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
* 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.
* 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.
* 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.
* 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
* 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
### Description
This change adds a new config property `druid.sql.planner.operatorConversion.denyList`, which allows a user to specify
any operator conversions that they wish to disallow. A user may want to do this for a number of reasons, including security concerns. The default value of this property is the empty list `[]`, which does not disallow any operator conversions.
An example usage of this property is `druid.sql.planner.operatorConversion.denyList=["extern"]`, which disallows the usage of the `extern` operator conversion. If the property is configured this way, and a user of the Druid cluster tries to submit a query that uses the `extern` function, such as the example given [here](https://druid.apache.org/docs/latest/multi-stage-query/examples.html#insert-with-no-rollup), a response with http response code `400` is returned with en error body similar to the following:
```
{
"taskId": "4ec5b0b6-fa9b-4c3a-827d-2308294e9985",
"state": "FAILED",
"error": {
"error": "Plan validation failed",
"errorMessage": "org.apache.calcite.runtime.CalciteContextException: From line 28, column 5 to line 32, column 5: No match found for function signature EXTERN(<CHARACTER>, <CHARACTER>, <CHARACTER>)",
"errorClass": "org.apache.calcite.tools.ValidationException",
"host": null
}
}
```
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
* 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)
* 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
Co-authored-by: Paul Rogers <progers@apache.org>
Much improved table functions
* Revises properties, definitions in the catalog
* Adds a "table function" abstraction to model such functions
* Specific functions for HTTP, inline, local and S3.
* Extended SQL types in the catalog
* Restructure external table definitions to use table functions
* EXTEND syntax for Druid's extern table function
* Support for array-valued table function parameters
* Support for array-valued SQL query parameters
* Much new documentation
* 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.
* 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
* 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
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>
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
Paul Rogers
Clint Wylie
Soumyava
Adarsh Sanjeev
Suneet Saldanha
Gian Merlino
imply-cheddar
zachjsh
AmatyaAvadhanula
Laksh Singla
Rohan Garg
Kashif Faraz