* Vectorized theta sketch aggregator.
Also a refactoring of BufferAggregator and VectorAggregator such that
they share a common interface, BaseBufferAggregator. This allows
implementing both in the same file with an abstract + dual subclass
structure.
* Rework implementation to use composition instead of inheritance.
* Rework things to enable working properly for both complex types and
regular types.
Involved finally moving makeVectorProcessor from DimensionHandlerUtils
into ColumnProcessors and harmonizing the two things.
* Add missing method.
* Style and name changes.
* Fix issues from inspections.
* Fix style issue.
* better type tracking: add typed postaggs, finalized types for agg factories
* more javadoc
* adjustments
* transition to getTypeName to be used exclusively for complex types
* remove unused fn
* adjust
* more better
* rename getTypeName to getComplexTypeName
* setup expression post agg for type inference existing
* more javadocs
* fixup
* oops
* more test
* more test
* more comments/javadoc
* nulls
* explicitly handle only numeric and complex aggregators for incremental index
* checkstyle
* more tests
* adjust
* more tests to showcase difference in behavior
* timeseries longsum array
* new average aggregator
* method to create count aggregator factory
* test everything
* update other usages
* fix style
* fix more tests
* fix datasketches tests
* Fix join
* Fix Subquery could not be converted to groupBy query
* Fix Subquery could not be converted to groupBy query
* Fix Subquery could not be converted to groupBy query
* Fix Subquery could not be converted to groupBy query
* Fix Subquery could not be converted to groupBy query
* Fix Subquery could not be converted to groupBy query
* Fix Subquery could not be converted to groupBy query
* Fix Subquery could not be converted to groupBy query
* add tests
* address comments
* fix failing tests
* Add REGEXP_LIKE, fix empty-pattern bug in REGEXP_EXTRACT.
- Add REGEXP_LIKE function that returns a boolean, and is useful in
WHERE clauses.
- Fix REGEXP_EXTRACT return type (should be nullable; causes incorrect
filter elision).
- Fix REGEXP_EXTRACT behavior for empty patterns: should always match
(previously, they threw errors).
- Improve error behavior when REGEXP_EXTRACT and REGEXP_LIKE are passed
non-literal patterns.
- Improve documentation of REGEXP_EXTRACT.
* Changes based on PR review.
* Fix arg check.
* Important fixes!
* Add speller.
* wip
* Additional tests.
* Fix up tests.
* Add validation error tests.
* Additional tests.
* Remove useless call.
* IntelliJ inspections cleanup
* Standard Charset object can be used
* Redundant Collection.addAll() call
* String literal concatenation missing whitespace
* Statement with empty body
* Redundant Collection operation
* StringBuilder can be replaced with String
* Type parameter hides visible type
* fix warnings in test code
* more test fixes
* remove string concatenation inspection error
* fix extra curly brace
* cleanup AzureTestUtils
* fix charsets for RangerAdminClient
* review comments
* Broker: Add ability to inline subqueries.
The main changes:
- ClientQuerySegmentWalker: Add ability to inline queries.
- Query: Add "getSubQueryId" and "withSubQueryId" methods.
- QueryMetrics: Add "subQueryId" dimension.
- ServerConfig: Add new "maxSubqueryRows" parameter, which is used by
ClientQuerySegmentWalker to limit how many rows can be inlined per
query.
- IndexedTableJoinMatcher: Allow creating keys on top of unknown types,
by assuming they are strings. This is useful because not all types are
known for fields in query results.
- InlineDataSource: Store RowSignature rather than component parts. Add
more zealous "equals" and "hashCode" methods to ease testing.
- Moved QuerySegmentWalker test code from CalciteTests and
SpecificSegmentsQueryWalker in druid-sql to QueryStackTests in
druid-server. Use this to spin up a new ClientQuerySegmentWalkerTest.
* Adjustments from CI.
* Fix integration test.
* Move RowSignature from druid-sql to druid-processing and make use of it.
1) Moved (most of) RowSignature from sql to processing. Left behind the SQL-specific
stuff in a RowSignatures utility class. It also picked up some new convenience
methods along the way.
2) There were a lot of places in the code where Map<String, ValueType> was used to
associate columns with type info. These are now all replaced with RowSignature.
3) QueryToolChest's resultArrayFields method is replaced with resultArraySignature,
and it now provides type info.
* Fix up extensions.
* Various fixes
* Harmonization and bug-fixing for selector and filter behavior on unknown types.
- Migrate ValueMatcherColumnSelectorStrategy to newer ColumnProcessorFactory
system, and set defaultType COMPLEX so unknown types can be dynamically matched.
- Remove ValueGetters in favor of ColumnComparisonFilter doing its own thing.
- Switch various methods to use convertObjectToX when casting to numbers, rather
than ad-hoc and inconsistent logic.
- Fix bug in RowBasedExpressionColumnValueSelector: isBindingArray should return
true even for 0- or 1- element arrays.
- Adjust various javadocs.
* Add throwParseExceptions option to Rows.objectToNumber, switch back to that.
* Update tests.
* Adjust moment sketch tests.
* Add MemoryOpenHashTable, a table similar to ByteBufferHashTable.
With some key differences to improve speed and design simplicity:
1) Uses Memory rather than ByteBuffer for its backing storage.
2) Uses faster hashing and comparison routines (see HashTableUtils).
3) Capacity is always a power of two, allowing simpler design and more
efficient implementation of findBucket.
4) Does not implement growability; instead, leaves that to its callers.
The idea is this removes the need for subclasses, while still giving
callers flexibility in how to handle table-full scenarios.
* Fix LGTM warnings.
* Adjust dependencies.
* Remove easymock from druid-benchmarks.
* Adjustments from review.
* Fix datasketches unit tests.
* Fix checkstyle.
* Guicify druid sql module
Break up the SQLModule in to smaller modules and provide a binding that
modules can use to register schemas with druid sql.
* fix some tests
* address code review
* tests compile
* Working tests
* Add all the tests
* fix up licenses and dependencies
* add calcite dependency to druid-benchmarks
* tests pass
* rename the schemas
* SQL join support for lookups.
1) Add LookupSchema to SQL, so lookups show up in the catalog.
2) Add join-related rels and rules to SQL, allowing joins to be planned into
native Druid queries.
* Add two missing LookupSchema calls in tests.
* Fix tests.
* Fix typo.
* intelliJ inspections cleanup
- remove redundant escapes
- performance warnings
- access static member via instance reference
- static method declared final
- inner class may be static
Most of these changes are aesthetic, however, they will allow inspections to
be enabled as part of CI checks going forward
The valuable changes in this delta are:
- using StringBuilder instead of string addition in a loop
indexing-hadoop/.../Utils.java
processing/.../ByteBufferMinMaxOffsetHeap.java
- Use class variables instead of static variables for parameterized test
processing/src/.../ScanQueryLimitRowIteratorTest.java
* Add intelliJ inspection warnings as errors to druid profile
* one more static inner 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
* IndexerSQLMetadataStorageCoordinator.getTimelineForIntervalsWithHandle() don't fetch abutting intervals; simplify getUsedSegmentsForIntervals()
* Add VersionedIntervalTimeline.findNonOvershadowedObjectsInInterval() method; Propagate the decision about whether only visible segmetns or visible and overshadowed segments should be returned from IndexerMetadataStorageCoordinator's methods to the user logic; Rename SegmentListUsedAction to RetrieveUsedSegmentsAction, SegmetnListUnusedAction to RetrieveUnusedSegmentsAction, and UsedSegmentLister to UsedSegmentsRetriever
* Fix tests
* More fixes
* Add javadoc notes about returning Collection instead of Set. Add JacksonUtils.readValue() to reduce boilerplate code
* Fix KinesisIndexTaskTest, factor out common parts from KinesisIndexTaskTest and KafkaIndexTaskTest into SeekableStreamIndexTaskTestBase
* More test fixes
* More test fixes
* Add a comment to VersionedIntervalTimelineTestBase
* Fix tests
* Set DataSegment.size(0) in more tests
* Specify DataSegment.size(0) in more places in tests
* Fix more tests
* Fix DruidSchemaTest
* Set DataSegment's size in more tests and benchmarks
* Fix HdfsDataSegmentPusherTest
* Doc changes addressing comments
* Extended doc for visibility
* Typo
* Typo 2
* Address comment
* remove select query
* thanks teamcity
* oops
* oops
* add back a SelectQuery class that throws RuntimeExceptions linking to docs
* adjust text
* update docs per review
* deprecated
* Fix dependency analyze warnings
Update the maven dependency plugin to the latest version and fix all
warnings for unused declared and used undeclared dependencies in the
compile scope. Added new travis job to add the check to CI. Also fixed
some source code files to use the correct packages for their imports and
updated druid-forbidden-apis to prevent regressions.
* Address review comments
* Adjust scope for org.glassfish.jaxb:jaxb-runtime
* Fix dependencies for hdfs-storage
* Consolidate netty4 versions
* check ctyle for constant field name
* check ctyle for constant field name
* check ctyle for constant field name
* check ctyle for constant field name
* check ctyle for constant field name
* check ctyle for constant field name
* check ctyle for constant field name
* check ctyle for constant field name
* check ctyle for constant field name
* merging with upstream
* review-1
* unknow changes
* unknow changes
* review-2
* merging with master
* review-2 1 changes
* review changes-2 2
* bug fix
* GroupBy array-based result rows.
Fixes#8118; see that proposal for details.
Other than the GroupBy changes, the main other "interesting" classes are:
- ResultRow: The array-based result type.
- BaseQuery: T is no longer required to be Comparable.
- QueryToolChest: Adds "decorateObjectMapper" to enable query-aware serialization
and deserialization of result rows (necessary due to their positional nature).
- QueryResource: Uses the new decoration functionality.
- DirectDruidClient: Also uses the new decoration functionality.
- QueryMaker (in Druid SQL): Modifications to read ResultRows.
These classes weren't changed, but got some new javadocs:
- BySegmentQueryRunner
- FinalizeResultsQueryRunner
- Query
* Adjustments for TC stuff.
* Fix dependency analyze warnings
Update the maven dependency plugin to the latest version and fix all
warnings for unused declared and used undeclared dependencies in the
compile scope. Added new travis job to add the check to CI. Also fixed
some source code files to use the correct packages for their imports.
* Fix licenses and dependencies
* Fix licenses and dependencies again
* Fix integration test dependency
* Address review comments
* Fix unit test dependencies
* Fix integration test dependency
* Fix integration test dependency again
* Fix integration test dependency third time
* Fix integration test dependency fourth time
* Fix compile error
* Fix assert package
* Benchmarks: New SqlBenchmark, add caching & vectorization to some others.
- Introduce a new SqlBenchmark geared towards benchmarking a wide
variety of SQL queries. Rename the old SqlBenchmark to
SqlVsNativeBenchmark.
- Add (optional) caching to SegmentGenerator to enable easier
benchmarking of larger segments.
- Add vectorization to FilteredAggregatorBenchmark and GroupByBenchmark.
* Query vectorization.
This patch includes vectorized timeseries and groupBy engines, as well
as some analogs of your favorite Druid classes:
- VectorCursor is like Cursor. (It comes from StorageAdapter.makeVectorCursor.)
- VectorColumnSelectorFactory is like ColumnSelectorFactory, and it has
methods to create analogs of the column selectors you know and love.
- VectorOffset and ReadableVectorOffset are like Offset and ReadableOffset.
- VectorAggregator is like BufferAggregator.
- VectorValueMatcher is like ValueMatcher.
There are some noticeable differences between vectorized and regular
execution:
- Unlike regular cursors, vector cursors do not understand time
granularity. They expect query engines to handle this on their own,
which a new VectorCursorGranularizer class helps with. This is to
avoid too much batch-splitting and to respect the fact that vector
selectors are somewhat more heavyweight than regular selectors.
- Unlike FilteredOffset, FilteredVectorOffset does not leverage indexes
for filters that might partially support them (like an OR of one
filter that supports indexing and another that doesn't). I'm not sure
that this behavior is desirable anyway (it is potentially too eager)
but, at any rate, it'd be better to harmonize it between the two
classes. Potentially they should both do some different thing that
is smarter than what either of them is doing right now.
- When vector cursors are created by QueryableIndexCursorSequenceBuilder,
they use a morphing binary-then-linear search to find their start and
end rows, rather than linear search.
Limitations in this patch are:
- Only timeseries and groupBy have vectorized engines.
- GroupBy doesn't handle multi-value dimensions yet.
- Vector cursors cannot handle virtual columns or descending order.
- Only some filters have vectorized matchers: "selector", "bound", "in",
"like", "regex", "search", "and", "or", and "not".
- Only some aggregators have vectorized implementations: "count",
"doubleSum", "floatSum", "longSum", "hyperUnique", and "filtered".
- Dimension specs other than "default" don't work yet (no extraction
functions or filtered dimension specs).
Currently, the testing strategy includes adding vectorization-enabled
tests to TimeseriesQueryRunnerTest, GroupByQueryRunnerTest,
GroupByTimeseriesQueryRunnerTest, CalciteQueryTest, and all of the
filtering tests that extend BaseFilterTest. In all of those classes,
there are some test cases that don't support vectorization. They are
marked by special function calls like "cannotVectorize" or "skipVectorize"
that tell the test harness to either expect an exception or to skip the
test case.
Testing should be expanded in the future -- a project in and of itself.
Related to #3011.
* WIP
* Adjustments for unused things.
* Adjust javadocs.
* DimensionDictionarySelector adjustments.
* Add "clone" to BatchIteratorAdapter.
* ValueMatcher javadocs.
* Fix benchmark.
* Fixups post-merge.
* Expect exception on testGroupByWithStringVirtualColumn for IncrementalIndex.
* BloomDimFilterSqlTest: Tag two non-vectorizable tests.
* Minor adjustments.
* Update surefire, bump up Xmx in Travis.
* Some more adjustments.
* Javadoc adjustments
* AggregatorAdapters adjustments.
* Additional comments.
* Remove switching search.
* Only missiles.
* Add round support for DS-HLL
Since the Cardinality aggregator has a "round" option to round off estimated
values generated from the HyperLogLog algorithm, add the same "round" option to
the DataSketches HLL Sketch module aggregators to be consistent.
* Fix checkstyle errors
* Change HllSketchSqlAggregator to do rounding
* Fix test for standard-compliant null handling mode
* SQL: Allow select-sort-project query shapes.
Fixes#7768.
Design changes:
- In PartialDruidQuery, allow projection after select + sort by removing
the SELECT_SORT query stage and instead allowing the SORT and
SORT_PROJECT stages to apply either after aggregation or after a plain
non-aggregating select. This is different from prior behavior, where
SORT and SORT_PROJECT were only considered valid after aggregation
stages. This logic change is in the "canAccept" method.
- In DruidQuery, represent either kind of sorting with a single "Sorting"
class (instead of DefaultLimitSpec). The Sorting class is still
convertible into a DefaultLimitSpec, but is also convertible into the
sorting parameters accepted by a Scan query.
- In DruidQuery, represent post-select and post-sorting projections with
a single "Projection" class. This obsoletes the SortProject and
SelectProjection classes, and simplifies the DruidQuery by allowing us
to move virtual-column and post-aggregator-creation logic into the
new Projection class.
- Split "DruidQuerySignature" into RowSignature and VirtualColumnRegistry.
This effectively means that instead of having mutable and immutable
versions of DruidQuerySignature, we instead of RowSignature (always
immutable) and VirtualColumnRegistry (always mutable, but sometimes
null). This change wasn't required, but IMO it this makes the logic
involving them easier to follow, and makes it more clear when the
virtual column registry is active and when it's not.
Other changes:
- ConvertBoundsToSelectors now just accepts a RowSignature, but we
use the VirtualColumnRegistry.getFullRowSignature() method to get
a signature that includes all columns, and therefore allows us to
simplify the logic (no need to special-case virtual columns).
- Add `__time` to the Scan column list if the query is ordering by time.
* Remove unused import.
* Upgrade various build and doc links to https.
Where it wasn't possible to upgrade build-time dependencies to https,
I kept http in place but used hardcoded checksums or GPG keys to ensure
that artifacts fetched over http are verified properly.
* Switch to https://apache.org.
* Bump Checkstyle to 8.20
Moderate severity vulnerability that affects:
com.puppycrawl.tools:checkstyle
Checkstyle prior to 8.18 loads external DTDs by default,
which can potentially lead to denial of service attacks
or the leaking of confidential information.
Affected versions: < 8.18
* Oops, missed one
* Oops, missed a few
* refactor sql planning to re-use expression virtual columns when possible when constructing a DruidQuery, allowing virtual columns to be defined in filter expressions, and making resulting native druid queries more concise. also minor refactor of built-in sql aggregators to maximize code re-use
* fix it
* fix it in the right place
* fixup for base64 stuff
* fixup tests
* fix merge conflict on import order
* fixup
* fix imports
* fix tests
* review comments
* refactor
* re-arrange
* better javadoc
* fixup merge
* fixup tests
* fix accidental changes
* Added checkstyle for "Methods starting with Capital Letters" and changed the method names violating this.
* Un-abbreviate the method names in the calcite tests
* Fixed checkstyle errors
* Changed asserts position in the code
* * Add few methods about base64 into StringUtils
* Use `java.util.Base64` instead of others
* Add org.apache.commons.codec.binary.Base64 & com.google.common.io.BaseEncoding into druid-forbidden-apis
* Rename encodeBase64String & decodeBase64String
* Update druid-forbidden-apis
Not putting this to 0.13 milestone because the found bugs are not critical (one is a harmless DI config duplicate, and another is in a benchmark.
Change in `DumpSegment` is just an indentation change.
* Add checkstyle rules about imports and empty lines between members
* Add suppressions
* Update Eclipse import order
* Add empty line
* Fix StatsDEmitter
* Prohibit some guava collection APIs and use JDK APIs directly
* reset files that changed by accident
* sort codestyle/druid-forbidden-apis.txt alphabetically
This PR accumulates many refactorings and small improvements that I did while preparing the next change set of https://github.com/druid-io/druid/projects/2. I finally decided to make them a separate PR to minimize the volume of the main PR.
Some of the changes:
- Renamed confusing "Generic Column" term to "Numeric Column" (what it actually implies) in many class names.
- Generified `ComplexMetricExtractor`
Gian Merlino
Jihoon Son
Jonathan Wei
Clint Wylie
Maytas Monsereenusorn
Franklyn Dsouza
Maytas Monsereenusorn
Alexander Saydakov
Suneet Saldanha
Atul Mohan
Chi Cao Minh
jon-wei
Roman Leventov
SandishKumarHN
Fokko Driesprong
Chi Cao Minh
Xue Yu
Gian Merlino
Merlin Lee
es1220
Alexander Saydakov
Clint Wylie
Himanshu Pandey
Benedict Jin
Alexander Saydakov
David Lim
QiuMM
David Lim