Updated the Direct Druid Client so as to make Connection Count Server Selector Strategy work more efficiently.
If creating connection to a node is slow, then that slowness wouldn't be accounted for if we count the open connections after sending the request. So we increment the counter and then send the request.
* docs: add mermaid diagram support
* fix crash when parsing data in data loader that can not be parsed (#15983)
* update jetty to address CVE (#16000)
* Concurrent replace should work with supervisors using concurrent locks (#15995)
* Concurrent replace should work with supervisors using concurrent locks
* Ignore supervisors with useConcurrentLocks set to false
* Apply feedback
* Add pre-check for heavy debug logs (#15706)
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
Co-authored-by: Benedict Jin <asdf2014@apache.org>
* Remove helm paths from CodeQL config (#16006)
* docs: mention acid-compliance for metadb
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Co-authored-by: Vadim Ogievetsky <vadim@ogievetsky.com>
Co-authored-by: Jan Werner <105367074+janjwerner-confluent@users.noreply.github.com>
Co-authored-by: AmatyaAvadhanula <amatya.avadhanula@imply.io>
Co-authored-by: Sensor <fectrain@outlook.com>
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
Co-authored-by: Benedict Jin <asdf2014@apache.org>
* MSQ: Nicer error when sortMerge join falls back to broadcast.
In certain cases, joins run as broadcast even when the user hinted
that they wanted sortMerge. This happens when the sortMerge algorithm
is unable to process the join, because it isn't a direct comparison
between two fields on the LHS and RHS.
When this happens, the error message from BroadcastTablesTooLargeFault
is quite confusing, since it mentions that you should try sortMerge
to fix it. But the user may have already configured sortMerge.
This patch fixes it by having two error messages, based on whether
broadcast join was used as a primary selection or as a fallback selection.
* Style.
* Better message.
* 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
Currently, while reading results from realtime tasks, requests are sent on a segment level. This is slightly wasteful, as when contacting a data servers, it is possible to transfer results for all segments which it is hosting, instead of only one segment at a time.
One change this PR makes is to group the segments on the basis of servers. This reduces the number of queries to data servers made. Since we don't have access to the number of rows for realtime segments, the grouping is done with a fixed estimated number of rows for each realtime segment.
* Update basic-cluster-tuning.md
The sentence "When free system memory is greater than or equal to druid.segmentCache.locations, the more segment data the Historical can be held in the memory-mapped segment cache" didn't read well. Updated to clarify it.
* Update docs/operations/basic-cluster-tuning.md
* Update docs/operations/basic-cluster-tuning.md
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Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* All segments stored in the same batch have the same created_date entry.
In the absence of a group_id column, this metadata would allow us to easily
reason about and troubleshoot ingestion-related issues.
* Rename metric name and code references to eligibleUnusedSegments.
Address review comment from https://github.com/apache/druid/pull/15941#discussion_r1503631992
* Kill duty and test improvements.
Initial commit with:
- Bug fixes - auto-kill can throw NPE when there are no datasources present and defaults mismatch.
- Add new stat for candidate segment intervals killed.
- Move a couple of debug logs to info logs for improved visibility (should only log once per kill period).
- Remove redundant checks for code readability.
- Updated tests from using mocks (also the mocks weren't using last updated timestamp) and
add more test coverage for different config parameters.
- Add a couple of unit tests that are ignored for the eternity case to prove that
the kill duty doesn't clean up segments with ALL grain or that end in DateTimes.MAX.
- Migrate Druid exception from user to operator persona.
* Address review comments.
* Remove unused methods.
* fix up format specifier and validate bad config tests.
* Consolidate the helpers a bit more and add another test.
* Update test names. Add javadoc placeholders for slightly involved tests.
* Add docs for metric kill/candidateUnusedSegments/count.
Also, rename to disambiguate.
* Comments.
* Apply logging suggestions from code review
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
* Review comments
- Clarify docs on eligibility.
- Add test for multiple segments in the same interval. Clarify comment.
- Remove log line from test.
- Remove lastUpdatedDate = now.plus(10) from test.
* minor cleanup.
* Clarify javadocs for getUnusedSegmentIntervals().
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Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
The code in the groupBy engine and the topN engine assume that the dimensions are comparable and can call dimA.compareTo(dimB) to sort the dimensions and group them together.
This works well for the primitive dimensions, because they are Comparable, however falls apart when the dimensions can be arrays (or in future scenarios complex columns). In cases when the dimensions are not comparable, Druid resorts to having a wrapper type ComparableStringArray and ComparableList, which is a Comparable, based on the list comparator.
* Fix up typos, inaccuracies and clean up code related to PARTITIONED BY.
* Remove wrapper function and update tests to use DruidExceptionMatcher.
* Checkstyle and Intellij inspection fixes.
This PR creates symlinks when there are duplicate jars present in the extension. Docker image includes contrib extensions, too, and the size of the image has bloated up quite a lot of late. This change also fixes "ITNestedQueryPushDownTest integration test"
Changes:
- Add visibility into number of records processed by each streaming task per partition
- Add field `recordsProcessed` to `IngestionStatsAndErrorsTaskReportData`
- Populate number of records processed per partition in `SeekableStreamIndexTaskRunner`
This PR contains a portion of the changes from the inactive draft PR for integrating the catalog with the Calcite planner https://github.com/apache/druid/pull/13686 from @paul-rogers, Refactoring the IngestHandler and subclasses to produce a validated SqlInsert instance node instead of the previous Insert source node. The SqlInsert node is then validated in the calcite validator. The validation that is implemented as part of this pr, is only that for the source node, and some of the validation that was previously done in the ingest handlers. As part of this change, the partitionedBy clause can be supplied by the table catalog metadata if it exists, and can be omitted from the ingest time query in this case.
Fixes a bug when the undocumented castToType parameter is set on 'auto' column schema, which should have been using the 'nullable' comparator to allow null values to be present when merging columns, but wasn't which would lead to null pointer exceptions. Also fixes an issue I noticed while adding tests that if 'FLOAT' type was specified for the castToType parameter it would be an exception because that type is not expected to be present, since 'auto' uses the native expressions to determine the input types and expressions don't have direct support for floats, only doubles.
In the future I should probably split this functionality out of the 'auto' schema (maybe even have a simpler version of the auto indexer dedicated to handling non-nested data) but still have the same results of writing out the newer 'nested common format' columns used by 'auto', but I haven't taken that on in this PR.
Adithya Chakilam
Adarsh Sanjeev
Sree Charan Manamala
317brian
Gian Merlino
George Shiqi Wu
Zoltan Haindrich
Vadim Ogievetsky
Clint Wylie
Jan Werner
Sensor
AmatyaAvadhanula
Kashif Faraz
benkrug
AlbericByte
Abhishek Radhakrishnan
Karan Kumar
Laksh Singla
Soumyava
Abhishek Agarwal
Benjamin Hopp
dependabot[bot]
zachjsh
Katya Macedo
Jamie
Benedict Jin
Suneet Saldanha