* Create splits of multiple files for parallel indexing
* fix wrong import and npe in test
* use the single file split in tests
* rename
* import order
* Remove specific local input source
* Update docs/ingestion/native-batch.md
Co-Authored-By: sthetland <steve.hetland@imply.io>
* Update docs/ingestion/native-batch.md
Co-Authored-By: sthetland <steve.hetland@imply.io>
* doc and error msg
* fix build
* fix a test and address comments
Co-authored-by: sthetland <steve.hetland@imply.io>
* Add Azure config options for segment prefix and max listing length
Added configuration options to allow the user to specify the prefix
within the segment container to store the segment files. Also
added a configuration option to allow the user to specify the
maximum number of input files to stream for each iteration.
* * Fix test failures
* * Address review comments
* * add dependency explicitly to pom
* * update docs
* * Address review comments
* * Address review comments
* Doc update for new input source and input format.
- The input source and input format are promoted in all docs under docs/ingestion
- All input sources including core extension ones are located in docs/ingestion/native-batch.md
- All input formats and parsers including core extension ones are localted in docs/ingestion/data-formats.md
- New behavior of the parallel task with different partitionsSpecs are documented in docs/ingestion/native-batch.md
* parquet
* add warning for range partitioning with sequential mode
* hdfs + s3, gs
* add fs impl for gs
* address comments
* address comments
* gcs
* Fail superbatch range partition multi dim values
Change the behavior of parallel indexing range partitioning to fail
ingestion if any row had multiple values for the partition dimension.
After this change, the behavior matches that of hadoop indexing.
(Previously, rows with multiple dimension values would be skipped.)
* Improve err msg, rename method, rename test class
* Parallel indexing single dim partitions
Implements single dimension range partitioning for native parallel batch
indexing as described in #8769. This initial version requires the
druid-datasketches extension to be loaded.
The algorithm has 5 phases that are orchestrated by the supervisor in
`ParallelIndexSupervisorTask#runRangePartitionMultiPhaseParallel()`.
These phases and the main classes involved are described below:
1) In parallel, determine the distribution of dimension values for each
input source split.
`PartialDimensionDistributionTask` uses `StringSketch` to generate
the approximate distribution of dimension values for each input
source split. If the rows are ungrouped,
`PartialDimensionDistributionTask.UngroupedRowDimensionValueFilter`
uses a Bloom filter to skip rows that would be grouped. The final
distribution is sent back to the supervisor via
`DimensionDistributionReport`.
2) The range partitions are determined.
In `ParallelIndexSupervisorTask#determineAllRangePartitions()`, the
supervisor uses `StringSketchMerger` to merge the individual
`StringSketch`es created in the preceding phase. The merged sketch is
then used to create the range partitions.
3) In parallel, generate partial range-partitioned segments.
`PartialRangeSegmentGenerateTask` uses the range partitions
determined in the preceding phase and
`RangePartitionCachingLocalSegmentAllocator` to generate
`SingleDimensionShardSpec`s. The partition information is sent back
to the supervisor via `GeneratedGenericPartitionsReport`.
4) The partial range segments are grouped.
In `ParallelIndexSupervisorTask#groupGenericPartitionLocationsPerPartition()`,
the supervisor creates the `PartialGenericSegmentMergeIOConfig`s
necessary for the next phase.
5) In parallel, merge partial range-partitioned segments.
`PartialGenericSegmentMergeTask` uses `GenericPartitionLocation` to
retrieve the partial range-partitioned segments generated earlier and
then merges and publishes them.
* Fix dependencies & forbidden apis
* Fixes for integration test
* Address review comments
* Fix docs, strict compile, sketch check, rollup check
* Fix first shard spec, partition serde, single subtask
* Fix first partition check in test
* Misc rewording/refactoring to address code review
* Fix doc link
* Split batch index integration test
* Do not run parallel-batch-index twice
* Adjust last partition
* Split ITParallelIndexTest to reduce runtime
* Rename test class
* Allow null values in range partitions
* Indicate which phase failed
* Improve asserts in tests
* Fix the potential race SplittableInputSource.getNumSplits() and SplittableInputSource.createSplits() in TaskMonitor
* Fix docs and javadoc
* Add unit tests for large or small estimated num splits
* add override
* Adjust defaults for hashed partitioning
If neither the partition size nor the number of shards are specified,
default to partitions of 5,000,000 rows (similar to the behavior of
dynamic partitions). Previously, both could be null and cause incorrect
behavior.
Specifying both a partition size and a number of shards now results in
an error instead of ignoring the partition size in favor of using the
number of shards. This is a behavior change that makes it more apparent
to the user that only one of the two properties will be honored
(previously, a message was just logged when the specified partition size
was ignored).
* Fix test
* Handle -1 as null
* Add -1 as null tests for single dim partitioning
* Simplify logic to handle -1 as null
* Address review comments
* Rename partition spec fields
Rename partition spec fields to be consistent across the various types
(hashed, single_dim, dynamic). Specifically, use targetNumRowsPerSegment
and maxRowsPerSegment in favor of targetPartitionSize and
maxSegmentSize. Consistent and clearer names are easier for users to
understand and use.
Also fix various IntelliJ inspection warnings and doc spelling mistakes.
* Fix test
* Improve docs
* Add targetRowsPerSegment to HashedPartitionsSpec
* move google ext docs from contrib to core
* fix links
* revert unintended change
* more links, add note to example ext doc that it was removed, unlink from sidebar
Jihoon Son
zachjsh
Jonathan Wei
Chi Cao Minh
Gian Merlino
David Glasser
Clint Wylie