Upstream release: https://github.com/delta-io/delta/releases/tag/v3.2.0
- Upgrade kernel dependency to 3.2.0
- Notable breaking changes introduced in upstream that affects the Druid extension:
- Rename TableClient -> Engine
- Rename DefaultTableClient -> DefaultEngine
- Exceptions moved to a separate package
- Table.getPath() doesn't throw TableNotFoundException. Instead the exception is thrown
when getting snapshot info from the Table object
This commit aims to enable the re-ordering of window operators in order to optimise
the sort and partition operators.
Example :
```
SELECT m1, m2,
SUM(m1) OVER(PARTITION BY m2) as sum1,
SUM(m2) OVER() as sum2
from numFoo
GROUP BY m1,m2
```
In order to compute this query, we can order the operators as to first compute the operators
corresponding to sum2 and then place the operators corresponding to sum1 which would
help us in reducing one sort operator if we order our operators by sum1 and then sum2.
There are a few issues with using Jackson serialization in sending datasketches between controller and worker in MSQ. This caused a blowup due to holding multiple copies of the sketch being stored.
This PR aims to resolve this by switching to deserializing the sketch payload without Jackson.
The PR adds a new query parameter used during communication between controller and worker while fetching sketches, "sketchEncoding".
If the value of this parameter is OCTET, the sketch is returned as a binary encoding, done by ClusterByStatisticsSnapshotSerde.
If the value is not the above, the sketch is encoded by Jackson as before.
* fix issue with auto column grouping
changes:
* fixes bug where AutoTypeColumnIndexer reports incorrect cardinality, allowing it to incorrectly use array grouper algorithm for realtime queries producing incorrect results for strings
* fixes bug where auto LONG and DOUBLE type columns incorrectly report not having null values, resulting in incorrect null handling when grouping
* fix test
* Altered `QueryTestBuilder` to be able to switch to a backing quidem test
* added a small crc to ensure that the shadow testcase does not deviate from the original one
* Packaged all decoupled related things into a a single `DecoupledExtension` to reduce copy-paste
* `DecoupledTestConfig#quidemReason` must describe why its being used
* `DecoupledTestConfig#separateDefaultModeTest` can be used to make multiple case files based on `NullHandling` state
* fixed a cosmetic bug during decoupled join translation
* enhanced `!druidPlan` to report the final logical plan in non-decoupled mode as well
* add check to ensure that only supported params are present in a druidtest uri
* enabled shadow testcases for previously disabled testcases
This brings them in line with the behavior of other numeric aggregations.
It is important because otherwise ClassCastExceptions can arise if comparing
different numeric types that may arise from deserialization.
* Add SQL DIV function.
This function has been documented for some time, but lacked a binding,
so it wasn't usable.
* Add a case with two expression inputs.
* * add new catalog IT with failure to ensure that it is run in CI
* * actually add failing test referred to and fix checkstyle
* * add some tests
* * fix checkstyle
* * add test descriptions
* * add more tests
* Fix ExpressionPredicateIndexSupplier numeric replace-with-default behavior.
In replace-with-default mode, null numeric values from the index should be
interpreted as zeroes by expressions. This makes the index supplier more
consistent with the behavior of the selectors created by the expression
virtual column.
* Fix test case.
This PR updates CompactionTask to not load any lookups by default, unless transformSpec is present.
If transformSpec is present, we will make the decision based on context values, loading all lookups by default. This is done to ensure backward compatibility since transformSpec can reference lookups.
If transform spec is not present and no context value is passed, we donot load any lookup.
This behavior can be overridden by supplying lookupLoadingMode and lookupsToLoad in the task context.
* Speed up SQL IN using SCALAR_IN_ARRAY.
Main changes:
1) DruidSqlValidator now includes a rewrite of IN to SCALAR_IN_ARRAY, when the size of
the IN is above inFunctionThreshold. The default value of inFunctionThreshold
is 100. Users can restore the prior behavior by setting it to Integer.MAX_VALUE.
2) SearchOperatorConversion now generates SCALAR_IN_ARRAY when converting to a regular
expression, when the size of the SEARCH is above inFunctionExprThreshold. The default
value of inFunctionExprThreshold is 2. Users can restore the prior behavior by setting
it to Integer.MAX_VALUE.
3) ReverseLookupRule generates SCALAR_IN_ARRAY if the set of reverse-looked-up values is
greater than inFunctionThreshold.
* Revert test.
* Additional coverage.
* Update docs/querying/sql-query-context.md
Co-authored-by: Benedict Jin <asdf2014@apache.org>
* New test.
---------
Co-authored-by: Benedict Jin <asdf2014@apache.org>
Custom calcite rule mimicking AggregateProjectMergeRule to extend support to expressions.
The current calcite rule return null in such cases.
In addition, this removes the redundant references.
* change to using measure name
* Implment order by delta
* less paring, stricter types
* safeDivide0
* fix no query
* new DTQ alows parsing JSON_VALUE(...RETURNING...)
MSQ sorts the columns in a highly specialized manner by byte comparisons. As such the values are serialized differently. This works well for the primitive types and primitive arrays, however complex types cannot be serialized specially.
This PR adds the support for sorting the complex columns by deserializing the value from the field and comparing it via the type strategy. This is a lot slower than the byte comparisons, however, it's the only way to support sorting on complex columns that can have arbitrary serialization not optimized for MSQ.
The primitives and the arrays are still compared via the byte comparison, therefore this doesn't affect the performance of the queries supported before the patch. If there's a sorting key with mixed complex and primitive/primitive array types, for example: longCol1 ASC, longCol2 ASC, complexCol1 DESC, complexCol2 DESC, stringCol1 DESC, longCol3 DESC, longCol4 ASC, the comparison will happen like:
longCol1, longCol2 (ASC) - Compared together via byte-comparison, since both are byte comparable and need to be sorted in ascending order
complexCol1 (DESC) - Compared via deserialization, cannot be clubbed with any other field
complexCol2 (DESC) - Compared via deserialization, cannot be clubbed with any other field, even though the prior field was a complex column with the same order
stringCol1, longCol3 (DESC) - Compared together via byte-comparison, since both are byte comparable and need to be sorted in descending order
longCol4 (ASC) - Compared via byte-comparison, couldn't be coalesced with the previous fields as the direction was different
This way, we only deserialize the field wherever required
Katya Macedo
Atul Mohan
George Shiqi Wu
Kashif Faraz
Jill Osborne
Charles Smith
Adithya Chakilam
Abhishek Radhakrishnan
Sree Charan Manamala
Adarsh Sanjeev
Clint Wylie
Sensor
zachjsh
Pranav
Zoltan Haindrich
Vadim Ogievetsky
Gian Merlino
dependabot[bot]
Rishabh Singh
Akshat Jain
kaisun2000
Codegass
Laksh Singla
aho135
Alberic Liu
Benedict Jin