This PR fixes an issue that could occur if druid.query.scheduler.numThreads is configured and any exception occurs after QueryScheduler.run has been called to create a Sequence. This would result in total and/or lane specific locks being acquired, but because the sequence was not actually being evaluated, the "baggage" which typically releases these locks was not being executed. An example of how this can happen is if a group-by having filter, which wraps and transforms this sequence happens to explode while wrapping the sequence. The end result is that the locks are acquired, but never released, eventually halting the ability to execute any queries.
This PR fixes an issue when using 'auto' encoded LONG typed columns and the 'vectorized' query engine. These columns use a delta based bit-packing mechanism, and errors in the vectorized reader would cause it to incorrectly read column values for some bit sizes (1 through 32 bits). This is a regression caused by #11004, which added the optimized readers to improve performance, so impacts Druid versions 0.22.0+.
While writing the test I finally got sad enough about IndexSpec not having a "builder", so I made one, and switched all the things to use it. Apologies for the noise in this bug fix PR, the only real changes are in VSizeLongSerde, and the tests that have been modified to cover the buggy behavior, VSizeLongSerdeTest and ExpressionVectorSelectorsTest. Everything else is just cleanup of IndexSpec usage.
* Make LoggingEmitter more useful
* Skip code coverage for facade classes
* fix spellcheck
* code review
* fix dependency
* logging.md
* fix checkstyle
* Add back jacoco version to main pom
* TimeBoundary: Use cursor when datasource is not a regular table.
Fixes a bug where TimeBoundary could return incorrect results with
INNER Join or inline data.
* Addl Javadocs.
* Fix two concurrency issues with segment fetching.
1) SegmentLocalCacheManager: Fix a concurrency issue where certain directory
cleanup happened outside of directoryWriteRemoveLock. This created the
possibility that segments would be deleted by one thread, while being
actively downloaded by another thread.
2) TaskDataSegmentProcessor (MSQ): Fix a concurrency issue when two stages
in the same process both use the same segment. For example: a self-join
using distributed sort-merge. Prior to this change, the two stages could
delete each others' segments.
3) ReferenceCountingResourceHolder: increment() returns a new ResourceHolder,
rather than a Releaser. This allows it to be passed to callers without them
having to hold on to both the original ResourceHolder *and* a Releaser.
4) Simplify various interfaces and implementations by using ResourceHolder
instead of Pair and instead of split-up fields.
* Add test.
* Fix style.
* Remove Releaser.
* Updates from master.
* Add some GuardedBys.
* Use the correct GuardedBy.
* Adjustments.
* Improved handling for zero-length intervals.
1) Return an empty list from VersionedIntervalTimeline.lookup when
provided with an empty interval. (The logic doesn't quite work when
intervals are empty, which led to #14129.)
2) Don't return zero-length intervals from JodaUtils.condenseIntervals.
3) Detect "incorrect" comparator in JodaUtils.condenseIntervals, and
recreate the SortedSet if needed. (Not strictly related to the theme
of this patch. Just another thing in the same file.)
4) Remove unused method JodaUtils.containOverlappingIntervals.
Fixes#14129.
* Fix TimewarpOperatorTest.
* MSQ: Subclass CalciteJoinQueryTest, other supporting changes.
The main change is the new tests: we now subclass CalciteJoinQueryTest
in CalciteSelectJoinQueryMSQTest twice, once for Broadcast and once for
SortMerge.
Two supporting production changes for default-value mode:
1) InputNumberDataSource is marked as concrete, to allow leftFilter to
be pushed down to it.
2) In default-value mode, numeric frame field readers can now return nulls.
This is necessary when stacking joins on top of joins: nulls must be
preserved for semantics that match broadcast joins and native queries.
3) In default-value mode, StringFieldReader.isNull returns true on empty
strings in addition to nulls. This is more consistent with the behavior
of the selectors, which map empty strings to null as well in that mode.
As an effect of change (2), the InsertTimeNull change from #14020 (to
replace null timestamps with default timestamps) is reverted. IMO, this
is fine, as either behavior is defensible, and the change from #14020
hasn't been released yet.
* Adjust tests.
* Style fix.
* Additional tests.
* return task status reported by peon
* Write TaskStatus to file in AbstractTask.cleanUp
* Get TaskStatus from task log
* Fix merge conflicts in AbstractTaskTest
* Add unit tests for TaskLogPusher, TaskLogStreamer, NoopTaskLogs to satisfy code coverage
* Add license headerss
* Fix style
* Remove unknown exception declarations
* Allow for Log4J to be configured for peons but still ensure console logging is enforced
This change will allow for log4j to be configured for peons but require console logging is still
configured for them to ensure peon logs are saved to deep storage.
Also fixed the test ConsoleLoggingEnforcementTest to use a valid appender for the non console
Config as the previous config was incorrect and would never return a logger.
* fix checkstyle
* add warning to logger when it overwrites all loggers to be console
* optimize calls for altering logging config for ConsoleLoggingEnforcementConfigurationFactory
add getName to the druid logger class
* update docs, and error message
* edit docs to be more clear
* fix checkstyle issues
* CI fixes - LoggerTest code coverage and fix spelling issue for logging docs
* Updating segment map function for QueryDataSource to ensure group by of group by of join data source gets into proper segment map function path
* Adding unit tests for the failed case
* There you go coverage bot, be happy now
* MSQ: Support for querying lookup and inline data directly.
Main changes:
1) Add of LookupInputSpec and DataSourcePlan.forLookup.
2) Add InlineInputSpec, and modify of DataSourcePlan.forInline to use
this instead of an ExternalInputSpec with JSON. This allows the inline
data to act as the right-hand side of a join, if needed.
Supporting changes:
1) Modify JoinDataSource's leftFilter validation to be a little less
strict: it's now OK with leftFilter being attached to any concrete
leaf (no children) datasource, rather than requiring it be a table.
This allows MSQ to create JoinDataSource with InputNumberDataSource
as the base.
2) Add SegmentWranglerModule to CliIndexer, CliPeon. This allows them to
query lookups and inline data directly.
* Updates based on CI.
* Additional tests.
* Style fix.
* Remove unused import.
* MSQ: Support multiple result columns with the same name.
This is allowed in SQL, and is supported by the regular SQL endpoint.
We retain a validation that INSERT ... SELECT does not allow multiple
columns with the same name, because column names in segments must be
unique.
changes:
* adds support for boolean inputs to the classic long dimension indexer, which plays nice with LONG being the semi official boolean type in Druid, and even nicer when druid.expressions.useStrictBooleans is set to true, since the sampler when using the new 'auto' schema when 'useSchemaDiscovery' is specified on the dimensions spec will call the type out as LONG
* fix bugs with sampler response and new schema discovery stuff incorrectly using classic 'json' type for the logical schema instead of the new 'auto' type
* Frames: Ensure nulls are read as default values when appropriate.
Fixes a bug where LongFieldWriter didn't write a properly transformed
zero when writing out a null. This had no meaningful effect in SQL-compatible
null handling mode, because the field would get treated as a null anyway.
But it does have an effect in default-value mode: it would cause Long.MIN_VALUE
to get read out instead of zero.
Also adds NullHandling checks to the various frame-based column selectors,
allowing reading of nullable frames by servers in default-value mode.
Fixes#13837.
### Description
This change allows for input source type security in the native task layer.
To enable this feature, the user must set the following property to true:
`druid.auth.enableInputSourceSecurity=true`
The default value for this property is false, which will continue the existing functionality of needing authorization to write to the respective datasource.
When this config is enabled, the users will be required to be authorized for the following resource action, in addition to write permission on the respective datasource.
`new ResourceAction(new Resource(ResourceType.EXTERNAL, {INPUT_SOURCE_TYPE}, Action.READ`
where `{INPUT_SOURCE_TYPE}` is the type of the input source being used;, http, inline, s3, etc..
Only tasks that provide a non-default implementation of the `getInputSourceResources` method can be submitted when config `druid.auth.enableInputSourceSecurity=true` is set. Otherwise, a 400 error will be thrown.
* smarter nested column index utilization
changes:
* adds skipValueRangeIndexScale and skipValuePredicateIndexScale to ColumnConfig (e.g. DruidProcessingConfig) available as system config via druid.processing.indexes.skipValueRangeIndexScale and druid.processing.indexes.skipValuePredicateIndexScale
* NestedColumnIndexSupplier uses skipValueRangeIndexScale and skipValuePredicateIndexScale to multiply by the total number of rows to be processed to determine the threshold at which we should no longer consider using bitmap indexes because it will be too many operations
* Default values for skipValueRangeIndexScale and skipValuePredicateIndexScale have been initially set to 0.08, but are separate to allow independent tuning
* these are not documented on purpose yet because they are kind of hard to explain, the mainly exist to help conduct larger scale experiments than the jmh benchmarks used to derive the initial set of values
* these changes provide a pretty sweet performance boost for filter processing on nested columns
* Always use file sizes when determining batch ingest splits.
Main changes:
1) Update CloudObjectInputSource and its subclasses (S3, GCS,
Azure, Aliyun OSS) to use SplitHintSpecs in all cases. Previously, they
were only used for prefixes, not uris or objects.
2) Update ExternalInputSpecSlicer (MSQ) to consider file size. Previously,
file size was ignored; all files were treated as equal weight when
determining splits.
A side effect of these changes is that we'll make additional network
calls to find the sizes of objects when users specify URIs or objects
as opposed to prefixes. IMO, this is worth it because it's the only way
to respect the user's split hint and task assignment settings.
Secondary changes:
1) S3, Aliyun OSS: Use getObjectMetadata instead of listObjects to get
metadata for a single object. This is a simpler call that is also
expected to be less expensive.
2) Azure: Fix a bug where getBlobLength did not populate blob
reference attributes, and therefore would not actually retrieve the
blob length.
3) MSQ: Align dynamic slicing logic between ExternalInputSpecSlicer and
TableInputSpecSlicer.
4) MSQ: Adjust WorkerInputs to ensure there is always at least one
worker, even if it has a nil slice.
* Add msqCompatible to testGroupByWithImpossibleTimeFilter.
* Fix tests.
* Add additional tests.
* Remove unused stuff.
* Remove more unused stuff.
* Adjust thresholds.
* Remove irrelevant test.
* Fix comments.
* Fix bug.
* Updates.
changes:
* introduce ColumnFormat to separate physical storage format from logical type. ColumnFormat is now used instead of ColumnCapabilities to get column handlers for segment creation
* introduce new 'auto' type indexer and merger which produces a new common nested format of columns, which is the next logical iteration of the nested column stuff. Essentially this is an automatic type column indexer that produces the most appropriate column for the given inputs, making either STRING, ARRAY<STRING>, LONG, ARRAY<LONG>, DOUBLE, ARRAY<DOUBLE>, or COMPLEX<json>.
* revert NestedDataColumnIndexer, NestedDataColumnMerger, NestedDataColumnSerializer to their version pre #13803 behavior (v4) for backwards compatibility
* fix a bug in RoaringBitmapSerdeFactory if anything actually ever wrote out an empty bitmap using toBytes and then later tried to read it (the nerve!)
* select sum(c) on an unnested column now does not return 'Type mismatch' error and works properly
* Making sure an inner join query works properly
* Having on unnested column with a group by now works correctly
* count(*) on an unnested query now works correctly
While using intermediateSuperSorterStorageMaxLocalBytes the super sorter was retaining references of the memory allocator.
The fix clears the current outputChannel when close() is called on the ComposingWritableFrameChannel.java
* Reworking s3 connector with
1. Adding retries
2. Adding max fetch size
3. Using s3Utils for most of the api's
4. Fixing bugs in DurableStorageCleaner
5. Moving to Iterator for listDir call
array columns!
changes:
* add support for storing nested arrays of string, long, and double values as specialized nested columns instead of breaking them into separate element columns
* nested column type mimic behavior means that columns ingested with only root arrays of primitive values will be ARRAY typed columns
* neat test refactor stuff
* add v4 segment test
* add array element indexes
* add tests for unnest and array columns
* fix unnest column value selector cursor handling of null and empty arrays
* Refactoring and bug fixes on top of unnest. The filter now is passed inside the unnest cursors. Added tests for scenarios such as
1. filter on unnested column which involves a left filter rewrite
2. filter on unnested virtual column which pushes the filter to the right only and involves no rewrite
3. not filters
4. SQL functions applied on top of unnested column
5. null present in first row of the column to be unnested
changes:
* fixes inconsistent handling of byte[] values between ExprEval.bestEffortOf and ExprEval.ofType, which could cause byte[] values to end up as java toString values instead of base64 encoded strings in ingest time transforms
* improved ExpressionTransform binding to re-use ExprEval.bestEffortOf when evaluating a binding instead of throwing it away
* improved ExpressionTransform array handling, added RowFunction.evalDimension that returns List<String> to back Row.getDimension and remove the automatic coercing of array types that would typically happen to expression transforms unless using Row.getDimension
* added some tests for ExpressionTransform with array inputs
* improved ExpressionPostAggregator to use partial type information from decoration
* migrate some test uses of InputBindings.forMap to use other methods
* Adds new implementation of 'frontCoded' string encoding strategy, which writes out a v1 FrontCodedIndexed which stores buckets on a prefix of the previous value instead of the first value in the bucket
* Refactoring and bug fixes on top of unnest. The filter now is passed inside the unnest cursors. Added tests for scenarios such as
1. filter on unnested column which involves a left filter rewrite
2. filter on unnested virtual column which pushes the filter to the right only and involves no rewrite
3. not filters
4. SQL functions applied on top of unnested column
5. null present in first row of the column to be unnested
* Various changes and fixes to UNNEST.
Native changes:
1) UnnestDataSource: Replace "column" and "outputName" with "virtualColumn".
This enables pushing expressions into the datasource. This in turn
allows us to do the next thing...
2) UnnestStorageAdapter: Logically apply query-level filters and virtual
columns after the unnest operation. (Physically, filters are pulled up,
when possible.) This is beneficial because it allows filters and
virtual columns to reference the unnested column, and because it is
consistent with how the join datasource works.
3) Various documentation updates, including declaring "unnest" as an
experimental feature for now.
SQL changes:
1) Rename DruidUnnestRel (& Rule) to DruidUnnestRel (& Rule). The rel
is simplified: it only handles the UNNEST part of a correlated join.
Constant UNNESTs are handled with regular inline rels.
2) Rework DruidCorrelateUnnestRule to focus on pulling Projects from
the left side up above the Correlate. New test testUnnestTwice verifies
that this works even when two UNNESTs are stacked on the same table.
3) Include ProjectCorrelateTransposeRule from Calcite to encourage
pushing mappings down below the left-hand side of the Correlate.
4) Add a new CorrelateFilterLTransposeRule and CorrelateFilterRTransposeRule
to handle pulling Filters up above the Correlate. New tests
testUnnestWithFiltersOutside and testUnnestTwiceWithFilters verify
this behavior.
5) Require a context feature flag for SQL UNNEST, since it's undocumented.
As part of this, also cleaned up how we handle feature flags in SQL.
They're now hooked into EngineFeatures, which is useful because not
all engines support all features.
With SuperSorter using the PartitionedOutputChannels for sorting, it might OOM on inputs of reasonable size because the channel consists of both the writable frame channel and the frame allocator, both of which are not required once the output channel has been written to.
This change adds a readOnly to the output channel which contains only the readable channel, due to which unnecessary memory references to the writable channel and the memory allocator are lost once the output channel has been written to, preventing the OOM.
* Window planning: use collation traits, improve subquery logic.
SQL changes:
1) Attach RelCollation (sorting) trait to any PartialDruidQuery
that ends in AGGREGATE or AGGREGATE_PROJECT. This allows planning to
take advantage of the fact that Druid sorts by dimensions when
doing aggregations.
2) Windowing: inspect RelCollation trait from input, and insert naiveSort
if, and only if, necessary.
3) Windowing: add support for Project after Window, when the Project
is a simple mapping. Helps eliminate subqueries.
4) DruidRules: update logic for considering subqueries to reflect that
subqueries are not required to be GroupBys, and that we have a bunch
of new Stages now. With all of this evolution that has happened, the
old logic didn't quite make sense.
Native changes:
1) Use merge sort (stable) rather than quicksort when sorting
RowsAndColumns. Makes it easier to write test cases for plans that
involve re-sorting the data.
* Changes from review.
* Mark the bad test as failing.
* Additional update.
* Fix failingTest.
* Fix tests.
* Mark a var final.
* Improve memory efficiency of WrappedRoaringBitmap.
Two changes:
1) Use an int[] for sizes 4 or below.
2) Remove the boolean compressRunOnSerialization. Doesn't save much
space, but it does save a little, and it isn't adding a ton of value
to have it be configurable. It was originally configurable in case
anything broke when enabling it, but it's been a while and nothing
has broken.
* Slight adjustment.
* Adjust for inspection.
* Updates.
* Update snaps.
* Update test.
* Adjust test.
* Fix snaps.
* use custom case operator conversion instead of direct operator conversion, to produce native nvl expression for SQL NVL and 2 argument COALESCE, and add optimization for certain case filters from coalesce and nvl statements
* Sort-merge join and hash shuffles for MSQ.
The main changes are in the processing, multi-stage-query, and sql modules.
processing module:
1) Rename SortColumn to KeyColumn, replace boolean descending with KeyOrder.
This makes it nicer to model hash keys, which use KeyOrder.NONE.
2) Add nullability checkers to the FieldReader interface, and an
"isPartiallyNullKey" method to FrameComparisonWidget. The join
processor uses this to detect null keys.
3) Add WritableFrameChannel.isClosed and OutputChannel.isReadableChannelReady
so callers can tell which OutputChannels are ready for reading and which
aren't.
4) Specialize FrameProcessors.makeCursor to return FrameCursor, a random-access
implementation. The join processor uses this to rewind when it needs to
replay a set of rows with a particular key.
5) Add MemoryAllocatorFactory, which is embedded inside FrameWriterFactory
instead of a particular MemoryAllocator. This allows FrameWriterFactory
to be shared in more scenarios.
multi-stage-query module:
1) ShuffleSpec: Add hash-based shuffles. New enum ShuffleKind helps callers
figure out what kind of shuffle is happening. The change from SortColumn
to KeyColumn allows ClusterBy to be used for both hash-based and sort-based
shuffling.
2) WorkerImpl: Add ability to handle hash-based shuffles. Refactor the logic
to be more readable by moving the work-order-running code to the inner
class RunWorkOrder, and the shuffle-pipeline-building code to the inner
class ShufflePipelineBuilder.
3) Add SortMergeJoinFrameProcessor and factory.
4) WorkerMemoryParameters: Adjust logic to reserve space for output frames
for hash partitioning. (We need one frame per partition.)
sql module:
1) Add sqlJoinAlgorithm context parameter; can be "broadcast" or
"sortMerge". With native, it must always be "broadcast", or it's a
validation error. MSQ supports both. Default is "broadcast" in
both engines.
2) Validate that MSQs do not use broadcast join with RIGHT or FULL join,
as results are not correct for broadcast join with those types. Allow
this in native for two reasons: legacy (the docs caution against it,
but it's always been allowed), and the fact that it actually *does*
generate correct results in native when the join is processed on the
Broker. It is much less likely that MSQ will plan in such a way that
generates correct results.
3) Remove subquery penalty in DruidJoinQueryRel when using sort-merge
join, because subqueries are always required, so there's no reason
to penalize them.
4) Move previously-disabled join reordering and manipulation rules to
FANCY_JOIN_RULES, and enable them when using sort-merge join. Helps
get to better plans where projections and filters are pushed down.
* Work around compiler problem.
* Updates from static analysis.
* Fix @param tag.
* Fix declared exception.
* Fix spelling.
* Minor adjustments.
* wip
* Merge fixups
* fixes
* Fix CalciteSelectQueryMSQTest
* Empty keys are sortable.
* Address comments from code review. Rename mux -> mix.
* Restore inspection config.
* Restore original doc.
* Reorder imports.
* Adjustments
* Fix.
* Fix imports.
* Adjustments from review.
* Update header.
* Adjust docs.
This function is notorious for causing memory exhaustion and excessive
CPU usage; so much so that it was valuable to work around it in the
SQL planner in #13206. Hopefully, a warning comment will encourage
developers to stay away and come up with solutions that do not involve
computing all possible buckets.
You can now do the following operations with TupleSketches in Post Aggregation Step
Get the Sketch Output as Base64 String
Provide a constant Tuple Sketch in post-aggregation step that can be used in Set Operations
Get the Estimated Value(Sum) of Summary/Metrics Objects associated with Tuple Sketch
The FiniteFirehoseFactory and InputRowParser classes were deprecated in 0.17.0 (#8823) in favor of InputSource & InputFormat. This PR removes the FiniteFirehoseFactory and all its implementations along with classes solely used by them like Fetcher (Used by PrefetchableTextFilesFirehoseFactory). Refactors classes including tests using FiniteFirehoseFactory to use InputSource instead.
Removing InputRowParser may not be as trivial as many classes that aren't deprecated depends on it (with no alternatives), like EventReceiverFirehoseFactory. Hence FirehoseFactory, EventReceiverFirehoseFactory, and Firehose are marked deprecated.
* move numeric null value coercion out of expression processing engine
* add ExprEval.valueOrDefault() to allow consumers to automatically coerce to default values
* rename Expr.buildVectorized as Expr.asVectorProcessor more consistent naming with Function and ApplyFunction; javadocs for some stuff
* Speed up composite key joins on IndexedTable.
Prior to this patch, IndexedTable indexes are sorted IntList. This works
great when we have a single-column join key: we simply retrieve the list
and we know what rows match. However, when we have a composite key, we
need to merge the sorted lists. This is inefficient when one is very dense
and others are very sparse.
This patch switches from sorted IntList to IntSortedSet, and changes
to the following intersection algorithm:
1) Initialize the intersection set to the smallest matching set from the
various parts of the composite key.
2) For each element in that smallest set, check other sets for that element.
If any do *not* include it, then remove the element from the intersection
set.
This way, complexity scales with the size of the smallest set, not the
largest one.
* RangeIntSet stuff.
* merge druid-core, extendedset, and druid-hll into druid-processing to simplify everything
* fix poms and license stuff
* mockito is evil
* allow reset of JvmUtils RuntimeInfo if tests used static injection to override
* fix array_agg to work with complex types and bugs with expression aggregator complex array handling
* more consistent handling of array expressions, numeric arrays more consistently honor druid.generic.useDefaultValueForNull, fix array_ordinal sql output type
* Allow users to add additional metadata to ingestion metrics
When submitting an ingestion spec, users may pass a map of metadata
in the ingestion spec config that will be added to ingestion metrics.
This will make it possible for operators to tag metrics with other
metadata that doesn't necessarily line up with the existing tags
like taskId.
Druid clusters that ingest these metrics can take advantage of the
nested data columns feature to process this additional metadata.
* rename to tags
* docs
* tests
* fix test
* make code cov happy
* checkstyle
changes:
* modified druid schema column type compution to special case COMPLEX<json> handling to choose COMPLEX<json> if any column in any segment is COMPLEX<json>
* NestedFieldVirtualColumn can now work correctly on any type of column, returning either a column selector if a root path, or nil selector if not
* fixed a random bug with NilVectorSelector when using a vector size larger than the default and druid.generic.useDefaultValueForNull=false would have the nulls vector set to all false instead of true
* fixed an overly aggressive check in ExprEval.ofType when handling complex types which would try to treat any string as base64 without gracefully falling back if it was not in fact base64 encoded, along with special handling for complex<json>
* added ExpressionVectorSelectors.castValueSelectorToObject and ExpressionVectorSelectors.castObjectSelectorToNumeric as convience methods to cast vector selectors using cast expressions without the trouble of constructing an expression. the polymorphic nature of the non-vectorized engine (and significantly larger overhead of non-vectorized expression processing) made adding similar methods for non-vectorized selectors less attractive and so have not been added at this time
* fix inconsistency between nested column indexer and serializer in handling values (coerce non primitive and non arrays of primitives using asString)
* ExprEval best effort mode now handles byte[] as string
* added test for ExprEval.bestEffortOf, and add missing conversion cases that tests uncovered
* more tests more better
* Fallback virtual column
This virtual columns enables falling back to another column if
the original column doesn't exist. This is useful when doing
column migrations and you have some old data with column X,
new data with column Y and you want to use Y if it exists, X
otherwise so that you can run a consistent query against all of
the data.
* Adjust Operators to be Pausable
This enables "merge" style operations that
combine multiple streams.
This change includes a naive implementation
of one such merge operator just to provide
concrete evidence that the refactoring is
effective.
* adds the SQL component of the native unnest functionality in Druid to unnest SQL queries on a table dimension, virtual column or a constant array and convert them into native Druid queries
* unnest in SQL is implemented as a combination of Correlate (the comma join part) and Uncollect (the unnest part)
* discover nested columns when using nested column indexer for schemaless
* move useNestedColumnIndexerForSchemaDiscovery from AppendableIndexSpec to DimensionsSpec
* Semantic Implementations for ArrayListRAC
This adds implementations of semantic interfaces
to optimize (eliminate object creation) the
window processing on top of an ArrayListSegment.
Tests are also added to cover the interplay
between the semantic interfaces that are expected
for this use case
* Kinesis: More robust default fetch settings.
1) Default recordsPerFetch and recordBufferSize based on available memory
rather than using hardcoded numbers. For this, we need an estimate
of record size. Use 10 KB for regular records and 1 MB for aggregated
records. With 1 GB heaps, 2 processors per task, and nonaggregated
records, recordBufferSize comes out to the same as the old
default (10000), and recordsPerFetch comes out slightly lower (1250
instead of 4000).
2) Default maxRecordsPerPoll based on whether records are aggregated
or not (100 if not aggregated, 1 if aggregated). Prior default was 100.
3) Default fetchThreads based on processors divided by task count on
Indexers, rather than overall processor count.
4) Additionally clean up the serialized JSON a bit by adding various
JsonInclude annotations.
* Updates for tests.
* Additional important verify.
* single typed "root" only nested columns now mimic "regular" columns of those types
* incremental index can now use nested column indexer instead of string indexer for discovered columns
* Addition of NaiveSortMaker and Default implementation
Add the NaiveSortMaker which makes a sorter
object and a default implementation of the
interface.
This also allows us to plan multiple different window
definitions on the same query.
* Validate response headers and fix exception logging
A class of QueryException were throwing away their
causes making it really hard to determine what's
going wrong when something goes wrong in the SQL
planner specifically. Fix that and adjust tests
to do more validation of response headers as well.
We allow 404s and 307s to be returned even without
authorization validated, but others get converted to 403
* Unify the handling of HTTP between SQL and Native
The SqlResource and QueryResource have been
using independent logic for things like error
handling and response context stuff. This
became abundantly clear and painful during a
change I was making for Window Functions, so
I unified them into using the same code for
walking the response and serializing it.
Things are still not perfectly unified (it would
be the absolute best if the SqlResource just
took SQL, planned it and then delegated the
query run entirely to the QueryResource), but
this refactor doesn't take that fully on.
The new code leverages async query processing
from our jetty container, the different
interaction model with the Resource means that
a lot of tests had to be adjusted to align with
the async query model. The semantics of the
tests remain the same with one exception: the
SqlResource used to not log requests that failed
authorization checks, now it does.
This PR expands `StringDimensionIndexer` to handle conversion of `byte[]` to base64 encoded strings, rather than the current behavior of calling java `toString`.
This issue was uncovered by a regression of sorts introduced by #13519, which updated the protobuf extension to directly convert stuff to java types, resulting in `bytes` typed values being converted as `byte[]` instead of a base64 string which the previous JSON based conversion created. While outputting `byte[]` is more consistent with other input formats, and preferable when the bytes can be consumed directly (such as complex types serde), when fed to a `StringDimensionIndexer`, it resulted in an ugly java `toString` because `processRowValsToUnsortedEncodedKeyComponent` is fed the output of `row.getRaw(..)`. Converting `byte[]` to a base64 string within `StringDimensionIndexer` is consistent with the behavior of calling `row.getDimension(..)` which does do this coercion (and why many tests on binary types appeared to be doing the expected thing).
I added some protobuf `bytes` tests, but they don't really hit the new `StringDimensionIndexer` behavior because they operate on the `InputRow` directly, and call `getDimension` to validate stuff. The parser based version still uses the old conversion mechanisms, so when not using a flattener incorrectly calls `toString` on the `ByteString`. I have encoded this behavior in the test for now, if we either update the parser to use the new flattener or just .. remove parsers we can remove this test stuff.
* bump nested column format version
changes:
* nested field files are now named by their position in field paths list, rather than directly by the path itself. this fixes issues with valid json properties with commas and newlines breaking the csv file meta.smoosh
* update StructuredDataProcessor to deal in NestedPathPart to be consistent with other abstract path handling rather than building JQ syntax strings directly
* add v3 format segment and test
This commit fixes a bug with nested column "value set" indexes caused by not properly
validating that the globalId looked up for value is present in the global dictionary prior to
looking it up in the local dictionary, which when "adjusting" the global ids for value type
can cause incorrect selection of value indexes.
To use an example of a variant typed nested column with 3 values `["1", null, -2]`.
The string dictionary is `[null, "1"]`, the long dictionary is `[-2]` and our local dictionary is `[0, 1, 2]`.
The code for variant typed indexes checks if the value is present in all global dictionaries
and returns indexes for all matches. So in this case, we first lookup "1" in the string dictionary,
find it at global id 1, all is good. Now, we check the long dictionary for `1`, which due to
`-(insertionpoint + 1)` gives us `-(1 + 2) = -2`. Since the global id space is actually stacked
dictionaries, global ids for long and double values must be "adjusted" by the size of string
dictionary, and size of string + size of long for doubles.
Prior to this patch we were not checking that the globalId is 0 or larger, we then immediately
looked up the `localDictionary.indexOf(-2 + adjustLong) = localDictionary.indexOf(-2 + 2) = localDictionary.indexOf(0)` ... which is an actual value contained in the dictionary! The fix is
to skip the longs completely since there were no global matches.
On to doubles, `-(insertionPoint + 1)` gives us `-(0 + 1) = -1`. The double adjust value is '3'
since 2 strings and 1 long, so `localDictionary.indexOf(-1 + 3)` = `localDictionary.indexOf(2)`
which is also a real value in our local dictionary that is definitely not '1'.
So in this one case, looking for '1' incorrectly ended up matching every row.
* Support Framing for Window Aggregations
This adds support for framing over ROWS
for window aggregations.
Still not implemented as yet:
1. RANGE frames
2. Multiple different frames in the same query
3. Frames on last/first functions
This commit adds a new class `InputStats` to track the total bytes processed by a task.
The field `processedBytes` is published in task reports along with other row stats.
Major changes:
- Add class `InputStats` to track processed bytes
- Add method `InputSourceReader.read(InputStats)` to read input rows while counting bytes.
> Since we need to count the bytes, we could not just have a wrapper around `InputSourceReader` or `InputEntityReader` (the way `CountableInputSourceReader` does) because the `InputSourceReader` only deals with `InputRow`s and the byte information is already lost.
- Classic batch: Use the new `InputSourceReader.read(inputStats)` in `AbstractBatchIndexTask`
- Streaming: Increment `processedBytes` in `StreamChunkParser`. This does not use the new `InputSourceReader.read(inputStats)` method.
- Extend `InputStats` with `RowIngestionMeters` so that bytes can be exposed in task reports
Other changes:
- Update tests to verify the value of `processedBytes`
- Rename `MutableRowIngestionMeters` to `SimpleRowIngestionMeters` and remove duplicate class
- Replace `CacheTestSegmentCacheManager` with `NoopSegmentCacheManager`
- Refactor `KafkaIndexTaskTest` and `KinesisIndexTaskTest`
Refactor DataSource to have a getAnalysis method()
This removes various parts of the code where while loops and instanceof
checks were being used to walk through the structure of DataSource objects
in order to build a DataSourceAnalysis. Instead we just ask the DataSource
for its analysis and allow the stack to rebuild whatever structure existed.
* Processors for Window Processing
This is an initial take on how to use Processors
for Window Processing. A Processor is an interface
that transforms RowsAndColumns objects.
RowsAndColumns objects are essentially combinations
of rows and columns.
The intention is that these Processors are the start
of a set of operators that more closely resemble what
DB engineers would be accustomed to seeing.
* Wire up windowed processors with a query type that
can run them end-to-end. This code can be used to
actually run a query, so yay!
* Wire up windowed processors with a query type that
can run them end-to-end. This code can be used to
actually run a query, so yay!
* Some SQL tests for window functions. Added wikipedia
data to the indexes available to the
SQL queries and tests validating the windowing
functionality as it exists now.
Co-authored-by: Gian Merlino <gianmerlino@gmail.com>
* Moving all unnest cursor code atop refactored code for unnest
* Updating unnest cursor
* Removing dedup and fixing up some null checks
* AllowList changes
* Fixing some NPEs
* Using bitset for allowlist
* Updating the initialization only when cursor is in non-done state
* Updating code to skip rows not in allow list
* Adding a flag for cases when first element is not in allowed list
* Updating for a null in allowList
* Splitting unnest cursor into 2 subclasses
* Intercepting some apis with columnName for new unnested column
* Adding test cases and renaming some stuff
* checkstyle fixes
* Moving to an interface for Unnest
* handling null rows in a dimension
* Updating cursors after comments part-1
* Addressing comments and adding some more tests
* Reverting a change to ScanQueryRunner and improving a comment
* removing an unused function
* Updating cursors after comments part 2
* One last fix for review comments
* Making some functions private, deleting some comments, adding a test for unnest of unnest with allowList
* Adding an exception for a case
* Closure for unnest data source
* Adding some javadocs
* One minor change in makeDimSelector of columnarCursor
* Updating an error message
* Update processing/src/main/java/org/apache/druid/segment/DimensionUnnestCursor.java
Co-authored-by: Abhishek Agarwal <1477457+abhishekagarwal87@users.noreply.github.com>
* Unnesting on virtual columns was missing an object array, adding that to support virtual columns unnesting
* Updating exceptions to use UOE
* Renamed files, added column capability test on adapter, return statement and made unnest datasource not cacheable for the time being
* Handling for null values in dim selector
* Fixing a NPE for null row
* Updating capabilities
* Updating capabilities
Co-authored-by: Abhishek Agarwal <1477457+abhishekagarwal87@users.noreply.github.com>
SQL test framework extensions
* Capture planner artifacts: logical plan, etc.
* Planner test builder validates the logical plan
* Validation for the SQL resut schema (we already have
validation for the Druid row signature)
* Better Guice integration: properties, reuse Guice modules
* Avoid need for hand-coded expr, macro tables
* Retire some of the test-specific query component creation
* Fix query log hook race condition
* fixes BlockLayoutColumnarLongs close method to nullify internal buffer.
* fixes other BlockLayoutColumnar supplier close methods to nullify internal buffers.
* fix spotbugs
* we can read where we want to
we can leave your bounds behind
'cause if the memory is not there
we really don't care
and we'll crash this process of mine
Clint Wylie
Suneet Saldanha
Adarsh Sanjeev
Gian Merlino
Nicholas Lippis
TSFenwick
Soumyava
zachjsh
Abhishek Agarwal
Karan Kumar
Paul Rogers
abhagraw
imply-cheddar
Laksh Singla
Anshu Makkar
Tejaswini Bandlamudi
hqx871
Kashif Faraz
Jason Koch
Tijo Thomas
Rohan Garg