### What changes were proposed in this pull request?
RepairTableCommand respects `spark.sql.addPartitionInBatch.size` too
### Why are the changes needed?
Make RepairTableCommand add partition batch size configurable.
### Does this PR introduce _any_ user-facing change?
User can use `spark.sql.addPartitionInBatch.size` to change batch size when repair table.
### How was this patch tested?
Not need
Closes#32489 from AngersZhuuuu/SPARK-35360.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Change `failOnError` to false for `NativeAdd` in `V2FunctionBenchmark`.
### Why are the changes needed?
Since `NativeAdd` is simply doing addition on long it's better to set `failOnError` to false so it will use native long addition instead of `Math.addExact`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N/A
Closes#32481 from sunchao/SPARK-35261-follow-up.
Authored-by: Chao Sun <sunchao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Rename pattern strings and regexps of year-month and day-time intervals.
### Why are the changes needed?
To improve code maintainability.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By existing test suites.
Closes#32444 from AngersZhuuuu/SPARK-35111-followup.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
As title. We should use a more restrictive interface `ShuffledJoin` other than `BaseJoinExec` in `CoalesceBucketsInJoin`, as the rule only applies to sort merge join and shuffled hash join (i.e. `ShuffledJoin`).
### Why are the changes needed?
Code cleanup.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit test in `CoalesceBucketsInJoinSuite`.
Closes#32480 from c21/minor-cleanup.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
when `numSlices` is avaiable, `logical.Range` should compute a exact `maxRowsPerPartition`
### Why are the changes needed?
`maxRowsPerPartition` is used in optimizer, we should provide an exact value if possible
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing testsuites
Closes#32350 from zhengruifeng/range_maxRowsPerPartition.
Authored-by: Ruifeng Zheng <ruifengz@foxmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This patch proposes to use `MethodUtils` for looking up methods `Invoke` and `StaticInvoke` expressions.
### Why are the changes needed?
Currently we wrote our logic in `Invoke` and `StaticInvoke` expressions for looking up methods. It is tricky to consider all the cases and there is already existing utility package for this purpose. We should reuse the utility package.
### Does this PR introduce _any_ user-facing change?
No, internal change only.
### How was this patch tested?
Existing tests.
Closes#32474 from viirya/invoke-util.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR proposes to filter out TPCDS v1.4 q6 and q75 in `TPCDSQueryTestSuite`.
I saw`TPCDSQueryTestSuite` failed nondeterministically because output row orders were different with those in the golden files. For example, the failure in the GA job, https://github.com/linhongliu-db/spark/runs/2507928605?check_suite_focus=true, happened because the `tpcds/q6.sql` query output rows were only sorted by `cnt`:
a0c76a8755/sql/core/src/test/resources/tpcds/q6.sql (L20)
Actually, `tpcds/q6.sql` and `tpcds-v2.7.0/q6.sql` are almost the same and the only difference is that `tpcds-v2.7.0/q6.sql` sorts both `cnt` and `a.ca_state`:
a0c76a8755/sql/core/src/test/resources/tpcds-v2.7.0/q6.sql (L22)
So, I think it's okay just to test `tpcds-v2.7.0/q6.sql` in this case (q75 has the same issue).
### Why are the changes needed?
For stable testing.
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
GA passed.
Closes#32454 from maropu/CleanUpTpcdsQueries.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR makes the below case work well.
```sql
select a b from values(1) t(a) distribute by a;
```
```logtalk
== Parsed Logical Plan ==
'RepartitionByExpression ['a]
+- 'Project ['a AS b#42]
+- 'SubqueryAlias t
+- 'UnresolvedInlineTable [a], [List(1)]
== Analyzed Logical Plan ==
org.apache.spark.sql.AnalysisException: cannot resolve 'a' given input columns: [b]; line 1 pos 62;
'RepartitionByExpression ['a]
+- Project [a#48 AS b#42]
+- SubqueryAlias t
+- LocalRelation [a#48]
```
### Why are the changes needed?
bugfix
### Does this PR introduce _any_ user-facing change?
yes, the original attributes can be used in `distribute by` / `cluster by` and hints like `/*+ REPARTITION(3, c) */`
### How was this patch tested?
new tests
Closes#32465 from yaooqinn/SPARK-35331.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Retain column metadata during the process of nested column pruning, when constructing `StructField`.
To test the above change, this also added the logic of column projection in `InMemoryTable`. Without the fix `DSV2CharVarcharDDLTestSuite` will fail.
### Why are the changes needed?
The column metadata is used in a few places such as re-constructing CHAR/VARCHAR information such as in [SPARK-33901](https://issues.apache.org/jira/browse/SPARK-33901). Therefore, we should retain the info during nested column pruning.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#32354 from sunchao/SPARK-35232.
Authored-by: Chao Sun <sunchao@apache.org>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This allows `ScalarFunction` implemented in Java to optionally specify the magic method `invoke` to be static, which can be used if the UDF is stateless. Comparing to the non-static method, it can potentially give better performance due to elimination of dynamic dispatch, etc.
Also added a benchmark to measure performance of: the default `produceResult`, non-static magic method and static magic method.
### Why are the changes needed?
For UDFs that are stateless (e.g., no need to maintain intermediate state between each function call), it's better to allow users to implement the UDF function as static method which could potentially give better performance.
### Does this PR introduce _any_ user-facing change?
Yes. Spark users can now have the choice to define static magic method for `ScalarFunction` when it is written in Java and when the UDF is stateless.
### How was this patch tested?
Added new UT.
Closes#32407 from sunchao/SPARK-35261.
Authored-by: Chao Sun <sunchao@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Instantiate a new Hive client through `Hive.getWithFastCheck(conf, false)` instead of `Hive.get(conf)`.
### Why are the changes needed?
[HIVE-10319](https://issues.apache.org/jira/browse/HIVE-10319) introduced a new API `get_all_functions` which is only supported in Hive 1.3.0/2.0.0 and up. As result, when Spark 3.x talks to a HMS service of version 1.2 or lower, the following error will occur:
```
Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: org.apache.thrift.TApplicationException: Invalid method name: 'get_all_functions'
at org.apache.hadoop.hive.ql.metadata.Hive.getAllFunctions(Hive.java:3897)
at org.apache.hadoop.hive.ql.metadata.Hive.reloadFunctions(Hive.java:248)
at org.apache.hadoop.hive.ql.metadata.Hive.registerAllFunctionsOnce(Hive.java:231)
... 96 more
Caused by: org.apache.thrift.TApplicationException: Invalid method name: 'get_all_functions'
at org.apache.thrift.TServiceClient.receiveBase(TServiceClient.java:79)
at org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$Client.recv_get_all_functions(ThriftHiveMetastore.java:3845)
at org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$Client.get_all_functions(ThriftHiveMetastore.java:3833)
```
The `get_all_functions` is called only when `doRegisterAllFns` is set to true:
```java
private Hive(HiveConf c, boolean doRegisterAllFns) throws HiveException {
conf = c;
if (doRegisterAllFns) {
registerAllFunctionsOnce();
}
}
```
what this does is to register all Hive permanent functions defined in HMS in Hive's `FunctionRegistry` class, via iterating through results from `get_all_functions`. To Spark, this seems unnecessary as it loads Hive permanent (not built-in) UDF via directly calling the HMS API, i.e., `get_function`. The `FunctionRegistry` is only used in loading Hive's built-in function that is not supported by Spark. At this time, it only applies to `histogram_numeric`.
### Does this PR introduce _any_ user-facing change?
Yes with this fix Spark now should be able to talk to HMS server with Hive 1.2.x and lower (with HIVE-24608 too)
### How was this patch tested?
Manually started a HMS server of Hive version 1.2.2, with patched Hive 2.3.8 using HIVE-24608. Without the PR it failed with the above exception. With the PR the error disappeared and I can successfully perform common operations such as create table, create database, list tables, etc.
Closes#32446 from sunchao/SPARK-35321.
Authored-by: Chao Sun <sunchao@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This patch proposes to make StaticInvoke able to find method with given method name even the parameter types do not exactly match to argument classes.
### Why are the changes needed?
Unlike `Invoke`, `StaticInvoke` only tries to get the method with exact argument classes. If the calling method's parameter types are not exactly matched with the argument classes, `StaticInvoke` cannot find the method.
`StaticInvoke` should be able to find the method under the cases too.
### Does this PR introduce _any_ user-facing change?
Yes. `StaticInvoke` can find a method even the argument classes are not exactly matched.
### How was this patch tested?
Unit test.
Closes#32413 from viirya/static-invoke.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This PR group exception messages in `sql/catalyst/src/main/scala/org/apache/spark/sql/connector/catalog`.
### Why are the changes needed?
It will largely help with standardization of error messages and its maintenance.
### Does this PR introduce _any_ user-facing change?
No. Error messages remain unchanged.
### How was this patch tested?
No new tests - pass all original tests to make sure it doesn't break any existing behavior.
Closes#32377 from beliefer/SPARK-35021.
Lead-authored-by: beliefer <beliefer@163.com>
Co-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Added the following TreePattern enums:
- APPEND_COLUMNS
- DESERIALIZE_TO_OBJECT
- LAMBDA_VARIABLE
- MAP_OBJECTS
- SERIALIZE_FROM_OBJECT
- PROJECT
- TYPED_FILTER
Added tree traversal pruning to the following rules dealing with objects:
- EliminateSerialization
- CombineTypedFilters
- EliminateMapObjects
- ObjectSerializerPruning
### Why are the changes needed?
Reduce the number of tree traversals and hence improve the query compilation latency.
### How was this patch tested?
Existing tests.
Closes#32451 from sigmod/object.
Authored-by: Yingyi Bu <yingyi.bu@databricks.com>
Signed-off-by: Gengliang Wang <ltnwgl@gmail.com>
### What changes were proposed in this pull request?
If `targetObject` is not nullable, we don't need the object null check in `Invoke`.
### Why are the changes needed?
small perf improvement
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing tests
Closes#32466 from cloud-fan/invoke.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR group exception messages in `sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util`.
### Why are the changes needed?
It will largely help with standardization of error messages and its maintenance.
### Does this PR introduce _any_ user-facing change?
No. Error messages remain unchanged.
### How was this patch tested?
No new tests - pass all original tests to make sure it doesn't break any existing behavior.
Closes#32367 from beliefer/SPARK-35020.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/32198
Before https://github.com/apache/spark/pull/32198, in `WriteTaskStatsTracker.newRow`, we know that the row is written to the current file. After https://github.com/apache/spark/pull/32198 , we no longer know this connection.
This PR adds the file path parameter in `WriteTaskStatsTracker.newRow` to bring back the connection.
### Why are the changes needed?
To not break some custom `WriteTaskStatsTracker` implementations.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
N/A
Closes#32459 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a follow up to https://github.com/apache/spark/pull/32032#discussion_r620928086. Basically, `children`/`innerChildren` should be mutually exclusive for `AlterViewAsCommand` and `CreateViewCommand`, which extend `AnalysisOnlyCommand`. Otherwise, there could be an issue in the `EXPLAIN` command. Currently, this is not an issue, because these commands will be analyzed (children will always be empty) when the `EXPLAIN` command is run.
### Why are the changes needed?
To be future-proof where these commands are directly used.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added new tsts
Closes#32447 from imback82/SPARK-34701-followup.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`EXPLAIN CODEGEN <query>` (and Dataset.explain("codegen")) prints out the generated code for each stage of plan. The current implementation is to match `WholeStageCodegenExec` operator in query plan and prints out generated code (https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala#L111-L118 ). This does not work with AQE as we wrap the whole query plan inside `AdaptiveSparkPlanExec` and do not run whole stage code-gen physical plan rule eagerly (`CollapseCodegenStages`). This introduces unexpected behavior change for EXPLAIN query (and Dataset.explain), as we enable AQE by default now.
The change is to explain code-gen for the current executed plan of AQE.
### Why are the changes needed?
Make `EXPLAIN CODEGEN` work same as before.
### Does this PR introduce _any_ user-facing change?
No (when comparing with latest Spark release 3.1.1).
### How was this patch tested?
Added unit test in `ExplainSuite.scala`.
Closes#32430 from c21/explain-aqe.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
When checking the path in `FileStreamSink.hasMetadata`, we should ignore the error and assume the user wants to read a batch output.
### Why are the changes needed?
Keep the original behavior of ignoring the error.
### Does this PR introduce _any_ user-facing change?
Yes.
The path checking will not throw an exception when checking file sink format
### How was this patch tested?
New UT added.
Closes#31638 from xuanyuanking/SPARK-34526.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This patch changes custom metric updating to update per certain rows (currently 100), instead of per row.
### Why are the changes needed?
Based on previous discussion https://github.com/apache/spark/pull/31451#discussion_r605413557, we should only update custom metrics per certain (e.g. 100) rows and also at the end of the task. Updating per row doesn't make too much benefit.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing unit test.
Closes#32330 from viirya/metric-update.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch changes a few places using `FileSystem` API to manipulate checkpoint file to `CheckpointFileManager`.
### Why are the changes needed?
`CheckpointFileManager` is designed to handle checkpoint file manipulation. However, there are a few places exposing `FileSystem` from checkpoint files/paths. We should use `CheckpointFileManager` to manipulate checkpoint files. For example, we may want to have one storage system for checkpoint file. If all checkpoint file manipulation is performed through `CheckpointFileManager`, we can only implement `CheckpointFileManager` for the storage system, and don't need to implement `FileSystem` API for it.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing unit tests.
Closes#32361 from viirya/checkpoint-manager.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Hide internal view properties for describe table command, because those
properties are generated by spark and should be transparent to the end-user.
### Why are the changes needed?
Avoid internal properties confusing the users.
### Does this PR introduce _any_ user-facing change?
Yes
Before this change, the user will see below output for `describe formatted test_view`
```
....
Table Properties [view.catalogAndNamespace.numParts=2, view.catalogAndNamespace.part.0=spark_catalog, view.catalogAndNamespace.part.1=default, view.query.out.col.0=c, view.query.out.col.1=v, view.query.out.numCols=2, view.referredTempFunctionsNames=[], view.referredTempViewNames=[]]
...
```
After this change, the internal properties will be hidden for `describe formatted test_view`
```
...
Table Properties []
...
```
### How was this patch tested?
existing UT
Closes#32441 from linhongliu-db/hide-properties.
Authored-by: Linhong Liu <linhong.liu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR intends to replace `maropu/spark-tpcds-datagen` with `databricks/tpcds-kit` for using a newer dsdgen and update the golden files in `tpcds-query-results`.
### Why are the changes needed?
For better testing.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
GA passed.
Closes#32420 from maropu/UseTpcdsKit.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR aims to enrich ORC encryption test coverage for nested columns.
### Why are the changes needed?
This will provide a test coverage for this feature.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CIs with the newly added test case.
Closes#32449 from dongjoon-hyun/SPARK-35325.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This pull request proposes a new API for streaming sources to signal that they can report metrics, and adds a use case to support Kafka micro batch stream to report the stats of # of offsets for the current offset falling behind the latest.
A public interface is added.
`metrics`: returns the metrics reported by the streaming source with given offset.
### Why are the changes needed?
The new API can expose any custom metrics for the "current" offset for streaming sources. Different from #31398, this PR makes metrics available to user through progress report, not through spark UI. A use case is that people want to know how the current offset falls behind the latest offset.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test for Kafka micro batch source v2 are added to test the Kafka use case.
Closes#31944 from yijiacui-db/SPARK-34297.
Authored-by: Yijia Cui <yijia.cui@databricks.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
To fix lambda variable name issues in nested DataFrame functions, this PR modifies code to use a global counter for `LambdaVariables` names created by higher order functions.
This is the rework of #31887. Closes#31887.
### Why are the changes needed?
This moves away from the current hard-coded variable names which break on nested function calls. There is currently a bug where nested transforms in particular fail (the inner variable shadows the outer variable)
For this query:
```
val df = Seq(
(Seq(1,2,3), Seq("a", "b", "c"))
).toDF("numbers", "letters")
df.select(
f.flatten(
f.transform(
$"numbers",
(number: Column) => { f.transform(
$"letters",
(letter: Column) => { f.struct(
number.as("number"),
letter.as("letter")
) }
) }
)
).as("zipped")
).show(10, false)
```
This is the current (incorrect) output:
```
+------------------------------------------------------------------------+
|zipped |
+------------------------------------------------------------------------+
|[{a, a}, {b, b}, {c, c}, {a, a}, {b, b}, {c, c}, {a, a}, {b, b}, {c, c}]|
+------------------------------------------------------------------------+
```
And this is the correct output after fix:
```
+------------------------------------------------------------------------+
|zipped |
+------------------------------------------------------------------------+
|[{1, a}, {1, b}, {1, c}, {2, a}, {2, b}, {2, c}, {3, a}, {3, b}, {3, c}]|
+------------------------------------------------------------------------+
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added the new test in `DataFrameFunctionsSuite`.
Closes#32424 from maropu/pr31887.
Lead-authored-by: dsolow <dsolow@sayari.com>
Co-authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Co-authored-by: dmsolow <dsolow@sayarianalytics.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Added the following TreePattern enums:
- CREATE_NAMED_STRUCT
- EXTRACT_VALUE
- JSON_TO_STRUCT
- OUTER_REFERENCE
- AGGREGATE
- LOCAL_RELATION
- EXCEPT
- LIMIT
- WINDOW
Used them in the following rules:
- DecorrelateInnerQuery
- LimitPushDownThroughWindow
- OptimizeCsvJsonExprs
- PropagateEmptyRelation
- PullOutGroupingExpressions
- PushLeftSemiLeftAntiThroughJoin
- ReplaceExceptWithFilter
- RewriteDistinctAggregates
- SimplifyConditionalsInPredicate
- UnwrapCastInBinaryComparison
### Why are the changes needed?
Reduce the number of tree traversals and hence improve the query compilation latency.
### How was this patch tested?
Existing tests.
Closes#32421 from sigmod/opt.
Authored-by: Yingyi Bu <yingyi.bu@databricks.com>
Signed-off-by: Gengliang Wang <ltnwgl@gmail.com>
### What changes were proposed in this pull request?
This is rather a followup of https://github.com/apache/spark/pull/30518 that should be ported back to `branch-3.1` too.
`STOP_AT_DELIMITER` was mistakenly used twice. The duplicated `STOP_AT_DELIMITER` should be `SKIP_VALUE` in the documentation.
### Why are the changes needed?
To correctly document.
### Does this PR introduce _any_ user-facing change?
Yes, it fixes the user-facing documentation.
### How was this patch tested?
I checked them via running linters.
Closes#32423 from HyukjinKwon/SPARK-35250.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Fixing some typos in the documenting comments.
### Why are the changes needed?
To make reading the docs more pleasant.
### Does this PR introduce _any_ user-facing change?
Yes, since the user sees the docs.
### How was this patch tested?
It was not tested, because no code was changed.
Closes#32400 from Dobiasd/patch-1.
Authored-by: Tobias Hermann <editgym@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In `StaticInvoke`, when result is nullable, don't box the return value if its type is primitive.
### Why are the changes needed?
It is unnecessary to apply boxing when the method return value is of primitive type, and it would hurt performance a lot if the method is simple. The check is done in `Invoke` but not in `StaticInvoke`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added a UT.
Closes#32416 from sunchao/SPARK-35281.
Authored-by: Chao Sun <sunchao@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1. Extend Spark SQL parser to support parsing of:
- `INTERVAL YEAR TO MONTH` to `YearMonthIntervalType`
- `INTERVAL DAY TO SECOND` to `DayTimeIntervalType`
2. Assign new names to the ANSI interval types according to the SQL standard to be able to parse the names back by Spark SQL parser. Override the `typeName()` name of `YearMonthIntervalType`/`DayTimeIntervalType`.
### Why are the changes needed?
To be able to use new ANSI interval types in SQL. The SQL standard requires the types to be defined according to the rules:
```
<interval type> ::= INTERVAL <interval qualifier>
<interval qualifier> ::= <start field> TO <end field> | <single datetime field>
<start field> ::= <non-second primary datetime field> [ <left paren> <interval leading field precision> <right paren> ]
<end field> ::= <non-second primary datetime field> | SECOND [ <left paren> <interval fractional seconds precision> <right paren> ]
<primary datetime field> ::= <non-second primary datetime field | SECOND
<non-second primary datetime field> ::= YEAR | MONTH | DAY | HOUR | MINUTE
<interval fractional seconds precision> ::= <unsigned integer>
<interval leading field precision> ::= <unsigned integer>
```
Currently, Spark SQL supports only `YEAR TO MONTH` and `DAY TO SECOND` as `<interval qualifier>`.
### Does this PR introduce _any_ user-facing change?
Should not since the types has not been released yet.
### How was this patch tested?
By running the affected tests such as:
```
$ build/sbt "sql/testOnly *SQLQueryTestSuite -- -z interval.sql"
$ build/sbt "sql/testOnly *SQLQueryTestSuite -- -z datetime.sql"
$ build/sbt "test:testOnly *ExpressionTypeCheckingSuite"
$ build/sbt "sql/testOnly *SQLQueryTestSuite -- -z windowFrameCoercion.sql"
$ build/sbt "sql/testOnly *SQLQueryTestSuite -- -z literals.sql"
```
Closes#32409 from MaxGekk/parse-ansi-interval-types.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to port minimal code to generate TPC-DS data from [databricks/spark-sql-perf](https://github.com/databricks/spark-sql-perf). The classes in a new class file `tpcdsDatagen.scala` are basically copied from the `databricks/spark-sql-perf` codebase.
Note that I've modified them a bit to follow the Spark code style and removed unnecessary parts from them.
The code authors of these classes are:
juliuszsompolski
npoggi
wangyum
### Why are the changes needed?
We frequently use TPCDS data now for benchmarks/tests, but the classes for the TPCDS schemas of datagen and benchmarks/tests are managed separately, e.g.,
- https://github.com/apache/spark/blob/master/sql/core/src/test/scala/org/apache/spark/sql/TPCDSBase.scala
- https://github.com/databricks/spark-sql-perf/blob/master/src/main/scala/com/databricks/spark/sql/perf/tpcds/TPCDSTables.scala
I think this causes some inconveniences, e.g., we need to update both files in the separate repositories if we update the TPCDS schema #32037. So, it would be useful for the Spark codebase to generate them by referring to the same schema definition.
### Does this PR introduce _any_ user-facing change?
dev only.
### How was this patch tested?
Manually checked and GA passed.
Closes#32243 from maropu/tpcdsDatagen.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Support Cast string to day-seconds interval
### Why are the changes needed?
Users can cast day-second interval string to DayTimeIntervalType.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#32271 from AngersZhuuuu/SPARK-35112.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR adds a new rule `PullOutGroupingExpressions` to pull out complex grouping expressions to a `Project` node under an `Aggregate`. These expressions are then referenced in both grouping expressions and aggregate expressions without aggregate functions to ensure that optimization rules don't change the aggregate expressions to invalid ones that no longer refer to any grouping expressions.
### Why are the changes needed?
If aggregate expressions (without aggregate functions) in an `Aggregate` node are complex then the `Optimizer` can optimize out grouping expressions from them and so making aggregate expressions invalid.
Here is a simple example:
```
SELECT not(t.id IS NULL) , count(*)
FROM t
GROUP BY t.id IS NULL
```
In this case the `BooleanSimplification` rule does this:
```
=== Applying Rule org.apache.spark.sql.catalyst.optimizer.BooleanSimplification ===
!Aggregate [isnull(id#222)], [NOT isnull(id#222) AS (NOT (id IS NULL))#226, count(1) AS c#224L] Aggregate [isnull(id#222)], [isnotnull(id#222) AS (NOT (id IS NULL))#226, count(1) AS c#224L]
+- Project [value#219 AS id#222] +- Project [value#219 AS id#222]
+- LocalRelation [value#219] +- LocalRelation [value#219]
```
where `NOT isnull(id#222)` is optimized to `isnotnull(id#222)` and so it no longer refers to any grouping expression.
Before this PR:
```
== Optimized Logical Plan ==
Aggregate [isnull(id#222)], [isnotnull(id#222) AS (NOT (id IS NULL))#234, count(1) AS c#232L]
+- Project [value#219 AS id#222]
+- LocalRelation [value#219]
```
and running the query throws an error:
```
Couldn't find id#222 in [isnull(id#222)#230,count(1)#226L]
java.lang.IllegalStateException: Couldn't find id#222 in [isnull(id#222)#230,count(1)#226L]
```
After this PR:
```
== Optimized Logical Plan ==
Aggregate [_groupingexpression#233], [NOT _groupingexpression#233 AS (NOT (id IS NULL))#230, count(1) AS c#228L]
+- Project [isnull(value#219) AS _groupingexpression#233]
+- LocalRelation [value#219]
```
and the query works.
### Does this PR introduce _any_ user-facing change?
Yes, the query works.
### How was this patch tested?
Added new UT.
Closes#32396 from peter-toth/SPARK-34581-keep-grouping-expressions-2.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch fixes `Invoke` expression when the target object has more than one method with the given method name.
### Why are the changes needed?
`Invoke` will find out the method on the target object with given method name. If there are more than one method with the name, currently it is undeterministic which method will be used. We should add the condition of parameter number when finding the method.
### Does this PR introduce _any_ user-facing change?
Yes, fixed a bug when using `Invoke` on a object where more than one method with the given method name.
### How was this patch tested?
Unit test.
Closes#32404 from viirya/verify-invoke-param-len.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This pr makes `CombineFilters` support non-deterministic expressions. For example:
```sql
spark.sql("CREATE TABLE t1(id INT, dt STRING) using parquet PARTITIONED BY (dt)")
spark.sql("CREATE VIEW v1 AS SELECT * FROM t1 WHERE dt NOT IN ('2020-01-01', '2021-01-01')")
spark.sql("SELECT * FROM v1 WHERE dt = '2021-05-01' AND rand() <= 0.01").explain()
```
Before this pr:
```
== Physical Plan ==
*(1) Filter (isnotnull(dt#1) AND ((dt#1 = 2021-05-01) AND (rand(-6723800298719475098) <= 0.01)))
+- *(1) ColumnarToRow
+- FileScan parquet default.t1[id#0,dt#1] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex(0 paths)[], PartitionFilters: [NOT dt#1 IN (2020-01-01,2021-01-01)], PushedFilters: [], ReadSchema: struct<id:int>
```
After this pr:
```
== Physical Plan ==
*(1) Filter (rand(-2400509328955813273) <= 0.01)
+- *(1) ColumnarToRow
+- FileScan parquet default.t1[id#0,dt#1] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex(0 paths)[], PartitionFilters: [isnotnull(dt#1), NOT dt#1 IN (2020-01-01,2021-01-01), (dt#1 = 2021-05-01)], PushedFilters: [], ReadSchema: struct<id:int>
```
### Why are the changes needed?
Improve query performance.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#32405 from wangyum/SPARK-35273.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
~~This PR aims to add a new AQE optimizer rule `DynamicJoinSelection`. Like other AQE partition number configs, this rule add a new broadcast threshold config `spark.sql.adaptive.autoBroadcastJoinThreshold`.~~
This PR amis to add a flag in `Statistics` to distinguish AQE stats or normal stats, so that we can make some sql configs isolation between AQE and normal.
### Why are the changes needed?
The main idea here is that make join config isolation between normal planner and aqe planner which shared the same code path.
Actually we do not very trust using the static stats to consider if it can build broadcast hash join. In our experience it's very common that Spark throw broadcast timeout or driver side OOM exception when execute a bit large plan. And due to braodcast join is not reversed which means if we covert join to braodcast hash join at first time, we(AQE) can not optimize it again, so it should make sense to decide if we can do broadcast at aqe side using different sql config.
### Does this PR introduce _any_ user-facing change?
Yes, a new config `spark.sql.adaptive.autoBroadcastJoinThreshold` added.
### How was this patch tested?
Add new test.
Closes#32391 from ulysses-you/SPARK-35264.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Support Cast string to year-month interval
Supported format as below
```
ANSI_STYLE, like
INTERVAL -'-10-1' YEAR TO MONTH
HIVE_STYLE like
10-1 or -10-1
Rules from the SQL standard about ANSI_STYLE:
<interval literal> ::=
INTERVAL [ <sign> ] <interval string> <interval qualifier>
<interval string> ::=
<quote> <unquoted interval string> <quote>
<unquoted interval string> ::=
[ <sign> ] { <year-month literal> | <day-time literal> }
<year-month literal> ::=
<years value> [ <minus sign> <months value> ]
| <months value>
<years value> ::=
<datetime value>
<months value> ::=
<datetime value>
<datetime value> ::=
<unsigned integer>
<unsigned integer> ::= <digit>...
```
### Why are the changes needed?
Support Cast string to year-month interval
### Does this PR introduce _any_ user-facing change?
User can cast year month interval string to YearMonthIntervalType
### How was this patch tested?
Added UT
Closes#32266 from AngersZhuuuu/SPARK-SPARK-35111.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This PR proposes to enable the JSON datasources to write non-ascii characters as codepoints.
To enable/disable this feature, I introduce a new option `writeNonAsciiCharacterAsCodePoint` for JSON datasources.
### Why are the changes needed?
JSON specification allows codepoints as literal but Spark SQL's JSON datasources don't support the way to do it.
It's great if we can write non-ascii characters as codepoints, which is a platform neutral representation.
### Does this PR introduce _any_ user-facing change?
Yes. Users can write non-ascii characters as codepoints with JSON datasources.
### How was this patch tested?
New test.
Closes#32147 from sarutak/json-unicode-write.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR extends `ADD FILE/JAR/ARCHIVE` commands to be able to take multiple path arguments like Hive.
### Why are the changes needed?
To make those commands more useful.
### Does this PR introduce _any_ user-facing change?
Yes. In the current implementation, those commands can take a path which contains whitespaces without enclose it by neither `'` nor `"` but after this change, users need to enclose such paths.
I've note this incompatibility in the migration guide.
### How was this patch tested?
New tests.
Closes#32205 from sarutak/add-multiple-files.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
### What changes were proposed in this pull request?
This PR proposes to introduce a new JDBC option `refreshKrb5Config` which allows to reflect the change of `krb5.conf`.
### Why are the changes needed?
In the current master, JDBC datasources can't accept `refreshKrb5Config` which is defined in `Krb5LoginModule`.
So even if we change the `krb5.conf` after establishing a connection, the change will not be reflected.
The similar issue happens when we run multiple `*KrbIntegrationSuites` at the same time.
`MiniKDC` starts and stops every KerberosIntegrationSuite and different port number is recorded to `krb5.conf`.
Due to `SecureConnectionProvider.JDBCConfiguration` doesn't take `refreshKrb5Config`, KerberosIntegrationSuites except the first running one see the wrong port so those suites fail.
You can easily confirm with the following command.
```
build/sbt -Phive Phive-thriftserver -Pdocker-integration-tests "testOnly org.apache.spark.sql.jdbc.*KrbIntegrationSuite"
```
### Does this PR introduce _any_ user-facing change?
Yes. Users can set `refreshKrb5Config` to refresh krb5 relevant configuration.
### How was this patch tested?
New test.
Closes#32344 from sarutak/kerberos-refresh-issue.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Kousuke Saruta <sarutak@oss.nttdata.com>
### What changes were proposed in this pull request?
Explicitly declare DecimalType(20, 0) for Parquet UINT_64, avoid use DecimalType.LongDecimal which only happens to have 20 as precision.
https://github.com/apache/spark/pull/31960#discussion_r622691560
### Why are the changes needed?
fix ambiguity
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
not needed, just current CI pass
Closes#32390 from yaooqinn/SPARK-34786-F.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Make sure we re-throw an exception that is not null.
### Why are the changes needed?
to be super safe
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
N/A
Closes#32387 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
Co-Authored-By: Chao Sun <sunchaoapple.com>
Co-Authored-By: Ryan Blue <rbluenetflix.com>
### What changes were proposed in this pull request?
This implements function resolution and evaluation for functions registered through V2 FunctionCatalog [SPARK-27658](https://issues.apache.org/jira/browse/SPARK-27658). In particular:
- Added documentation for how to define the "magic method" in `ScalarFunction`.
- Added a new expression `ApplyFunctionExpression` which evaluates input by delegating to `ScalarFunction.produceResult` method.
- added a new expression `V2Aggregator` which is a type of `TypedImperativeAggregate`. It's a wrapper of V2 `AggregateFunction` and mostly delegate methods to the implementation of the latter. It also uses plain Java serde for intermediate state.
- Added function resolution logic for `ScalarFunction` and `AggregateFunction` in `Analyzer`.
+ For `ScalarFunction` this checks if the magic method is implemented through Java reflection, and create a `Invoke` expression if so. Otherwise, it checks if the default `produceResult` is overridden. If so, it creates a `ApplyFunctionExpression` which evaluates through `InternalRow`. Otherwise an analysis exception is thrown.
+ For `AggregateFunction`, this checks if the `update` method is overridden. If so, it converts it to `V2Aggregator`. Otherwise an analysis exception is thrown similar to the case of `ScalarFunction`.
- Extended existing `InMemoryTableCatalog` to add the function catalog capability. Also renamed it to `InMemoryCatalog` since it no longer only covers tables.
**Note**: this currently can successfully detect whether a subclass overrides the default `produceResult` or `update` method from the parent interface **only for Java implementations**. It seems in Scala it's hard to differentiate whether a subclass overrides a default method from its parent interface. In this case, it will be a runtime error instead of analysis error.
A few TODOs:
- Extend `V2SessionCatalog` with function catalog. This seems a little tricky since API such V2 `FunctionCatalog`'s `loadFunction` is different from V1 `SessionCatalog`'s `lookupFunction`.
- Add magic method for `AggregateFunction`.
- Type coercion when looking up functions
### Why are the changes needed?
As V2 FunctionCatalog APIs are finalized, we should integrate it with function resolution and evaluation process so that they are actually useful.
### Does this PR introduce _any_ user-facing change?
Yes, now a function exposed through V2 FunctionCatalog can be analyzed and evaluated.
### How was this patch tested?
Added new unit tests.
Closes#32082 from sunchao/resolve-func-v2.
Lead-authored-by: Chao Sun <sunchao@apple.com>
Co-authored-by: Chao Sun <sunchao@apache.org>
Co-authored-by: Chao Sun <sunchao@uber.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Reorder `DemoteBroadcastHashJoin` and `EliminateUnnecessaryJoin`.
### Why are the changes needed?
Skip unnecessary check in `DemoteBroadcastHashJoin` if `EliminateUnnecessaryJoin` affects.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No result affect.
Closes#32380 from ulysses-you/SPARK-34781-FOLLOWUP.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add `ShuffledHashJoin` pattern check in `OptimizeSkewedJoin` so that we can optimize it.
### Why are the changes needed?
Currently, we have already supported all type of join through hint that make it easy to choose the join implementation.
We would choose `ShuffledHashJoin` if one table is not big but over the broadcast threshold. It's better that we can support optimize it in `OptimizeSkewedJoin`.
### Does this PR introduce _any_ user-facing change?
Probably yes, the execute plan in AQE mode may be changed.
### How was this patch tested?
Improve exists test in `AdaptiveQueryExecSuite`
Closes#32328 from ulysses-you/SPARK-35214.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR let JDBC clients identify ANSI interval columns properly.
### Why are the changes needed?
This PR is similar to https://github.com/apache/spark/pull/29539.
JDBC users can query interval values through thrift server, create views with ansi interval columns, e.g.
`CREATE global temp view view1 as select interval '1-1' year to month as I;`
but when they want to get the details of the columns of view1, the will fail with `Unrecognized type name: YEAR-MONTH INTERVAL`
```
Caused by: java.lang.IllegalArgumentException: Unrecognized type name: YEAR-MONTH INTERVAL
at org.apache.spark.sql.hive.thriftserver.SparkGetColumnsOperation.toJavaSQLType(SparkGetColumnsOperation.scala:190)
at org.apache.spark.sql.hive.thriftserver.SparkGetColumnsOperation.$anonfun$addToRowSet$1(SparkGetColumnsOperation.scala:206)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.sql.hive.thriftserver.SparkGetColumnsOperation.addToRowSet(SparkGetColumnsOperation.scala:198)
at org.apache.spark.sql.hive.thriftserver.SparkGetColumnsOperation.$anonfun$runInternal$7(SparkGetColumnsOperation.scala:109)
at org.apache.spark.sql.hive.thriftserver.SparkGetColumnsOperation.$anonfun$runInternal$7$adapted(SparkGetColumnsOperation.scala:109)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.sql.hive.thriftserver.SparkGetColumnsOperation.$anonfun$runInternal$5(SparkGetColumnsOperation.scala:109)
at org.apache.spark.sql.hive.thriftserver.SparkGetColumnsOperation.$anonfun$runInternal$5$adapted(SparkGetColumnsOperation.scala:107)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.sql.hive.thriftserver.SparkGetColumnsOperation.runInternal(SparkGetColumnsOperation.scala:107)
... 34 more
```
### Does this PR introduce _any_ user-facing change?
Yes. Let hive JDBC recognize ANSI interval.
### How was this patch tested?
Jenkins test.
Closes#32345 from beliefer/SPARK-35085.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
