* perf: indexing: Introduce a bulk getValuesInto function to read values in bulk
If large number of values are required from DimensionDictionary
during indexing, fetch them all in a single lock/unlock instead of
lock/unlock each individual item.
* refactor: rename key to keys in function args
* fix: check explicitly that argument length on arrays match
* refactor: getValuesInto renamed to getValues, now creates and returns a new T[] rather than filling
Azure Blob storage has multiple modes of authentication. One of them is Shared access resource
. This is very useful in cases when we do not want to add the account key in the druid properties .
Problem:
- When a kinesis stream is resharded, the original shards are closed.
Any intermediate shard created in the process is eventually closed as well.
- If a shard is closed before any record is put into it, it can be safely ignored for ingestion.
- It is expensive to determine if a closed shard is empty, since it requires a call to the Kinesis cluster.
Changes:
- Maintain a cache of closed empty and closed non-empty shards in `KinesisSupervisor`
- Add config `skipIngorableShards` to `KinesisSupervisorTuningConfig`
- The caches are used and updated only when `skipIgnorableShards = true`
Problem:
When using a `CachingCostBalancerStrategy` with segments of granularity ALL,
no segment gets loaded.
- With granularity ALL, segments of eternity interval are created which have
`start = Long.MIN_VALUE / 2` and `end = Long.MAX_VALUE / 2`.
- For cost calculation in the balancer strategy, `toLocalInterval()` method is invoked where
`Long.MIN_VALUE / 2` or `Long.MAX_VALUE / 2` cause an overflow thus resulting in no overlap.
- The strategy is unable to find any eligible server for loading a given segment.
Fix:
- Reverse order of operations to divide by `MILLIS_FACTOR` (~10^8) first,
then do the subtraction to prevent Long overflow.
As part of #12078 one of the followup's was to have a specific config which does not allow accidental unnesting of multi value columns if such columns become part of the grouping key.
Added a config groupByEnableMultiValueUnnesting which can be set in the query context.
The default value of groupByEnableMultiValueUnnesting is true, therefore it does not change the current engine behavior.
If groupByEnableMultiValueUnnesting is set to false, the query will fail if it encounters a multi-value column in the grouping key.
* Moving in filter check to broker
* Adding more unit tests, making error message meaningful
* Spelling and doc changes
* Updating default to -1 and making this feature hide by default. The number of IN filters can grow upto a max limit of 100
* Removing upper limit of 100, updated docs
* Making documentation more meaningful
* Moving check outside to PlannerConfig, updating test cases and adding back max limit
* Updated with some additional code comments
* Missed removing one line during the checkin
* Addressing doc changes and one forbidden API correction
* Final doc change
* Adding a speling exception, correcting a testcase
* Reading entire filter tree to address combinations of ANDs and ORs
* Specifying in docs that, this case works only for ORs
* Revert "Reading entire filter tree to address combinations of ANDs and ORs"
This reverts commit 81ca8f8496.
* Covering a class cast exception and updating docs
* Counting changed
Co-authored-by: Jihoon Son <jihoonson@apache.org>
In extreme cases where many parallel indexing jobs are submitted together, it is possible
that the `ParallelIndexSupervisorTasks` take up all slots leaving no slot to schedule
their own sub-tasks thus stalling progress of all the indexing jobs.
Key changes:
- Add config `druid.indexer.runner.parallelIndexTaskSlotRatio` to limit the task slots
for `ParallelIndexSupervisorTasks` per worker
- `ratio = 1` implies supervisor tasks can use all slots on a worker if needed (default behavior)
- `ratio = 0` implies supervisor tasks can not use any slot on a worker
(actually, at least 1 slot is always available to ensure progress of parallel indexing jobs)
- `ImmutableWorkerInfo.canRunTask()`
- `WorkerHolder`, `ZkWorker`, `WorkerSelectUtils`
This fixes intermittent, spurious failures that we've observed in
the Kinesis sharding integration tests due to Kinesis taking
longer than the code expected to start a sharding operation. The
method that's changed is part of the integration test suite and
only used by the test cases that we've seen are flaky.
Prior to this change, the tests expected a sharding operation to
start in 9 seconds (30 retries * 300ms delay/retry). This change
bumps the number of retries to 100, giving Kinesis 30 seconds to
start the sharding.
This PR also makes a small, clarifying change to the condition
used to determine if sharding has started. Instead of checking if
the number of shards has increased (which was technically correct
even if the test is reducing the number of shards due to a Kinesis
implementation detail), we now just check if the shard count has
changed.
* refactor and link fixes
* add sql docs to left nav
* code format for needle
* updated web console script
* link fixes
* update earliest/latest functions
* edits for grammar and style
* more link fixes
* another link
* update with #12226
* update .spelling file
#12163 makes PARTITIONED BY a required clause in INSERT queries. While this is required, if a user accidentally omits the clause, it emits a JavaCC/Calcite error, since it's syntactically incorrect. The error message is cryptic. Since it's a custom clause, this PR aims to make the clause optional on the syntactic side, but move the validation to DruidSqlInsert where we can surface a friendlier error.
When `ParallelIndexSupervisorTask` converts `BucketNumberedShardSpecs`
to corresponding `BuildingShardSpecs`, the bucketId order gets lost.
Particularly, for range partitioning, this results in the partitionIds not being in the same order
as increasing partition boundaries.
Changes
- Refactor `ParallelIndexSupervisorTask.groupGenericPartitionLocationsPerPartition()`
* rework sql planner expression and virtual column handling
* simplify a bit
* add back and deprecate old methods, more tests, fix multi-value string coercion bug and associated tests
* spotbugs
* fix bugs with multi-value string array expression handling
* javadocs and adjust test
* better
* fix tests
* working
* Lazily load segmentKillers, segmentMovers, and segmentArchivers
* more tests
* test-jar plugin
* more coverage
* lazy client
* clean up changes
* checkstyle
* i did not change the branch condition
* adjust failure rate to run tests faster
* javadocs
* checkstyle
* array_concat_agg and array_agg support for array inputs
changes:
* added array_concat_agg to aggregate arrays into a single array
* added array_agg support for array inputs to make nested array
* added 'shouldAggregateNullInputs' and 'shouldCombineAggregateNullInputs' to fix a correctness issue with STRING_AGG and ARRAY_AGG when merging results, with dual purpose of being an optimization for aggregating
* fix test
* tie capabilities type to legacy mode flag about coercing arrays to strings
* oops
* better javadoc
* Harmonize implementations of "visit" for Exprs from ExprMacros.
Many of them had bugs where they would not visit all of the original
arguments. I don't think this has user-visible consequences right now,
but it's possible it would in a future world where "visit" is used
for more stuff than it is today.
So, this patch all updates all implementations to a more consistent
style that emphasizes reapplying the macro to the shuttled args.
* Test fixes, test coverage, PR review comments.
Changes:
- Add `Range` header to the request before opening the connection
- Use header `Content-Range` instead of `Accept-Ranges` as `Content-Range` is guaranteed to be populated if the server is returning a partial response
* changes:
* remove SystemSchema duplicate ServerInventoryView in broker
* suppress duplicate segment added/removed warnings in HttpServerInventoryView when doing a full sync
* fixes
Fixes#12022
### Description
The current implementations of memory estimation in `OnHeapIncrementalIndex` and `StringDimensionIndexer` tend to over-estimate which leads to more persistence cycles than necessary.
This PR replaces the max estimation mechanism with getting the incremental memory used by the aggregator or indexer at each invocation of `aggregate` or `encode` respectively.
### Changes
- Add new flag `useMaxMemoryEstimates` in the task context. This overrides the same flag in DefaultTaskConfig i.e. `druid.indexer.task.default.context` map
- Add method `AggregatorFactory.factorizeWithSize()` that returns an `AggregatorAndSize` which contains
the aggregator instance and the estimated initial size of the aggregator
- Add method `Aggregator.aggregateWithSize()` which returns the incremental memory used by this aggregation step
- Update the method `DimensionIndexer.processRowValsToKeyComponent()` to return the encoded key component as well as its effective size in bytes
- Update `OnHeapIncrementalIndex` to use the new estimations only if `useMaxMemoryEstimates = false`
Follow up to #12205 to allow druid-mysql-extensions to work with mysql connector/j 8.x again, which does not contain MySQLTransientException, and while would have had the same problem as mariadb if a transient exception was checked, the new check eagerly loads the class when starting up, causing immediate failure.
Fixes an issue where a load-drop-load sequence for a segment and historical doesn't work correctly for http based load queue peon. The first cycle of load-drop works fine - the problem comes when there is an attempt to reload the segment. The historical caches load success for some recent segments and makes the reload as a no-op. But it doesn't consider that fact that the segment was also dropped in between the load requests.
This change invalidates the cache after a client tries to fetch a success result.
* VirtualizedColumnSelectorFactory: Allow virtual columns to reference each other.
This matches the behavior of QueryableIndex and IncrementalIndex based cursors.
* Fixes to getColumnCapabilities.
Jihoon Son
Jason Koch
Karan Kumar
Xavier Léauté
Jonathan Wei
Maytas Monsereenusorn
AmatyaAvadhanula
tejaswini-imply
Abhishek Agarwal
Laksh Singla
somu-imply
William Hyun
Daniel Koepke
Victoria Lim
Kashif Faraz
Clint Wylie
John Gozde
Vadim Ogievetsky
Suneet Saldanha
Gian Merlino
AshishKapoor
zachjsh
Rohan Garg
Gian Merlino