Fixes a bug because of which some SQL queries cannot be parsed using druid convention. Specifically, these queries translate to an inline datasource and have some null values. Calcite internally uses NULL as SQL type for these literals and that is not supported by the druid.
I am now allowing null column types to be returned while building RowSignature in org.apache.druid.sql.calcite.table.RowSignatures#fromRelDataType. RowSignature already allows null column type for any column. Doing so should also fix bindable queries such as select (1,2). When such queries are run with headers set to true, we get an exception in org.apache.druid.sql.http.ArrayWriter#writeHeader. This is again a similar exception to the one addressed in this PR. Because SQL type for the result column is RECORD and that doesn't have a corresponding columnType.
* init multiValue column group by
* Changing sorting to Lexicographic as default
* Adding initial tests
* 1.Fixing test cases adding
2.Optimized inmem structs
* Linking SQL layer to native layer
* Adding multiDimension support to group by column strategy
* 1. Removing array coercion in Calcite layer
2. Removing ResultRowDeserializer
* 1. Supporting all primitive array types
2. Removing dimension spec as part of columnSelector
* 1. Supporting all primitive array types
2. Removing dimension spec as part of columnSelector
* 1. Checkstyle things
2. Removing flag
* Minor naming things
* CheckStyle Things
* Fixing test case
* Fixing hashing
* 1. Adding the MV function
2. Added few test cases
* 1. Adding MV function test cases
* Adding Selector strategy function test cases
* Fixing ClientQuerySegmentWalkerTest
* Adding GroupByQueryRunnerTest test cases
* Fixing test cases
* Adding few more test cases
* Fixing Exception asset statement and intellij inspection
* Adding null compatibility tests
* Review comments
* Fixing few failing tests
* Fixing few failing tests
* Do no convert to topN Q incase of group by on array
* Fixing checkstyle
* Fixing differences between jdk's class cast exception message
* 1. Fixing ordering if the grouping key is an array
* Fixing DefaultLimitSpec
* Fixing CalciteArraysQueryTest
* Dummy commit for LGTM
* changes:
* only coerce multi-value string null values when `ExpressionPlan.Trait.NEEDS_APPLIED` is set
* correct return type inference for ARRAY_APPEND,ARRAY_PREPEND,ARRAY_SLICE,ARRAY_CONCAT
* fix bug with ExprEval.ofType when actual type of object from binding doesn't match its claimed type
* Review comments
* Fixing test cases
* Fixing spot bugs
* Fixing strict compile
Co-authored-by: Clint Wylie <cwylie@apache.org>
This PR changes the value of the property `druid.sql.planner.useGroupingSetForExactDistinct` from `false` to `true` in the runtime.properties files, so that newer installations have this property as `true`, while the default still remains as `false`.
The flag determines how queries which contain an aggregation over `DISTINCT` like `SELECT COUNT(DISTINCT foo.dim1) FILTER(WHERE foo.cnt = 1), SUM(foo.cnt) FROM druid.foo` get planned by Calcite. With the flag being set to false, it plans it via joins, whereas with it being set to true, the query is set using grouping sets.
There is a known issue with Calcite (https://github.com/apache/druid/issues/7953), where an NPE is thrown while planning the above query with joins. There is no such issue while planning the query using grouping sets.
Makes kinesis ingestion resilient to `LimitExceededException` caused by resharding.
Replace `describeStream` with `listShards` (recommended) to get shard related info.
`describeStream` has a limit (100) to the number of shards returned per call and a low default TPS limit of 10.
`listShards` returns the info for at most 1000 shards and has a higher TPS limit of 100 as well.
Key changed/added classes in this PR
* `KinesisRecordSupplier`
* `KinesisAdminClient`
Fixed an issue where the provisionerService which can be used to spawn resources as needed is left running on a non-leader coordinator/overlord, after it is removed from leadership. Provisioning should only be done by the leader. To fix the issue, a call to stop the provisionerService was added to the stop() method of HttpRemoteTaskRunner class. The provisionerService was properly closed on other TaskRunner types.
This fixes a bug that causes TaskClient in overlord to continuously retry to pause tasks. This can happen when a task is not responding to the pause command. Ideally, in such a case when the task is unresponsive, the overlord would have given up after a few retries and would have killed the task. However, due to this bug, retries go on forever.
* add new doc
* Apply suggestions from code review
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* reorder query laning properties
* rename doc
* new name in doc header
* organize material into "service tiering" section
* text edits and update sidebars.json
* update query laning
* how queries get assigned to lanes
* add more details to intro; use more consistent terminology
* more content
* Apply suggestions from code review
Co-authored-by: Jihoon Son <jihoonson@apache.org>
* Update docs/operations/mixed-workloads.md
* Apply suggestions from code review
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* typo
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Co-authored-by: Jihoon Son <jihoonson@apache.org>
Based on profiling data, about 25% of the time de-serializing DataSchema
is spent on formatting strings in validateId.
This can add up quickly, especially when de-serializing task information
in the overlord, where in can consume almost 2% of CPU if there are many
tasks.
Since the formatting is unnecessary unless the checks fail, we can
leverage the built-in formatting of Preconditions.checkArgument instead
to avoid the cost.
* Pass VirtualColumnRegistry in PlannerContext for join expression planning
* Allow for including VCs from join fact table expression
* Optmize MV_FILTER functions to use a VC when in join fact table expression
* fixup! Allow for including VCs from join fact table expression
* Address review comments
* Ingestion will fail for HLLSketchBuild instead of creating with incorrect values
* Addressing review comments for HLL< updated error message introduced test case
This change mimics what was done in PR #11917 to
fix the incompatibilities produced by #11713. #11917
fixed it with AggregatorFactory by creating default
methods to allow for extensions built against old
jars to still work. This does the same for PostAggregator
* fix delegated smoosh writer and some new facilities for segment writeout medium
changes:
* fixed issue with delegated `SmooshedWriter` when writing files that look like paths, causing `NoSuchFileException` exceptions when attempting to open a channel to the file
* `FileSmoosher.addWithSmooshedWriter` when _not_ delegating now checks that it is still open when closing, making it a no-op if already closed (allowing column serializers to add additional files and avoid delegated mode if they are finished writing out their own content and ned to add additional files)
* add `makeChildWriteOutMedium` to `SegmentWriteOutMedium` interface, which allows users of a shared medium to clean up `WriteOutBytes` if they fully control the lifecycle. there are no callers of this yet, adding for future functionality
* `OnHeapByteBufferWriteOutBytes` now can be marked as not open so it `OnHeapMemorySegmentWriteOutMedium` can now behave identically to other medium implementations
* fix to address nit - use AtomicLong
Related to #11188
The above mentioned PR allowed timeseries queries to return a default result, when queries of type: select count(*) from table where dim1="_not_present_dim_" were executed. Before the PR, it returned no row, after the PR, it would return a row with value of count(*) as 0 (as expected by SQL standards of different dbs).
In Grouping#applyProject, we can sometimes perform optimization of a groupBy query to a timeseries query if possible (when the keys of the groupBy are constants, as generated by automated tools). For example, in select count(*) from table where dim1="_present_dim_" group by "dummy_key", the groupBy clause can be removed. However, in the case when the filter doesn't return anything, i.e. select count(*) from table where dim1="_not_present_dim_" group by "dummy_key", the behavior of general databases would be to return nothing, while druid (due to above change) returns an empty row. This PR aims to fix this divergence of behavior.
Example cases:
select count(*) from table where dim1="_not_present_dim_" group by "dummy_key".
CURRENT: Returns a row with count(*) = 0
EXPECTED: Return no row
select 'A', dim1 from foo where m1 = 123123 and dim1 = '_not_present_again_' group by dim1
CURRENT: Returns a row with ('A', 'wat')
EXPECTED: Return no row
To do this, a boolean droppedDimensionsWhileApplyingProject has been added to Grouping which is true whenever we make changes to the original shape with optimization. Hence if a timeseries query has a grouping with this set to true, we set skipEmptyBuckets=true in the query context (i.e. donot return any row).
* Add http response status code to org.eclipse.jetty.server.RequestLog
* http response code is expressed as an int. Set log msg interpolation based on digit
* trying to add an unit test to verify if the logger.debug method is called
* trying to add an unit test to verify if the logger.debug method is called
* fix compilation issues
* remove test
* Use Druid's extension loading for integration test instead of maven
* fix maven command
* override config path
* load input format extensions and kafka by default; add prepopulated-data group
* all docker-composes are overridable
* fix s3 configs
* override config for all
* fix docker_compose_args
* fix security tests
* turn off debug logs for overlord api calls
* clean up stuff
* revert docker-compose.yml
* fix override config for query error test; fix circular dependency in docker compose
* add back some dependencies in docker compose
* new maven profile for integration test
* example file filter
zachjsh
Abhishek Agarwal
TSFenwick
Karan Kumar
Vadim Ogievetsky
Clint Wylie
Jihoon Son
Suneet Saldanha
somu-imply
Laksh Singla
JoyKing
AmatyaAvadhanula
Uwe Schindler
Gian Merlino
Maytas Monsereenusorn
Benedict Jin
Victoria Lim
Ivan Vankovich
aggarwalakshay
Jonathan Wei
Xavier Léauté
Rohan Garg
imply-cheddar
Frank Chen
Marcelo R Costa