* Update to Calcite 1.35.0
* Update from.ftl for Calcite 1.35.0.
* Fixed tests in Calcite upgrade by doing the following:
1. Added a new rule, CoreRules.PROJECT_FILTER_TRANSPOSE_WHOLE_PROJECT_EXPRESSIONS, to Base rules
2. Refactored the CorrelateUnnestRule
3. Updated CorrelateUnnestRel accordingly
4. Fixed a case with selector filters on the left where Calcite was eliding the virtual column
5. Additional test cases for fixes in 2,3,4
6. Update to StringListAggregator to fail a query if separators are not propagated appropriately
* Refactored for testcases to pass after the upgrade, introduced 2 new data sources for handling filters and select projects
* Added a literalSqlAggregator as the upgraded Calcite involved changes to subquery remove rule. This corrected plans for 2 queries with joins and subqueries by replacing an useless literal dimension with a post agg. Additionally a test with COUNT DISTINCT and FILTER which was failing with Calcite 1.21 is added here which passes with 1.35
* Updated to latest avatica and updated code as SqlUnknownTimeStamp is now used in Calcite which needs to be resolved to a timestamp literal
* Added a wrapper segment ref to use for unnest and filter segment reference
Fixes a case I missed in #14688 when the return type is STRING but its coming from a top level array typed column instead of a nested array column while making a vector object selector.
Also while here I noticed that the internal JSON_VALUE functions for array types were named inconsistently with the non-array functions, so I renamed them. These are not documented so it should not be disruptive in any way, since they are only used internally for rewrites while planning to make the correctly virtual column.
JSON_VALUE_RETURNING_ARRAY_VARCHAR -> JSON_VALUE_ARRAY_VARCHAR
JSON_VALUE_RETURNING_ARRAY_BIGINT -> JSON_VALUE_ARRAY_BIGINT
JSON_VALUE_RETURNING_ARRAY_DOUBLE -> JSON_VALUE_ARRAY_DOUBLE
The internal non-array functions are JSON_VALUE_VARCHAR, JSON_VALUE_BIGINT, and JSON_VALUE_DOUBLE.
* fix issue with nested virtual column array element vector selectors when input is numeric array but output is non-numeric
* add vector value selector for mixed numeric type variant and nested variant fields, tests
* fix issues with equality and range filters matching double values to long typed inputs
* adjust to ensure we never homogenize null, [], and [null] into [null] for expressions on real array columns
* Use OverlordClient for all Overlord RPCs.
Continuing the work from #12696, this patch removes HttpIndexingServiceClient
and the IndexingService flavor of DruidLeaderClient completely. All remaining
usages are migrated to OverlordClient.
Supporting changes include:
1) Add a variety of methods to OverlordClient.
2) Update MetadataTaskStorage to skip the complete-task lookup when
the caller requests zero completed tasks. This helps performance of
the "get active tasks" APIs, which don't want to see complete ones.
* Use less forbidden APIs.
* Fixes from CI.
* Add test coverage.
* Two more tests.
* Fix test.
* Updates from CR.
* Remove unthrown exceptions.
* Refactor to improve testability and test coverage.
* Add isNil tests.
* Remove unnecessary "deserialize" methods.
* Simplify bounds/range vs selectors/equality logic in SQL planning.
1) Consolidate duplicate code related to Expressions#buildTimeFloorFilter.
2) Cleaner logic in Expressions#toSimpleLeafFilter: choose bounds vs range
filter based solely on plannerContext.isUseBoundsAndSelectors, not also
considering rhs kind. Use parsed rhs in both paths (except for numerics
in the bound path).
3) Fix ArrayContains, ArrayOverlap to avoid equality filters when there is
an extractionFn present. Fixes a bug introduced in #14612.
* Avoid sending nonprimitives down the bound path.
* remove extractionFn from equality, null, and range filters
changes:
* EqualityFilter, NullFilter, and RangeFilter no longer support extractionFn
* SQL planner will use ExpressionFilter in the small number of cases where an extractionFn would have been used if sqlUseBoundsAndSelectors is set to false instead of equality/null/range filters
* fix bugs and add tests with serde, equals, and cache key for null, equality, and range filters
* test coverage fixes bugs
* adjust
* adjust again
* so persnickety
changes:
* new filters that preserve match value typing to better handle filtering different column types
* sql planner uses new filters by default in sql compatible null handling mode
* remove isFilterable from column capabilities
* proper handling of array filtering, add array processor to column processors
* javadoc for sql test filter functions
* range filter support for arrays, tons more tests, fixes
* add dimension selector tests for mixed type roots
* support json equality
* rename semantic index maker thingys to mostly have plural names since they typically make many indexes, e.g. StringValueSetIndex -> StringValueSetIndexes
* add cooler equality index maker, ValueIndexes
* fix missing string utf8 index supplier
* expression array comparator stuff
MSQ engine returns correct error codes for invalid user inputs in the query context. Also, using DruidExceptions for MSQ related errors happening in the Broker with improved error messages.
* Add aggregatorMergeStrategy property to SegmentMetadaQuery.
- Adds a new property aggregatorMergeStrategy to segmentMetadata query.
aggregatorMergeStrategy currently supports three types of merge strategies -
the legacy strict and lenient strategies, and the new latest strategy.
- The latest strategy considers the latest aggregator from the latest segment
by time order when there's a conflict when merging aggregators from different
segments.
- Deprecate lenientAggregatorMerge property; The API validates that both the new
and old properties are not set, and returns an exception.
- When merging segments as part of segmentMetadata query, the segments have a more
elaborate id -- <datasource>_<interval>_merged_<partition_number> format, similar to
the name format that segments usually contain. Previously it was simply "merged".
- Adjust unit tests to test the latest strategy, to assert the returned complete
SegmentAnalysis object instead of just the aggregators for completeness.
* Don't explicitly set strict strategy in tests
* Apply suggestions from code review
Co-authored-by: Katya Macedo <38017980+ektravel@users.noreply.github.com>
* Update docs/querying/segmentmetadataquery.md
* Apply suggestions from code review
Co-authored-by: Katya Macedo <38017980+ektravel@users.noreply.github.com>
---------
Co-authored-by: Katya Macedo <38017980+ektravel@users.noreply.github.com>
* Fix a resource leak with Window processing
Additionally, in order to find the leak, there were
adjustments to the StupidPool to track leaks a bit better.
It would appear that the pool objects get GC'd during testing
for some reason which was causing some incorrect identification
of leaks from objects that had been returned but were GC'd along
with the pool.
* Suppress unused warning
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.
* Claim full support for Java 17.
No production code has changed, except the startup scripts.
Changes:
1) Allow Java 17 without DRUID_SKIP_JAVA_CHECK.
2) Include the full list of opens and exports on both Java 11 and 17.
3) Document that Java 17 is both supported and preferred.
4) Switch some tests from Java 11 to 17 to get better coverage on the
preferred version.
* Doc update.
* Update errorprone.
* Update docker_build_containers.sh.
* Update errorprone in licenses.yaml.
* Add some more run-javas.
* Additional run-javas.
* Update errorprone.
* Suppress new errorprone error.
* Add exports and opens in ForkingTaskRunner for Java 11+.
Test, doc changes.
* Additional errorprone updates.
* Update for errorprone.
* Restore old fomatting in LdapCredentialsValidator.
* Copy bin/ too.
* Fix Java 15, 17 build line in docker_build_containers.sh.
* Update busybox image.
* One more java command.
* Fix interpolation.
* IT commandline refinements.
* Switch to busybox 1.34.1-glibc.
* POM adjustments, build and test one IT on 17.
* Additional debugging.
* Fix silly thing.
* Adjust command line.
* Add exports and opens one more place.
* Additional harmonization of strong encapsulation parameters.
* Fix ColumnSignature error message and jdk17 test issue.
On jdk17, the "problem" part of the error message could change from
NullPointerException to:
Cannot invoke "String.length()" because "s" is null
Due to the new more-helpful NPEs in Java 17. This broke the expectation
and led to test failures on this case.
This patch fixes the problem by improving the error message so it isn't
a generic NullPointerException.
* Fix format.
* Add support for DML WITH AS.
* One more UT for with as subquery.
* Add a test with join query
* Use root query prepared node instead of individual SqlNode types.
- Set the explain plan attributes after the query is prepared when
the query is planned and we've the finalized output names in the root
source rel node.
- Adjust tests; add unit test for negative ordinal case.
- Remove the exception / error handling logic from resolveClusteredBy
function since the validations now happen before it comes to the function
* Update comment.
This commit borrows some test definitions from Drill's test suite
and tries to use them to flesh out the full validation of window
function capbilities.
In order to be able to run these tests, we also add the ability to
run a Scan operation against segments, which also meant an
implementation of RowsAndColumns for frames.
with a RuntimeException. Now the RuntimeException is being replaced by an user facing DruidException of Invalid category which would allow calcite not to throw an uncategorized exception.
In these other cases, stick to plain "filter". This simplifies lots of
logic downstream, and doesn't hurt since we don't have intervals-specific
optimizations outside of tables.
Fixes an issue where we couldn't properly filter on a column from an
external datasource if it was named __time.
* Support complex variance object inputs for variance SQL agg function
* Add test
* Include complexTypeChecker, address PR comments
* Checkstyle, javadoc link
This PR aims to expose a new API called
"@path("/druid/v2/sql/statements/")" which takes the same payload as the current "/druid/v2/sql" endpoint and allows users to fetch results in an async manner.
* Cache parsed expressions and binding analysis in more places.
Main changes:
1) Cache parsed and analyzed expressions within PlannerContext for a
single SQL query.
2) Cache parsed expressions together with input binding analysis using
a new class AnalyzeExpr.
This speeds up SQL planning, because SQL planning involves parsing
analyzing the same expression strings over and over again.
* Fixes.
* Fix style.
* Fix test.
* Simplify: get rid of AnalyzedExpr, focus on caching.
* Rename parse -> parseExpression.
* Updates: use the target table directly, sanitized replace time chunks and clustered by cols.
* Add DruidSqlParserUtil and tests.
* minor refactor
* Use SqlUtil.isLiteral
* 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.
* minor naming
* move deprecated field to the bottom
* update docs.
* add one more example.
* Collapsible query and result
* checkstyle fixes
* Code cleanup
* order by changes
* conditionally set attributes only for explain queries.
* Cleaner ordinal check.
* Add limit test and update javadoc.
* Commentary and minor adjustments.
* Checkstyle fixes.
* One more checkArg.
* add unexpected kind to exception.
Users can now add a guardrail to prevent subquery’s results from exceeding the set number of bytes by setting druid.server.http.maxSubqueryRows in Broker's config or maxSubqueryRows in the query context. This feature is experimental for now and would default back to row-based limiting in case it fails to get the accurate size of the results consumed by the query.
* SqlResults: Coerce arrays to lists for VARCHAR.
Useful for STRING_TO_MV, which returns VARCHAR at the SQL layer and an
ExprEval with String[] at the native layer.
* Fix style.
* Improve test coverage.
* Remove unnecessary throws.
* SQL OperatorConversions: Introduce.aggregatorBuilder, allow CAST-as-literal.
Four main changes:
1) Provide aggregatorBuilder, a more consistent way of defining the
SqlAggFunction we need for all of our SQL aggregators. The mechanism
is analogous to the one we already use for SQL functions
(OperatorConversions.operatorBuilder).
2) Allow CASTs of constants to be considered as "literalOperands". This
fixes an issue where various of our operators are defined with
OperandTypes.LITERAL as part of their checkers, which doesn't allow
casts. However, in these cases we generally _do_ want to allow casts.
The important piece is that the value must be reducible to a constant,
not that the SQL text is literally a literal.
3) Update DataSketches SQL aggregators to use the new aggregatorBuilder
functionality. The main user-visible effect here is [2]: the aggregators
would now accept, for example, "CAST(0.99 AS DOUBLE)" as a literal
argument. Other aggregators could be updated in a future patch.
4) Rename "requiredOperands" to "requiredOperandCount", because the
old name was confusing. (It rhymes with "literalOperands" but the
arguments mean different things.)
* Adjust method calls.
New metrics:
- `segment/metadatacache/refresh/time`: time taken to refresh segments per datasource
- `segment/metadatacache/refresh/count`: number of segments being refreshed per datasource
Add a new planning strategy that explicitly decouples the DAG from building the native query.
With this mode, it is Calcite's job to generate a "logical DAG" which is all of the various
DruidProject, DruidFilter, etc. nodes. We then take those nodes and use them to build a native
query. The current commit doesn't pass all tests, but it does work for some things and is a
decent starting baseline.
Introduce DruidException, an exception whose goal in life is to be delivered to a user.
DruidException itself has javadoc on it to describe how it should be used. This commit both introduces the Exception and adjusts some of the places that are generating exceptions to generate DruidException objects instead, as a way to show how the Exception should be used.
This work was a 3rd iteration on top of work that was started by Paul Rogers. I don't know if his name will survive the squash-and-merge, so I'm calling it out here and thanking him for starting on this.
Description:
Druid allows a configuration of load rules that may cause a used segment to not be loaded
on any historical. This status is not tracked in the sys.segments table on the broker, which
makes it difficult to determine if the unavailability of a segment is expected and if we should
not wait for it to be loaded on a server after ingestion has finished.
Changes:
- Track replication factor in `SegmentReplicantLookup` during evaluation of load rules
- Update API `/druid/coordinator/v1metadata/segments` to return replication factor
- Add column `replication_factor` to the sys.segments virtual table and populate it in
`MetadataSegmentView`
- If this column is 0, the segment is not assigned to any historical and will not be loaded.
* 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
changes:
* auto columns no longer participate in generic 'null column' handling, this was a mistake to try to support and caused ingestion failures due to mismatched ColumnFormat, and will be replaced in the future with nested common format constant column functionality (not in this PR)
* fix bugs with auto columns which contain empty objects, empty arrays, or primitive types mixed with either of these empty constructs
* fix bug with bound filter when upper is null equivalent but is strict
* Add INFORMATION_SCHEMA.ROUTINES to expose Druid operators and functions.
* checkstyle
* remove IS_DETERMISITIC.
* test
* cleanup test
* remove logs and simplify
* fixup unit test
* Add docs for INFORMATION_SCHEMA.ROUTINES table.
* Update test and add another SQL query.
* add stuff to .spelling and checkstyle fix.
* Add more tests for custom operators.
* checkstyle and comment.
* Some naming cleanup.
* Add FUNCTION_ID
* The different Calcite function syntax enums get translated to FUNCTION
* Update docs.
* Cleanup markdown table.
* fixup test.
* fixup intellij inspection
* Review comment: nullable column; add a function to determine function syntax.
* More tests; add non-function syntax operators.
* More unit tests. Also add a separate test for DruidOperatorTable.
* actually just validate non-zero count.
* switch up the order
* checkstyle fixes.
This PR adds the following to the ATTRIBUTES column in the explain plan output:
- partitionedBy
- clusteredBy
- replaceTimeChunks
This PR leverages the work done in #14074, which added a new column ATTRIBUTES
to encapsulate all the statement-related attributes.
* Fix EarliestLatestBySqlAggregator signature; Include function name for all signatures.
* Single quote function signatures, space between args and remove \n.
* fixup UT assertion
It was found that several supported tasks / input sources did not have implementations for the methods used by the input source security feature, causing these tasks and input sources to fail when used with this feature. This pr adds the needed missing implementations. Also securing the sampling endpoint with input source security, when enabled.
This PR adds a new interface to control how SegmentMetadataCache chooses ColumnType when faced with differences between segments for SQL schemas which are computed, exposed as druid.sql.planner.metadataColumnTypeMergePolicy and adds a new 'least restrictive type' mode to allow choosing the type that data across all segments can best be coerced into and sets this as the default behavior.
This is a behavior change around when segment driven schema migrations take effect for the SQL schema. With latestInterval, the SQL schema will be updated as soon as the first job with the new schema has published segments, while using leastRestrictive, the schema will only be updated once all segments are reindexed to the new type. The benefit of leastRestrictive is that it eliminates a bunch of type coercion errors that can happen in SQL when types are varied across segments with latestInterval because the newest type is not able to correctly represent older data, such as if the segments have a mix of ARRAY and number types, or any other combinations that lead to odd query plans.
Rishabh Singh
Soumyava
Adarsh Sanjeev
Clint Wylie
Kashif Faraz
Gian Merlino
AmatyaAvadhanula
Laksh Singla
Abhishek Radhakrishnan
imply-cheddar
Jonathan Wei
Pranav
Karan Kumar
Rohan Garg
zachjsh