Changes:
- Break `NewestSegmentFirstIterator` into two parts
- `DatasourceCompactibleSegmentIterator` - this contains all the code from `NewestSegmentFirstIterator`
but now handles a single datasource and allows a priority to be specified
- `PriorityBasedCompactionSegmentIterator` - contains separate iterator for each datasource and
combines the results into a single queue to be used by a compaction search policy
- Update `NewestSegmentFirstPolicy` to use the above new classes
- Cleanup `CompactionStatistics` and `AutoCompactionSnapshot`
- Cleanup `CompactSegments`
- Remove unused methods from `Tasks`
- Remove unneeded `TasksTest`
- Move tests from `NewestSegmentFirstIteratorTest` to `CompactionStatusTest`
and `DatasourceCompactibleSegmentIteratorTest`
* When an ArrayList RAC creates a child RAC, the start and end offsets need to have the offset of parent's start offset
* Defaults the 2nd window bound to CURRENT ROW when only a single bound is specified
* Removes the windowingStrictValidation warning and throws a hard exception when Order By alongside RANGE clause is not provided with UNBOUNDED or CURRENT ROW as both bounds
Changes:
- No functional change
- Add class `TuningConfigBuilder` to build `IndexTuningConfig`, `CompactionTuningConfig`
- Remove old class `ParallelIndexTestingFactory.TuningConfigBuilder`
- Remove some unused fields and methods
Changes
- No functional change
- Remove unused method `IndexTuningConfig.withPartitionsSpec()`
- Remove unused method `ParallelIndexTuningConfig.withPartitionsSpec()`
- Remove redundant method `CompactTask.emitIngestionModeMetrics()`
- Remove Clock argument from `CompactionTask.createDataSchemasForInterval()` as it was only needed
for one test which was just verifying the value passed by the test itself. The code now uses a `Stopwatch`
instead and test simply verifies that the metric has been emitted.
- Other minor cleanup changes
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).
In case of few aggregators for example BloomSqlAggregator, BaseVarianceSqlAggregator etc, the aggName is being updated from a0 to a0:agg, breaching the contract as we would expect the aggName as the name which is passed. This is causing a mismatch while creating a column accessor.
This commit aims to correct those violating sql aggregators.
changes:
* fixes a bug with unnest storage adapter not preserving underlying columns dictionary uniqueness when allowing dimension selector cursor
* fixes a bug with unnest on realtime segments with empty rows incorrectly specifying index 0 as the row dictionary value
Add a shuffling based on the resultShuffleSpecFactory after a limit processor depending on the query destination. LimitFrameProcessors currently do not update the partition boosting column, so we also add the boost column to the previous stage, if one is required.
temp fix until CALCITE-6435 gets fixed (released&upgraded to)
added a custom rule (FixIncorrectInExpansionTypes) to fix-up types of the affected literals
added a testcase which will alert on upgrade
* fix equality and typed in filter behavior for numeric match values on string columns
changes:
* EqualityFilter and TypedInfilter numeric match values against string columns will now cast strings to numeric values instead of converting the numeric values directly to string for pure string equality, which is consistent with the casts which are eaten in the SQL layer, as well as classic druid behavior
* added tests to cover numeric equality matching. Double match values in particular would fail to match the string values since `1.0` would become `'1.0'` which does not match `'1'`.
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.
* Update examples/bin/dsql scripts to accept Python 3
Remove redundant urllib import
Translating to Python3: Changing xrange to range
Translating to Python3: Changing long to int
Translating to Python3: Change urllib2 methods, and fix encoding/decoding issues
Remove unnecessary import
Add option for Python2
Rename files
* Update examples/bin/dsql
Co-authored-by: Benedict Jin <asdf2014@apache.org>
* Resolve PR comments
Add comment in files indicating updates need to be made in both places
Update examples/bin/dsql
Co-authored-by: Benedict Jin <asdf2014@apache.org>
* Update error output when using Python 2.
Co-authored-by: Abhishek Radhakrishnan <abhishek.rb19@gmail.com>
---------
Co-authored-by: Benedict Jin <asdf2014@apache.org>
Co-authored-by: Abhishek Radhakrishnan <abhishek.rb19@gmail.com>
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.
Updated javadoc for `ColumnIndexSupplier.as` to elaborate on the types of indexes callers might want to ask for from the method, as well as help implementors know what kinds of indexes they should implement to participate in filtering
Zoltan Haindrich
Kashif Faraz
Adithya Chakilam
AmatyaAvadhanula
Sree Charan Manamala
Rishabh Singh
Laksh Singla
Vishesh Garg
Clint Wylie
Vadim Ogievetsky
YongGang
Adarsh Sanjeev
Abhishek Radhakrishnan
Alberic Liu
Lars Francke
Virushade
317brian
Victoria Lim
zachjsh
Jill Osborne
Akshat Jain