* 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
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.
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.
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
* Place __time in signatures according to sort order.
Updates a variety of places to put __time in row signatures according
to its position in the sort order, rather than always first, including:
- InputSourceSampler.
- ScanQueryEngine (in the default signature when "columns" is empty).
- Various StorageAdapters, which also have the effect of reordering
the column order in segmentMetadata queries, and therefore in SQL
schemas as well.
Follow-up to #16849.
* Fix compilation.
* Additional fixes.
* Fix.
* Fix style.
* Omit nonexistent columns from the row signature.
* Fix tests.
* Segments primarily sorted by non-time columns.
Currently, segments are always sorted by __time, followed by the sort
order provided by the user via dimensionsSpec or CLUSTERED BY. Sorting
by __time enables efficient execution of queries involving time-ordering
or granularity. Time-ordering is a simple matter of reading the rows in
stored order, and granular cursors can be generated in streaming fashion.
However, for various workloads, it's better for storage footprint and
query performance to sort by arbitrary orders that do not start with __time.
With this patch, users can sort segments by such orders.
For spec-based ingestion, users add "useExplicitSegmentSortOrder: true" to
dimensionsSpec. The "dimensions" list determines the sort order. To
define a sort order that includes "__time", users explicitly
include a dimension named "__time".
For SQL-based ingestion, users set the context parameter
"useExplicitSegmentSortOrder: true". The CLUSTERED BY clause is then
used as the explicit segment sort order.
In both cases, when the new "useExplicitSegmentSortOrder" parameter is
false (the default), __time is implicitly prepended to the sort order,
as it always was prior to this patch.
The new parameter is experimental for two main reasons. First, such
segments can cause errors when loaded by older servers, due to violating
their expectations that timestamps are always monotonically increasing.
Second, even on newer servers, not all queries can run on non-time-sorted
segments. Scan queries involving time-ordering and any query involving
granularity will not run. (To partially mitigate this, a currently-undocumented
SQL feature "sqlUseGranularity" is provided. When set to false the SQL planner
avoids using "granularity".)
Changes on the write path:
1) DimensionsSpec can now optionally contain a __time dimension, which
controls the placement of __time in the sort order. If not present,
__time is considered to be first in the sort order, as it has always
been.
2) IncrementalIndex and IndexMerger are updated to sort facts more
flexibly; not always by time first.
3) Metadata (stored in metadata.drd) gains a "sortOrder" field.
4) MSQ can generate range-based shard specs even when not all columns are
singly-valued strings. It merely stops accepting new clustering key
fields when it encounters the first one that isn't a singly-valued
string. This is useful because it enables range shard specs on
"someDim" to be created for clauses like "CLUSTERED BY someDim, __time".
Changes on the read path:
1) Add StorageAdapter#getSortOrder so query engines can tell how a
segment is sorted.
2) Update QueryableIndexStorageAdapter, IncrementalIndexStorageAdapter,
and VectorCursorGranularizer to throw errors when using granularities
on non-time-ordered segments.
3) Update ScanQueryEngine to throw an error when using the time-ordering
"order" parameter on non-time-ordered segments.
4) Update TimeBoundaryQueryRunnerFactory to perform a segment scan when
running on a non-time-ordered segment.
5) Add "sqlUseGranularity" context parameter that causes the SQL planner
to avoid using granularities other than ALL.
Other changes:
1) Rename DimensionsSpec "hasCustomDimensions" to "hasFixedDimensions"
and change the meaning subtly: it now returns true if the DimensionsSpec
represents an unchanging list of dimensions, or false if there is
some discovery happening. This is what call sites had expected anyway.
* Fixups from CI.
* Fixes.
* Fix missing arg.
* Additional changes.
* Fix logic.
* Fixes.
* Fix test.
* Adjust test.
* Remove throws.
* Fix styles.
* Fix javadocs.
* Cleanup.
* Smoother handling of null ordering.
* Fix tests.
* Missed a spot on the merge.
* Fixups.
* Avoid needless Filters.and.
* Add timeBoundaryInspector to test.
* Fix tests.
* Fix FrameStorageAdapterTest.
* Fix various tests.
* Use forceSegmentSortByTime instead of useExplicitSegmentSortOrder.
* Pom fix.
* Fix doc.
changes:
* Added `CursorBuildSpec` which captures all of the 'interesting' stuff that goes into producing a cursor as a replacement for the method arguments of `CursorFactory.canVectorize`, `CursorFactory.makeCursor`, and `CursorFactory.makeVectorCursor`
* added new interface `CursorHolder` and new interface `CursorHolderFactory` as a replacement for `CursorFactory`, with method `makeCursorHolder`, which takes a `CursorBuildSpec` as an argument and replaces `CursorFactory.canVectorize`, `CursorFactory.makeCursor`, and `CursorFactory.makeVectorCursor`
* `CursorFactory.makeCursors` previously returned a `Sequence<Cursor>` corresponding to the query granularity buckets, with a separate `Cursor` per bucket. `CursorHolder.asCursor` instead returns a single `Cursor` (equivalent to 'ALL' granularity), and a new `CursorGranularizer` has been added for query engines to iterate over the cursor and divide into granularity buckets. This makes the non-vectorized engine behave the same way as the vectorized query engine (with its `VectorCursorGranularizer`), and simplifies a lot of stuff that has to read segments particularly if it does not care about bucketing the results into granularities.
* Deprecated `CursorFactory`, `CursorFactory.canVectorize`, `CursorFactory.makeCursors`, and `CursorFactory.makeVectorCursor`
* updated all `StorageAdapter` implementations to implement `makeCursorHolder`, transitioned direct `CursorFactory` implementations to instead implement `CursorMakerFactory`. `StorageAdapter` being a `CursorMakerFactory` is intended to be a transitional thing, ideally will not be released in favor of moving `CursorMakerFactory` to be fetched directly from `Segment`, however this PR was already large enough so this will be done in a follow-up.
* updated all query engines to use `makeCursorHolder`, granularity based engines to use `CursorGranularizer`.
* Fix build
* Run coldSchemaExec thread periodically
* Bugfix: Run cold schema refresh periodically
* Rename metrics for deep storage only segment schema process
Bug description:
Peons to fail to start up when `WorkerTaskCountStatsMonitor` is used on MiddleManagers.
This is because MiddleManagers pass on their properties to peons and peons are unable to
find `IndexerTaskCountStatsProvider` as that is bound only for indexer nodes.
Fix:
Check if node is an indexer before trying to get instance of `IndexerTaskCountStatsProvider`.
Fixes#13936
In cases where a supervisor is idle and the overlord is restarted for some reason, the supervisor would
start spinning tasks again. In clusters where there are many low throughput streams, this would spike
the task count unnecessarily.
This commit compares the latest stream offset with the ones in metadata during the startup of supervisor
and sets it to idle state if they match.
Adds a configuration clientConnectTimeout to our http client config which controls the connection timeout for our http client requests.
It was observed that on busy K8S clusters, the default connect timeout of 500ms is sometimes not enough time to complete syn/acks for a request and in these cases, the requests timeout with the error:
exceptionType=java.net.SocketTimeoutException, exceptionMessage=Connect Timeout
This behavior was mostly observed on the router while forwarding queries to the broker.
Having a slightly higher connect timeout helped resolve these issues.
This PR adds checks for verification of DataSourceCompactionConfig and CompactionTask with msq engine to ensure:
each aggregator in metricsSpec is idempotent
metricsSpec is non-null when rollup is set to true
Unit tests and existing compaction ITs have been updated accordingly.
Background:
ZK-based segment loading has been completely disabled in #15705 .
ZK `servedSegmentsPath` has been deprecated since Druid 0.7.1, #1182 .
This legacy path has been replaced by the `liveSegmentsPath` and is not used in the code anymore.
Changes:
- Never create ZK loadQueuePath as it is never used.
- Never create ZK servedSegmentsPath as it is never used.
- Do not create ZK liveSegmentsPath if announcement on ZK is disabled
- Fix up tests
* enables to launch a fake broker based on test resources (druidtest uri)
* could record queries into new testfiles during usage
* instead of re-purpose Calcite's Hook migrates to use DruidHook which we can add further keys
* added a quidem-ut module which could be the place for tests which could iteract with modules/etc
This PR adds indexer-level task metrics-
"indexer/task/failed/count"
"indexer/task/success/count"
the current "worker/task/completed/count" metric shows all the tasks completed irrespective of success or failure status so these metrics would help us get more visibility into the status of the completed tasks
This patch introduces an optional cluster configuration, druid.indexing.formats.stringMultiValueHandlingMode, allowing operators to override the default mode SORTED_SET for string dimensions. The possible values for the config are SORTED_SET, SORTED_ARRAY, or ARRAY (SORTED_SET is the default). Case insensitive values are allowed.
While this cluster property allows users to manage the multi-value handling mode for string dimension types, it's recommended to migrate to using real array types instead of MVDs.
This fixes a long-standing issue where compaction will honor the configured cluster wide property instead of rewriting it as the default SORTED_ARRAY always, even if the data was originally ingested with ARRAY or SORTED_SET.
Design:
The loading rate is computed as a moving average of at least the last 10 GiB of successful segment loads.
To account for multiple loading threads on a server, we use the concept of a batch to track load times.
A batch is a set of segments added by the coordinator to the load queue of a server in one go.
Computation:
batchDurationMillis = t(load queue becomes empty) - t(first load request in batch is sent to server)
batchBytes = total bytes successfully loaded in batch
avg loading rate in batch (kbps) = (8 * batchBytes) / batchDurationMillis
overall avg loading rate (kbps) = (8 * sumOverWindow(batchBytes)) / sumOverWindow(batchDurationMillis)
Changes:
- Add `LoadingRateTracker` which computes a moving average load rate based on
the last few GBs of successful segment loads.
- Emit metric `segment/loading/rateKbps` from the Coordinator. In the future, we may
also consider emitting this metric from the historicals themselves.
- Add `expectedLoadTimeMillis` to response of API `/druid/coordinator/v1/loadQueue?simple`
Changes:
- Rename `CoordinatorCompactionConfig` to `DruidCompactionConfig`
- Rename `CompactionConfigUpdateRequest` to `ClusterCompactionConfig`
- Refactor methods in `DruidCompactionConfig`
- Clean up `DataSourceCompactionConfigHistory` and its tests
- Clean up tests and add new tests
- Change API path `/druid/coordinator/v1/config/global` to `/druid/coordinator/v1/config/cluster`
Follow-up to #16291, this commit enables a subset of existing native compaction ITs on the MSQ engine.
In the process, the following changes have been introduced in the MSQ compaction flow:
- Populate `metricsSpec` in `CompactionState` from `querySpec` in `MSQControllerTask` instead of `dataSchema`
- Add check for pre-rolled-up segments having `AggregatorFactory` with different input and output column names
- Fix passing missing cluster-by clause in scan queries
- Add annotation of `CompactionState` to tombstone segments
Changes:
- Add API `/druid/coordinator/v1/config/compaction/global` to update cluster level compaction config
- Add class `CompactionConfigUpdateRequest`
- Fix bug in `CoordinatorCompactionConfig` which caused compaction engine to not be persisted.
Use json field name `engine` instead of `compactionEngine` because JSON field names must align
with the getter name.
- Update MSQ validation error messages
- Complete overhaul of `CoordinatorCompactionConfigResourceTest` to remove unnecessary mocking
and add more meaningful tests.
- Add `TuningConfigBuilder` to easily build tuning configs for tests.
- Add `DatasourceCompactionConfigBuilder`
* Round-robin iterator for datasources to kill.
Currently there's a fairness problem in the KillUnusedSegments duty
where the duty consistently selects the same set of datasources as discovered
from the metadata store or dynamic config params. This is a problem especially
when there are multiple unused. In a medium to large cluster, while we can increase
the task slots to increase the likelihood of broader coverage. This patch adds a simple
round-robin iterator to select datasources and has the following properties:
1. Starts with an initial random cursor position in an ordered list of candidates.
2. Consecutive {@code next()} iterations from {@link #getIterator()} are guaranteed to be deterministic
unless the set of candidates change when {@link #updateCandidates(Set)} is called.
3. Guarantees that no duplicate candidates are returned in two consecutive {@code next()} iterations.
* Renames in RoundRobinIteratorTest.
* Address review comments.
1. Clarify javadocs on the ordered list. Also flesh out the details a bit more.
2. Rename the test hooks to make intent clearer and fix typo.
3. Add NotThreadSafe annotation.
4. Remove one potentially noisy log that's in the path of iteration.
* Add null check to input candidates.
* More commentary.
* Addres review feedback: downgrade some new info logs to debug; invert condition.
Remove redundant comments.
Remove rendundant variable tracking.
* CircularList adjustments.
* Updates to CircularList and cleanup RoundRobinInterator.
* One more case and add more tests.
* Make advanceCursor private for now.
* Review comments.
* more aggressive cancellation of broker parallel merge, more chill blocking queue timeouts
* wire parallel merge into query cancellation system
* oops
* style
* adjust metrics initialization
* fix timeout, fix cleanup to not block
* javadocs to clarify why cancellation future and gizmo are split
* cancelled -> canceled, simplify QueuePusher since it always takes a ResultBatch, non-static terminal marker to make stuff stop complaining about types, specialize tryOffer to be tryOfferTerminal so it wont be misused, add comments to clarify reason for non-blocking offers that might fail
changes:
* removes `druid.indexer.task.batchProcessingMode` in favor of always using `CLOSED_SEGMENT_SINKS` which uses `BatchAppenderator`. This was intended to become the default for native batch, but that was missed so `CLOSED_SEGMENTS` was the default (using `AppenderatorImpl`), however MSQ has been exclusively using `BatchAppenderator` with no problems so it seems safe to just roll it out as the only option for batch ingestion everywhere.
* with `batchProcessingMode` gone, there is no use for `AppenderatorImpl` so it has been removed
* implify `Appenderator` construction since there are only separate stream and batch versions now
* simplify tests since `batchProcessingMode` is gone
changes:
* removed `Firehose` and `FirehoseFactory` and remaining implementations which were mostly no longer used after #16602
* Moved `IngestSegmentFirehose` which was still used internally by Hadoop ingestion to `DatasourceRecordReader.SegmentReader`
* Rename `SQLFirehoseFactoryDatabaseConnector` to `SQLInputSourceDatabaseConnector` and similar renames for sub-classes
* Moved anything remaining in a 'firehose' package somewhere else
* Clean up docs on firehose stuff
Description:
Task action audit logging was first deprecated and disabled by default in Druid 0.13, #6368.
As called out in the original discussion #5859, there are several drawbacks to persisting task action audit logs.
- Only usage of the task audit logs is to serve the API `/indexer/v1/task/{taskId}/segments`
which returns the list of segments created by a task.
- The use case is really narrow and no prod clusters really use this information.
- There can be better ways of obtaining this information, such as the metric
`segment/added/bytes` which reports both the segment ID and task ID
when a segment is committed by a task. We could also include committed segment IDs in task reports.
- A task persisting several segments would bloat up the audit logs table putting unnecessary strain
on metadata storage.
Changes:
- Remove `TaskAuditLogConfig`
- Remove method `TaskAction.isAudited()`. No task action is audited anymore.
- Remove `SegmentInsertAction` as it is not used anymore. `SegmentTransactionalInsertAction`
is the new incarnation which has been in use for a while.
- Deprecate `MetadataStorageActionHandler.addLog()` and `getLogs()`. These are not used anymore
but need to be retained for backward compatibility of extensions.
- Do not create `druid_taskLog` metadata table anymore.
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`
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).
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.
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.
Clint Wylie
Rishabh Singh
Vishesh Garg
Kashif Faraz
Gian Merlino
Virushade
Maytas Monsereenusorn
Rushikesh Bankar
Adithya Chakilam
Zoltan Haindrich
Atul Mohan
Laksh Singla
Abhishek Radhakrishnan
AmatyaAvadhanula
zachjsh