149 Commits (cf12656a148cee6a605c19c45b8c27e4050f2ece)

Author	SHA1	Message	Date
benclive	077e4196ef	chore: Add indexes to LogQL benchmarks (#18500)	11 months ago
Robert Fratto	86c57c8616	chore: consistently use new labels.Labels API (#18490)	11 months ago
benclive	517c317c97	chore: Integrate indexes with the new query engine (#18427)	11 months ago
benclive	d0a3cd86a7	chore: Remove lookback from stats in unsupported sections (#18489)	11 months ago
Periklis Tsirakidis	17d6853535	chore: Add support for indexpointers section (#18485)	11 months ago
Periklis Tsirakidis	bcf24e4cfb	chore(dataobj): Use indexpointer sections in metastore reads and writes (#18448)	11 months ago
Periklis Tsirakidis	6eedcaaded	chore(dataobj): Add new index pointers section type (#18441)	11 months ago
benclive	1f0edcd4ac	feat: Initial index-builder implementation (#18297)	11 months ago
Christian Haudum	d9f7d785ff	chore(engine): Sort entries in dataobj sections by timestamp descending (#18367) ... Sorting sections by `timestamp DESC` first and `streamID ASC` second allows for more efficient reading (by section) for BACKWARD log queries. - This change breaks FORWARD log queries. Which is ok for the time being. We will add support later again when columnar reading of the dataobj is introduced. - This change renders existing dataobj in storage incompatible. Signed-off-by: Christian Haudum <christian.haudum@gmail.com>	11 months ago
benclive	5429647b81	chore: Add pointers to dataobj-inspect UI (#18372)	11 months ago
benclive	f66781d426	fix: Row reader with no stream ID predicates (#18368)	11 months ago
Robert Fratto	26cb50e65a	chore(dataobj): Improve performance of dataset.Value API (#18339) ... Co-authored-by: Ashwanth <iamashwanth@gmail.com>	11 months ago
benclive	a238816751	perf: Use map for InPredicate when reading dataobj (#18325)	11 months ago
Robert Fratto	807f06baf3	chore(dataobj/uploader): expose logs for uploaded objects (#18338)	11 months ago
Robert Fratto	9c3d86be52	chore(dataobj): cut logs section based on uncompressed size (#18329)	11 months ago
Ashwanth	5c02493fe2	chore(dataobj): avoid reading a predicate column more than once (#18324)	11 months ago
Christian Haudum	2cbbbd1f46	chore: Fixup metric names for dataobj-consumer and histogram bucket (#18295) ... Signed-off-by: Christian Haudum <christian.haudum@gmail.com>	11 months ago
Christian Haudum	b1fc685397	chore: Extend instrumentation of dataobject uploader (#18292) ... These changes provide better visibility into the dataobj uploader. Signed-off-by: Christian Haudum <christian.haudum@gmail.com>	11 months ago
Robert Fratto	30ccf5172c	chore(util/arrowtest): export arrowtest package (#18255)	12 months ago
benclive	9642af3ac6	chore: Add new pointers section type for dataobj (#18243)	12 months ago
Christian Haudum	b316d52612	chore(engine): Use shard information from query frontend in the new query engine (#17792) ... **What this PR does / why we need it**: This PR introduces the sharding to the new query engine to be able to test the engine using the exsiting Loki architecture with query frontend, query scheduler, and queriers. **Note, this is only an interim solution used for validating and testing.** By design, the first phase of the query engine implementation does only local execution of queries without sharding or time-based splitting. However, for testing the results of this first phase, we can utilise dual-ingestion (writing both chunks/tsdb and data objects in a way that sharding and splitting is performed in the query frontend using the existing middlewares. On the queriers themselves, the sub-queries are parsed and planned using the new logical and physical planner. If the query can successfully be planned, it will be executed by the new engine, otherwise it falls back to the old engine. **Shard size considerations** While performing time-splitting in the frontend works for data objects as well, sharding by information from TSDB is not directly mappable to data objects. The default target shard size in TSDB is 600MB (decompressed), whereas target size of data objects is 1GB compressed or roughly 10-15GB uncompressed. However, individual logs sections of a data object have a target size of 128MB, which is roughly 0.9-1.2GB. That is 1.5-2x larger than the TSDB target shard size. So when using the sharding calculation from TSDB, it would over-shard for data object sections, which is likely acceptable for testing and good enough for proving that local execution with the new engine works. **How does sharding with data objects in this PR work?** The query frontend passes down the calculated shards as part of the query parameters of the serialised sub-request. The logical planner on the querier stores the Shard annotation on the `MAKETABLE` alongside the stream selector. This is then used by the physical planner to filter out only the relevant sections of the resolved data objects from the metastore lookup. During exeuction, only readers for the relevant sections are initialised when performing the `DataObjScan`. Signed-off-by: Christian Haudum <christian.haudum@gmail.com> Co-authored-by: Ashwanth <iamashwanth@gmail.com>	12 months ago
Robert Fratto	06cf52757b	chore(dataobj): Add columnar reading APIs to logs and streams sections (#17976) ... This PR introduces columnar reading APIs to both the logs and streams sections. As part of this change, both sections now expose the columns stored in the section, which are then used to define predicates on the readers. The columnar reading APIs emit a sequence of `arrow.Record`s, where each record has no more than the batch size passed to the `Reader.Read` method. Unlike the original row-based readers, the columnar readers: * Allow reading a subset of columns * Have column-based predicates that map literally to the new query engine To be able to represent a column's value for use in predicates, we import [github.com/apache/arrow-go/v18/arrow/scalar](https://pkg.go.dev/github.com/apache/arrow-go/v18@v18.3.0/arrow/scalar). Section Reader implementations perform mapping to and from the internal `dataset.Value`. The implementation of `logs.Reader` is almost an identical copy of the `streams.Reader` implementation. I've opted for duplicating the implementation in the short term, since there's no obvious way for how to deduplicate it in a way keeps the package structure easy to understand. We can change this in the future if a clean and simple approach presents itself. This PR does not yet update `DataObjScan` to make use of these new APIs; that's being left out of scope for another PR to handle.	1 year ago
Robert Fratto	c5ac922e55	chore(dataobj): Use pooled buffers when decoding pages (#17955)	1 year ago
Charles Korn	545fb1597c	refactor(stringlabels): Support stringlabels in `logql/log` package (#17838) ... This is another step towards supporting Prometheus' stringlabels implementation in Loki. It adds support in the `logql/log` package. Part of https://github.com/grafana/loki/issues/17122 The tests should now compile and pass with `-tags stringlabels`. --- Co-authored-by: Karsten Jeschkies <karsten.jeschkies@grafana.com>	1 year ago
Christian Haudum	48f1d0ebc0	chore(dataobj): Decompress zstd compressed pages upfront (#17841) ... This PR changes the decompression of data object pages, so that they are decompressed fully upfront into a bytes buffer, instead of passing a streaming zstd decompressor (reader) to the page decoder. This this allows to release the zstd decompressor to the pool earlier.Signed-off-by: Christian Haudum <christian.haudum@gmail.com>	1 year ago
Oleg Zaytsev	c8a15f451c	refactor: Use OTel tracing library (#17859) ... Signed-off-by: Oleg Zaytsev <mail@olegzaytsev.com>	1 year ago
benclive	e6fe98c7c3	chore: Emit metastore event to kafka after object build (#17842)	1 year ago
benclive	02a6b81c66	chore: Estimate builder size when using multiple sections (#17840)	1 year ago
Trevor Whitney	3d2286a840	chore: implement multi-variant extractor that can return multiple samples (#17149)	1 year ago
Jackson Coelho	8f58df6e47	chore: rename dataobj consumer metrics (#17830)	1 year ago
Jackson Coelho	8001beef13	chore: add metrics (#17823)	1 year ago
Robert Fratto	36bcb5aa2d	chore(dataobj): fix regression in allocations of row readers (#17784)	1 year ago
Robert Fratto	a237444f0d	refactor(dataobj): invert dependency between dataobj and sections (#17762) ... Originally, the dataobj package was a higher-level API around sections. This design caused it to become a bottleneck: * Implementing any new public behaviour for a section required bubbling it up to the dataobj API for it to be exposed, making it tedious to add new sections or update existing ones. * The `dataobj.Builder` pattern was focused on constructing dataobjs for storing log data, which will cause friction as we build objects around other use cases. This PR builds on top of the foundation laid out by #17704 and #17708, fully inverting the dependency between dataobj and sections: * The `dataobj` package has no knowledge of what sections exist, and can now be used for writing and reading generic sections. Section packages now create higher-level APIs around the abstractions provided by `dataobj`. * Section packages are now public, and callers interact directly with these packages for writing and reading section-specific data. * All logic for a section (encoding, decoding, buffering, reading) is now fully self-contained inside the section package. Previously, the implementation of each section was spread across three packages (`pkg/dataobj/internal/encoding`, `pkg/dataobj/internal/sections/SECTION`, `pkg/dataobj`). * Cutting a section is now a decision made by the caller rather than the section implementation. Previously, the logs section builder would create multiple sections. For the most part, this change is a no-op, with two exceptions: 1. Section cutting is now performed by the caller; however, this shouldn't result in any issues. 2. Removing the high-level `dataobj.Stream` and `dataobj.Record` types will temporarily reduce the allocation gains from #16988. I will address this after this PR is merged.	1 year ago
Robert Fratto	7b0d6d7144	chore(dataobj): Migrate to dynamic section types (#17708)	1 year ago
Robert Fratto	3fbebd3bc4	chore(dataobj): migrate to section-relative offsets (#17704)	1 year ago
Ashwanth	3ad1a64bc1	chore(dataobj): fixes a memory aliasing bug in dataobj reader (#17537)	1 year ago
Trevor Whitney	577d501839	refactor: extractors can return multiple samples (#17064) ... Signed-off-by: Trevor Whitney <trevorjwhitney@gmail.com>	1 year ago
Robert Fratto	4841b2abe2	chore(engine): provide basic implementation of DataObjScan (#17568) ... Co-authored-by: Ashwanth Goli <iamashwanth@gmail.com>	1 year ago
Ashwanth	009600cb51	chore(dataobj): order predicates based on row selectivity and cost of reading (#17376)	1 year ago
Ashwanth	5961923836	chore(dataobj): apply predicates sequentially (#17283)	1 year ago
Ashwanth	42e2407137	chore(dataobj): backfill sparse rows using EncodeN (#16485)	1 year ago
aarogoss	ab65ac5e8e	feat: refactors kafka/client to improve metric collection (#17308)	1 year ago
Ashwanth	7dd4ab4b09	chore(dataobj): correct stats calculation for prepredicate bytes read (#17279)	1 year ago
Karsten Jeschkies	a864f8b986	refactor(stringlabels): Support stringlabels in log.StreamPipeline (#17216)	1 year ago
benclive	9e68fb01f4	feat(dataobj): Add query stats collection to the dataobj readers (#17128)	1 year ago
benclive	e5784d7a82	perf: Reuse Values in dataobj.Reader to reduce allocs (#16988) ... Co-authored-by: Robert Fratto <robertfratto@gmail.com>	1 year ago
Robert Fratto	bd4331363b	chore: align benchmark results between chunks and dataobjs (#17127)	1 year ago
benclive	8e421186df	chore: Collect basic stats from dataobj queries (#17111)	1 year ago
aarogoss	e962398462	feat: Optionally enable kafka histograms for read/write latency (#17089)	1 year ago
Christian Haudum	e297c51d20	chore(metastore): Use constants for label names (#17048) ... This PR contains improvements for: 1. Make label names contants 2. Use `strconv.FormatInt` for encoding timestamp into label value (equivalent to `strconv.ParseInt` when decoding) 3. Use `labels.New` to construct labels, rather then string formatting Signed-off-by: Christian Haudum <christian.haudum@gmail.com>	1 year ago