* 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
* Thread pool for broker
* Updating two tests to improve coverage for new method added
* Updating druidProcessingConfigTest to cover coverage
* Adding missed spelling errors caused in doc
* Adding test to cover lines of new function added
Segment pruning for multi-dim partitioning for a given query
DimensionRangeShardSpec#possibleInDomain has been modified to enhance pruning when multi-dim partitioning is used.
Idea
While iterating through each dimension,
If query domain doesn't overlap with the set of permissible values in the segment, the segment is pruned.
If the overlap happens on a boundary, consider the next dimensions.
If there is an overlap within the segment boundaries, the segment cannot be pruned.
This index helps in faster query results during kill task's query on interval based unused segment listing. This can become a bottleneck in some production loads causing coordinator to wait longer for metadata db replies and impacting Kafka ingestion. The modified index has helped reduce the query times for such queries.
DruidLogicalValuesRule while transforming to DruidRel can return incorrect values, if during the creation of the literal it was created from a float value. The BigDecimal representation stores 123.0, and it seems that using RexLiteral's method while conversion returns the inflated value (which is 1230). I am unsure if this is intentional from Calcite's perspective, and the actual change should be done somewhere else.
Extract the values of INT/LONG from the RexLiteral in the DruidLogicalValuesRule, via BigDecimal.longValue() method.
changes:
* IncrementalIndex is now a ColumnInspector
* fixes performance regression from using map of ColumnCapabilities from IncrementalIndex as a RowSignature
* Add jsonPath functions support
* Add jsonPath function test for Avro
* Add jsonPath function length() to Orc
* Add jsonPath function length() to Parquet
* Add more tests to ORC format
* update doc
* Fix exception during ingestion
* Add IT test case
* Revert "Fix exception during ingestion"
This reverts commit 5a5484b9ea.
* update IT test case
* Add 'keys()'
* Commit IT test case
* Fix UT
* Allow for appending tasks to co-exist with each other.
Add a config parameter for appending tasks to allow them to
use a SHARED lock. This will allow multiple appending tasks
to add segments to the same datasource at the same time.
This config should actually be the default, but it is added
as a config to enable a smooth transition/validation in
production settings before forcing it as the default
behavior going forward.
This change leverages the TaskLockType.SHARED that existed
previously, this used to carry the semantics of a READ lock,
which was "escalated" when the task wanted to actually
persist the segment. As of many moons before this diff, the
SHARED lock had stopped being used but was still piped into
the code. It turns out that with a few tweaks, it can be
adjusted to be a shared lock for append tasks to allow them
all to write to the same datasource, so that is what this does.
* Can only reuse the shared lock if using the same groupId
* Need to serialize out the task lock type
* Adjust Unit tests to expect new field in JSON
* Remove use of deprecated PMD ruleset
This fixes annoying warnings we were getting during build.
- Use a custom PMD ruleset, since the built-in one uses deprecated rules.
- UnnecessaryImport replaces most of the deprecated rules
- Update maven-pmd-plugin to 3.15
- Exclude ancient asm version from caliper, since this was causing
incompatibility warnings with PMD and could also affect our tests runs
in unexpected ways
In this PR, we will now return 400 instead of 500 when SQL query cannot be planned. I also fixed a bug where error messages were not getting sent to the users in case the rules throw UnsupportSQLQueryException.
DruidSchema consists of a concurrent HashMap of DataSource -> Segement -> AvailableSegmentMetadata. AvailableSegmentMetadata contains RowSignature of the segment, and for each segment, a new object is getting created. RowSignature is an immutable class, and hence it can be interned, and this can lead to huge savings of memory being used in broker, since a lot of the segments of a table would potentially have same RowSignature.
Vadim Ogievetsky
Clint Wylie
Laksh Singla
Rohan Garg
imply-cheddar
somu-imply
Frank Chen
Marcelo R Costa
Jihoon Son
Victoria Lim
Maytas Monsereenusorn
Jonathan Wei
AmatyaAvadhanula
Abhishek Agarwal
lokesh-lingarajan
Karan Kumar
Gian Merlino
Lucas Capistrant
Suneet Saldanha
Xavier Léauté
shallada