* Update data-formats.md
Field error and light rewording of new Avro material (and working through the doc authoring process).
* Update data-formats.md
Make default statements consistent. Future change: s/=/is.
* null handling for numeric first/last aggregators, refactor to not extend nullable numeric agg since they are complex typed aggs
* initially null or not based on config
* review stuff, make string first/last consistent with null handling of numeric columns, more tests
* docs
* handle nil selectors, revert to primitive first/last types so groupby v1 works...
* 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
* Tutorials use new ingestion spec where possible
There are 2 main changes
* Use task type index_parallel instead of index
* Remove the use of parser + firehose in favor of inputFormat + inputSource
index_parallel is the preferred method starting in 0.17. Setting the job to
index_parallel with the default maxNumConcurrentSubTasks(1) is the equivalent
of an index task
Instead of using a parserSpec, dimensionSpec and timestampSpec have been
promoted to the dataSchema. The format is described in the ioConfig as the
inputFormat.
There are a few cases where the new format is not supported
* Hadoop must use firehoses instead of the inputSource and inputFormat
* There is no equivalent of a combining firehose as an inputSource
* A Combining firehose does not support index_parallel
* fix typo
* 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
* Allow startup scripts to specify java home
The startup scripts now look for java in 3 locations. The order is from
most related to druid to least, ie
${DRUID_JAVA_HOME}
${JAVA_HOME}
${PATH}
* Update fn names and clean up code
* final round of fixes
* fix spellcheck
* 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
* add prefixes support to google input source, making it symmetrical-ish with s3
* docs
* more better, and tests
* unused
* formatting
* javadoc
* dependencies
* oops
* review comments
* better javadoc
* add s3 input source for native batch ingestion
* add docs
* fixes
* checkstyle
* lazy splits
* fixes and hella tests
* fix it
* re-use better iterator
* use key
* javadoc and checkstyle
* exception
* oops
* refactor to use S3Coords instead of URI
* remove unused code, add retrying stream to handle s3 stream
* remove unused parameter
* update to latest master
* use list of objects instead of object
* serde test
* refactor and such
* now with the ability to compile
* fix signature and javadocs
* fix conflicts yet again, fix S3 uri stuffs
* more tests, enforce uri for bucket
* javadoc
* oops
* abstract class instead of interface
* null or empty
* better error
* 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
* add parquet support to native batch
* cleanup
* implement toJson for sampler support
* better binaryAsString test
* docs
* i hate spellcheck
* refactor toMap conversion so can be shared through flattenerMaker, default impls should be good enough for orc+avro, fixup for merge with latest
* add comment, fix some stuff
* adjustments
* fix accident
* tweaks
If the JDBC drivers are missing from the lookup extensions, throw an
exception that directs the user how to resolve the issue. This change is
a follow up to #8825.
* transformSpec + array expressions
changes:
* added array expression support to transformSpec
* removed ParseSpec.verify since its only use afaict was preventing transform expr that did not replace their input from functioning
* hijacked index task test to test changes
* remove docs about being unsupported
* re-arrange test assert
* unused imports
* imports
* fix tests
* preserve types
* suppress warning, fixes, add test
* formatting
* cleanup
* better list to array type conversion and tests
* fix oops
* Add reference to `druid.storage.type`
This should be in here. Without setting storage type to S3 globally it will obviously not be used, even if all other parameters are correct.
* Update s3.md
Add global storage parameter to knob table.
* Update s3.md
* SQL: EARLIEST, LATEST aggregators.
I chose these names instead of FIRST, LAST because those are already
reserved functions in Calcite that mean something different. I think
these are also better names anyway.
* Finalify.
* SQL updates.
* Adjust aggregator calls.
* Validations, test updates.
* Review docs.
* sketch of broker parallel merges done in small batches on fork join pool
* fix non-terminating sequences, auto compute parallelism
* adjust benches
* adjust benchmarks
* now hella more faster, fixed dumb
* fix
* remove comments
* log.info for debug
* javadoc
* safer block for sequence to yielder conversion
* refactor LifecycleForkJoinPool into LifecycleForkJoinPoolProvider which wraps a ForkJoinPool
* smooth yield rate adjustment, more logs to help tune
* cleanup, less logs
* error handling, bug fixes, on by default, more parallel, more tests
* remove unused var
* comments
* timeboundary mergeFn
* simplify, more javadoc
* formatting
* pushdown config
* use nanos consistently, move logs back to debug level, bit more javadoc
* static terminal result batch
* javadoc for nullability of createMergeFn
* cleanup
* oops
* fix race, add docs
* spelling, remove todo, add unhandled exception log
* cleanup, revert unintended change
* another unintended change
* review stuff
* add ParallelMergeCombiningSequenceBenchmark, fixes
* hyper-threading is the enemy
* fix initial start delay, lol
* parallelism computer now balances partition sizes to partition counts using sqrt of sequence count instead of sequence count by 2
* fix those important style issues with the benchmarks code
* lazy sequence creation for benchmarks
* more benchmark comments
* stable sequence generation time
* update defaults to use 100ms target time, 4096 batch size, 16384 initial yield, also update user docs
* add jmh thread based benchmarks, cleanup some stuff
* oops
* style
* add spread to jmh thread benchmark start range, more comments to benchmarks parameters and purpose
* retool benchmark to allow modeling more typical heterogenous heavy workloads
* spelling
* fix
* refactor benchmarks
* formatting
* docs
* add maxThreadStartDelay parameter to threaded benchmark
* why does catch need to be on its own line but else doesnt
Zhenxiao Luo
sthetland
Clint Wylie
Suneet Saldanha
Gian Merlino
Jihoon Son
singh
Maytas Monsereenusorn
Jonathan Wei
Chi Cao Minh
Himanshu
Benjamin Hopp
Parag Jain
Vadim Ogievetsky
Roman Leventov
Lucas Capistrant
Fangyuan Deng
SeKing
Surekha
fst0
Jad Naous