Better fallback strategy when the broker is unable to materialize the subquery's results as frames for estimating the bytes:
a. We don't touch the subquery sequence till we know that we can materialize the result as frames
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).
Motivation:
- Improve code hygeiene
- Make `SegmentLoadDropHandler` easily extensible
Changes:
- Add `SegmentBootstrapper`
- Move code for bootstrapping segments already cached on disk and fetched from coordinator to
`SegmentBootstrapper`.
- No functional change
- Use separate executor service in `SegmentBootstrapper`
- Bind `SegmentBootstrapper` to `ManageLifecycle` explicitly in `CliBroker`, `CliHistorical` etc.
Previously, the segment granularity for tables in the catalog had to be defined in period format, ie `'PT1H'` , `'P1D'`, etc. This disallows a user from defining segment granularity of `'ALL'` for a table in the catalog, which may be a valid use case. This change makes it so that a user may define the segment granularity of a table in the catalog, as any string that results in a valid granularity using either the `Granularity.fromString(str)` method, or `new PeriodGranularity(new Period(value), null, null)`, and that granularity maps to a standard supported granularity, where `GranularityType.isStandard(granularity)` returns true. As a result a user may who wants to assign a catalog table's segment granularity to be hourly, may assign the segment granularity property of the table to be either `PT1H`, or `HOUR`. These are the same formats accepted at query time.
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()`
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>
* Initial support for bootstrap segments.
- Adds a new API in the coordinator.
- All processes that have storage locations configured (including tasks)
talk to the coordinator if they can, and fetch bootstrap segments from it.
- Then load the segments onto the segment cache as part of startup.
- This addresses the segment bootstrapping logic required by processes before
they can start serving queries or ingesting.
This patch also lays the foundation to speed up upgrades.
* Fail open by default if there are any errors talking to the coordinator.
* Add test for failure scenario and cleanup logs.
* Cleanup and add debug log
* Assert the events so we know the list exactly.
* Revert RunRules test.
The rules aren't evaluated if there are no clusters.
* Revert RunRulesTest too.
* Remove debug info.
* Make the API POST and update log.
* Fix up UTs.
* Throw 503 from MetadataResource; clean up exception handling and DruidException.
* Remove unused logger, add verification of metrics and docs.
* Update error message
* Update server/src/main/java/org/apache/druid/server/coordination/SegmentLoadDropHandler.java
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
* Apply suggestions from code review
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
* Adjust test metric expectations with the rename.
* Add BootstrapSegmentResponse container in the response for future extensibility.
* Rename to BootstrapSegmentsInfo for internal consistency.
* Remove unused log.
* Use a member variable for broadcast segments instead of segmentAssigner.
* Minor cleanup
* Add test for loadable bootstrap segments and clarify comment.
* Review suggestions.
---------
Co-authored-by: Kashif Faraz <kashif.faraz@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
* Fix flakey BrokerClientTest.
The testError() method reliably fails in the IDE. This is because the
the test runner has
<surefire.rerunFailingTestsCount>3</surefire.rerunFailingTestsCount> is set to 3, so maven
retries this "flaky test" multiple times and the test code returns a successful response
in the third attempt.
The exception handling in BrokerClientTest was broken:
- All non-2xx errors were being turned as 5xx errors. Remove that block of
code. If we need to handle retries of more specific 5xx error codes, that should be
hanlded explicitly. Or if there's a source of 4xx class error that needs to be 5xx,
fix that in the source of error.
* Fix CodeQL warning for unused parameter.
* 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
* Router: Authorize permissionless internal requests.
Router-internal requests like /proxy/enabled and errors for invalid
requests should not require permissions, but they still need to be
authorized in order to satisfy the PreResponseAuthorizationCheckFilter.
This patch adds authorization checks that do not require any particular
permissions.
* Fix tests.
* Add interface method for returning canonical lookup name
* Address review comment
* Add test in LookupReferencesManagerTest for coverage check
* Add test in LookupSerdeModuleTest for coverage check
* 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
* fix issue with auto column grouping
changes:
* fixes bug where AutoTypeColumnIndexer reports incorrect cardinality, allowing it to incorrectly use array grouper algorithm for realtime queries producing incorrect results for strings
* fixes bug where auto LONG and DOUBLE type columns incorrectly report not having null values, resulting in incorrect null handling when grouping
* fix test
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 deprecated `markAsUnused` parameter from `KillUnusedSegmentsTask`
- Allow `kill` task to use `REPLACE` lock when `useConcurrentLocks` is true
- Use `EXCLUSIVE` lock by default
This parameter has been removed for awhile now as of Druid 0.23.0
https://github.com/apache/druid/pull/12187.
The code was only used in tests to verify that serialization works.
Now remove all references to avoid any confusion.
* * add another catalog clustering columns unit test
* * dissallow clusterKeys with descending order
* * make more clear that clustering is re-written into ingest node
whether a catalog table or not
* * when partitionedBy is stored in catalog, user shouldnt need to specify
it in order to specify clustering
* * fix intellij inspection failure
Changes:
- Rename `DataSegmentChangeRequestAndStatus` to `DataSegmentChangeResponse`
- Rename `SegmentLoadDropHandler.Status` to `SegmentChangeStatus`
- Remove method `CoordinatorRunStats.getSnapshotAndReset()` as it was used only in
load queue peon implementations. Using an atomic reference is much simpler.
- Remove `ServerTestHelper.MAPPER`. Use existing `TestHelper.makeJsonMapper()` instead.
* 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.
AmatyaAvadhanula
Laksh Singla
Vishesh Garg
Kashif Faraz
Abhishek Radhakrishnan
zachjsh
Rishabh Singh
Clint Wylie
Zoltan Haindrich
razinbouzar
Gian Merlino
Akshat Jain
George Shiqi Wu
kaisun2000
Igor Berman
Alberic Liu
Adithya Chakilam
Andreas Maechler
Adarsh Sanjeev