The Azure connector is introduced and MSQ's fault tolerance and durable storage can now be used with Microsoft Azure's blob storage. Also, the results of newly introduced queries from deep storage can now store and fetch the results from Azure's blob storage.
sqlJoinAlgorithm is now a hint to the planner to execute the join in the specified manner. The planner can decide to ignore the hint if it deduces that the specified algorithm can be detrimental to the performance of the join beforehand.
Adds support for automatic cleaning of a "query-results" directory in durable storage. This directory will be cleaned up only if the task id is not known to the overlord. This will allow the storage of query results after the task has finished running.
Changes:
- Throw an `InsertCannotAllocateSegmentFault` if the allocated segment is not aligned with
the requested granularity.
- Tests to verify new behaviour
* Throw ValidationException if CLUSTERED BY column descending order is specified.
- Fails query planning
* Some more tests.
* fixup existing comment
* Update comment
* checkstyle fix: remove unused imports
* Remove InsertCannotOrderByDescendingFault and deprecate the fault in readme.
* move deprecated field to the bottom
### Description
This change allows for consideration of the input format and compression when computing how to split the input files among available tasks, in MSQ ingestion, when considering the value of the `maxInputBytesPerWorker` query context parameter. This query parameter allows users to control the maximum number of bytes, with granularity of input file / object, that ingestion tasks will be assigned to ingest. With this change, this context parameter now denotes the estimated weighted size in bytes of the input to split on, with consideration for input format and compression format, rather than the actual file size, reported by the file system. We assume uncompressed newline delimited json as a baseline, with scaling factor of `1`. This means that when computing the byte weight that a file has towards the input splitting, we take the file size as is, if uncompressed json, 1:1. It was found during testing that gzip compressed json, and parquet, has scale factors of `4` and `8` respectively, meaning that each byte of data is weighted 4x and 8x respectively, when computing input splits. This weighted byte scaling is only considered for MSQ ingestion that uses either LocalInputSource or CloudObjectInputSource at the moment. The default value of the `maxInputBytesPerWorker` query context parameter has been updated from 10 GiB, to 512 MiB
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com>
Co-authored-by: Victoria Lim <lim.t.victoria@gmail.com>
* Be able to load segments on Peons
This change introduces a new config on WorkerConfig
that indicates how many bytes of each storage
location to use for storage of a task. Said config
is divided up amongst the locations and slots
and then used to set TaskConfig.tmpStorageBytesPerTask
The Peons use their local task dir and
tmpStorageBytesPerTask as their StorageLocations for
the SegmentManager such that they can accept broadcast
segments.
* MSQ: Subclass CalciteJoinQueryTest, other supporting changes.
The main change is the new tests: we now subclass CalciteJoinQueryTest
in CalciteSelectJoinQueryMSQTest twice, once for Broadcast and once for
SortMerge.
Two supporting production changes for default-value mode:
1) InputNumberDataSource is marked as concrete, to allow leftFilter to
be pushed down to it.
2) In default-value mode, numeric frame field readers can now return nulls.
This is necessary when stacking joins on top of joins: nulls must be
preserved for semantics that match broadcast joins and native queries.
3) In default-value mode, StringFieldReader.isNull returns true on empty
strings in addition to nulls. This is more consistent with the behavior
of the selectors, which map empty strings to null as well in that mode.
As an effect of change (2), the InsertTimeNull change from #14020 (to
replace null timestamps with default timestamps) is reverted. IMO, this
is fine, as either behavior is defensible, and the change from #14020
hasn't been released yet.
* Adjust tests.
* Style fix.
* Additional tests.
*
Adds new run time parameter druid.indexer.task.tmpStorageBytesPerTask. This sets a limit for the amount of temporary storage disk space used by tasks. This limit is currently only respected by MSQ tasks.
* Removes query context parameters intermediateSuperSorterStorageMaxLocalBytes and composedIntermediateSuperSorterStorageEnabled. Composed intermediate super sorter (which was enabled by composedIntermediateSuperSorterStorageEnabled) is now enabled automatically if durableShuffleStorage is set to true. intermediateSuperSorterStorageMaxLocalBytes is calculated from the limit set by the run time parameter druid.indexer.task.tmpStorageBytesPerTask.
* "maxResultsSize" has been removed from the S3OutputConfig and a default "chunkSize" of 100MiB is now present. This change primarily affects users who wish to use durable storage for MSQ jobs.
* Frames: Ensure nulls are read as default values when appropriate.
Fixes a bug where LongFieldWriter didn't write a properly transformed
zero when writing out a null. This had no meaningful effect in SQL-compatible
null handling mode, because the field would get treated as a null anyway.
But it does have an effect in default-value mode: it would cause Long.MIN_VALUE
to get read out instead of zero.
Also adds NullHandling checks to the various frame-based column selectors,
allowing reading of nullable frames by servers in default-value mode.
* Add a new fault "QueryRuntimeError" to MSQ engine to capture native query errors.
* Fixed bug in MSQ fault tolerance where worker were being retried if `UnexpectedMultiValueDimensionException` was thrown.
* An exception from the query runtime with `org.apache.druid.query` as the package name is thrown as a QueryRuntimeError
* Removing intermediateSuperSorterStorageMaxLocalBytes, maxInputBytesPerWorker, composedIntermediateSuperSorterStorageEnabled, clusterStatisticsMergeMode from docs
* Adding documentation in the context class.
* Sort-merge join and hash shuffles for MSQ.
The main changes are in the processing, multi-stage-query, and sql modules.
processing module:
1) Rename SortColumn to KeyColumn, replace boolean descending with KeyOrder.
This makes it nicer to model hash keys, which use KeyOrder.NONE.
2) Add nullability checkers to the FieldReader interface, and an
"isPartiallyNullKey" method to FrameComparisonWidget. The join
processor uses this to detect null keys.
3) Add WritableFrameChannel.isClosed and OutputChannel.isReadableChannelReady
so callers can tell which OutputChannels are ready for reading and which
aren't.
4) Specialize FrameProcessors.makeCursor to return FrameCursor, a random-access
implementation. The join processor uses this to rewind when it needs to
replay a set of rows with a particular key.
5) Add MemoryAllocatorFactory, which is embedded inside FrameWriterFactory
instead of a particular MemoryAllocator. This allows FrameWriterFactory
to be shared in more scenarios.
multi-stage-query module:
1) ShuffleSpec: Add hash-based shuffles. New enum ShuffleKind helps callers
figure out what kind of shuffle is happening. The change from SortColumn
to KeyColumn allows ClusterBy to be used for both hash-based and sort-based
shuffling.
2) WorkerImpl: Add ability to handle hash-based shuffles. Refactor the logic
to be more readable by moving the work-order-running code to the inner
class RunWorkOrder, and the shuffle-pipeline-building code to the inner
class ShufflePipelineBuilder.
3) Add SortMergeJoinFrameProcessor and factory.
4) WorkerMemoryParameters: Adjust logic to reserve space for output frames
for hash partitioning. (We need one frame per partition.)
sql module:
1) Add sqlJoinAlgorithm context parameter; can be "broadcast" or
"sortMerge". With native, it must always be "broadcast", or it's a
validation error. MSQ supports both. Default is "broadcast" in
both engines.
2) Validate that MSQs do not use broadcast join with RIGHT or FULL join,
as results are not correct for broadcast join with those types. Allow
this in native for two reasons: legacy (the docs caution against it,
but it's always been allowed), and the fact that it actually *does*
generate correct results in native when the join is processed on the
Broker. It is much less likely that MSQ will plan in such a way that
generates correct results.
3) Remove subquery penalty in DruidJoinQueryRel when using sort-merge
join, because subqueries are always required, so there's no reason
to penalize them.
4) Move previously-disabled join reordering and manipulation rules to
FANCY_JOIN_RULES, and enable them when using sort-merge join. Helps
get to better plans where projections and filters are pushed down.
* Work around compiler problem.
* Updates from static analysis.
* Fix @param tag.
* Fix declared exception.
* Fix spelling.
* Minor adjustments.
* wip
* Merge fixups
* fixes
* Fix CalciteSelectQueryMSQTest
* Empty keys are sortable.
* Address comments from code review. Rename mux -> mix.
* Restore inspection config.
* Restore original doc.
* Reorder imports.
* Adjustments
* Fix.
* Fix imports.
* Adjustments from review.
* Update header.
* Adjust docs.
*When running REPLACE queries, the segments which contain no data are dropped (marked as unused). This PR aims to generate tombstones in place of segments which contain no data to mark their deletion, as is the behavior with the native ingestion.
This will cause InsertCannotReplaceExistingSegmentFault to be removed since it was generated if the interval to be marked unused didn't fully overlap one of the existing segments to replace.
Much improved table functions
* Revises properties, definitions in the catalog
* Adds a "table function" abstraction to model such functions
* Specific functions for HTTP, inline, local and S3.
* Extended SQL types in the catalog
* Restructure external table definitions to use table functions
* EXTEND syntax for Druid's extern table function
* Support for array-valued table function parameters
* Support for array-valued SQL query parameters
* Much new documentation
* Add validation checks to worker chat handler apis
* Merge things and polishing the error messages.
* Minor error message change
* Fixing race and adding some tests
* Fixing controller fetching stats from wrong workers.
Fixing race
Changing default mode to Parallel
Adding logging.
Fixing exceptions not propagated properly.
* Changing to kernel worker count
* Added a better logic to figure out assigned worker for a stage.
* Nits
* Moving to existing kernel methods
* Adding more coverage
Co-authored-by: cryptoe <karankumar1100@gmail.com>
1) Edited the TooManyBuckets error message to mention PARTITIONED BY
instead of segmentGranularity.
2) Added error-code-specific anchors in the docs.
3) Add information to various error codes in the docs about common
causes and solutions.
* Fix typo
* Fix some spacing
* Add missing fields
* Cleanup table spacing
* Remove durable storage docs again
Thanks Brian for pointing out previous discussions.
* Update docs/multi-stage-query/reference.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Mark codes as code
* And even more codes as code
* Another set of spaces
* Combine `ColumnTypeNotSupported`
Thanks Karan.
* More whitespaces and typos
* Add spelling and fix links
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* scratch
* s3 ls fix, add docs
* add documentation, update method name
* Add tests, address commits, change default value of the helper
* fix test
* update the default value of config, remove initial delay config
* Trigger Build
* update class
* add more tests
* docs update
* spellcheck
* remove ioe from the signature
* add back dmmy constructor for initialization
* fix guice bindings, intellij inspections
* MSQ: Consider PARTITION_STATS_MAX_BYTES in WorkerMemoryParameters.
This consideration is important, because otherwise we can run out of
memory due to large statistics-tracking objects.
* Improved calculations.
317brian
Laksh Singla
Karan Kumar
Abhishek Radhakrishnan
Adarsh Sanjeev
zachjsh
Nhi Pham
Katya Macedo
imply-cheddar
Gian Merlino
Paul Rogers
Rohan Garg
Jill Osborne
Andreas Maechler