ago |
Arve Knudsen |
5c4310aa37
|
[ENHANCEMENT] TSDB: Optimize querying with regexp matchers
Add method `PostingsForLabelMatching` to `tsdb.IndexReader`, to obtain postings for labels with a certain name and values accepted by a provided callback, and use it from `tsdb.PostingsForMatchers`. The intention is to optimize regexp matcher paths, especially not having to load all label values before matching on them. Plus tests, and refactor some `tsdb/index.Reader` methods. Benchmarking shows memory reduction up to ~100%, and speedup of up to ~50%. Signed-off-by: Arve Knudsen <arve.knudsen@gmail.com> Co-authored-by: Bartlomiej Plotka <bwplotka@gmail.com> |
7 months ago |
Marco Pracucci |
501bc6419e
|
Add ShardedPostings() support to TSDB (#10421)
This PR is a reference implementation of the proposal described in #10420. In addition to what described in #10420, in this PR I've introduced labels.StableHash(). The idea is to offer an hashing function which doesn't change over time, and that's used by query sharding in order to get a stable behaviour over time. The implementation of labels.StableHash() is the hashing function used by Prometheus before stringlabels, and what's used by Grafana Mimir for query sharding (because built before stringlabels was a thing). Follow up work As mentioned in #10420, if this PR is accepted I'm also open to upload another foundamental piece used by Grafana Mimir query sharding to accelerate the query execution: an optional, configurable and fast in-memory cache for the series hashes. Signed-off-by: Marco Pracucci <marco@pracucci.com> |
10 months ago |
Matthieu MOREL |
9c4782f1cc
|
golangci-lint: enable testifylint linter (#13254)
Signed-off-by: Matthieu MOREL <matthieu.morel35@gmail.com> |
12 months ago |
Fiona Liao |
5bee0cfce2
|
Change `ChunkReader.Chunk()` to `ChunkOrIterable()`
The ChunkReader interface's Chunk() has been changed to ChunkOrIterable(). This is a precursor to OOO native histogram support - with OOO native histograms, the chunks.Meta passed to Chunk() can result in multiple chunks being returned rather than just a single chunk (e.g. if oooMergedChunk has a counter reset in the middle). To support this, ChunkOrIterable() requires either a single chunk or an iterable to be returned. If an iterable is returned, the caller has the responsibility of converting the samples from the iterable into possibly multiple chunks. The OOOHeadChunkReader now returns an iterable rather than a chunk to prepare for the native histograms case. Also as a beneficial side effect, oooMergedChunk and boundedChunk has been simplified as they only need to implement the Iterable interface now, not the full Chunk interface. --------- Signed-off-by: Fiona Liao <fiona.y.liao@gmail.com> Co-authored-by: George Krajcsovits <krajorama@users.noreply.github.com> |
1 year ago |
Łukasz Mierzwa |
3c80963e81
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Use a linked list for memSeries.headChunk (#11818)
Currently memSeries holds a single head chunk in-memory and a slice of mmapped chunks. When append() is called on memSeries it might decide that a new headChunk is needed to use for given append() call. If that happens it will first mmap existing head chunk and only after that happens it will create a new empty headChunk and continue appending our sample to it. Since appending samples uses write lock on memSeries no other read or write can happen until any append is completed. When we have an append() that must create a new head chunk the whole memSeries is blocked until mmapping of existing head chunk finishes. Mmapping itself uses a lock as it needs to be serialised, which means that the more chunks to mmap we have the longer each chunk might wait for it to be mmapped. If there's enough chunks that require mmapping some memSeries will be locked for long enough that it will start affecting queries and scrapes. Queries might timeout, since by default they have a 2 minute timeout set. Scrapes will be blocked inside append() call, which means there will be a gap between samples. This will first affect range queries or calls using rate() and such, since the time range requested in the query might have too few samples to calculate anything. To avoid this we need to remove mmapping from append path, since mmapping is blocking. But this means that when we cut a new head chunk we need to keep the old one around, so we can mmap it later. This change makes memSeries.headChunk a linked list, memSeries.headChunk still points to the 'open' head chunk that receives new samples, while older, yet to be mmapped, chunks are linked to it. Mmapping is done on a schedule by iterating all memSeries one by one. Thanks to this we control when mmapping is done, since we trigger it manually, which reduces the risk that it will have to compete for mmap locks with other chunks. Signed-off-by: Łukasz Mierzwa <l.mierzwa@gmail.com> |
1 year ago |
Signed-off-by: Jesus Vazquez |
3362bf6d79
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Fix merge conflicts
Signed-off-by: Jesus Vazquez <jesus.vazquez@grafana.com> Signed-off-by: Ganesh Vernekar <ganeshvern@gmail.com> Co-authored-by: Ganesh Vernekar <ganeshvern@gmail.com> |
2 years ago |
Jesus Vazquez |
c1b669bf9b
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Add out-of-order sample support to the TSDB (#11075)
* Introduce out-of-order TSDB support This implementation is based on this design doc: https://docs.google.com/document/d/1Kppm7qL9C-BJB1j6yb6-9ObG3AbdZnFUBYPNNWwDBYM/edit?usp=sharing This commit adds support to accept out-of-order ("OOO") sample into the TSDB up to a configurable time allowance. If OOO is enabled, overlapping querying are automatically enabled. Most of the additions have been borrowed from https://github.com/grafana/mimir-prometheus/ Here is the list ist of the original commits cherry picked from mimir-prometheus into this branch: - |
2 years ago |