Bug:
In the `MarkOvershadowedSegmentsAsUnused` duty, the coordinator marks a segment
as unused if it is overshadowed by a segment currently being served by a historical or broker.
But it is possible to have segments that are eligible for a load rule but require zero replicas
to be loaded. (Such segments can be queried only using the MSQ engine).
If such a zero-replica segment overshadows any other segment, the overshadowed segment will
never be marked as unused and will continue to exist in the metadata store as a dangling segment.
Fix:
- In a coordinator run, keep track of segments that are eligible for a load rule but require zero replicas
- Allow the zero-replicas segments to overshadow old segments and hence mark the latter as unused
Other changes:
- Add simulation test to verify new behaviour. This test fails with the current code.
- Clean up javadocs
Changes:
Add the following indexer level task metrics:
- `worker/task/running/count`
- `worker/task/assigned/count`
- `worker/task/completed/count`
These metrics will provide more visibility into the tasks distribution across indexers
(We often see a task skew issue across indexers and with this issue it would be easier
to catch the imbalance)
* Mark used and unused APIs by versions.
* remove the conditional invocations.
* isValid() and test updates.
* isValid() and tests.
* Remove warning logs for invalid user requests. Also, downgrade visibility.
* Update resp message, etc.
* tests and some cleanup.
* Docs draft
* Clarify docs
* Update server/src/main/java/org/apache/druid/server/http/DataSourcesResource.java
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
* Review comments
* Remove default interface methods only used in tests and update docs.
* Clarify javadocs and @Nullable.
* Add more tests.
* Parameterized versions.
---------
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
* Update Calcite*Test to use junit5
* change the way temp dirs are handled
* add openrewrite workflow to safeguard upgrade
* replace junitparamrunner with standard junit5 parametered tests
* update a few rules to junit5 api
* lots of boring changes
* cleanup QueryLogHook
* cleanup
* fix compile error: ARRAYS_DATASOURCE
* fix test
* remove enclosed
* empty
+TEST:TDigestSketchSqlAggregatorTest,HllSketchSqlAggregatorTest,DoublesSketchSqlAggregatorTest,ThetaSketchSqlAggregatorTest,ArrayOfDoublesSketchSqlAggregatorTest,BloomFilterSqlAggregatorTest,BloomDimFilterSqlTest,CatalogIngestionTest,CatalogQueryTest,FixedBucketsHistogramQuantileSqlAggregatorTest,QuantileSqlAggregatorTest,MSQArraysTest,MSQDataSketchesTest,MSQExportTest,MSQFaultsTest,MSQInsertTest,MSQLoadedSegmentTests,MSQParseExceptionsTest,MSQReplaceTest,MSQSelectTest,InsertLockPreemptedFaultTest,MSQWarningsTest,SqlMSQStatementResourcePostTest,SqlStatementResourceTest,CalciteSelectJoinQueryMSQTest,CalciteSelectQueryMSQTest,CalciteUnionQueryMSQTest,MSQTestBase,VarianceSqlAggregatorTest,SleepSqlTest,SqlRowTransformerTest,DruidAvaticaHandlerTest,DruidStatementTest,BaseCalciteQueryTest,CalciteArraysQueryTest,CalciteCorrelatedQueryTest,CalciteExplainQueryTest,CalciteExportTest,CalciteIngestionDmlTest,CalciteInsertDmlTest,CalciteJoinQueryTest,CalciteLookupFunctionQueryTest,CalciteMultiValueStringQueryTest,CalciteNestedDataQueryTest,CalciteParameterQueryTest,CalciteQueryTest,CalciteReplaceDmlTest,CalciteScanSignatureTest,CalciteSelectQueryTest,CalciteSimpleQueryTest,CalciteSubqueryTest,CalciteSysQueryTest,CalciteTableAppendTest,CalciteTimeBoundaryQueryTest,CalciteUnionQueryTest,CalciteWindowQueryTest,DecoupledPlanningCalciteJoinQueryTest,DecoupledPlanningCalciteQueryTest,DecoupledPlanningCalciteUnionQueryTest,DrillWindowQueryTest,DruidPlannerResourceAnalyzeTest,IngestTableFunctionTest,QueryTestRunner,SqlTestFrameworkConfig,SqlAggregationModuleTest,ExpressionsTest,GreatestExpressionTest,IPv4AddressMatchExpressionTest,IPv4AddressParseExpressionTest,IPv4AddressStringifyExpressionTest,LeastExpressionTest,TimeFormatOperatorConversionTest,CombineAndSimplifyBoundsTest,FiltrationTest,SqlQueryTest,CalcitePlannerModuleTest,CalcitesTest,DruidCalciteSchemaModuleTest,DruidSchemaNoDataInitTest,InformationSchemaTest,NamedDruidSchemaTest,NamedLookupSchemaTest,NamedSystemSchemaTest,RootSchemaProviderTest,SystemSchemaTest,CalciteTestBase,SqlResourceTest
* use @Nested
* add rule to remove enclosed; upgrade surefire
* remove enclosed
* cleanup
* add comment about surefire exclude
* Add update() in TestDerbyConnectorRule
* use common function.
* fixup build.
* fixup indentations.
* Revert "fixup indentations."
This reverts commit a9d6b73e79.
* fixup indentataions.
* Remove Thread.sleep() by directly calling updateUsedStatusLastUpdated.
* another indentation slip.
* Move common segment initialization to setup().
* Fix for checkstyle.
* review comments: indentation fixes, type.
* Wrapper class for Segments table
* Add KillUnusedSegmentsTaskBuilder in test class
* Remove javadocs for self-explanatory methods.
Changes:
- Remove deprecated `DruidException` (old one) and `EntryExistsException`
- Use newly added comprehensive `DruidException` instead
- Update error message in `SqlMetadataStorageActionHandler` when max packet limit is violated.
- Factor out common code from several faults into `BaseFault`.
- Slightly update javadoc in `DruidException` to render it correctly
- Remove unused classes `SegmentToMove`, `SegmentToDrop`
- Move `ServletResourceUtils` from module `druid-processing` to `druid-server`
- Add utility method to build error Response from `DruidException`.
Changes
- Replace usages of `UnknownSegmentIdsException` with `DruidException`
- Add method `SqlMetadataQuery.retrieveSegments`
- Add new field `used` to `DataSegmentPlus`
* Kill task version support.
Kill tasks by default kill all versions of unused segments in the specified
interval. Users wanting to delete specific versions (for example, data compliance
reasons) and keep rest of the versions can specify the optional version in the
kill task payload.
* Formatting changes.
* Multi version tests in RetrieveSegmentsActionsTest
Sort of like method-level parameterized tests.
* Address review feedback
* Accept a list of versions instead of a single version.
Support multiple versions.
* Tests for multiple versions.
* Update docs
* Cleanup
* Address review comments.
Retain the old interface method and make it default and route it to
the method with nullable versions variant. Update usages to use the
default method where versions doesn't matter.
* Remove versions from retreive used segments action.
* Some updates.
* Apply suggestions from code review
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
* /s/actual/observed/g
* minor test cleanup
* WIP: Test fixes and updates. Also add test for kill by version with used load spec.
Checkpoint.
---------
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
Changes:
- Use an actual SqlSegmentsMetadataManager instead of TestSqlSegmentsMetadataManager
- Simplify TestSegmentsMetadataManager
- Add a test for large interval segments.
Changes:
Improve `SqlSegmentsMetadataManager`
- Break the loop in `populateUsedStatusLastUpdated` before going to sleep if there are no more segments to update
- Add comments and clean up logs
Refactor `SqlSegmentsMetadataManagerTest`
- Merge `SqlSegmentsMetadataManagerEmptyTest` into this test
- Add method `testPollEmpty`
- Shave a few seconds off of the tests by reducing poll duration
- Simplify creation of test segments
- Some renames here and there
- Remove unused methods
- Move `TestDerbyConnector.allowLastUsedFlagToBeNull` to this class
Other minor changes
- Add javadoc to `NoneShardSpec`
- Use lambda in `SqlSegmentMetadataPublisher`
While converting Sequence<ScanResultValue> to Sequence<Frames>, when maxSubqueryBytes is enabled, we batch the results to prevent creating a single frame per ScanResultValue. Batching requires peeking into the actual value, and checking if the row signature of the scan result’s value matches that of the previous value.
Since we can do this indefinitely (in the worst case all of them have the same signature), we keep fetching them and accumulating them in a list (on the heap). We don’t really know how much to batch before we actually write the value as frames.
The PR modifies the batching logic to not accumulate the results in an intermediary list
BaseNodeRoleWatcher counts down cacheInitialized after a timeout, but also sets some flag that it was a timed-out initialization. and call nodeViewInitializationTimedOut (new method on listeners) instead of nodeViewInitialized. Then listeners can do what is most appropriate with this information.
* Move retries into DataSegmentPusher implementations.
The individual implementations know better when they should and should
not retry. They can also generate better error messages.
The inspiration for this patch was a situation where EntityTooLarge was
generated by the S3DataSegmentPusher, and retried uselessly by the
retry harness in PartialSegmentMergeTask.
* Fix missing var.
* Adjust imports.
* Tests, comments, style.
* Remove unused import.
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.
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.
* 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().
---------
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.
During ingestion, incremental segments are created in memory for the different time chunks and persisted to disk when certain thresholds are reached (max number of rows, max memory, incremental persist period etc). In the case where there are a lot of dimension and metrics (1000+) it was observed that the creation/serialization of incremental segment file format for persistence and persisting the file took a while and it was blocking ingestion of new data. This affected the real-time ingestion. This serialization and persistence can be parallelized across the different time chunks. This update aims to do that.
The patch adds a simple configuration parameter to the ingestion tuning configuration to specify number of persistence threads. The default value is 1 if it not specified which makes it the same as it is today.
Proposal #13469
Original PR #14024
A new method is being added in QueryLifecycle class to authorise a query based on authentication result.
This method is required since we authenticate the query by intercepting it in the grpc extension and pass down the authentication result.
### Description
The unusedSegment api response was extended to include the original DataSegment object with the creation time and last used update time added to it. A new object `DataSegmentPlus` was created for this purpose, and the metadata queries used were updated as needed.
example response:
```
[
{
"dataSegment": {
"dataSource": "inline_data",
"interval": "2023-01-02T00:00:00.000Z/2023-01-03T00:00:00.000Z",
"version": "2024-01-25T16:06:42.835Z",
"loadSpec": {
"type": "local",
"path": "/Users/zachsherman/projects/opensrc-druid/distribution/target/apache-druid-30.0.0-SNAPSHOT/var/druid/segments/inline_data/2023-01-02T00:00:00.000Z_2023-01-03T00:00:00.000Z/2024-01-25T16:06:42.835Z/0/index/"
},
"dimensions": "str_dim,double_measure1,double_measure2",
"metrics": "",
"shardSpec": {
"type": "numbered",
"partitionNum": 0,
"partitions": 1
},
"binaryVersion": 9,
"size": 1402,
"identifier": "inline_data_2023-01-02T00:00:00.000Z_2023-01-03T00:00:00.000Z_2024-01-25T16:06:42.835Z"
},
"createdDate": "2024-01-25T16:06:44.196Z",
"usedStatusLastUpdatedDate": "2024-01-25T16:07:34.909Z"
}
]
```
* Merge hydrant runners flatly for realtime queries.
Prior to this patch, we have two layers of mergeRunners for realtime
queries: one for each Sink (a logical segment) and one across all
Sinks. This is done because to keep metrics and results grouped by Sink,
given that each FireHydrant within a Sink has its own separate storage
adapter.
However, it costs extra memory usage due to the extra layer of
materialization. This is especially pronounced for groupBy queries,
which only use their merge buffers at the top layer of merging. The
lower layer of merging materializes ResultRows directly into the heap,
which can cause heap exhaustion if there are enough ResultRows.
This patch changes to a single layer of merging when bySegment: false,
just like Historicals. To accommodate that, segment metrics like
query/segment/time are now per-FireHydrant instead of per-Sink.
Two layers of merging are retained when bySegment: true. This isn't
common, because it's typically only used when segment level caching
is enabled on the Broker, which is off by default.
* Use SinkQueryRunners.
* Remove unused method.
* Kill tasks should honor the buffer period of unused segments.
- The coordinator duty KillUnusedSegments determines an umbrella interval
for each datasource to determine the kill interval. There can be multiple unused
segments in an umbrella interval with different used_status_last_updated timestamps.
For example, consider an unused segment that is 30 days old and one that is 1 hour old. Currently
the kill task after the 30-day mark would kill both the unused segments and not retain the 1-hour
old one.
- However, when a kill task is instantiated with this umbrella interval, it’d kill
all the unused segments regardless of the last updated timestamp. We need kill
tasks and RetrieveUnusedSegmentsAction to honor the bufferPeriod to avoid killing
unused segments in the kill interval prematurely.
* Clarify default behavior in docs.
* test comments
* fix canDutyRun()
* small updates.
* checkstyle
* forbidden api fix
* doc fix, unused import, codeql scan error, and cleanup logs.
* Address review comments
* Rename maxUsedFlagLastUpdatedTime to maxUsedStatusLastUpdatedTime
This is consistent with the column name `used_status_last_updated`.
* Apply suggestions from code review
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
* Make period Duration type
* Remove older variants of runKilLTask() in OverlordClient interface
* Test can now run without waiting for canDutyRun().
* Remove previous variants of retrieveUnusedSegments from internal metadata storage coordinator interface.
Removes the following interface methods in favor of a new method added:
- retrieveUnusedSegmentsForInterval(String, Interval)
- retrieveUnusedSegmentsForInterval(String, Interval, Integer)
* Chain stream operations
* cleanup
* Pass in the lastUpdatedTime to markUnused test function and remove sleep.
---------
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
* Reverse, pull up lookups in the SQL planner.
Adds two new rules:
1) ReverseLookupRule, which eliminates calls to LOOKUP by doing
reverse lookups.
2) AggregatePullUpLookupRule, which pulls up calls to LOOKUP above
GROUP BY, when the lookup is injective.
Adds configs `sqlReverseLookup` and `sqlPullUpLookup` to control whether
these rules fire. Both are enabled by default.
To minimize the chance of performance problems due to many keys mapping to
the same value, ReverseLookupRule refrains from reversing a lookup if there
are more keys than `inSubQueryThreshold`. The rationale for using this setting
is that reversal works by generating an IN, and the `inSubQueryThreshold`
describes the largest IN the user wants the planner to create.
* Add additional line.
* Style.
* Remove commented-out lines.
* Fix tests.
* Add test.
* Fix doc link.
* Fix docs.
* Add one more test.
* Fix tests.
* Logic, test updates.
* - Make FilterDecomposeConcatRule more flexible.
- Make CalciteRulesManager apply reduction rules til fixpoint.
* Additional tests, simplify code.
Currently, If 2 tasks are consuming from the same partitions, try to publish the segment and update the metadata, the second task can fail because the end offset stored in the metadata store doesn't match with the start offset of the second task. We can fix this by retrying instead of failing.
AFAIK apart from the above issue, the metadata mismatch can happen in 2 scenarios:
- when we update the input topic name for the data source
- when we run 2 replicas of ingestion tasks(1 replica will publish and 1 will fail as the first replica has already updated the metadata).
Implemented the comparable function to compare the last committed end offset and new Sequence start offset. And return a specific error msg for this.
Add retry logic on indexers to retry for this specific error msg.
Updated the existing test case.
The initial step in optimizing segment metadata was to centralize the construction of datasource schema in the Coordinator (#14985). Subsequently, our goal is to eliminate the requirement for regularly executing queries to obtain segment schema information. This task encompasses addressing both realtime and finalized segments.
This modification specifically addresses the issue with realtime segments. Tasks will now routinely communicate the schema for realtime segments during the segment announcement process. The Coordinator will identify the schema alongside the segment announcement and subsequently update the schema for realtime segments in the metadata cache.
This PR enables the flag by default to queue excess query requests in the jetty queue. Still keeping the flag so that it can be turned off if necessary. But the flag will be removed in the future.
* Allow overwriting ServerConnector accept queue size
* Use a single config
* Fix spacing
* fix spacing
* fixed value
* read value from environment
* fix spacing
* Unpack value before reading
* check somaxconn on linux only
* Reverse lookup fixes and enhancements.
1) Add a "mayIncludeUnknown" parameter to DimFilter#optimize. This is important
because otherwise the reverse-lookup optimization is done improperly when
the "in" filter appears under a "not", and the lookup extractionFn may return
null for some possible values of the filtered column. The "includeUnknown" test
cases in InDimFilterTest illustrate the difference in behavior.
2) Enhance InDimFilter#optimizeLookup to handle "mayIncludeUnknown", and to be able
to do a reverse lookup in a wider variety of cases.
3) Make "unapply" protected in LookupExtractor, and move callers to "unapplyAll".
The main reason is that MapLookupExtractor, a common implementation, lacks a
reverse mapping and therefore does a scan of the map for each call to "unapply".
For performance sake these calls need to be batched.
* Remove optimize call from BloomDimFilter.
* Follow the law.
* Fix tests.
* Fix imports.
* Switch function.
* Fix tests.
* More tests.
* Minor fixes
* Update docs/development/extensions-contrib/prometheus.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
---------
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Currently in the realtime ingestion (Kafka/Kinesis) case, after publishing the segments, upon acknowledgement from the coordinator that the segments are already placed in some historicals, the peon would unannounce the segments (basically saying the segments are not in this peon anymore to the whole cluster) and drop the segments from cache and sink timeline in one shot.
The in transit queries from the brokers that still thinks the segments are in the peon can get a NullPointer exception when the peon is unsetting the hydrants in the sinks.
The fix would let the peon to wait for a configurable delay period before dropping segments, remove segments from cache etc after the peon unannounce the segments.
This delayed approach is similar to how the historicals handle segments moving out.
Changes
- Add `log` implementation for `AuditManager` alongwith `SQLAuditManager`
- `LoggingAuditManager` simply logs the audit event. Thus, it returns empty for
all `fetchAuditHistory` calls.
- Add new config `druid.audit.manager.type` which can take values `log`, `sql` (default)
- Add new config `druid.audit.manager.logLevel` which can take values `DEBUG`, `INFO`, `WARN`.
This gets activated only if `type` is `log`.
- Remove usage of `ConfigSerde` from `AuditManager` as audit is not just limited to configs
- Add `AuditSerdeHelper` for a single implementation of serialization/deserialization of
audit payload and other utility methods.
The segment allocation algorithm reuses an already allocated pending segment if the new allocation request is made for the same parameters:
datasource
sequence name
same interval
same value of skipSegmentLineageCheck (false for batch append, true for streaming append)
same previous segment id (used only when skipSegmentLineageCheck = false)
The above parameters can thus uniquely identify a pending segment (enforced by the UNIQUE constraint on the sequence_name_prev_id_sha1 column in druid_pendingSegments metadata table).
This reuse is done in order to
allow replica tasks (in case of streaming ingestion) to use the same set of segment IDs.
allow re-run of a failed batch task to use the same segment ID and prevent unnecessary allocations
This PR builds on #13304 to skip compaction for datasources with segments that have their interval start or end coinciding with Eternity interval end-points.
This is needed in order to prevent an issue similar to #13208 as the Coordinator tries to iterate over a large number of intervals when trying to compact an interval with infinite start or end.
### Description
This pr adds an api for retrieving unused segments for a particular datasource. The api supports pagination by the addition of `limit` and `lastSegmentId` parameters. The resulting unused segments are returned with optional `sortOrder`, `ASC` or `DESC` with respect to the matching segments `id`, `start time`, and `end time`, or not returned in any guarenteed order if `sortOrder` is not specified
`GET /druid/coordinator/v1/datasources/{dataSourceName}/unusedSegments?interval={interval}&limit={limit}&lastSegmentId={lastSegmentId}&sortOrder={sortOrder}`
Returns a list of unused segments for a datasource in the cluster contained within an optionally specified interval.
Optional parameters for limit and lastSegmentId can be given as well, to limit results and enable paginated results.
The results may be sorted in either ASC, or DESC order depending on specifying the sortOrder parameter.
`dataSourceName`: The name of the datasource
`interval`: the specific interval to search for unused segments for.
`limit`: the maximum number of unused segments to return information about. This property helps to
support pagination
`lastSegmentId`: the last segment id from which to search for results. All segments returned are > this segment
lexigraphically if sortOrder is null or ASC, or < this segment lexigraphically if sortOrder is DESC.
`sortOrder`: Specifies the order with which to return the matching segments by start time, end time. A null
value indicates that order does not matter.
This PR has:
- [x] been self-reviewed.
- [ ] using the [concurrency checklist](https://github.com/apache/druid/blob/master/dev/code-review/concurrency.md) (Remove this item if the PR doesn't have any relation to concurrency.)
- [x] added documentation for new or modified features or behaviors.
- [ ] a release note entry in the PR description.
- [x] added Javadocs for most classes and all non-trivial methods. Linked related entities via Javadoc links.
- [ ] added or updated version, license, or notice information in [licenses.yaml](https://github.com/apache/druid/blob/master/dev/license.md)
- [x] added comments explaining the "why" and the intent of the code wherever would not be obvious for an unfamiliar reader.
- [x] added unit tests or modified existing tests to cover new code paths, ensuring the threshold for [code coverage](https://github.com/apache/druid/blob/master/dev/code-review/code-coverage.md) is met.
- [ ] added integration tests.
- [x] been tested in a test Druid cluster.
* Add initial draft of MarkDanglingTombstonesAsUnused duty.
* Use overshadowed segments instead of all used segments.
* Add unit test for MarkDanglingSegmentsAsUnused duty.
* Add mock call
* Simplify code.
* Docs
* shorter lines formatting
* metric doc
* More tests, refactor and fix up some logic.
* update javadocs; other review comments.
* Make numCorePartitions as 0 in the TombstoneShardSpec.
* fix up test
* Add tombstone core partition tests
* Update docs/design/coordinator.md
Co-authored-by: 317brian <53799971+317brian@users.noreply.github.com>
* review comment
* Minor cleanup
* Only consider tombstones with 0 core partitions
* Need to register the test shard type to make jackson happy
* test comments
* checkstyle
* fixup misc typos in comments
* Update logic to use overshadowed segments
* minor cleanup
* Rename duty to eternity tombstone instead of dangling. Add test for full eternity tombstone.
* Address review feedback.
---------
Co-authored-by: 317brian <53799971+317brian@users.noreply.github.com>
Description
With CentralizedDatasourceSchema (#14989) feature enabled, metadata for appended segments was not being refreshed. This caused numRows to be 0 for the new segments and would probably cause the datasource schema to not include columns from the new segments.
Analysis
The problem turned out in the new QuerySegmentWalker implementation in the Coordinator. It first finds the segment to be queried in the Coordinator timeline. Then it creates a new timeline of the segments present in the timeline.
The problem was that it is looking up complete partition set in the new timeline. Since the appended segments by themselves do not make a complete partition set, no SegmentMetadataQuery were executed.
Update guava to 32.0.1-jre to address two CVEs: CVE-2020-8908, CVE-2023-2976
This change requires a minor test change to remove assumptions about ordering.
---------
Co-authored-by: Xavier Léauté <xl+github@xvrl.net>
* Fixing failing compaction/parallel index jobs during upgrade due to new actions not available on the overlord.
* Fixing build
* Removing extra space.
* Fixing json getter.
* Review comments.
* Fix NPE caused by realtime segment closing race, fix possible missing-segment retry bug.
Fixes#12168, by returning empty from FireHydrant when the segment is
swapped to null. This causes the SinkQuerySegmentWalker to use
ReportTimelineMissingSegmentQueryRunner, which causes the Broker to look
for the segment somewhere else.
In addition, this patch changes SinkQuerySegmentWalker to acquire references
to all hydrants (subsegments of a sink) at once, and return a
ReportTimelineMissingSegmentQueryRunner if *any* of them could not be acquired.
I suspect, although have not confirmed, that the prior behavior could lead to
segments being reported as missing even though results from some hydrants were
still included.
* Some more test coverage.
* Make numCorePartitions as 0 in the TombstoneShardSpec.
* fix up test
* Add tombstone core partition tests
* review comment
* Need to register the test shard type to make jackson happy
In pull request #14985, a bug was introduced where periodic refresh would skip rebuilding a datasource's schema after encountering a non-existent datasource. This resulted in remaining datasources having stale schema information.
This change addresses the bug and adds a unit test to validate the refresh mechanism's behaviour when a datasource is removed, and other datasources have schema changes.
* Add a unit test that fails when used segments with too many intervals are retrieved.
- This is a failing test case that needs to be ignored.
* Batch the intervals (use 100 as it's consistent with batching in other places).
* move the filtering inside the batch
* Account for limit cross the batch splits.
* Adjustments
* Fixup and add tests
* small refactor
* add more tests.
* remove wrapper.
* Minor edits
* assert out of range
In the current design, brokers query both data nodes and tasks to fetch the schema of the segments they serve. The table schema is then constructed by combining the schemas of all segments within a datasource. However, this approach leads to a high number of segment metadata queries during broker startup, resulting in slow startup times and various issues outlined in the design proposal.
To address these challenges, we propose centralizing the table schema management process within the coordinator. This change is the first step in that direction. In the new arrangement, the coordinator will take on the responsibility of querying both data nodes and tasks to fetch segment schema and subsequently building the table schema. Brokers will now simply query the Coordinator to fetch table schema. Importantly, brokers will still retain the capability to build table schemas if the need arises, ensuring both flexibility and resilience.
* Use filters for pruning properly for hash-joins.
Native used them too aggressively: it might use filters for the RHS
to prune the LHS. MSQ used them not at all. Now, both use them properly,
pruning based on base (LHS) columns only.
* Fix tests.
* Fix style.
* Clear filterFields too.
* Update.
* Add system fields to input sources.
Main changes:
1) The SystemField enum defines system fields "__file_uri", "__file_path",
and "__file_bucket". They are associated with each input entity.
2) The SystemFieldInputSource interface can be added to any InputSource
to make it system-field-capable. It sets up serialization of a list
of configured "systemFields" in the JSON form of the input source, and
provides a method getSystemFieldValue for computing the value of each
system field. Cloud object, HDFS, HTTP, and Local now have this.
* Fix various LocalInputSource calls.
* Fix style stuff.
* Fixups.
* Fix tests and coverage.
ServiceClientImpl logs the cause of every retry, even though we are retrying the connection attempt. This leads to slight pollution in the logs because a lot of the time, the reason for retrying is the same. This is seen primarily in MSQ, when the worker task hasn't launched yet however controller attempts to connect to the worker task, which can lead to scary-looking messages (with INFO log level), even though they are normal.
This PR changes the logging logic to log every 10 (arbitrary number) retries instead of every retry, to reduce the pollution of the logs.
Note: If there are no retries left, the client returns an exception, which would get thrown up by the caller, and therefore this change doesn't hide any important information.
* Use min of scheduler threads and server threads for subquery guardrails.
This allows more memory to be used for subqueries when the query scheduler
is configured to limit queries below the number of server threads. The patch
also refactors the code so SubqueryGuardrailHelper is provided by a Guice
Provider rather than being created by ClientQuerySegmentWalker, to achieve
better separation of concerns.
* Exclude provider from coverage.
* Frames: consider writing singly-valued column when input column hasMultipleValues is UNKNOWN.
Prior to this patch, columnar frames would always write multi-valued columns if
the input column had hasMultipleValues = UNKNOWN. This had the effect of flipping
UNKNOWN to TRUE when copying data into frames, which is problematic because TRUE
causes expressions to assume that string inputs must be treated as arrays.
We now avoid this by flipping UNKNOWN to FALSE if no multi-valuedness
is encountered, and flipping it to TRUE if multi-valuedness is encountered.
* Add regression test case.
Currently the inter Druid communication via rest endpoints is based on json formatted payload. Upon parsing error, there is only a generic exception stating expected json token type and current json token type. There is no detailed error log about the content of the payload causing the violation.
In the micro-service world, the trend is to deploy the Druid servers in k8 with the mesh network. Often the istio proxy or other proxies is used to intercept the network connection between Druid servers. The proxy may give error messages for various reasons. These error messages are not expected by the json parser. The generic error message from Druid can be very misleading as the user may think the message is based on the response from the other Druid server.
For example, this is an example of mysterious error message
QueryInterruptedException{msg=Next token wasn't a START_ARRAY, was[VALUE_STRING] from url[http://xxxxx:8088/druid/v2/], code=Unknown exception, class=org.apache.druid.java.util.common.IAE, host=xxxxx:8088}"
While the context of the message is the following from the proxy when it can't tunnel the network connection.
pstream connect error or disconnect/reset before header
So this very simple PR is just to enhance the logging and get the real underlying message printed out. This would save a lot of head scratching time if Druid is deployed with mesh network.
Co-authored-by: Kai Sun <kai.sun@salesforce.com>
This PR aims to add the capabilities to:
1. Fetch the realtime segment metadata from the coordinator server view,
2. Adds the ability for workers to query indexers, similar to how brokers do the same for native queries.
This PR updates the library used for Memcached client to AWS Elasticache Client : https://github.com/awslabs/aws-elasticache-cluster-client-memcached-for-java
This enables us to use the option of encrypting data in transit:
Amazon ElastiCache for Memcached now supports encryption of data in transit
For clusters running the Memcached engine, ElastiCache supports Auto Discovery—the ability for client programs to automatically identify all of the nodes in a cache cluster, and to initiate and maintain connections to all of these nodes.
Benefits of Auto Discovery - Amazon ElastiCache
AWS has forked spymemcached 2.12.1, and has since added all the patches included in 2.12.2 and 2.12.3 as part of the 1.2.0 release. So, this can now be considered as an equivalent drop-in replacement.
GitHub - awslabs/aws-elasticache-cluster-client-memcached-for-java: Amazon ElastiCache Cluster Client for Java - enhanced library to connect to ElastiCache clusters.
https://docs.aws.amazon.com/AWSJavaSDK/latest/javadoc/com/amazonaws/services/elasticache/AmazonElastiCacheClient.html#AmazonElastiCacheClient--
How to enable TLS with Elasticache
On server side:
https://docs.aws.amazon.com/AmazonElastiCache/latest/mem-ug/in-transit-encryption-mc.html#in-transit-encryption-enable-existing-mc
On client side:
GitHub - awslabs/aws-elasticache-cluster-client-memcached-for-java: Amazon ElastiCache Cluster Client for Java - enhanced library to connect to ElastiCache clusters.
The aggregators had incorrect types for getResultType when shouldFinalze
is false. They had the finalized type, but they should have had the
intermediate type.
Also includes a refactor of how ExprMacroTable is handled in tests, to make
it easier to add tests for this to the MSQ module. The bug was originally
noticed because the incorrect result types caused MSQ queries with DS_HLL
to behave erratically.
Changes:
- Add task context parameter `taskLockType`. This determines the type of lock used by a batch task.
- Add new task actions for transactional replace and append of segments
- Add methods StorageCoordinator.commitAppendSegments and commitReplaceSegments
- Upgrade segments to appropriate versions when performing replace and append
- Add new metadata table `upgradeSegments` to track segments that need to be upgraded
- Add tests
- Add `KillTaskReport` that contains stats for `numSegmentsKilled`,
`numBatchesProcessed`, `numSegmentsMarkedAsUnused`
- Fix bug where exception message had no formatter but was was still being passed some args.
- Add some comments regarding deprecation of `markAsUnused` flag.
Kashif Faraz
Rushikesh Bankar
AmatyaAvadhanula
Abhishek Radhakrishnan
Zoltan Haindrich
zachjsh
George Shiqi Wu
Laksh Singla
Parth Agrawal
Gian Merlino
Sree Charan Manamala
Sensor
Adarsh Sanjeev
Bartosz Mikulski
Pramod Immaneni
Rishabh Singh
Abhishek Agarwal
Karan Kumar
Sam Rash
PANKAJ KUMAR
Pranav
Jonathan Wei
kaisun2000
Clint Wylie
Vishesh Garg
Jan Werner
Vadim Ogievetsky
Suneet Saldanha