* introduces `UnionQuery`
* some changes to enable a `UnionQuery` to have multiple input datasources
* `UnionQuery` execution is driven by the `QueryLogic` - which could later enable to reduce some complexity in `ClientQuerySegmentWalker`
* to run the subqueries of `UnionQuery` there was a need to access the `conglomerate` from the `Runner`; to enable that some refactors were done
* renamed `UnionQueryRunner` to `UnionDataSourceQueryRunner`
* `QueryRunnerFactoryConglomerate` have taken the place of `QueryToolChestWarehouse` which shaves of some unnecessary things here and there
* small cleanup/refactors
changes:
* adds `SqlBenchmarkDatasets` which contains commonly used benchmark data generator schemas
* adds `SqlBaseBenchmark` which contains common benchmark segment generation methods for any benchmark using `SqlBenchmarkDatasets`
* adds `SqlBaseQueryBenchmark` and `SqlBasePlanBenchmark` for benchmarks measuring queries and planning respectively
* migrate all existing SQL jmh benchmarks to extend `SqlBaseQueryBenchmark`, quite dramatically reducing the boilerplate needed to create benchmarks, and allowing the use of multiple datasources within a benchmark file
* adjustments to data generator stuff to allow passing in an ObjectMapper so that the same mapper can be used for both benchmark queries and segment generation, avoiding the need to register stuff with both mappers for benchmarks
* adds `SqlProjectionsBenchmark` and `SqlComplexMetricsColumnsBenchmark` for measuring projections and measuring complex metric compression respectively
Description
-----------
The `OverlordCompactionScheduler` may sometimes launch a duplicate compaction
task for an interval that has just been compacted.
This may happen as follows:
- Scheduler launches a compaction task for an uncompacted interval.
- While the compaction task is running, the `CompactionStatusTracker` does not consider
this interval as compactible and returns the `CompactionStatus` as `SKIPPED` for it.
- As soon as the compaction task finishes, the `CompactionStatusTracker` starts considering
the interval eligible for compaction again.
- This interval remains eligible for compaction until the newly published segments are polled
from the database.
- Once the new segments have been polled, the `CompactionStatus` of the interval changes
to `COMPLETE`.
Change
--------
- Keep track of the `snapshotTime` in `DataSourcesSnapshot`. This time represents the start of the poll.
- Use the `snapshotTime` to determine if a poll has happened after a compaction task completed.
- If not, then skip the interval to avoid launching duplicate tasks.
- For tests, use a future `snapshotTime` to ensure that compaction is always triggered.
changes:
* filter index processing is now automatically ordered based on estimated 'cost', which is approximated based on how many expected bitmap operations are required to construct the bitmap used for the 'offset'
* cursorAutoArrangeFilters context flag now defaults to true, but can be set to false to disable cost based filter index sorting
Text-based input formats like csv and tsv currently parse inputs only as strings, following the RFC4180Parser spec).
To workaround this, the web-console and other tools need to further inspect the sample data returned to sample data returned by the Druid sampler API to parse them as numbers.
This patch introduces a new optional config, tryParseNumbers, for the csv and tsv input formats. If enabled, any numbers present in the input will be parsed in the following manner -- long data type for integer types and double for floating-point numbers, and if parsing fails for whatever reason, the input is treated as a string. By default, this configuration is set to false, so numeric strings will be treated as strings.
* MSQ: Improved worker cancellation.
Four changes:
1) FrameProcessorExecutor now requires that cancellationIds be registered
with "registerCancellationId" prior to being used in "runFully" or "runAllFully".
2) FrameProcessorExecutor gains an "asExecutor" method, which allows that
executor to be used as an executor for future callbacks in such a way
that respects cancellationId.
3) RunWorkOrder gains a "stop" method, which cancels the current
cancellationId and closes the current FrameContext. It blocks until
both operations are complete.
4) Fixes a bug in RunAllFullyWidget where "processorManager.result()" was
called outside "runAllFullyLock", which could cause it to be called
out-of-order with "cleanup()" in case of cancellation or other error.
Together, these changes help ensure cancellation does not have races.
Once "cancel" is called for a given cancellationId, all existing processors
and running callbacks are canceled and exit in an orderly manner. Future
processors and callbacks with the same cancellationId are rejected
before being executed.
* Fix test.
* Use execute, which doesn't return, to avoid errorprone complaints.
* Fix some style stuff.
* Further enhancements.
* Fix style.
* MSQ: Rework memory management.
This patch reworks memory management to better support multi-threaded
workers running in shared JVMs. There are two main changes.
First, processing buffers and threads are moved from a per-JVM model to
a per-worker model. This enables queries to hold processing buffers
without blocking other concurrently-running queries. Changes:
- Introduce ProcessingBuffersSet and ProcessingBuffers to hold the
per-worker and per-work-order processing buffers (respectively). On Peons,
this is the JVM-wide processing pool. On Indexers, this is a per-worker
pool of on-heap buffers. (This change fixes a bug on Indexers where
excessive processing buffers could be used if MSQ tasks ran concurrently
with realtime tasks.)
- Add "bufferPool" argument to GroupingEngine#process so a per-worker pool
can be passed in.
- Add "druid.msq.task.memory.maxThreads" property, which controls the
maximum number of processing threads to use per task. This allows usage of
multiple processing buffers per task if admins desire.
- IndexerWorkerContext acquires processingBuffers when creating the FrameContext
for a work order, and releases them when closing the FrameContext.
- Add "usesProcessingBuffers()" to FrameProcessorFactory so workers know
how many sets of processing buffers are needed to run a given query.
Second, adjustments to how WorkerMemoryParameters slices up bundles, to
favor more memory for sorting and segment generation. Changes:
- Instead of using same-sized bundles for processing and for sorting,
workers now use minimally-sized processing bundles (just enough to read
inputs plus a little overhead). The rest is devoted to broadcast data
buffering, sorting, and segment-building.
- Segment-building is now limited to 1 concurrent segment per work order.
This allows each segment-building action to use more memory. Note that
segment-building is internally multi-threaded to a degree. (Build and
persist can run concurrently.)
- Simplify frame size calculations by removing the distinction between
"standard" and "large" frames. The new default frame size is the same
as the old "standard" frames, 1 MB. The original goal of of the large
frames was to reduce the number of temporary files during sorting, but
I think we can achieve the same thing by simply merging a larger number
of standard frames at once.
- Remove the small worker adjustment that was added in #14117 to account
for an extra frame involved in writing to durable storage. Instead,
account for the extra frame whenever we are actually using durable storage.
- Cap super-sorter parallelism using the number of output partitions, rather
than using a hard coded cap at 4. Note that in practice, so far, this cap
has not been relevant for tasks because they have only been using a single
processing thread anyway.
* Remove unused import.
* Fix errorprone annotation.
* Fixes for javadocs and inspections.
* Additional test coverage.
* Fix test.
* transition away from StorageAdapter
changes:
* CursorHolderFactory has been renamed to CursorFactory and moved off of StorageAdapter, instead fetched directly from the segment via 'asCursorFactory'. The previous deprecated CursorFactory interface has been merged into StorageAdapter
* StorageAdapter is no longer used by any engines or tests and has been marked as deprecated with default implementations of all methods that throw exceptions indicating the new methods to call instead
* StorageAdapter methods not covered by CursorFactory (CursorHolderFactory prior to this change) have been moved into interfaces which are retrieved by Segment.as, the primary classes are the previously existing Metadata, as well as new interfaces PhysicalSegmentInspector and TopNOptimizationInspector
* added UnnestSegment and FilteredSegment that extend WrappedSegmentReference since their StorageAdapter implementations were previously provided by WrappedSegmentReference
* added PhysicalSegmentInspector which covers some of the previous StorageAdapter functionality which was primarily used for segment metadata queries and other metadata uses, and is implemented for QueryableIndexSegment and IncrementalIndexSegment
* added TopNOptimizationInspector to cover the oddly specific StorageAdapter.hasBuiltInFilters implementation, which is implemented for HashJoinSegment, UnnestSegment, and FilteredSegment
* Updated all engines and tests to no longer use StorageAdapter
Description
-----------
Auto-compaction currently poses several challenges as it:
1. may get stuck on a failing interval.
2. may get stuck on the latest interval if more data keeps coming into it.
3. always picks the latest interval regardless of the level of compaction in it.
4. may never pick a datasource if its intervals are not very recent.
5. requires setting an explicit period which does not cater to the changing needs of a Druid cluster.
This PR introduces various improvements to compaction scheduling to tackle the above problems.
Change Summary
--------------
1. Run compaction for a datasource as a supervisor of type `autocompact` on Overlord.
2. Make compaction policy extensible and configurable.
3. Track status of recently submitted compaction tasks and pass this info to policy.
4. Add `/simulate` API on both Coordinator and Overlord to run compaction simulations.
5. Redirect compaction status APIs to the Overlord when compaction supervisors are enabled.
* Segments primarily sorted by non-time columns.
Currently, segments are always sorted by __time, followed by the sort
order provided by the user via dimensionsSpec or CLUSTERED BY. Sorting
by __time enables efficient execution of queries involving time-ordering
or granularity. Time-ordering is a simple matter of reading the rows in
stored order, and granular cursors can be generated in streaming fashion.
However, for various workloads, it's better for storage footprint and
query performance to sort by arbitrary orders that do not start with __time.
With this patch, users can sort segments by such orders.
For spec-based ingestion, users add "useExplicitSegmentSortOrder: true" to
dimensionsSpec. The "dimensions" list determines the sort order. To
define a sort order that includes "__time", users explicitly
include a dimension named "__time".
For SQL-based ingestion, users set the context parameter
"useExplicitSegmentSortOrder: true". The CLUSTERED BY clause is then
used as the explicit segment sort order.
In both cases, when the new "useExplicitSegmentSortOrder" parameter is
false (the default), __time is implicitly prepended to the sort order,
as it always was prior to this patch.
The new parameter is experimental for two main reasons. First, such
segments can cause errors when loaded by older servers, due to violating
their expectations that timestamps are always monotonically increasing.
Second, even on newer servers, not all queries can run on non-time-sorted
segments. Scan queries involving time-ordering and any query involving
granularity will not run. (To partially mitigate this, a currently-undocumented
SQL feature "sqlUseGranularity" is provided. When set to false the SQL planner
avoids using "granularity".)
Changes on the write path:
1) DimensionsSpec can now optionally contain a __time dimension, which
controls the placement of __time in the sort order. If not present,
__time is considered to be first in the sort order, as it has always
been.
2) IncrementalIndex and IndexMerger are updated to sort facts more
flexibly; not always by time first.
3) Metadata (stored in metadata.drd) gains a "sortOrder" field.
4) MSQ can generate range-based shard specs even when not all columns are
singly-valued strings. It merely stops accepting new clustering key
fields when it encounters the first one that isn't a singly-valued
string. This is useful because it enables range shard specs on
"someDim" to be created for clauses like "CLUSTERED BY someDim, __time".
Changes on the read path:
1) Add StorageAdapter#getSortOrder so query engines can tell how a
segment is sorted.
2) Update QueryableIndexStorageAdapter, IncrementalIndexStorageAdapter,
and VectorCursorGranularizer to throw errors when using granularities
on non-time-ordered segments.
3) Update ScanQueryEngine to throw an error when using the time-ordering
"order" parameter on non-time-ordered segments.
4) Update TimeBoundaryQueryRunnerFactory to perform a segment scan when
running on a non-time-ordered segment.
5) Add "sqlUseGranularity" context parameter that causes the SQL planner
to avoid using granularities other than ALL.
Other changes:
1) Rename DimensionsSpec "hasCustomDimensions" to "hasFixedDimensions"
and change the meaning subtly: it now returns true if the DimensionsSpec
represents an unchanging list of dimensions, or false if there is
some discovery happening. This is what call sites had expected anyway.
* Fixups from CI.
* Fixes.
* Fix missing arg.
* Additional changes.
* Fix logic.
* Fixes.
* Fix test.
* Adjust test.
* Remove throws.
* Fix styles.
* Fix javadocs.
* Cleanup.
* Smoother handling of null ordering.
* Fix tests.
* Missed a spot on the merge.
* Fixups.
* Avoid needless Filters.and.
* Add timeBoundaryInspector to test.
* Fix tests.
* Fix FrameStorageAdapterTest.
* Fix various tests.
* Use forceSegmentSortByTime instead of useExplicitSegmentSortOrder.
* Pom fix.
* Fix doc.
changes:
* Added `CursorBuildSpec` which captures all of the 'interesting' stuff that goes into producing a cursor as a replacement for the method arguments of `CursorFactory.canVectorize`, `CursorFactory.makeCursor`, and `CursorFactory.makeVectorCursor`
* added new interface `CursorHolder` and new interface `CursorHolderFactory` as a replacement for `CursorFactory`, with method `makeCursorHolder`, which takes a `CursorBuildSpec` as an argument and replaces `CursorFactory.canVectorize`, `CursorFactory.makeCursor`, and `CursorFactory.makeVectorCursor`
* `CursorFactory.makeCursors` previously returned a `Sequence<Cursor>` corresponding to the query granularity buckets, with a separate `Cursor` per bucket. `CursorHolder.asCursor` instead returns a single `Cursor` (equivalent to 'ALL' granularity), and a new `CursorGranularizer` has been added for query engines to iterate over the cursor and divide into granularity buckets. This makes the non-vectorized engine behave the same way as the vectorized query engine (with its `VectorCursorGranularizer`), and simplifies a lot of stuff that has to read segments particularly if it does not care about bucketing the results into granularities.
* Deprecated `CursorFactory`, `CursorFactory.canVectorize`, `CursorFactory.makeCursors`, and `CursorFactory.makeVectorCursor`
* updated all `StorageAdapter` implementations to implement `makeCursorHolder`, transitioned direct `CursorFactory` implementations to instead implement `CursorMakerFactory`. `StorageAdapter` being a `CursorMakerFactory` is intended to be a transitional thing, ideally will not be released in favor of moving `CursorMakerFactory` to be fetched directly from `Segment`, however this PR was already large enough so this will be done in a follow-up.
* updated all query engines to use `makeCursorHolder`, granularity based engines to use `CursorGranularizer`.
Refactors the SemanticCreator annotation.
Moves the interface to the semantic package.
Create a SemanticUtils to hold logic for storing semantic maps.
Add FrameMaker interface.
* enables to launch a fake broker based on test resources (druidtest uri)
* could record queries into new testfiles during usage
* instead of re-purpose Calcite's Hook migrates to use DruidHook which we can add further keys
* added a quidem-ut module which could be the place for tests which could iteract with modules/etc
This patch introduces an optional cluster configuration, druid.indexing.formats.stringMultiValueHandlingMode, allowing operators to override the default mode SORTED_SET for string dimensions. The possible values for the config are SORTED_SET, SORTED_ARRAY, or ARRAY (SORTED_SET is the default). Case insensitive values are allowed.
While this cluster property allows users to manage the multi-value handling mode for string dimension types, it's recommended to migrate to using real array types instead of MVDs.
This fixes a long-standing issue where compaction will honor the configured cluster wide property instead of rewriting it as the default SORTED_ARRAY always, even if the data was originally ingested with ARRAY or SORTED_SET.
Changes:
- Add API `/druid/coordinator/v1/config/compaction/global` to update cluster level compaction config
- Add class `CompactionConfigUpdateRequest`
- Fix bug in `CoordinatorCompactionConfig` which caused compaction engine to not be persisted.
Use json field name `engine` instead of `compactionEngine` because JSON field names must align
with the getter name.
- Update MSQ validation error messages
- Complete overhaul of `CoordinatorCompactionConfigResourceTest` to remove unnecessary mocking
and add more meaningful tests.
- Add `TuningConfigBuilder` to easily build tuning configs for tests.
- Add `DatasourceCompactionConfigBuilder`
Changes:
- Break `NewestSegmentFirstIterator` into two parts
- `DatasourceCompactibleSegmentIterator` - this contains all the code from `NewestSegmentFirstIterator`
but now handles a single datasource and allows a priority to be specified
- `PriorityBasedCompactionSegmentIterator` - contains separate iterator for each datasource and
combines the results into a single queue to be used by a compaction search policy
- Update `NewestSegmentFirstPolicy` to use the above new classes
- Cleanup `CompactionStatistics` and `AutoCompactionSnapshot`
- Cleanup `CompactSegments`
- Remove unused methods from `Tasks`
- Remove unneeded `TasksTest`
- Move tests from `NewestSegmentFirstIteratorTest` to `CompactionStatusTest`
and `DatasourceCompactibleSegmentIteratorTest`
Description:
Compaction operations issued by the Coordinator currently run using the native query engine.
As majority of the advancements that we are making in batch ingestion are in MSQ, it is imperative
that we support compaction on MSQ to make Compaction more robust and possibly faster.
For instance, we have seen OOM errors in native compaction that MSQ could have handled by its
auto-calculation of tuning parameters.
This commit enables compaction on MSQ to remove the dependency on native engine.
Main changes:
* `DataSourceCompactionConfig` now has an additional field `engine` that can be one of
`[native, msq]` with `native` being the default.
* if engine is MSQ, `CompactSegments` duty assigns all available compaction task slots to the
launched `CompactionTask` to ensure full capacity is available to MSQ. This is to avoid stalling which
could happen in case a fraction of the tasks were allotted and they eventually fell short of the number
of tasks required by the MSQ engine to run the compaction.
* `ClientCompactionTaskQuery` has a new field `compactionRunner` with just one `engine` field.
* `CompactionTask` now has `CompactionRunner` interface instance with its implementations
`NativeCompactinRunner` and `MSQCompactionRunner` in the `druid-multi-stage-query` extension.
The objectmapper deserializes `ClientCompactionRunnerInfo` in `ClientCompactionTaskQuery` to the
`CompactionRunner` instance that is mapped to the specified type [`native`, `msq`].
* `CompactTask` uses the `CompactionRunner` instance it receives to create the indexing tasks.
* `CompactionTask` to `MSQControllerTask` conversion logic checks whether metrics are present in
the segment schema. If present, the task is created with a native group-by query; if not, the task is
issued with a scan query. The `storeCompactionState` flag is set in the context.
* Each created `MSQControllerTask` is launched in-place and its `TaskStatus` tracked to determine the
final status of the `CompactionTask`. The id of each of these tasks is the same as that of `CompactionTask`
since otherwise, the workers will be unable to determine the controller task's location for communication
(as they haven't been launched via the overlord).
* Defer more expressions in vectorized groupBy.
This patch adds a way for columns to provide GroupByVectorColumnSelectors,
which controls how the groupBy engine operates on them. This mechanism is used
by ExpressionVirtualColumn to provide an ExpressionDeferredGroupByVectorColumnSelector
that uses the inputs of an expression as the grouping key. The actual expression
evaluation is deferred until the grouped ResultRow is created.
A new context parameter "deferExpressionDimensions" allows users to control when
this deferred selector is used. The default is "fixedWidthNonNumeric", which is a
behavioral change from the prior behavior. Users can get the prior behavior by setting
this to "singleString".
* Fix style.
* Add deferExpressionDimensions to SqlExpressionBenchmark.
* Fix style.
* Fix inspections.
* Add more testing.
* Use valueOrDefault.
* Compute exprKeyBytes a bit lighter-weight.
MSQ cannot process null bytes in string fields, and the current workaround is to remove them using the REPLACE function. 'removeNullBytes' context parameter has been added which sanitizes the input string fields by removing these null bytes.
* fix NestedDataColumnIndexerV4 to not report cardinality
changes:
* fix issue similar to #16489 but for NestedDataColumnIndexerV4, which can report STRING type if it only processes a single type of values. this should be less common than the auto indexer problem
* fix some issues with sql benchmarks
* Add interface method for returning canonical lookup name
* Address review comment
* Add test in LookupReferencesManagerTest for coverage check
* Add test in LookupSerdeModuleTest for coverage check
There are a few issues with using Jackson serialization in sending datasketches between controller and worker in MSQ. This caused a blowup due to holding multiple copies of the sketch being stored.
This PR aims to resolve this by switching to deserializing the sketch payload without Jackson.
The PR adds a new query parameter used during communication between controller and worker while fetching sketches, "sketchEncoding".
If the value of this parameter is OCTET, the sketch is returned as a binary encoding, done by ClusterByStatisticsSnapshotSerde.
If the value is not the above, the sketch is encoded by Jackson as before.
* Speed up SQL IN using SCALAR_IN_ARRAY.
Main changes:
1) DruidSqlValidator now includes a rewrite of IN to SCALAR_IN_ARRAY, when the size of
the IN is above inFunctionThreshold. The default value of inFunctionThreshold
is 100. Users can restore the prior behavior by setting it to Integer.MAX_VALUE.
2) SearchOperatorConversion now generates SCALAR_IN_ARRAY when converting to a regular
expression, when the size of the SEARCH is above inFunctionExprThreshold. The default
value of inFunctionExprThreshold is 2. Users can restore the prior behavior by setting
it to Integer.MAX_VALUE.
3) ReverseLookupRule generates SCALAR_IN_ARRAY if the set of reverse-looked-up values is
greater than inFunctionThreshold.
* Revert test.
* Additional coverage.
* Update docs/querying/sql-query-context.md
Co-authored-by: Benedict Jin <asdf2014@apache.org>
* New test.
---------
Co-authored-by: Benedict Jin <asdf2014@apache.org>
MSQ sorts the columns in a highly specialized manner by byte comparisons. As such the values are serialized differently. This works well for the primitive types and primitive arrays, however complex types cannot be serialized specially.
This PR adds the support for sorting the complex columns by deserializing the value from the field and comparing it via the type strategy. This is a lot slower than the byte comparisons, however, it's the only way to support sorting on complex columns that can have arbitrary serialization not optimized for MSQ.
The primitives and the arrays are still compared via the byte comparison, therefore this doesn't affect the performance of the queries supported before the patch. If there's a sorting key with mixed complex and primitive/primitive array types, for example: longCol1 ASC, longCol2 ASC, complexCol1 DESC, complexCol2 DESC, stringCol1 DESC, longCol3 DESC, longCol4 ASC, the comparison will happen like:
longCol1, longCol2 (ASC) - Compared together via byte-comparison, since both are byte comparable and need to be sorted in ascending order
complexCol1 (DESC) - Compared via deserialization, cannot be clubbed with any other field
complexCol2 (DESC) - Compared via deserialization, cannot be clubbed with any other field, even though the prior field was a complex column with the same order
stringCol1, longCol3 (DESC) - Compared together via byte-comparison, since both are byte comparable and need to be sorted in descending order
longCol4 (ASC) - Compared via byte-comparison, couldn't be coalesced with the previous fields as the direction was different
This way, we only deserialize the field wherever required
* test scoped jdbc driver for druidtest:/// backed DruidAvaticaTestDriver
** DecoupledTestConfig is used inside the URI - this will make it possible to attach to existing things more easily
* DruidQuidemTestBase can be used to create module level set of quidem tests
* added quidem commands: !convertedPlan, !logicalPlan, !druidPlan, !nativePlan
** for these I've used some values of the Hook which was there in calcite
* there are some shortcuts with proxies(they are only used during testing) - we can probably remove those later
Issue: #14989
The initial step in optimizing segment metadata was to centralize the construction of datasource schema in the Coordinator (#14985). Thereafter, we addressed the problem of publishing schema for realtime segments (#15475). Subsequently, our goal is to eliminate the requirement for regularly executing queries to obtain segment schema information.
This is the final change which involves publishing segment schema for finalized segments from task and periodically polling them in the Coordinator.
* Avoid conversion to String in JsonReader, JsonNodeReader.
These readers were running UTF-8 decode on the provided entity to
convert it to a String, then parsing the String as JSON. The patch
changes them to parse the provided entity's input stream directly.
In order to preserve the nice error messages that include parse errors,
the readers now need to open the entity again on the error path, to
re-read the data. To make this possible, the InputEntity#open contract
is tightened to require the ability to re-open entities, and existing
InputEntity implementations are updated to allow re-opening.
This patch also renames JsonLineReaderBenchmark to JsonInputFormatBenchmark,
updates it to benchmark all three JSON readers, and adds a case that reads
fields out of the parsed row (not just creates it).
* Fixes for static analysis.
* Implement intermediateRowAsString in JsonReader.
* Enhanced JsonInputFormatBenchmark.
Renames JsonLineReaderBenchmark to JsonInputFormatBenchmark, and enhances it to
test various readers (JsonReader, JsonLineReader, JsonNodeReader) as well as
to test with/without field discovery.
changes:
* adds TypedInFilter which preserves matching sets in the native match value type
* SQL planner uses new TypedInFilter when druid.generic.useDefaultValueForNull=false (the default)
changes:
* fix issues with array_contains and array_overlap with null left side arguments
* modify singleThreaded stuff to allow optimizing Function similar to how we do for ExprMacro - removed SingleThreadSpecializable in favor of default impl of asSingleThreaded on Expr with clear javadocs that most callers shouldn't be calling it directly and should be using Expr.singleThreaded static method which uses a shuttle and delegates to asSingleThreaded instead
* add optimized 'singleThreaded' versions of array_contains and array_overlap
* add mv_harmonize_nulls native expression to use with MV_CONTAINS and MV_OVERLAP to allow them to behave consistently with filter rewrites, coercing null and [] into [null]
* fix bug with casting rhs argument for native array_contains and array_overlap expressions
* `Expr#singleThreaded` which creates a singleThreaded version of the actual expression (caching ExprEval is allowed)
* `Expr#makeSingleThreaded` to make a whole subtree of expressions 'singleThreaded' - uses `Shuttle` to create the new expression tree
* `ConstantExpr#singleThreaded` creates a specialized `ConstantExpr` which does cache the `ExprEval`
* some `@Immutable` annotations were added to make it more likely to notice that there might be something off if a similar change will be made around here for some reason
* cooler cursor filter processing allowing much smart utilization of indexes by feeding selectivity forward, with implementations for range and predicate based filters
* added new method Filter.makeFilterBundle which cursors use to get indexes and matchers for building offsets
* AND filter partitioning is now pushed all the way down, even to nested AND filters
* vector engine now uses same indexed base value matcher strategy for OR filters which partially support indexes
If lots of keys map to the same value, reversing a LOOKUP call can slow
things down unacceptably. To protect against this, this patch introduces
a parameter sqlReverseLookupThreshold representing the maximum size of an
IN filter that will be created as part of lookup reversal.
If inSubQueryThreshold is set to a smaller value than
sqlReverseLookupThreshold, then inSubQueryThreshold will be used instead.
This allows users to use that single parameter to control IN sizes if they
wish.
Adds a set of benchmark queries for measuring the planning time with the IN operator. Current results indicate that with the recent optimizations, the IN planning time with 100K expressions in the IN clause is just 3s and with 1M is 46s. For IN clause paired with OR <col>=<val> expr, the numbers are 10s and 155s for 100K and 1M, resp.
Zoltan Haindrich
Akshat Jain
Clint Wylie
Kashif Faraz
Cece Mei
Sree Charan Manamala
Abhishek Radhakrishnan
Gian Merlino
Abhishek Agarwal
Adarsh Sanjeev
Laksh Singla
Vishesh Garg
Rishabh Singh
Tim Williamson
AmatyaAvadhanula
Karan Kumar