Refactors the SemanticCreator annotation.
Moves the interface to the semantic package.
Create a SemanticUtils to hold logic for storing semantic maps.
Add FrameMaker interface.
This PR adds checks for verification of DataSourceCompactionConfig and CompactionTask with msq engine to ensure:
each aggregator in metricsSpec is idempotent
metricsSpec is non-null when rollup is set to true
Unit tests and existing compaction ITs have been updated accordingly.
This PR adds indexer-level task metrics-
"indexer/task/failed/count"
"indexer/task/success/count"
the current "worker/task/completed/count" metric shows all the tasks completed irrespective of success or failure status so these metrics would help us get more visibility into the status of the completed tasks
Follow-up to #16291, this commit enables a subset of existing native compaction ITs on the MSQ engine.
In the process, the following changes have been introduced in the MSQ compaction flow:
- Populate `metricsSpec` in `CompactionState` from `querySpec` in `MSQControllerTask` instead of `dataSchema`
- Add check for pre-rolled-up segments having `AggregatorFactory` with different input and output column names
- Fix passing missing cluster-by clause in scan queries
- Add annotation of `CompactionState` to tombstone segments
Changes:
- Add API `/druid/coordinator/v1/config/compaction/global` to update cluster level compaction config
- Add class `CompactionConfigUpdateRequest`
- Fix bug in `CoordinatorCompactionConfig` which caused compaction engine to not be persisted.
Use json field name `engine` instead of `compactionEngine` because JSON field names must align
with the getter name.
- Update MSQ validation error messages
- Complete overhaul of `CoordinatorCompactionConfigResourceTest` to remove unnecessary mocking
and add more meaningful tests.
- Add `TuningConfigBuilder` to easily build tuning configs for tests.
- Add `DatasourceCompactionConfigBuilder`
changes:
* removes `druid.indexer.task.batchProcessingMode` in favor of always using `CLOSED_SEGMENT_SINKS` which uses `BatchAppenderator`. This was intended to become the default for native batch, but that was missed so `CLOSED_SEGMENTS` was the default (using `AppenderatorImpl`), however MSQ has been exclusively using `BatchAppenderator` with no problems so it seems safe to just roll it out as the only option for batch ingestion everywhere.
* with `batchProcessingMode` gone, there is no use for `AppenderatorImpl` so it has been removed
* implify `Appenderator` construction since there are only separate stream and batch versions now
* simplify tests since `batchProcessingMode` is gone
changes:
* removed `Firehose` and `FirehoseFactory` and remaining implementations which were mostly no longer used after #16602
* Moved `IngestSegmentFirehose` which was still used internally by Hadoop ingestion to `DatasourceRecordReader.SegmentReader`
* Rename `SQLFirehoseFactoryDatabaseConnector` to `SQLInputSourceDatabaseConnector` and similar renames for sub-classes
* Moved anything remaining in a 'firehose' package somewhere else
* Clean up docs on firehose stuff
Description:
Overlord guice dependencies are currently a little difficult to plug into.
This was encountered while working on a separate PR where a class needed to depend
on `TaskMaster.getTaskQueue()` to query some task related info but this class itself
needs to be a dependency of `TaskMaster` so that it can be registered to the leader lifecycle.
The approach taken here is to simply decouple the leadership lifecycle of the overlord from
manipulation or querying of its state.
Changes:
- No functional change
- Add new class `DruidOverlord` to contain leadership logic after the model of `DruidCoordinator`
- The new class `DruidOverlord` should not be a dependency of any class with the exception of
REST endpoint `*Resource` classes.
- All classes that need to listen to leadership changes must be a dependency of `DruidOverlord`
so that they can be registered to the leadership lifecycle.
- Move all querying logic from `OverlordResource` to `TaskQueryTool` so that other classes can
leverage this logic too (required for follow up PR).
- Update tests
Changes:
- Do not hold a reference to `TaskQueue` in `TaskStorageQueryAdapter`
- Use `TaskStorage` instead of `TaskStorageQueryAdapter` in `IndexerMetadataStorageAdapter`
- Rename `TaskStorageQueryAdapter` to `TaskQueryTool`
- Fix newly added task actions `RetrieveUpgradedFromSegmentIds` and `RetrieveUpgradedToSegmentIds`
by removing `isAudited` method.
Description:
Task action audit logging was first deprecated and disabled by default in Druid 0.13, #6368.
As called out in the original discussion #5859, there are several drawbacks to persisting task action audit logs.
- Only usage of the task audit logs is to serve the API `/indexer/v1/task/{taskId}/segments`
which returns the list of segments created by a task.
- The use case is really narrow and no prod clusters really use this information.
- There can be better ways of obtaining this information, such as the metric
`segment/added/bytes` which reports both the segment ID and task ID
when a segment is committed by a task. We could also include committed segment IDs in task reports.
- A task persisting several segments would bloat up the audit logs table putting unnecessary strain
on metadata storage.
Changes:
- Remove `TaskAuditLogConfig`
- Remove method `TaskAction.isAudited()`. No task action is audited anymore.
- Remove `SegmentInsertAction` as it is not used anymore. `SegmentTransactionalInsertAction`
is the new incarnation which has been in use for a while.
- Deprecate `MetadataStorageActionHandler.addLog()` and `getLogs()`. These are not used anymore
but need to be retained for backward compatibility of extensions.
- Do not create `druid_taskLog` metadata table anymore.
Changes:
- Break `NewestSegmentFirstIterator` into two parts
- `DatasourceCompactibleSegmentIterator` - this contains all the code from `NewestSegmentFirstIterator`
but now handles a single datasource and allows a priority to be specified
- `PriorityBasedCompactionSegmentIterator` - contains separate iterator for each datasource and
combines the results into a single queue to be used by a compaction search policy
- Update `NewestSegmentFirstPolicy` to use the above new classes
- Cleanup `CompactionStatistics` and `AutoCompactionSnapshot`
- Cleanup `CompactSegments`
- Remove unused methods from `Tasks`
- Remove unneeded `TasksTest`
- Move tests from `NewestSegmentFirstIteratorTest` to `CompactionStatusTest`
and `DatasourceCompactibleSegmentIteratorTest`
Changes:
- No functional change
- Add class `TuningConfigBuilder` to build `IndexTuningConfig`, `CompactionTuningConfig`
- Remove old class `ParallelIndexTestingFactory.TuningConfigBuilder`
- Remove some unused fields and methods
Changes
- No functional change
- Remove unused method `IndexTuningConfig.withPartitionsSpec()`
- Remove unused method `ParallelIndexTuningConfig.withPartitionsSpec()`
- Remove redundant method `CompactTask.emitIngestionModeMetrics()`
- Remove Clock argument from `CompactionTask.createDataSchemasForInterval()` as it was only needed
for one test which was just verifying the value passed by the test itself. The code now uses a `Stopwatch`
instead and test simply verifies that the metric has been emitted.
- Other minor cleanup changes
Description:
Compaction operations issued by the Coordinator currently run using the native query engine.
As majority of the advancements that we are making in batch ingestion are in MSQ, it is imperative
that we support compaction on MSQ to make Compaction more robust and possibly faster.
For instance, we have seen OOM errors in native compaction that MSQ could have handled by its
auto-calculation of tuning parameters.
This commit enables compaction on MSQ to remove the dependency on native engine.
Main changes:
* `DataSourceCompactionConfig` now has an additional field `engine` that can be one of
`[native, msq]` with `native` being the default.
* if engine is MSQ, `CompactSegments` duty assigns all available compaction task slots to the
launched `CompactionTask` to ensure full capacity is available to MSQ. This is to avoid stalling which
could happen in case a fraction of the tasks were allotted and they eventually fell short of the number
of tasks required by the MSQ engine to run the compaction.
* `ClientCompactionTaskQuery` has a new field `compactionRunner` with just one `engine` field.
* `CompactionTask` now has `CompactionRunner` interface instance with its implementations
`NativeCompactinRunner` and `MSQCompactionRunner` in the `druid-multi-stage-query` extension.
The objectmapper deserializes `ClientCompactionRunnerInfo` in `ClientCompactionTaskQuery` to the
`CompactionRunner` instance that is mapped to the specified type [`native`, `msq`].
* `CompactTask` uses the `CompactionRunner` instance it receives to create the indexing tasks.
* `CompactionTask` to `MSQControllerTask` conversion logic checks whether metrics are present in
the segment schema. If present, the task is created with a native group-by query; if not, the task is
issued with a scan query. The `storeCompactionState` flag is set in the context.
* Each created `MSQControllerTask` is launched in-place and its `TaskStatus` tracked to determine the
final status of the `CompactionTask`. The id of each of these tasks is the same as that of `CompactionTask`
since otherwise, the workers will be unable to determine the controller task's location for communication
(as they haven't been launched via the overlord).
Changes:
- Rename `UsedSegmentChecker` to `PublishedSegmentsRetriever`
- Remove deprecated single `Interval` argument from `RetrieveUsedSegmentsAction`
as it is now unused and has been deprecated since #1988
- Return `Set` of segments instead of a `Collection` from `IndexerMetadataStorageCoordinator.retrieveUsedSegments()`
* first pass
* more changes
* fix tests and formatting
* fix kinesis failing tests
* fix kafka tests
* add dimension name to float parse errors
* double and convertToType handling of dimensionName can report parse errors with dimension name
* fix checkstyle issue
* fix tests
* more cases to have better parse exception messages
* fix test
* fix tests
* partially address comments
* annotate method parameter with nullable
* address comments
* fix tests
* let float, double, long dimensionIndexer pass dimensionName down to dimensionHandlerUtils
* fix compilation error and clean up formatting
* clean up whitespace
* address feedback. undo change, pass down report parse exception for convertToType
* fix test
* Support ListBasedInputRow in Kafka ingestion with header
* Fix up buildBlendedEventMap
* Add new test for KafkaInputFormat with csv value and headers
* Do not use forbidden APIs
* Move utility method to TestUtils
index_realtime tasks were removed from the documentation in #13107. Even
at that time, they weren't really documented per se— just mentioned. They
existed solely to support Tranquility, which is an obsolete ingestion
method that predates migration of Druid to ASF and is no longer being
maintained. Tranquility docs were also de-linked from the sidebars and
the other doc pages in #11134. Only a stub remains, so people with
links to the page can see that it's no longer recommended.
index_realtime_appenderator tasks existed in the code base, but were
never documented, nor as far as I am aware were they used for any purpose.
This patch removes both task types completely, as well as removes all
supporting code that was otherwise unused. It also updates the stub
doc for Tranquility to be firmer that it is not compatible. (Previously,
the stub doc said it wasn't recommended, and pointed out that it is
built against an ancient 0.9.2 version of Druid.)
ITUnionQueryTest has been migrated to the new integration tests framework and updated to use Kafka ingestion.
Co-authored-by: Gian Merlino <gianmerlino@gmail.com>
Changes:
- Add new task action `RetrieveSegmentsByIdAction`
- Use new task action to retrieve segments irrespective of their visibility
- During rolling upgrades, this task action would fail as Overlord would be on old version
- If new action fails, fall back to just fetching used segments as before
Update default value of druid.indexer.tasklock.batchAllocationWaitTime to 0.
Thus, a segment allocation request is processed immediately unless there are already some requests queued before this one. While in queue, a segment allocation request may get clubbed together with other similar requests into a batch to reduce load on the metadata store.
* Handle null values of centralized schema config in PartialMergeTask
* Fix checkstyle
* Do not pass centralized schema config from supervisor task to sub-tasks
* Do not pass ObjectMapper in constructor of task
* Fix logs
* Fix tests
* Fix task bootstrap locations.
* Remove dependency of SegmentCacheManager from SegmentLoadDropHandler.
- The load drop handler code talks to the local cache manager via
SegmentManager.
* Clean up unused imports and stuff.
* Test fixes.
* Intellij inspections and test bind.
* Clean up dependencies some more
* Extract test load spec and factory to its own class.
* Cleanup test util
* Pull SegmentForTesting out to TestSegmentUtils.
* Fix up.
* Minor changes to infoDir
* Replace server announcer mock and verify that.
* Add tests.
* Update javadocs.
* Address review comments.
* Separate methods for download and bootstrap load
* Clean up return types and exception handling.
* No callback for loadSegment().
* Minor cleanup
* Pull out the test helpers into its own static class so it can have better state control.
* LocalCacheManager stuff
* Fix build.
* Fix build.
* Address some CI warnings.
* Minor updates to javadocs and test code.
* Address some CodeQL test warnings and checkstyle fix.
* Pass a Consumer<DataSegment> instead of boolean & rename variables.
* Small updates
* Remove one test constructor.
* Remove the other constructor that wasn't initializing fully and update usages.
* Cleanup withInfoDir() builder and unnecessary test hooks.
* Remove mocks and elaborate on comments.
* Commentary
* Fix a few Intellij inspection warnings.
* Suppress corePoolSize intellij-inspect warning.
The intellij-inspect tool doesn't seem to correctly inspect
lambda usages. See ScheduledExecutors.
* Update docs and add more tests.
* Use hamcrest for asserting order on expectation.
* Shutdown bootstrap exec.
* Fix checkstyle
This PR updates CompactionTask to not load any lookups by default, unless transformSpec is present.
If transformSpec is present, we will make the decision based on context values, loading all lookups by default. This is done to ensure backward compatibility since transformSpec can reference lookups.
If transform spec is not present and no context value is passed, we donot load any lookup.
This behavior can be overridden by supplying lookupLoadingMode and lookupsToLoad in the task context.
Changes:
- Remove `SegmentLockReleaseAction` as it is not used anywhere.
It is not even registered as a known sub-type of `TaskAction`.
- Minor refactor in `TaskLockbox`. No functional change.
- Remove `ExpectedException` from `TaskLockboxTest`
Changes:
- Remove deprecated `markAsUnused` parameter from `KillUnusedSegmentsTask`
- Allow `kill` task to use `REPLACE` lock when `useConcurrentLocks` is true
- Use `EXCLUSIVE` lock by default
Description:
All the streaming ingestion tasks for a given datasource share the same lock for a given interval.
Changing lock types in the supervisor can lead to segment allocation errors due to lock conflicts
for the new tasks while the older tasks are still running.
Fix:
Allow locks of different types (EXCLUSIVE, SHARED, APPEND, REPLACE) to co-exist if they have
the same interval and the same task group.
* MSQ controller: Support in-memory shuffles; towards JVM reuse.
This patch contains two controller changes that make progress towards a
lower-latency MSQ.
First, support for in-memory shuffles. The main feature of in-memory shuffles,
as far as the controller is concerned, is that they are not fully buffered. That
means that whenever a producer stage uses in-memory output, its consumer must run
concurrently. The controller determines which stages run concurrently, and when
they start and stop.
"Leapfrogging" allows any chain of sort-based stages to use in-memory shuffles
even if we can only run two stages at once. For example, in a linear chain of
stages 0 -> 1 -> 2 where all do sort-based shuffles, we can use in-memory shuffling
for each one while only running two at once. (When stage 1 is done reading input
and about to start writing its output, we can stop 0 and start 2.)
1) New OutputChannelMode enum attached to WorkOrders that tells workers
whether stage output should be in memory (MEMORY), or use local or durable
storage.
2) New logic in the ControllerQueryKernel to determine which stages can use
in-memory shuffling (ControllerUtils#computeStageGroups) and to launch them
at the appropriate time (ControllerQueryKernel#createNewKernels).
3) New "doneReadingInput" method on Controller (passed down to the stage kernels)
which allows stages to transition to POST_READING even if they are not
gathering statistics. This is important because it enables "leapfrogging"
for HASH_LOCAL_SORT shuffles, and for GLOBAL_SORT shuffles with 1 partition.
4) Moved result-reading from ControllerContext#writeReports to new QueryListener
interface, which ControllerImpl feeds results to row-by-row while the query
is still running. Important so we can read query results from the final
stage using an in-memory channel.
5) New class ControllerQueryKernelConfig holds configs that control kernel
behavior (such as whether to pipeline, maximum number of concurrent stages,
etc). Generated by the ControllerContext.
Second, a refactor towards running workers in persistent JVMs that are able to
cache data across queries. This is helpful because I believe we'll want to reuse
JVMs and cached data for latency reasons.
1) Move creation of WorkerManager and TableInputSpecSlicer to the
ControllerContext, rather than ControllerImpl. This allows managing workers and
work assignment differently when JVMs are reusable.
2) Lift the Controller Jersey resource out from ControllerChatHandler to a
reusable resource.
3) Move memory introspection to a MemoryIntrospector interface, and introduce
ControllerMemoryParameters that uses it. This makes it easier to run MSQ in
process types other than Indexer and Peon.
Both of these areas will have follow-ups that make similar changes on the
worker side.
* Address static checks.
* Address static checks.
* Fixes.
* Report writer tests.
* Adjustments.
* Fix reports.
* Review updates.
* Adjust name.
* Small changes.
This PR fixes the first and last vector aggregators and improves their readability. Following changes are introduced
The folding is broken in the vectorized versions. We consider time before checking the folded object.
If the numerical aggregator gets passed any other object type for some other reason (like String), then the aggregator considers it to be folded, even though it shouldn’t be. We should convert these objects to the desired type, and aggregate them properly.
The aggregators must properly use generics. This would minimize the ClassCastException issues that can happen with mixed segment types. We are unifying the string first/last aggregators with numeric versions as well.
The aggregators must aggregate null values (https://github.com/apache/druid/blob/master/processing/src/main/java/org/apache/druid/query/aggregation/first/StringFirstLastUtils.java#L55-L56 ). The aggregator should only ignore pairs with time == null, and not value == null
Time nullity is ignored when trying to vectorize the data.
String versions initialized with DateTimes.MIN that is equal to Long.MIN / 2. This can cause incorrect results in case the user enters a custom time column. NOTE: This is still present because it would require a larger refactor in all of the versions.
There is a difference in what users might expect from the results because the code flow is changed (for example, the direction of the for loops, etc), however, this will only change the results, and not the contract set by first/last aggregators, which is that if multiple values have the same timestamp, then any of them can get picked.
If the column is non-existent, the users might expect a change in the timestamp from DateTime.MAX to Long.MAX, because the code incorrectly used DateTime.MAX to initialize the aggregator, however, in case of a custom timestamp column, this might not be the case. The SQL query might be prohibited from using any Long since it requires a cast to the timestamp function that can fail, but AFAICT native queries don't have such limitations.
Changes:
- Add new config `lagAggregate` to `LagBasedAutoScalerConfig`
- Add field `aggregateForScaling` to `LagStats`
- Use the new field/config to determine which aggregate to use to compute lag
- Remove method `Supervisor.computeLagForAutoScaler()`
Changes:
- Add `LookupLoadingSpec` to support 3 modes of lookup loading: ALL, NONE, ONLY_REQUIRED
- Add method `Task.getLookupLoadingSpec()`
- Do not load any lookups for `KillUnusedSegmentsTask`
Changes:
1) Check for handoff of upgraded realtime segments.
2) Drop sink only when all associated realtime segments have been abandoned.
3) Delete pending segments upon commit to prevent unnecessary upgrades and
partition space exhaustion when a concurrent replace happens. This also prevents
potential data duplication.
4) Register pending segment upgrade only on those tasks to which the segment is associated.
Issue: #14989
The initial step in optimizing segment metadata was to centralize the construction of datasource schema in the Coordinator (#14985). Thereafter, we addressed the problem of publishing schema for realtime segments (#15475). Subsequently, our goal is to eliminate the requirement for regularly executing queries to obtain segment schema information.
This is the final change which involves publishing segment schema for finalized segments from task and periodically polling them in the Coordinator.
Changes:
- Use non-static fields to track task counts in `ForkingTaskRunner`
- Update assertions in `ForkingTaskRunnerTest` to ensure that the tests are idempotent
Tries to address the comments made on #16284 after merged.
Changes:
- Remove method `Supervisor.getLagMetric()`
- Add method `Supervisor.computeLagForAutoScaler()`
- Remove classes `LagMetric` and `LagMetricTest`
Adarsh Sanjeev
Vishesh Garg
Rushikesh Bankar
AmatyaAvadhanula
Kashif Faraz
Clint Wylie
Tom
Maytas Monsereenusorn
Abhishek Radhakrishnan
George Shiqi Wu
Akshat Jain
Igor Berman
Rishabh Singh
Gian Merlino
Laksh Singla
Alberic Liu
Adithya Chakilam
Arun Ramani
YongGang