In this PR, I have gotten rid of multiTopic parameter and instead added a topicPattern parameter. Kafka supervisor will pass topicPattern or topic as the stream name to the core ingestion engine. There is validation to ensure that only one of topic or topicPattern will be set. This new setting is easier to understand than overloading the topic field that earlier could be interpreted differently depending on the value of some other field.
This PR adds support to read from multiple Kafka topics in the same supervisor. A multi-topic ingestion can be useful in scenarios where a cluster admin has no control over input streams. Different teams in an org may create different input topics that they can write the data to. However, the cluster admin wants all this data to be queryable in one data source.
* 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
### Description
Added the following metrics, which are calculated from the `KillUnusedSegments` coordinatorDuty
`"killTask/availableSlot/count"`: calculates the number remaining task slots available for auto kill
`"killTask/maxSlot/count"`: calculates the maximum number of tasks available for auto kill
`"killTask/task/count"`: calculates the number of tasks submitted by auto kill.
#### Release note
NEW: metrics added for auto kill
`"killTask/availableSlot/count"`: calculates the number remaining task slots available for auto kill
`"killTask/maxSlot/count"`: calculates the maximum number of tasks available for auto kill
`"killTask/task/count"`: calculates the number of tasks submitted by auto kill.
The Azure connector is introduced and MSQ's fault tolerance and durable storage can now be used with Microsoft Azure's blob storage. Also, the results of newly introduced queries from deep storage can now store and fetch the results from Azure's blob storage.
### Description
Previously, the `KillUnusedSegments` coordinator duty, in charge of periodically deleting unused segments, could spawn an unlimited number of kill tasks for unused segments. This change adds 2 new coordinator dynamic configs that can be used to control the limit of tasks spawned by this coordinator duty
`killTaskSlotRatio`: Ratio of total available task slots, including autoscaling if applicable that will be allowed for kill tasks. This limit only applies for kill tasks that are spawned automatically by the coordinator's auto kill duty. Default is 1, which allows all available tasks to be used, which is the existing behavior
`maxKillTaskSlots`: Maximum number of tasks that will be allowed for kill tasks. This limit only applies for kill tasks that are spawned automatically by the coordinator's auto kill duty. Default is INT.MAX, which essentially allows for unbounded number of tasks, which is the existing behavior.
Realize that we can effectively get away with just the one `killTaskSlotRatio`, but following similarly to the compaction config, which has similar properties; I thought it was good to have some control of the upper limit regardless of ratio provided.
#### Release note
NEW: `killTaskSlotRatio` and `maxKillTaskSlots` coordinator dynamic config properties added that allow control of task resource usage spawned by `KillUnusedSegments` coordinator task (auto kill)
### Description
Previously, the `maxSegments` configured for auto kill could be ignored if an interval of data for a given datasource had more than this number of unused segments, causing the kill task spawned with the task of deleting unused segments in that given interval of data to delete more than the `maxSegments` configured. Now each kill task spawned by the auto kill coordinator duty, will kill at most `limit` segments. This is done by adding a new config property to the `KillUnusedSegmentTask` which allows users to specify this limit.
* Remove chatAsync parameter, so chat is always async.
chatAsync has been made default in Druid 26. I have seen good
battle-testing of it in production, and am comfortable removing the
older sync client.
This was the last remaining usage of IndexTaskClient, so this patch
deletes all that stuff too.
* Remove unthrown exception.
* Remove unthrown exception.
* No more TimeoutException.
split KillUnusedSegmentsTask to smaller batches
Processing in smaller chunks allows the task execution to yield the TaskLockbox lock,
which allows the overlord to continue being responsive to other tasks and users while
this particular kill task is executing.
* introduce KillUnusedSegmentsTask batchSize parameter to control size of batching
* provide an explanation for kill task batchSize parameter
* add logging details for kill batch progress
* Add ingest/input/bytes metric and Kafka consumer metrics.
New metrics:
1) ingest/input/bytes. Equivalent to processedBytes in the task reports.
2) kafka/consumer/bytesConsumed: Equivalent to the Kafka consumer
metric "bytes-consumed-total". Only emitted for Kafka tasks.
3) kafka/consumer/recordsConsumed: Equivalent to the Kafka consumer
metric "records-consumed-total". Only emitted for Kafka tasks.
* Fix anchor.
* Fix KafkaConsumerMonitor.
* Interface updates.
* Doc changes.
* Update indexing-service/src/main/java/org/apache/druid/indexing/seekablestream/SeekableStreamIndexTask.java
Co-authored-by: Benedict Jin <asdf2014@apache.org>
---------
Co-authored-by: Benedict Jin <asdf2014@apache.org>
* Add EARLIEST aggregator merge strategy.
- More unit tests.
- Include the aggregators analysis type by default in tests.
* Docs.
* Some comments and a test
* Collapse into individual code blocks.
This adds a new contrib extension: druid-iceberg-extensions which can be used to ingest data stored in Apache Iceberg format. It adds a new input source of type iceberg that connects to a catalog and retrieves the data files associated with an iceberg table and provides these data file paths to either an S3 or HDFS input source depending on the warehouse location.
Two important dependencies associated with Apache Iceberg tables are:
Catalog : This extension supports reading from either a Hive Metastore catalog or a Local file-based catalog. Support for AWS Glue is not available yet.
Warehouse : This extension supports reading data files from either HDFS or S3. Adapters for other cloud object locations should be easy to add by extending the AbstractInputSourceAdapter.
* Change default handoffConditionTimeout to 15 minutes.
Most of the time, when handoff is taking this long, it's because something
is preventing Historicals from loading new data. In this case, we have
two choices:
1) Stop making progress on ingestion, wait for Historicals to load stuff,
and keep the waiting-for-handoff segments available on realtime tasks.
(handoffConditionTimeout = 0, the current default)
2) Continue making progress on ingestion, by exiting the realtime tasks
that were waiting for handoff. Once the Historicals get their act
together, the segments will be loaded, as they are still there on
deep storage. They will just not be continuously available.
(handoffConditionTimeout > 0)
I believe most users would prefer [2], because [1] risks ingestion falling
behind the stream, which causes many other problems. It can cause data loss
if the stream ages-out data before we have a chance to ingest it.
Due to the way tuningConfigs are serialized -- defaults are baked into the
serialized form that is written to the database -- this default change will
not change anyone's existing supervisors. It will take effect for newly
created supervisors.
* Fix tests.
* Update docs/development/extensions-core/kafka-supervisor-reference.md
Co-authored-by: Katya Macedo <38017980+ektravel@users.noreply.github.com>
* Update docs/development/extensions-core/kinesis-ingestion.md
Co-authored-by: Katya Macedo <38017980+ektravel@users.noreply.github.com>
---------
Co-authored-by: Katya Macedo <38017980+ektravel@users.noreply.github.com>
* 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>
* Add ZooKeeper connection state alerts and metrics.
- New metric "zk/connected" is an indicator showing 1 when connected,
0 when disconnected.
- New metric "zk/disconnected/time" measures time spent disconnected.
- New alert when Curator connection state enters LOST or SUSPENDED.
* Use right GuardedBy.
* Test fixes, coverage.
* Adjustment.
* Fix tests.
* Fix ITs.
* Improved injection.
* Adjust metric name, add tests.
Cache is disabled for GroupByStrategyV2 on broker since the pr #3820 [groupBy v2: Results not fully merged when caching is enabled on the broker]. But we can enable the result-level cache on broker for GroupByStrategyV2 and keep the segment-level cache disabled.
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.
If a server is removed during `HttpServerInventoryView.serverInventoryInitialized`,
the initialization gets stuck as this server is never synced. The method eventually times
out (default 250s).
Fix: Mark a server as stopped if it is removed. `serverInventoryInitialized` only waits for
non-stopped servers to sync.
Other changes:
- Add new metrics for better debugging of slow broker/coordinator startup
- `segment/serverview/sync/healthy`: whether the server view is syncing properly with a server
- `segment/serverview/sync/unstableTime`: time for which sync with a server has been unstable
- Clean up logging in `HttpServerInventoryView` and `ChangeRequestHttpSyncer`
- Minor refactor for readability
- Add utility class `Stopwatch`
- Add tests and stubs
After #13197 , several coordinator configs are now redundant as they are not being
used anymore, neither with `smartSegmentLoading` nor otherwise.
Changes:
- Remove dynamic configs `emitBalancingStats`: balancer error stats are always
emitted, debug stats can be logged by using `debugDimensions`
- `useBatchedSegmentSampler`, `percentOfSegmentsToConsiderPerMove`:
batched segment sampling is always used
- Add test to verify deserialization with unknown properties
- Update `CoordinatorRunStats` to always track stats, this can be optimized later.
* combine string column implementations
changes:
* generic indexed, front-coded, and auto string columns now all share the same column and index supplier implementations
* remove CachingIndexed implementation, which I think is largely no longer needed by the switch of many things to directly using ByteBuffer, avoiding the cost of creating Strings
* remove ColumnConfig.columnCacheSizeBytes since CachingIndexed was the only user
Adds support for automatic cleaning of a "query-results" directory in durable storage. This directory will be cleaned up only if the task id is not known to the overlord. This will allow the storage of query results after the task has finished running.
Changes:
- Throw an `InsertCannotAllocateSegmentFault` if the allocated segment is not aligned with
the requested granularity.
- Tests to verify new behaviour
* 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.
* Add OverlordStatusMonitor and CoordinatorStatusMonitor to monitor service leader status
* make the monitor more general
* resolve conflict
* use Supplier pattern to provide metrics
* reformat code and doc
* move service specific tag to dimension
* minor refine
* update doc
* reformat code
* address comments
* remove declared exception
* bind HeartbeatSupplier conditionally in Coordinator
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.
* Clarify compaction docs.
The prior wording made it sound like segmentGranularity, queryGranularity,
and rollup are always required for granularitySpec. They are not required,
but they are strongly recommended. The adjusted wording hopefully does
a better job of making that clear.
* Fix link.
* Wording adjustments.
New metrics:
- `segment/metadatacache/refresh/time`: time taken to refresh segments per datasource
- `segment/metadatacache/refresh/count`: number of segments being refreshed per datasource
Changes:
- Add property `useDefaultTierForNull` for all load rules. This property determines the default
value of `tieredReplicants` if it is not specified. When true, the default is `_default_tier => 2 replicas`.
When false, the default is empty, i.e. no replicas on any tier.
- Fix validation to allow empty replicants map, so that the segment is used but not loaded anywhere.
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
* Limit select results in MSQ
* reduce number of files in test
* add truncated flag
* avoid materializing select results to list, use iterable instead
* javadocs
* 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.
In this PR, we are enhancing KafkaEmitter, to emit metadata about published segments (SegmentMetadataEvent) into a Kafka topic. This segment metadata information that gets published into Kafka, can be used by any other downstream services to query Druid intelligently based on the segments published. The segment metadata gets published into kafka topic in json string format similar to other events.
### Description
This change allows for consideration of the input format and compression when computing how to split the input files among available tasks, in MSQ ingestion, when considering the value of the `maxInputBytesPerWorker` query context parameter. This query parameter allows users to control the maximum number of bytes, with granularity of input file / object, that ingestion tasks will be assigned to ingest. With this change, this context parameter now denotes the estimated weighted size in bytes of the input to split on, with consideration for input format and compression format, rather than the actual file size, reported by the file system. We assume uncompressed newline delimited json as a baseline, with scaling factor of `1`. This means that when computing the byte weight that a file has towards the input splitting, we take the file size as is, if uncompressed json, 1:1. It was found during testing that gzip compressed json, and parquet, has scale factors of `4` and `8` respectively, meaning that each byte of data is weighted 4x and 8x respectively, when computing input splits. This weighted byte scaling is only considered for MSQ ingestion that uses either LocalInputSource or CloudObjectInputSource at the moment. The default value of the `maxInputBytesPerWorker` query context parameter has been updated from 10 GiB, to 512 MiB
The defaults of the following config values in the `CoordinatorDynamicConfig` are being updated.
1. `maxSegmentsInNodeLoadingQueue = 500` (previous = 100)
2. `replicationThrottleLimit = 500` (previous = 10)
Rationale: With round-robin segment assignment now being the default assignment technique,
the Coordinator can assign a large number of under-replicated/unavailable segments very quickly,
without getting stuck in `RunRules` duty due to very slow strategy-based cost computations.
3. `maxSegmentsToMove = 100` (previous = 5)
Rationale: A very low value (say 5) is ineffective in balancing especially if there are many segments
to balance. A very large value can cause excessive moves, which has these disadvantages:
- Load of moving segments competing with load of unavailable/under-replicated segments
- Unnecessary network costs due to constant download and delete of segments
These defaults will be revisited after #13197 is merged.
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.
* Make resources an ordered collection so it's deterministic.
* test cleanup
* fixup docs.
* Replace deprecated ObjectNode#put() calls with ObjectNode#set().
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com>
Co-authored-by: Victoria Lim <lim.t.victoria@gmail.com>
* Be able to load segments on Peons
This change introduces a new config on WorkerConfig
that indicates how many bytes of each storage
location to use for storage of a task. Said config
is divided up amongst the locations and slots
and then used to set TaskConfig.tmpStorageBytesPerTask
The Peons use their local task dir and
tmpStorageBytesPerTask as their StorageLocations for
the SegmentManager such that they can accept broadcast
segments.
* Make LoggingEmitter more useful
* Skip code coverage for facade classes
* fix spellcheck
* code review
* fix dependency
* logging.md
* fix checkstyle
* Add back jacoco version to main pom
* fix typo in s3 docs. add readme to s3 module.
* Update extensions-core/s3-extensions/README.md
Co-authored-by: 317brian <53799971+317brian@users.noreply.github.com>
* cleanup readme for s3 extension and link to repo markdown doc instead of web docs
---------
Co-authored-by: 317brian <53799971+317brian@users.noreply.github.com>
Hadoop 2 often causes red security scans on Druid distribution because of the dependencies it brings. We want to move away from Hadoop 2 and provide Hadoop 3 distribution available. Switch druid to building with Hadoop 3 by default. Druid will still be compatible with Hadoop 2 and users can build hadoop-2 compatible distribution using hadoop2 profile.
* Compaction: Block input specs not aligned with segmentGranularity.
When input intervals are not aligned with segmentGranularity, data may be
overshadowed if it lies in the space between the input intervals and the
output segmentGranularity.
In MSQ REPLACE, this is a validation error. IMO the same behavior makes
sense for compaction tasks. In case anyone was depending on the ability
to compact nonaligned intervals, a configuration parameter
allowNonAlignedInterval is provided. I don't expect it to be used much.
* Remove unused.
* ITCompactionTaskTest uses non-aligned intervals.
* MSQ: Subclass CalciteJoinQueryTest, other supporting changes.
The main change is the new tests: we now subclass CalciteJoinQueryTest
in CalciteSelectJoinQueryMSQTest twice, once for Broadcast and once for
SortMerge.
Two supporting production changes for default-value mode:
1) InputNumberDataSource is marked as concrete, to allow leftFilter to
be pushed down to it.
2) In default-value mode, numeric frame field readers can now return nulls.
This is necessary when stacking joins on top of joins: nulls must be
preserved for semantics that match broadcast joins and native queries.
3) In default-value mode, StringFieldReader.isNull returns true on empty
strings in addition to nulls. This is more consistent with the behavior
of the selectors, which map empty strings to null as well in that mode.
As an effect of change (2), the InsertTimeNull change from #14020 (to
replace null timestamps with default timestamps) is reverted. IMO, this
is fine, as either behavior is defensible, and the change from #14020
hasn't been released yet.
* Adjust tests.
* Style fix.
* Additional tests.
* Allow for Log4J to be configured for peons but still ensure console logging is enforced
This change will allow for log4j to be configured for peons but require console logging is still
configured for them to ensure peon logs are saved to deep storage.
Also fixed the test ConsoleLoggingEnforcementTest to use a valid appender for the non console
Config as the previous config was incorrect and would never return a logger.
* fix checkstyle
* add warning to logger when it overwrites all loggers to be console
* optimize calls for altering logging config for ConsoleLoggingEnforcementConfigurationFactory
add getName to the druid logger class
* update docs, and error message
* edit docs to be more clear
* fix checkstyle issues
* CI fixes - LoggerTest code coverage and fix spelling issue for logging docs
*
Adds new run time parameter druid.indexer.task.tmpStorageBytesPerTask. This sets a limit for the amount of temporary storage disk space used by tasks. This limit is currently only respected by MSQ tasks.
* Removes query context parameters intermediateSuperSorterStorageMaxLocalBytes and composedIntermediateSuperSorterStorageEnabled. Composed intermediate super sorter (which was enabled by composedIntermediateSuperSorterStorageEnabled) is now enabled automatically if durableShuffleStorage is set to true. intermediateSuperSorterStorageMaxLocalBytes is calculated from the limit set by the run time parameter druid.indexer.task.tmpStorageBytesPerTask.
* "maxResultsSize" has been removed from the S3OutputConfig and a default "chunkSize" of 100MiB is now present. This change primarily affects users who wish to use durable storage for MSQ jobs.
This commit adds attributes that contain metadata information about the query
in the EXPLAIN PLAN output. The attributes currently contain two items:
- `statementTyp`: SELECT, INSERT or REPLACE
- `targetDataSource`: provides the target datasource name for DML statements
It is added to both the legacy and native query plan outputs.
* Make the tasks run with only a single directory
There was a change that tried to get indexing to run on multiple disks
It made a bunch of changes to how tasks run, effectively hiding the
"safe" directory for tasks to write files into from the task code itself
making it extremely difficult to do anything correctly inside of a task.
This change reverts those changes inside of the tasks and makes it so that
only the task runners are the ones that make decisions about which
mount points should be used for storing task-related files.
It adds the config druid.worker.baseTaskDirs which can be used by the
task runners to know which directories they should schedule tasks inside of.
The TaskConfig remains the authoritative source of configuration for where
and how an individual task should be operating.
* Frames: Ensure nulls are read as default values when appropriate.
Fixes a bug where LongFieldWriter didn't write a properly transformed
zero when writing out a null. This had no meaningful effect in SQL-compatible
null handling mode, because the field would get treated as a null anyway.
But it does have an effect in default-value mode: it would cause Long.MIN_VALUE
to get read out instead of zero.
Also adds NullHandling checks to the various frame-based column selectors,
allowing reading of nullable frames by servers in default-value mode.
* use new sampler features
* supprot kafka format
* update DQT, fix tests
* prefer non numeric formats
* fix input format step
* boost SQL data loader
* delete dimension in auto discover mode
* inline example specs
* feedback updates
* yeet the format into valueFormat when switching to kafka
* kafka format is now a toggle
* even better form layout
* rename
* Update api.md
I have created changes in api call of python according to latest version of requests 2.28.1 library. Along with this there are some irregularities between use of <your-instance> and <hostname> so I have tried to fix that also.
* Update api.md
made some changes in declaring USER and PASSWORD
* Add a new fault "QueryRuntimeError" to MSQ engine to capture native query errors.
* Fixed bug in MSQ fault tolerance where worker were being retried if `UnexpectedMultiValueDimensionException` was thrown.
* An exception from the query runtime with `org.apache.druid.query` as the package name is thrown as a QueryRuntimeError
This PR is a follow-up to #13819 so that the Tuple sketch functionality can be used in SQL for both ingestion using Multi-Stage Queries (MSQ) and also for analytic queries against Tuple sketch columns.
Abhishek Agarwal
317brian
YongGang
Nhi Pham
Soumyava
zachjsh
Laksh Singla
Tejaswini Bandlamudi
Clint Wylie
Suneet Saldanha
Abhishek Radhakrishnan
Victoria Lim
Kashif Faraz
George Shiqi Wu
Gian Merlino
Jason Koch
Katya Macedo
slfan1989
Karan Kumar
Jaehui Lee
Atul Mohan
cristian-popa
hqx871
Jakub Matyszewski
Adarsh Sanjeev
Rishabh Singh
George Shiqi Wu
Maytas Monsereenusorn
Harini Rajendran
Andreas Maechler
Pramod Immaneni
Peter Marshall
imply-cheddar
Shingo Kitagawa
Vadim Ogievetsky
Jill Osborne
TSFenwick
Charles Smith
Suraj Sanjay Kadam
Paul Rogers
Nicholas Lippis
frankgrimes97