* transition away from StorageAdapter
changes:
* CursorHolderFactory has been renamed to CursorFactory and moved off of StorageAdapter, instead fetched directly from the segment via 'asCursorFactory'. The previous deprecated CursorFactory interface has been merged into StorageAdapter
* StorageAdapter is no longer used by any engines or tests and has been marked as deprecated with default implementations of all methods that throw exceptions indicating the new methods to call instead
* StorageAdapter methods not covered by CursorFactory (CursorHolderFactory prior to this change) have been moved into interfaces which are retrieved by Segment.as, the primary classes are the previously existing Metadata, as well as new interfaces PhysicalSegmentInspector and TopNOptimizationInspector
* added UnnestSegment and FilteredSegment that extend WrappedSegmentReference since their StorageAdapter implementations were previously provided by WrappedSegmentReference
* added PhysicalSegmentInspector which covers some of the previous StorageAdapter functionality which was primarily used for segment metadata queries and other metadata uses, and is implemented for QueryableIndexSegment and IncrementalIndexSegment
* added TopNOptimizationInspector to cover the oddly specific StorageAdapter.hasBuiltInFilters implementation, which is implemented for HashJoinSegment, UnnestSegment, and FilteredSegment
* Updated all engines and tests to no longer use StorageAdapter
Problem
Currently, the delta input source only supports reading from the latest snapshot of the given Delta Lake table. This is a known documented limitation.
Description
Add support for reading Delta snapshot. By default, the Druid-Delta connector reads the latest snapshot of the Delta table in order to preserve compatibility. Users can specify a snapshotVersion to ingest change data events from Delta tables into Druid.
In the future, we can also add support for time-based snapshot reads. The Delta API to read time-based snapshots is not clear currently.
This commit aims to reject MVDs in window processing as we do not support them.
Earlier to this commit, query running a window aggregate partitioned by an MVD column would fail with ClassCastException
* Add framework for running MSQ tests with taskSpec instead of SQL
* Allow configurable datasegment for tests
* Add test
* Revert "Add test"
This reverts commit 79fb241545.
* Revert "Allow configurable datasegment for tests"
This reverts commit caf04ede2b.
In the compaction config, a range type partitionsSpec supports setting one of maxRowsPerSegment and targetRowsPerSegment. When compaction is run with the native engine, while maxRowsPerSegment = x results in segments of size x, targetRowsPerSegment = y results in segments of size 1.5 * y.
MSQ only supports rowsPerSegment = x as part of its tuning config, the resulting segment size being approx. x -- which is in line with maxRowsPerSegment behaviour in native compaction.
This PR makes the following changes:
use effective maxRowsPerSegment to pass as rowsPerSegment parameter for MSQ
persist rowsPerSegment as maxRowsPerSegment in lastCompactionState for MSQ
Use effective maxRowsPerSegment-based range spec in CompactionStatus check for both Native and MSQ.
This reverts commit f1d24c868f.
Updating nimbus to version 9+ is causing HTTP ERROR 500 java.lang.NoSuchMethodError: 'net.minidev.json.JSONObject com.nimbusds.jwt.JWTClaimsSet.toJSONObject()'
Refer to SAP/cloud-security-services-integration-library#429 (comment) for more details.
We would need to upgrade other libraries as well for updating nimbus.jose.jwt
This patch adds "TypeCastSelectors", which is used when writing frames to
perform two coercions:
- When a numeric type is desired and the underlying type is non-numeric or
unknown, the underlying selector is wrapped, "getObject" is called and the
result is coerced using "ExprEval.ofType". This differs from the prior
behavior where the primitive methods like "getLong", "getDouble", etc, would
be called directly. This fixes an issue where a column would be read as
all-zeroes when its SQL type is numeric and its physical type is string, which
can happen when evolving a column's type from string to number.
- When an array type is desired, the underlying selector is wrapped,
"getObject" is called, and the result is coerced to Object[]. This coercion
replaces some earlier logic from #15917.
Description:
#16768 introduces new compaction APIs on the Overlord `/compact/status` and `/compact/progress`.
But the corresponding `OverlordClient` methods do not return an object compatible with the actual
endpoints defined in `OverlordCompactionResource`.
This patch ensures that the objects are compatible.
Changes:
- Add `CompactionStatusResponse` and `CompactionProgressResponse`
- Use these as the return type in `OverlordClient` methods and as the response entity in `OverlordCompactionResource`
- Add `SupervisorCleanupModule` bound on the Coordinator to perform cleanup of supervisors.
Without this module, Coordinator cannot deserialize compaction supervisors.
Currently, an alert is thrown while merging datasource schema with realtime
segment schema when the datasource schema already has update columns from the delta schema.
This isn't an error condition since the datasource schema can have those columns from a different segment.
One scenario in which this can occur is when multiple replicas for a task is run.
Currently compaction with MSQ engine doesn't work for rollup on multi-value dimensions (MVDs), the reason being the default behaviour of grouping on MVD dimensions to unnest the dimension values; for instance grouping on `[s1,s2]` with aggregate `a` will result in two rows: `<s1,a>` and `<s2,a>`.
This change enables rollup on MVDs (without unnest) by converting MVDs to Arrays before rollup using virtual columns, and then converting them back to MVDs using post aggregators. If segment schema is available to the compaction task (when it ends up downloading segments to get existing dimensions/metrics/granularity), it selectively does the MVD-Array conversion only for known multi-valued columns; else it conservatively performs this conversion for all `string` columns.
#15025 adds mergeBuffer/pendingRequests metric in QueryCountStatsMonitor. Since real-time nodes also use the same merge buffers for queries and have QueryCountStatsMonitor , the documentation is being updated to include this metric.
* MSQ: Add limitHint to global-sort shuffles.
This allows pushing down limits into the SuperSorter.
* Test fixes.
* Add limitSpec to ScanQueryKit. Fix SuperSorter tracking.
Bug: When coordinator period is less than 30s, `maxSegmentsToMove` is always computed as 0
irrespective of number of available threads.
Changes:
- Fix lower bound condition and set minimum value to 100.
- Add new test which fails without this fix
Description
-----------
Auto-compaction currently poses several challenges as it:
1. may get stuck on a failing interval.
2. may get stuck on the latest interval if more data keeps coming into it.
3. always picks the latest interval regardless of the level of compaction in it.
4. may never pick a datasource if its intervals are not very recent.
5. requires setting an explicit period which does not cater to the changing needs of a Druid cluster.
This PR introduces various improvements to compaction scheduling to tackle the above problems.
Change Summary
--------------
1. Run compaction for a datasource as a supervisor of type `autocompact` on Overlord.
2. Make compaction policy extensible and configurable.
3. Track status of recently submitted compaction tasks and pass this info to policy.
4. Add `/simulate` API on both Coordinator and Overlord to run compaction simulations.
5. Redirect compaction status APIs to the Overlord when compaction supervisors are enabled.
* Make IntelliJ's MethodIsIdenticalToSuperMethod an error
* Change codebase to follow new IntelliJ inspection
* Restore non-short-circuit boolean expressions to pass tests
* MSQ: Add CPU and thread usage counters.
The main change adds "cpu" and "wall" counters. The "cpu" counter measures
CPU time (using JvmUtils.getCurrentThreadCpuTime) taken up by processors
in processing threads. The "wall" counter measures the amount of wall time
taken up by processors in those same processing threads. Both counters are
broken down by type of processor.
This patch also includes changes to support adding new counters. Due to an
oversight in the original design, older deserializers are not forwards-compatible;
they throw errors when encountering an unknown counter type. To manage this,
the following changes are made:
1) The defaultImpl NilQueryCounterSnapshot is added to QueryCounterSnapshot's
deserialization configuration. This means that any unrecognized counter types
will be read as "nil" by deserializers. Going forward, once all servers are
on the latest code, this is enough to enable easily adding new counters.
2) A new context parameter "includeAllCounters" is added, which defaults to "false".
When this parameter is set "false", only legacy counters are included. When set
to "true", all counters are included. This is currently undocumented. In a future
version, we should set the default to "true", and at that time, include a release
note that people updating from versions prior to Druid 31 should set this to
"false" until their upgrade is complete.
* Style, coverage.
* Fix.
Changes:
- Simplify exception handling in `CryptoService` by just catching a `Exception`
- Throw a `DruidException` as the exception is user facing
- Log the exception for easier debugging
- Add a test to verify thrown exception
Currently, if we have a query with window function having PARTITION BY xyz, and we have a million unique values for xyz each having 1 row, we'd end up creating a million individual RACs for processing, each having a single row. This is unnecessary, and we can batch the PARTITION BY keys together for processing, and process them only when we can't batch further rows to adhere to maxRowsMaterialized config.
The previous iteration of this PR was simplifying WindowOperatorQueryFrameProcessor to run all operators on all the rows instead of creating smaller RACs per partition by key. That approach was discarded in favor of the batching approach, and the details are summarized here: #16823 (comment).
Clint Wylie
Abhishek Radhakrishnan
Sree Charan Manamala
Rishabh Singh
Adarsh Sanjeev
Vishesh Garg
Parth Agrawal
Edgar Melendrez
Katya Macedo
Virushade
Gian Merlino
Edgar Melendrez
Vadim Ogievetsky
Kashif Faraz
Jill Osborne
Hugh Evans
AmatyaAvadhanula
Laksh Singla
Hardik Bajaj
Akshat Jain
Zoltan Haindrich
Jonathan Wei
Parag Jain
317brian
Charles Smith