9 Commits (60c76318e66d33a726b41d8542e71663e581c92a)

Author	SHA1	Message	Date
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.	7 months ago
Christian Haudum	0ea1097c8f	chore(engine): Use configured storage bucket in execution context (#17583) ... Signed-off-by: Christian Haudum <christian.haudum@gmail.com>	8 months ago
Robert Fratto	4841b2abe2	chore(engine): provide basic implementation of DataObjScan (#17568) ... Co-authored-by: Ashwanth Goli <iamashwanth@gmail.com>	8 months ago
Christian Haudum	1f8e4eebfb	chore(engine): Implement execution pipeline for SortMerge operator (#17406) ... This PR contains an implementation of the k-way merge operation without using a heap, like @rfratto described [here](https://github.com/grafana/loki/pull/17280). The SortMerge is implemented only using slices: * Maintain the following invariant: * For each input pipeline, we store the next record to process. (this already exists as `HeapSortMerge.batches`) * Additionally for each record, track the starting slice offset (which resets to zero whenever a new record is loaded in). * Iteration stops when all input pipelines have been exhausted (no change from how this is now). * To get the next record: * Iterate through each record, looking at the value from their starting slice offset. * Track the top _two_ winners (e.g., the record whose next value is the smallest and the record whose next value is the next smallest). * Find the largest offset in the starting record whose value is still less than the value of the runner-up record from the previous step. * Return the slice of that record using the two offsets, and update the stored offset of the returned record for the next call to `Read`. This approach, like the one with heap, still requires to concatenate (coalesce) the single row records - which is not implemented in this PR yet. On that note, single row records are the worst case scenario with this implementation, not necessarily the regular case. **Update:** After an offline discussion, @owen-d and I agreed on ignoring the worst-case scenario of single-row records for now. Signed-off-by: Christian Haudum <christian.haudum@gmail.com>	8 months ago
Owen Diehl	cf54d502fc	feat(dataobj, executor): filter node execution (#17327) ... # Add Filter Pipeline Implementation This PR implements a filter pipeline for Loki's engine executor component. The filter operator enables row-level filtering of Arrow record batches based on boolean expressions. This is a fundamental component for query execution. ## Changes - Implement `NewFilterPipeline` for evaluating filter predicates on record batches - Add `filterBatch` utility to efficiently filter Arrow records - Update `executeFilter` in the executor to use the new implementation - Add comprehensive test suite for the filter pipeline: - Basic literal predicates (true/false) - Column reference predicates - Empty batch handling - Multiple batch processing ## Implementation Notes The filter implementation follows the same pattern as the project pipeline, evaluating predicates against each record and creating a new filtered record based on the results. The implementation supports compound predicates with AND logic. This implementation provides a foundation for more advanced filtering operations as the expression evaluator is enhanced to support more complex expressions.	8 months ago
Owen Diehl	395030b130	feat(dataobj executor): project node (#17312)	9 months ago
Owen Diehl	920e3b23ed	chore(dataobj,testing,executor): pipeline equality testing (#17311)	9 months ago
Christian Haudum	e828c48702	chore(engine): Implement execution pipeline for the limit operator (#17264) ... Signed-off-by: Christian Haudum <christian.haudum@gmail.com>	9 months ago
Christian Haudum	64c477b1f7	chore(engine): Add framework for query executor (#17260) ... Signed-off-by: Christian Haudum <christian.haudum@gmail.com>	9 months ago