Follow up to #15705
Changes:
- Remove references to ZK-based segment loading in the docs
- Fix doc for existing config `druid.coordinator.loadqueuepeon.http.repeatDelay`
* Delta Lake support for filters.
* Updates
* cleanup comments
* Docs
* Remmove Enclosed runner
* Rename
* Cleanup test
* Serde test for the Delta input source and fix jackson annotation.
* Updates and docs.
* Update error messages to be clearer
* Fixes
* Handle NumberFormatException to provide a nicer error message.
* Apply suggestions from code review
Co-authored-by: 317brian <53799971+317brian@users.noreply.github.com>
* Doc fixes based on feedback
* Yes -> yes in docs; reword slightly.
* Update docs/ingestion/input-sources.md
Co-authored-by: Laksh Singla <lakshsingla@gmail.com>
* Update docs/ingestion/input-sources.md
Co-authored-by: Laksh Singla <lakshsingla@gmail.com>
* Documentation, javadoc and more updates.
* Not with an or expression end-to-end test.
* Break up =, >, >=, <, <= into its own types instead of sub-classing.
---------
Co-authored-by: 317brian <53799971+317brian@users.noreply.github.com>
Co-authored-by: Laksh Singla <lakshsingla@gmail.com>
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()`
* Four changes to scalar_in_array as follow-ups to #16306:
1) Align behavior for `null` scalars to the behavior of the native `in` and `inType` filters: return `true` if the array itself contains null, else return `null`.
2) Rename the class to more closely match the function name.
3) Add a specialization for constant arrays, where we build a `HashSet`.
4) Use `castForEqualityComparison` to properly handle cross-type comparisons.
Additional tests verify comparisons between LONG and DOUBLE are now
handled properly.
* Fix spelling.
* Adjustments from review.
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.
Statsd client sometimes drops metrics when this queueSize of statsd client with max unprocessed messages is completely full. This causes some high cardinality metrics like per partition lag being droppped.
There are multiple parameters of statsdclient that can be initialized and can help increase the load/capacity of client to not to drop metrics more frequently.
Properties like queueSize, poolSize, processorWorkers and senderWorkers will now be configurable at runtime
* Adds Druid SQL query examples for the Timeseries and GroupBy Native queries in the stats aggregator docs page
* Updates intervals in Native Query to remove excess Time part in timestamp
* Moves Druid SQL section above Native query because sql used more often by users
* removes old Druid SQL sections
* Adds TopN Druid SQL query using ORDER BY and LIMIT
* Adds table for Druid SQL variance and standard deviation functions
* Update docs/development/extensions-core/stats.md
Co-authored-by: Abhishek Radhakrishnan <abhishek.rb19@gmail.com>
---------
Co-authored-by: Karan Kumar <karankumar1100@gmail.com>
Co-authored-by: Abhishek Radhakrishnan <abhishek.rb19@gmail.com>
Currently, export creates the files at the provided destination. The addition of the manifest file will provide a list of files created as part of the manifest. This will allow easier consumption of the data exported from Druid, especially for automated data pipelines
The default value for druid.coordinator.kill.period (if unspecified) has changed from P1D to the value of druid.coordinator.period.indexingPeriod. Operators can choose to override druid.coordinator.kill.period and that will take precedence over the default behavior.
The default value for the coordinator dynamic config killTaskSlotRatio is updated from 1.0 to 0.1. This ensures that that kill tasks take up only 1 task slot right out-of-the-box instead of taking up all the task slots.
* Remove stale comment and inline canDutyRun()
* druid.coordinator.kill.period defaults to druid.coordinator.period.indexingPeriod if not set.
- Remove the default P1D value for druid.coordinator.kill.period. Instead default
druid.coordinator.kill.period to whatever value druid.coordinator.period.indexingPeriod is set
to if the former config isn't specified.
- If druid.coordinator.kill.period is set, the value will take precedence over
druid.coordinator.period.indexingPeriod
* Update server/src/test/java/org/apache/druid/server/coordinator/DruidCoordinatorConfigTest.java
* Fix checkstyle error
* Clarify comment
* Update server/src/main/java/org/apache/druid/server/coordinator/DruidCoordinatorConfig.java
* Put back canDutyRun()
* Default killTaskSlotsRatio to 0.1 instead of 1.0 (all slots)
* Fix typo DEFAULT_MAX_COMPACTION_TASK_SLOTS
* Remove unused test method.
* Update default value of killTaskSlotsRatio in docs and web-console default mock
* Move initDuty() after params and config setup.
Changes:
- Add `TaskContextEnricher` interface to improve task management and monitoring
- Invoke `enrichContext` in `TaskQueue.add()` whenever a new task is submitted to the Overlord
- Add `TaskContextReport` to write out task context information in reports
Compaction in the native engine by default records the state of compaction for each segment in the lastCompactionState segment field. This PR adds support for doing the same in the MSQ engine, targeted for future cases such as REPLACE and compaction done via MSQ.
Note that this PR doesn't implicitly store the compaction state for MSQ replace tasks; it is stored with flag "storeCompactionState": true in the query context.
Current Runtime Exceptions generated while writing frames only include the exception itself without including the name of the column they were encountered in. This patch introduces the further information in the error and makes it non-retryable.
This PR logs the segment type and reason chosen. It also adds it to the query report, to be displayed in the UI.
This PR adds a new section to the reports, segmentReport. This contains the segment type created, if the query is an ingestion, and null otherwise.
Support for exporting msq results to gcs bucket. This is essentially copying the logic of s3 export for gs, originally done by @adarshsanjeev in this PR - #15689
This PR creates an interface for ImmutableRTree and moved the existing implementation to new class which represent 32 bit implementation (stores coordinate as floats). This PR makes the ImmutableRTree extendable to create higher precision implementation as well (64 bit).
In all spatial bound filters, we accept float as input which might not be accurate in the case of high precision implementation of ImmutableRTree. This PR changed the bound filters to accepts the query bounds as double instead of float and it is backward compatible change as it compares double to existing float values in RTree. Previously it was comparing input float to RTree floats which can cause precision loss, now it is little better as it compares double to float which is still not 100% accurate.
There are no changes in the way that we query spatial dimension today except input bound parsing. There is little improvement in string filter predicate which now parse double strings instead of float and compares double to double which is 100% accurate but string predicate is only called when we dont have spatial index.
With allowing the interface to extend ImmutableRTree, we allow to create high precision (HP) implementation and defines new search strategies to perform HP search Iterable<ImmutableBitmap> search(ImmutableDoubleNode node, Bound bound);
With possible HP implementations, Radius bound filter can not really focus on accuracy, it is calculating Euclidean distance in comparing. As EARTH 🌍 is round and not flat, Euclidean distances are not accurate in geo system. This PR adds new param called 'radiusUnit' which allows you to specify units like meters, km, miles etc. It uses https://en.wikipedia.org/wiki/Haversine_formula to check if given geo point falls inside circle or not. Added a test that generates set of points inside and outside in RadiusBoundTest.
This PR aims to introduce Window functions on MSQ by doing the following:
Introduce a Window querykit for handling window queries along with its factory and a processor for window queries
If a window operator is present with a partition by clause, pushes the partition as a shuffle spec of the previous stage
In presence of empty OVER() clause lets all operators loose on a single rac
In presence of no empty OVER() clause, breaks down each window into individual stages
Associated machinery to handle window functions in MSQ
Introduced a separate hidden engine feature WINDOW_LEAF_OPERATOR which is set only for MSQ engine. In presence of this feature, the planner plans without the leaf operators by creating a window query over an inner scan query. In case of native this is set to false and the planner generates the leafOperators
Guardrails around materialization
Comprehensive UTs
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>
* MSQ: Validate that strings and string arrays are not mixed.
When multi-value strings and string arrays coexist in the same column,
it causes problems with "classic MVD" style queries such as:
select * from wikipedia -- fails at runtime
select count(*) from wikipedia where flags = 'B' -- fails at planning time
select flags, count(*) from wikipedia group by 1 -- fails at runtime
To avoid these problems, this patch adds type verification for INSERT
and REPLACE. It is targeted: the only type changes that are blocked are
string-to-array and array-to-string. There is also a way to exclude
certain columns from the type checks, if the user really knows what
they're doing.
* Fixes.
* Tests and docs and error messages.
* More docs.
* Adjustments.
* Adjust message.
* Fix tests.
* Fix test in DV mode.
* docs: clarify description of uri/uripath
* Apply suggestions from code review
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
---------
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* 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:
- Add visibility into number of segments read/published by each parallel compaction
- Add new fields `segmentsRead`, `segmentsPublished` to `IngestionStatsAndErrorsTaskReportData`
- Update `ParallelIndexSupervisorTask` to populate the new stats
* updated description of rowsPerPage in export operations
* Update docs/multi-stage-query/reference.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
---------
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Add support for AzureDNSZone enabled storage accounts used for deep storage
Added a new config to AzureAccountConfig
`storageAccountEndpointSuffix`
which allows the user to specify a storage account endpoint suffix where the underlying
storage account is enabled for AzureDNSZone. The previous config `endpointSuffix`, did not allow
support for such accounts. The previous config has been deprecated in favor of this new config. Also
fixed an issue where `managedIdentityClientId` was not being set properly
* * address review comments
* * add back azure government link and docs
* 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.
* docs: add mermaid diagram support
* fix crash when parsing data in data loader that can not be parsed (#15983)
* update jetty to address CVE (#16000)
* Concurrent replace should work with supervisors using concurrent locks (#15995)
* Concurrent replace should work with supervisors using concurrent locks
* Ignore supervisors with useConcurrentLocks set to false
* Apply feedback
* Add pre-check for heavy debug logs (#15706)
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
Co-authored-by: Benedict Jin <asdf2014@apache.org>
* Remove helm paths from CodeQL config (#16006)
* docs: mention acid-compliance for metadb
---------
Co-authored-by: Vadim Ogievetsky <vadim@ogievetsky.com>
Co-authored-by: Jan Werner <105367074+janjwerner-confluent@users.noreply.github.com>
Co-authored-by: AmatyaAvadhanula <amatya.avadhanula@imply.io>
Co-authored-by: Sensor <fectrain@outlook.com>
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
Co-authored-by: Benedict Jin <asdf2014@apache.org>
* Update basic-cluster-tuning.md
The sentence "When free system memory is greater than or equal to druid.segmentCache.locations, the more segment data the Historical can be held in the memory-mapped segment cache" didn't read well. Updated to clarify it.
* Update docs/operations/basic-cluster-tuning.md
* Update docs/operations/basic-cluster-tuning.md
---------
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* All segments stored in the same batch have the same created_date entry.
In the absence of a group_id column, this metadata would allow us to easily
reason about and troubleshoot ingestion-related issues.
* Rename metric name and code references to eligibleUnusedSegments.
Address review comment from https://github.com/apache/druid/pull/15941#discussion_r1503631992
* Kill duty and test improvements.
Initial commit with:
- Bug fixes - auto-kill can throw NPE when there are no datasources present and defaults mismatch.
- Add new stat for candidate segment intervals killed.
- Move a couple of debug logs to info logs for improved visibility (should only log once per kill period).
- Remove redundant checks for code readability.
- Updated tests from using mocks (also the mocks weren't using last updated timestamp) and
add more test coverage for different config parameters.
- Add a couple of unit tests that are ignored for the eternity case to prove that
the kill duty doesn't clean up segments with ALL grain or that end in DateTimes.MAX.
- Migrate Druid exception from user to operator persona.
* Address review comments.
* Remove unused methods.
* fix up format specifier and validate bad config tests.
* Consolidate the helpers a bit more and add another test.
* Update test names. Add javadoc placeholders for slightly involved tests.
* Add docs for metric kill/candidateUnusedSegments/count.
Also, rename to disambiguate.
* Comments.
* Apply logging suggestions from code review
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
* Review comments
- Clarify docs on eligibility.
- Add test for multiple segments in the same interval. Clarify comment.
- Remove log line from test.
- Remove lastUpdatedDate = now.plus(10) from test.
* minor cleanup.
* Clarify javadocs for getUnusedSegmentIntervals().
---------
Co-authored-by: Kashif Faraz <kashif.faraz@gmail.com>
* Fix up typos, inaccuracies and clean up code related to PARTITIONED BY.
* Remove wrapper function and update tests to use DruidExceptionMatcher.
* Checkstyle and Intellij inspection fixes.
Changes:
- Add visibility into number of records processed by each streaming task per partition
- Add field `recordsProcessed` to `IngestionStatsAndErrorsTaskReportData`
- Populate number of records processed per partition in `SeekableStreamIndexTaskRunner`
Starting the process to officially deprecate non SQL compatible modes by updating docs to aggressively call out that Druids non SQL compliant modes are deprecated and will go away someday. There are no code or behavior changes at this PR.
Merging the work so far. @ektravel , @vogievetsky if there are additional improvements, let's track them & make another pr.
* Refactor streaming ingestion docs
* Update property definition
* Update after review
* Update known issues
* Move kinesis and kafka topics to ingestion, add redirects
* Saving changes
* Saving
* Add input format text
* Update after review
* Minor text edit
* Update example syntax
* Revert back to colon
* Fix merge conflicts
* Fix broken links
* Fix spelling error
This PR contains a portion of the changes from the inactive draft PR for integrating the catalog with the Calcite planner https://github.com/apache/druid/pull/13686 from @paul-rogers, extending the PARTITION BY clause to accept string literals for the time partitioning
* allow for kafka-emitter to have extra dimensions be set for each event it emits
* fix checktsyle issue in kafkaemitterconfig
* make changes to fix docs, and cleanup copy paste error in #toString()
* undo formatting to markdown table
* add more branches so test passes
* fix checkstyle issue
* Update the group id to org.apache.druid.extensions.contrib for contrib exts.
* Note iceberg and delta lake extensions in extensions.md
* properties and shell backticks
* Update groupId in distribution/pom.xml
* remove delta-lake from dist.
* Add note on downloading extension.
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.
If lots of keys map to the same value, reversing a LOOKUP call can slow
things down unacceptably. To protect against this, this patch introduces
a parameter sqlReverseLookupThreshold representing the maximum size of an
IN filter that will be created as part of lookup reversal.
If inSubQueryThreshold is set to a smaller value than
sqlReverseLookupThreshold, then inSubQueryThreshold will be used instead.
This allows users to use that single parameter to control IN sizes if they
wish.
* something
* test commit
* compilation fix
* more compilation fixes (fixme placeholders)
* Comment out druid-kereberos build since it conflicts with newly added transitive deps from delta-lake
Will need to sort out the dependencies later.
* checkpoint
* remove snapshot schema since we can get schema from the row
* iterator bug fix
* json json json
* sampler flow
* empty impls for read(InputStats) and sample()
* conversion?
* conversion, without timestamp
* Web console changes to show Delta Lake
* Asset bug fix and tile load
* Add missing pieces to input source info, etc.
* fix stuff
* Use a different delta lake asset
* Delta lake extension dependencies
* Cleanup
* Add InputSource, module init and helper code to process delta files.
* Test init
* Checkpoint changes
* Test resources and updates
* some fixes
* move to the correct package
* More tests
* Test cleanup
* TODOs
* Test updates
* requirements and javadocs
* Adjust dependencies
* Update readme
* Bump up version
* fixup typo in deps
* forbidden api and checkstyle checks
* Trim down dependencies
* new lines
* Fixup Intellij inspections.
* Add equals() and hashCode()
* chain splits, intellij inspections
* review comments and todo placeholder
* fix up some docs
* null table path and test dependencies. Fixup broken link.
* run prettify
* Different test; fixes
* Upgrade pyspark and delta-spark to latest (3.5.0 and 3.0.0) and regenerate tests
* yank the old test resource.
* add a couple of sad path tests
* Updates to readme based on latest.
* Version support
* Extract Delta DateTime converstions to DeltaTimeUtils class and add test
* More comprehensive split tests.
* Some test renames.
* Cleanup and update instructions.
* add pruneSchema() optimization for table scans.
* Oops, missed the parquet files.
* Update default table and rename schema constants.
* Test setup and misc changes.
* Add class loader logic as the context class loader is unaware about extension classes
* change some table client creation logic.
* Add hadoop-aws, hadoop-common and related exclusions.
* Remove org.apache.hadoop:hadoop-common
* Apply suggestions from code review
Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com>
* Add entry to .spelling to fix docs static check
---------
Co-authored-by: abhishekagarwal87 <1477457+abhishekagarwal87@users.noreply.github.com>
Co-authored-by: Laksh Singla <lakshsingla@gmail.com>
Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com>
* New: Add DDSketch-Druid extension
- Based off of http://www.vldb.org/pvldb/vol12/p2195-masson.pdf and uses
the corresponding https://github.com/DataDog/sketches-java library
- contains tests for post building and using aggregation/post
aggregation.
- New aggregator: `ddSketch`
- New post aggregators: `quantileFromDDSketch` and
`quantilesFromDDSketch`
* Fixing easy CodeQL warnings/errors
* Fixing docs, and dependencies
Also moved aggregator ids to AggregatorUtil and PostAggregatorIds
* Adding more Docs and better null/empty handling for aggregators
* Fixing docs, and pom version
* DDSketch documentation format and wording
A low value of inSubQueryThreshold can cause queries with IN filter to plan as joins more commonly. However, some of these join queries may not get planned as IN filter on data nodes and causes significant perf regression.
### Description
Our Kinesis consumer works by using the [GetRecords API](https://docs.aws.amazon.com/kinesis/latest/APIReference/API_GetRecords.html) in some number of `fetchThreads`, each fetching some number of records (`recordsPerFetch`) and each inserting into a shared buffer that can hold a `recordBufferSize` number of records. The logic is described in our documentation at: https://druid.apache.org/docs/27.0.0/development/extensions-core/kinesis-ingestion/#determine-fetch-settings
There is a problem with the logic that this pr fixes: the memory limits rely on a hard-coded “estimated record size” that is `10 KB` if `deaggregate: false` and `1 MB` if `deaggregate: true`. There have been cases where a supervisor had `deaggregate: true` set even though it wasn’t needed, leading to under-utilization of memory and poor ingestion performance.
Users don’t always know if their records are aggregated or not. Also, even if they could figure it out, it’s better to not have to. So we’d like to eliminate the `deaggregate` parameter, which means we need to do memory management more adaptively based on the actual record sizes.
We take advantage of the fact that GetRecords doesn’t return more than 10MB (https://docs.aws.amazon.com/streams/latest/dev/service-sizes-and-limits.html ):
This pr:
eliminates `recordsPerFetch`, always use the max limit of 10000 records (the default limit if not set)
eliminate `deaggregate`, always have it true
cap `fetchThreads` to ensure that if each fetch returns the max (`10MB`) then we don't exceed our budget (`100MB` or `5% of heap`). In practice this means `fetchThreads` will never be more than `10`. Tasks usually don't have that many processors available to them anyway, so in practice I don't think this will change the number of threads for too many deployments
add `recordBufferSizeBytes` as a bytes-based limit rather than records-based limit for the shared queue. We do know the byte size of kinesis records by at this point. Default should be `100MB` or `10% of heap`, whichever is smaller.
add `maxBytesPerPoll` as a bytes-based limit for how much data we poll from shared buffer at a time. Default is `1000000` bytes.
deprecate `recordBufferSize`, use `recordBufferSizeBytes` instead. Warning is logged if `recordBufferSize` is specified
deprecate `maxRecordsPerPoll`, use `maxBytesPerPoll` instead. Warning is logged if maxRecordsPerPoll` is specified
Fixed issue that when the record buffer is full, the fetchRecords logic throws away the rest of the GetRecords result after `recordBufferOfferTimeout` and starts a new shard iterator. This seems excessively churny. Instead, wait an unbounded amount of time for queue to stop being full. If the queue remains full, we’ll end up right back waiting for it after the restarted fetch.
There was also a call to `newQ::offer` without check in `filterBufferAndResetBackgroundFetch`, which seemed like it could cause data loss. Now checking return value here, and failing if false.
### Release Note
Kinesis ingestion memory tuning config has been greatly simplified, and a more adaptive approach is now taken for the configuration. Here is a summary of the changes made:
eliminates `recordsPerFetch`, always use the max limit of 10000 records (the default limit if not set)
eliminate `deaggregate`, always have it true
cap `fetchThreads` to ensure that if each fetch returns the max (`10MB`) then we don't exceed our budget (`100MB` or `5% of heap`). In practice this means `fetchThreads` will never be more than `10`. Tasks usually don't have that many processors available to them anyway, so in practice I don't think this will change the number of threads for too many deployments
add `recordBufferSizeBytes` as a bytes-based limit rather than records-based limit for the shared queue. We do know the byte size of kinesis records by at this point. Default should be `100MB` or `10% of heap`, whichever is smaller.
add `maxBytesPerPoll` as a bytes-based limit for how much data we poll from shared buffer at a time. Default is `1000000` bytes.
deprecate `recordBufferSize`, use `recordBufferSizeBytes` instead. Warning is logged if `recordBufferSize` is specified
deprecate `maxRecordsPerPoll`, use `maxBytesPerPoll` instead. Warning is logged if maxRecordsPerPoll` is specified
* 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>
* Undocument unused segments retrieval API.
* Mark API deprecated and unstable. Note that it'll be removed.
* Cleanup .spelling entries
* Remove the Unstable annotation
* Add SpectatorHistogram extension
* Clarify documentation
Cleanup comments
* Use ColumnValueSelector directly
so that we support being queried as a Number using longSum or doubleSum aggregators as well as a histogram.
When queried as a Number, we're returning the count of entries in the histogram.
* Apply suggestions from code review
Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com>
* Fix references
* Fix spelling
* Update docs/development/extensions-contrib/spectator-histogram.md
Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com>
---------
Co-authored-by: Victoria Lim <vtlim@users.noreply.github.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.
Added support for Azure Government storage in Druid Azure-Extensions. This enhancement allows the Azure-Extensions to be compatible with different Azure storage types by updating the endpoint suffix from a hardcoded value to a configurable one.
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.
Rishabh Singh
Kashif Faraz
Abhishek Radhakrishnan
Adithya Chakilam
Gian Merlino
Atul Mohan
Katya Macedo
Charles Smith
Sree Charan Manamala
Hardik Bajaj
Nikhil Rao
Adarsh Sanjeev
YongGang
Pranav
317brian
Vishesh Garg
Parag Jain
Sergio Ferragut
Zoltan Haindrich
Soumyava
Rushikesh Bankar
George Shiqi Wu
Sensor
Jill Osborne
zachjsh
benkrug
AlbericByte
Karan Kumar
Benjamin Hopp
Jamie
Peter Marshall
Clint Wylie
Tom
Vadim Ogievetsky
Pramod Immaneni
Aru Raghuwanshi
Laksh Singla
Abhishek Agarwal
Hiroshi Fukada
Ben Sykes
Misha
Victoria Lim