8 Commits (JStickler-patch-1)

Author	SHA1	Message	Date
Robert Fratto	9129d8cdd0	chore(engine): use columnar logs.Reader for DataObjScan (#18577)	5 months ago
Christian Haudum	cea56469b9	chore(engine): Pass context to Pipeline's Read() function (#18459) ... This change unifies how the request context is passed through the execution of pipelines. Instead of storing the context on pipelines, pass them as argument to the `Read()` function. This change alters the interface method of `Pipeline` from `Read() bool` to `Read(context.Context) bool`. Signed-off-by: Christian Haudum <christian.haudum@gmail.com>	6 months ago
Christian Haudum	4305a6aa61	chore(engine): Make literals simple types (#17733) ... Representing data types as simple types (string, int64, float64) makes them more efficient to cast without conversion. Signed-off-by: Christian Haudum <christian.haudum@gmail.com>	6 months ago
Christian Haudum	4fbe7c1a21	fix(engine): Fix bug in `SortMerge` which caused rows to be skipped due to incorrect sorting (#18334) ... This PR fixes two problems with the sorting of rows of input records: 1) When sorting input indexes (`batchIndexes []int`) based on their current timestamp at index `offset` (`timestamps []int64`), the positional correspondence was not maintained leading to pull rows from wrong records. 2) When calculating how many rows from the current batch could be yield, the comparison of the timestamp with the next highest timestamp was wrong. Signed-off-by: Christian Haudum <christian.haudum@gmail.com>	6 months ago
Owen Diehl	ccb65f9670	feat(engine, dataobj): logql bench wiring for new engine [non-fork] (#17627) ... Signed-off-by: Christian Haudum <christian.haudum@gmail.com> Co-authored-by: Christian Haudum <christian.haudum@gmail.com> Co-authored-by: Robert Fratto <robertfratto@gmail.com>	8 months ago
Christian Haudum	94b1d2d5ce	chore(engine): Improve type system of the new query engine (#17456) ... **What this PR does / why we need it**: The original type system for the new engine was introduced before adding Apache Arrow and with that an additional type system. One aspect of the new type system of this PR is that the logical types of the engine are aligned/interchangeable with the arrow logical types. ```go type Type uint8 const ( NULL = Type(arrow.NULL) BOOL = Type(arrow.BOOL) STRING = Type(arrow.STRING) INT64 = Type(arrow.INT64) FLOAT64 = Type(arrow.FLOAT64) ) ``` The actual data types of Loki (`Boolean`, `String`, `Integer`, `Float`, `Timestamp`, `Duration`, and `Bytes`) are implemented as wrapper around a logical type. Different data types can use the same logical type as representation, such as `Integer`, `Timestamp` and `Duration` both use `INT64`. Since Arrow array types are bound to logical types, and one logical type can have multiple Loki data types, it is required to "annotate" Fields of an arrow.Record using its Metadata, similar to how it is done for distinguishing builtin columns from label/metadata/parsed columns. Additionally, the PR contains a mapping between Loki types and Arrow types. The `Literal` has been updated to use the new types. The old system also used UINT64 as representation of the `Timestamp` type, which is incorrect, since both LogQL and the DataObjects allow timestamps before unix epoch. --- Signed-off-by: Christian Haudum <christian.haudum@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
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