### What changes were proposed in this pull request?
This PR is a follow-up to fix Scala 2.13 compilation.
### Why are the changes needed?
To support Scala 2.13 in Apache Spark 3.1.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the GitHub Action Scala 2.13 compilation job.
Closes#30502 from dongjoon-hyun/SPARK-31257.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This patch proposes to support subexpression elimination for interpreted predicate.
### Why are the changes needed?
Similar to interpreted projection, there are use cases when codegen predicate is not able to work, e.g. too complex schema, non-codegen expression, etc. When there are frequently occurring expressions (subexpressions) among predicate expression, the performance is quite bad as we need to re-compute same expressions. We should be able to support subexpression elimination for interpreted predicate like interpreted projection.
### Does this PR introduce _any_ user-facing change?
No, this doesn't change user behavior.
### How was this patch tested?
Unit test and benchmark.
Closes#30497 from viirya/SPARK-33540.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
After https://github.com/apache/spark/pull/30260, there are some type conversions disallowed under ANSI mode.
We should tell users what they can do if they have to use the disallowed casting.
### Why are the changes needed?
Make it more user-friendly.
### Does this PR introduce _any_ user-facing change?
Yes, the error message is improved on casting failure when ANSI mode is enabled
### How was this patch tested?
Unit tests.
Closes#30440 from gengliangwang/improveAnsiCastErrorMSG.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
* Unify the create table syntax in the parser by merging Hive and DataSource clauses
* Add `SerdeInfo` and `external` boolean to statement plans and update AstBuilder to produce them
* Add conversion from create statement plan to v1 create plans in ResolveSessionCatalog
* Support new statement clauses in ResolveCatalogs conversion to v2 create plans
* Remove SparkSqlParser rules for Hive syntax
* Add "option." namespace to distinguish SERDEPROPERTIES and OPTIONS in table properties
### Why are the changes needed?
* Current behavior is confusing.
* A way to pass the Hive create options to DSv2 is needed for a Hive source.
### Does this PR introduce any user-facing change?
Not by default, but v2 sources will be able to handle STORED AS and other Hive clauses.
### How was this patch tested?
Existing tests validate there are no behavior changes.
Update unit tests for using a statement plan for Hive create syntax:
* Move create tests from spark-sql DDLParserSuite into PlanResolutionSuite
* Add parser tests to spark-catalyst DDLParserSuite
Closes#28026 from rdblue/unify-create-table.
Lead-authored-by: Ryan Blue <blue@apache.org>
Co-authored-by: Wenchen Fan <cloud0fan@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Add new method `listPartitionByNames` to the `SupportsPartitionManagement` interface. It allows to list partitions by partition names and their values.
2. Implement new method in `InMemoryPartitionTable` which is used in DSv2 tests.
### Why are the changes needed?
Currently, the `SupportsPartitionManagement` interface exposes only `listPartitionIdentifiers` which allows to list partitions by partition values. And it requires to specify all values for partition schema fields in the prefix. This restriction does not allow to list partitions by some of partition names (not all of them).
For example, the table `tableA` is partitioned by two column `year` and `month`
```
CREATE TABLE tableA (price int, year int, month int)
USING _
partitioned by (year, month)
```
and has the following partitions:
```
PARTITION(year = 2015, month = 1)
PARTITION(year = 2015, month = 2)
PARTITION(year = 2016, month = 2)
PARTITION(year = 2016, month = 3)
```
If we want to list all partitions with `month = 2`, we have to specify `year` for **listPartitionIdentifiers()** which not always possible as we don't know all `year` values in advance. New method **listPartitionByNames()** allows to specify partition values only for `month`, and get two partitions:
```
PARTITION(year = 2015, month = 2)
PARTITION(year = 2016, month = 2)
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the affected test suite `SupportsPartitionManagementSuite`.
Closes#30452 from MaxGekk/column-names-listPartitionIdentifiers.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Remove SQL configuration spark.sql.legacy.allowCastNumericToTimestamp
### Why are the changes needed?
In the current master branch, there is a new configuration `spark.sql.legacy.allowCastNumericToTimestamp` which controls whether to cast Numeric types to Timestamp or not. The default value is true.
After https://github.com/apache/spark/pull/30260, the type conversion between Timestamp type and Numeric type is disallowed in ANSI mode. So, we don't need to a separate configuration `spark.sql.legacy.allowCastNumericToTimestamp` for disallowing the conversion. Users just need to set `spark.sql.ansi.enabled` for the behavior.
As the configuration is not in any released yet, we should remove the configuration to make things simpler.
### Does this PR introduce _any_ user-facing change?
No, since the configuration is not released yet.
### How was this patch tested?
Existing test cases
Closes#30493 from gengliangwang/LEGACY_ALLOW_CAST_NUMERIC_TO_TIMESTAMP.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to migrate `SHOW COLUMNS` to use `UnresolvedTableOrView` to resolve the table/view identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
Note that `SHOW COLUMNS` is not yet supported for v2 tables.
### Why are the changes needed?
To use `UnresolvedTableOrView` for table/view resolution. Note that `ShowColumnsCommand` internally resolves to a temp view first, so there is no resolution behavior change with this PR.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Updated existing tests.
Closes#30490 from imback82/show_columns.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Structured Streaming UI is not containing state custom metrics information. In this PR I've added it.
### Why are the changes needed?
Missing state custom metrics information.
### Does this PR introduce _any_ user-facing change?
Additional UI elements appear.
### How was this patch tested?
Existing unit tests + manual test.
```
#Compile Spark
echo "spark.sql.streaming.ui.enabledCustomMetricList stateOnCurrentVersionSizeBytes" >> conf/spark-defaults.conf
sbin/start-master.sh
sbin/start-worker.sh spark://gsomogyi-MBP16:7077
./bin/spark-submit --master spark://gsomogyi-MBP16:7077 --deploy-mode client --class com.spark.Main ../spark-test/target/spark-test-1.0-SNAPSHOT-jar-with-dependencies.jar
```
<img width="1119" alt="Screenshot 2020-11-18 at 12 45 36" src="https://user-images.githubusercontent.com/18561820/99527506-2f979680-299d-11eb-9187-4ae7fbd2596a.png">
Closes#30336 from gaborgsomogyi/SPARK-33287.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This PR proposes to migrate `TRUNCATE TABLE` to use `UnresolvedTable` to resolve the table identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
Note that `TRUNCATE TABLE` works only with v1 tables, and not supported for v2 tables.
### Why are the changes needed?
The changes allow consistent resolution behavior when resolving the table identifier. For example, the following is the current behavior:
```scala
sql("CREATE TEMPORARY VIEW t AS SELECT 1")
sql("CREATE DATABASE db")
sql("CREATE TABLE t using csv AS SELECT 1")
sql("USE db")
sql("TRUNCATE TABLE t") // Succeeds
```
With this PR, `TRUNCATE TABLE` above fails with the following:
```
org.apache.spark.sql.AnalysisException: t is a temp view not table.; line 1 pos 0
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveTempViews$$anonfun$apply$7.$anonfun$applyOrElse$42(Analyzer.scala:866)
```
, which is expected since temporary view is resolved first and `TRUNCATE TABLE` doesn't support a temporary view.
### Does this PR introduce _any_ user-facing change?
After this PR, `TRUNCATE TABLE` is resolved to a temp view `t` instead of table `db.t` in the above scenario.
### How was this patch tested?
Updated existing tests.
Closes#30457 from imback82/truncate_table.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to changes the resolver of partition specs used in V2 `ALTER TABLE .. ADD/DROP PARTITION` (at the moment), and re-use `CAST` in conversion partition values to desired types according to the partition schema.
### Why are the changes needed?
Currently, the resolver of V2 partition specs supports just a few types: 23e9920b39/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/ResolvePartitionSpec.scala (L72), and fails on other types like date/timestamp.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By running `AlterTablePartitionV2SQLSuite`
Closes#30474 from MaxGekk/dsv2-partition-value-types.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to change `InMemoryTable` not to use `Tuple.hashCode` for `BucketTransform`.
### Why are the changes needed?
SPARK-32168 made `InMemoryTable` to handle `BucketTransform` as a hash of `Tuple` which is dependents on Scala versions.
- https://github.com/apache/spark/blob/master/sql/catalyst/src/test/scala/org/apache/spark/sql/connector/InMemoryTable.scala#L159
**Scala 2.12.10**
```scala
$ bin/scala
Welcome to Scala 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272).
Type in expressions for evaluation. Or try :help.
scala> (1, 1).hashCode
res0: Int = -2074071657
```
**Scala 2.13.3**
```scala
Welcome to Scala 2.13.3 (OpenJDK 64-Bit Server VM, Java 1.8.0_272).
Type in expressions for evaluation. Or try :help.
scala> (1, 1).hashCode
val res0: Int = -1669302457
```
### Does this PR introduce _any_ user-facing change?
Yes. This is a correctness issue.
### How was this patch tested?
Pass the UT with both Scala 2.12/2.13.
Closes#30477 from dongjoon-hyun/SPARK-33524.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR aims the followings.
1. Upgrade from Scala 2.13.3 to 2.13.4 for Apache Spark 3.1
2. Fix exhaustivity issues in both Scala 2.12/2.13 (Scala 2.13.4 requires this for compilation.)
3. Enforce the improved exhaustive check by using the existing Scala 2.13 GitHub Action compilation job.
### Why are the changes needed?
Scala 2.13.4 is a maintenance release for 2.13 line and improves JDK 15 support.
- https://github.com/scala/scala/releases/tag/v2.13.4
Also, it improves exhaustivity check.
- https://github.com/scala/scala/pull/9140 (Check exhaustivity of pattern matches with "if" guards and custom extractors)
- https://github.com/scala/scala/pull/9147 (Check all bindings exhaustively, e.g. tuples components)
### Does this PR introduce _any_ user-facing change?
Yep. Although it's a maintenance version change, it's a Scala version change.
### How was this patch tested?
Pass the CIs and do the manual testing.
- Scala 2.12 CI jobs(GitHub Action/Jenkins UT/Jenkins K8s IT) to check the validity of code change.
- Scala 2.13 Compilation job to check the compilation
Closes#30455 from dongjoon-hyun/SCALA_3.13.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
https://github.com/apache/spark/pull/30178 provided `OptimizeWindowFunctions` used to transfer `first` to `nth_value`.
If the window frame is `UNBOUNDED PRECEDING AND CURRENT ROW` or `UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING`, `nth_value` has better performance than `first`.
But the `OptimizeWindowFunctions` need to exclude other window frame.
### Why are the changes needed?
Improve `OptimizeWindowFunctions` to avoid transfer `first` to `nth_value` if the specified window frame isn't `UNBOUNDED PRECEDING AND CURRENT ROW` or `UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING`.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#30419 from beliefer/SPARK-33278_followup.
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?
1. Pre-process partition specs in `ResolvePartitionSpec`, and convert partition names according to the partition schema and the SQL config `spark.sql.caseSensitive`. In the PR, I propose to invoke `normalizePartitionSpec` for that. The function is used in DSv1 commands, so, the behavior will be similar to DSv1.
2. Move `normalizePartitionSpec()` from `sql/core/.../datasources/PartitioningUtils` to `sql/catalyst/.../util/PartitioningUtils` to use it in Catalyst's rule `ResolvePartitionSpec`
### Why are the changes needed?
DSv1 commands like `ALTER TABLE .. ADD PARTITION` and `ALTER TABLE .. DROP PARTITION` respect the SQL config `spark.sql.caseSensitive` while resolving partition specs. For example:
```sql
spark-sql> CREATE TABLE tbl1 (id bigint, data string) USING parquet PARTITIONED BY (id);
spark-sql> ALTER TABLE tbl1 ADD PARTITION (ID=1);
spark-sql> SHOW PARTITIONS tbl1;
id=1
```
The same command fails on V2 Table catalog with error:
```
AnalysisException: Partition key ID not exists
```
### Does this PR introduce _any_ user-facing change?
Yes. After the changes, partition spec resolution works as for DSv1 (without the exception showed above).
### How was this patch tested?
By running `AlterTablePartitionV2SQLSuite`.
Closes#30454 from MaxGekk/partition-spec-case-sensitivity.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to improve the exception messages while `UnresolvedTable` is handled based on this suggestion: https://github.com/apache/spark/pull/30321#discussion_r521127001.
Currently, when an identifier is resolved to a view when a table is expected, the following exception message is displayed (e.g., for `COMMENT ON TABLE`):
```
v is a temp view not table.
```
After this PR, the message will be:
```
v is a temp view. 'COMMENT ON TABLE' expects a table.
```
Also, if an identifier is not resolved, the following exception message is currently used:
```
Table not found: t
```
After this PR, the message will be:
```
Table not found for 'COMMENT ON TABLE': t
```
### Why are the changes needed?
To improve the exception message.
### Does this PR introduce _any_ user-facing change?
Yes, the exception message will be changed as described above.
### How was this patch tested?
Updated existing tests.
Closes#30461 from imback82/unresolved_table_message.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This reverts commit 065f17386d, which is not part of any released version. That is, this is an unreleased feature
### Why are the changes needed?
I like the concept of Trash, but I think this PR might just resolve a very specific issue by introducing a mechanism without a proper design doc. This could make the usage more complex.
I think we need to consider the big picture. Trash directory is an important concept. If we decide to introduce it, we should consider all the code paths of Spark SQL that could delete the data, instead of Truncate only. We also need to consider what is the current behavior if the underlying file system does not provide the API `Trash.moveToAppropriateTrash`. Is the exception good? How about the performance when users are using the object store instead of HDFS? Will it impact the GDPR compliance?
In sum, I think we should not merge the PR https://github.com/apache/spark/pull/29552 without the design doc and implementation plan. That is why I reverted it before the code freeze of Spark 3.1
### Does this PR introduce _any_ user-facing change?
Reverted the original commit
### How was this patch tested?
The existing tests.
Closes#30463 from gatorsmile/revertSpark-32481.
Authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This follow-up fixes an issue when inserting key/value pairs into `IdentityHashMap` in `SubExprEvaluationRuntime`.
### Why are the changes needed?
The last commits to #30341 follows review comment to use `IdentityHashMap`. Because we leverage `IdentityHashMap` to compare keys in reference, we should not convert expression pairs to Scala map before inserting. Scala map compares keys by equality so we will loss keys with different references.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Run benchmark to verify.
Closes#30459 from viirya/SPARK-33427-map.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add a `CurrentTimeZone` function and replace the value at `Optimizer` side.
### Why are the changes needed?
Let user get current timezone easily. Then user can call
```
SELECT current_timezone()
```
Presto: https://prestodb.io/docs/current/functions/datetime.html
SQL Server: https://docs.microsoft.com/en-us/sql/t-sql/functions/current-timezone-transact-sql?view=sql-server-ver15
### Does this PR introduce _any_ user-facing change?
Yes, a new function.
### How was this patch tested?
Add test.
Closes#30400 from ulysses-you/SPARK-33469.
Lead-authored-by: ulysses <youxiduo@weidian.com>
Co-authored-by: ulysses-you <youxiduo@weidian.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
1. Add a hook method to `addPartitionKey()` of `InMemoryTable` which is called per every row.
2. Override `addPartitionKey()` in `InMemoryPartitionTable`, and add partition key every time when new row is inserted to the table.
### Why are the changes needed?
To be able to write unified tests for datasources V1 and V2. Currently, INSERT INTO a V1 table creates partitions but the same doesn't work for the custom catalog `InMemoryPartitionTableCatalog` used in DSv2 tests.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the affected test suite `DataSourceV2SQLSuite`.
Closes#30449 from MaxGekk/insert-into-InMemoryPartitionTable.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Group all messages of `AnalysisExcpetions` created and thrown directly in org.apache.spark.sql.catalyst.analysis.Analyzer in one file.
* Create a new object: `org.apache.spark.sql.CatalystErrors` with many exception-creating functions.
* When the `Analyzer` wants to create and throw a new `AnalysisException`, call functions of `CatalystErrors`
### Why are the changes needed?
This is the sample PR that groups exception messages together in several files. 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.
### Naming of exception functions
All function names ended with `Error`.
* For specific errors like `groupingIDMismatch` and `groupingColInvalid`, directly use them as name, just like `groupingIDMismatchError` and `groupingColInvalidError`.
* For generic errors like `dataTypeMismatch`,
* if confident with the context, prefix and condition can be added, like `pivotValDataTypeMismatchError`
* if not sure about the context, add a `For` suffix of the specific component that this exception is related to, like `dataTypeMismatchForDeserializerError`
Closes#29497 from anchovYu/32670.
Lead-authored-by: anchovYu <aureole@sjtu.edu.cn>
Co-authored-by: anchovYu <xyyu15@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
With `ParseUrl`, instead of return null we throw exception if input string is not a vaild url.
### Why are the changes needed?
For ANSI mode.
### Does this PR introduce _any_ user-facing change?
Yes, user will get exception if `set spark.sql.ansi.enabled=true`.
### How was this patch tested?
Add test.
Closes#30399 from ulysses-you/SPARK-33468.
Lead-authored-by: ulysses <youxiduo@weidian.com>
Co-authored-by: ulysses-you <youxiduo@weidian.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Explicitly convert `scala.collection.mutable.Buffer` to `Seq`. In Scala 2.13 `Seq` is an alias of `scala.collection.immutable.Seq` instead of `scala.collection.Seq`.
### Why are the changes needed?
Without the change build with Scala 2.13 fails with the following:
```
[error] /home/runner/work/spark/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/AstBuilder.scala:1417:41: type mismatch;
[error] found : scala.collection.mutable.Buffer[org.apache.spark.unsafe.types.UTF8String]
[error] required: Seq[org.apache.spark.unsafe.types.UTF8String]
[error] case null => LikeAll(e, patterns)
[error] ^
[error] /home/runner/work/spark/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/AstBuilder.scala:1418:41: type mismatch;
[error] found : scala.collection.mutable.Buffer[org.apache.spark.unsafe.types.UTF8String]
[error] required: Seq[org.apache.spark.unsafe.types.UTF8String]
[error] case _ => NotLikeAll(e, patterns)
[error] ^
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N/A
Closes#30431 from sunchao/SPARK-33045-followup.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Spark already support `LIKE ALL` syntax, but it will throw `StackOverflowError` if there are many elements(more than 14378 elements). We should implement built-in function for LIKE ALL to fix this issue.
Why the stack overflow can happen in the current approach ?
The current approach uses reduceLeft to connect each `Like(e, p)`, this will lead the the call depth of the thread is too large, causing `StackOverflowError` problems.
Why the fix in this PR can avoid the error?
This PR support built-in function for `LIKE ALL` and avoid this issue.
### Why are the changes needed?
1.Fix the `StackOverflowError` issue.
2.Support built-in function `like_all`.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#29999 from beliefer/SPARK-33045-like_all.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Change `CombineLimits` name to `EliminateLimits` and add check if `Limit` child max row <= limit.
### Why are the changes needed?
In Add-hoc scene, we always add limit for the query if user have no special limit value, but not all limit is nesessary.
A general negative example is
```
select count(*) from t limit 100000;
```
It will be great if we can eliminate limit at Spark side.
Also, we make a benchmark for this case
```
runBenchmark("Sort and Limit") {
val N = 100000
val benchmark = new Benchmark("benchmark sort and limit", N)
benchmark.addCase("TakeOrderedAndProject", 3) { _ =>
spark.range(N).toDF("c").repartition(200).sort("c").take(200000)
}
benchmark.addCase("Sort And Limit", 3) { _ =>
withSQLConf("spark.sql.execution.topKSortFallbackThreshold" -> "-1") {
spark.range(N).toDF("c").repartition(200).sort("c").take(200000)
}
}
benchmark.addCase("Sort", 3) { _ =>
spark.range(N).toDF("c").repartition(200).sort("c").collect()
}
benchmark.run()
}
```
and the result is
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_191-b12 on Mac OS X 10.15.6
Intel(R) Core(TM) i5-5257U CPU 2.70GHz
benchmark sort and limit: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
TakeOrderedAndProject 1833 2259 382 0.1 18327.1 1.0X
Sort And Limit 1417 1658 285 0.1 14167.5 1.3X
Sort 1324 1484 225 0.1 13238.3 1.4X
```
It shows that it makes sense to replace `TakeOrderedAndProjectExec` with `Sort + Project`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add test.
Closes#30368 from ulysses-you/SPARK-33442.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR is a follow up for #30093 to updates the config `spark.sql.execution.removeRedundantSorts` version to 2.4.8.
### Why are the changes needed?
To update the rule version it has been backported to 2.4. #30194
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N/A
Closes#30420 from allisonwang-db/spark-33183-follow-up.
Authored-by: allisonwang-db <66282705+allisonwang-db@users.noreply.github.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This pr add a new Scala compile arg to `pom.xml` to defense against new unused imports:
- `-Ywarn-unused-import` for Scala 2.12
- `-Wconf:cat=unused-imports:e` for Scala 2.13
The other fIles change are remove all unused imports in Spark code
### Why are the changes needed?
Cleanup code and add guarantee to defense against new unused imports
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or GitHub Action
Closes#30351 from LuciferYang/remove-imports-core-module.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add missing license headers for new files added in #28027.
### Why are the changes needed?
To fix licenses.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
This is a purely non-functional change.
Closes#30415 from rdblue/license-headers.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Similar to `InterpretedUnsafeProjection`, this patch proposes to extend interpreted subexpression elimination to `InterpretedMutableProjection` and `InterpretedSafeProjection`.
### Why are the changes needed?
Enabling subexpression elimination can improve the performance of interpreted projections, as shown in `InterpretedUnsafeProjection`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Closes#30406 from viirya/SPARK-33473.
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 followup is to prevent possible test flakyness of `SubExprEvaluationRuntimeSuite`.
### Why are the changes needed?
Because HashMap doesn't guarantee the order, in `proxyExpressions` the proxy expression id is not deterministic. So in `SubExprEvaluationRuntimeSuite` we should not test against it.
### Does this PR introduce _any_ user-facing change?
No, dev only.
### How was this patch tested?
Unit test.
Closes#30414 from viirya/SPARK-33427-followup.
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?
In section 6.13 of the ANSI SQL standard, there are syntax rules for valid combinations of the source and target data types.
Comparing the ANSI CAST syntax rules with the current default behavior of Spark:
To make Spark's ANSI mode more ANSI SQL Compatible，I propose to disallow the following casting in ANSI mode:
```
TimeStamp <=> Boolean
Date <=> Boolean
Numeric <=> Timestamp
Numeric <=> Date
Numeric <=> Binary
String <=> Array
String <=> Map
String <=> Struct
```
The following castings are considered invalid in ANSI SQL standard, but they are quite straight forward. Let's Allow them for now
```
Numeric <=> Boolean
String <=> Binary
```
### Why are the changes needed?
Better ANSI SQL compliance
### Does this PR introduce _any_ user-facing change?
Yes, the following castings will not be allowed in ANSI mode:
```
TimeStamp <=> Boolean
Date <=> Boolean
Numeric <=> Timestamp
Numeric <=> Date
Numeric <=> Binary
String <=> Array
String <=> Map
String <=> Struct
```
### How was this patch tested?
Unit test
The ANSI Compliance doc preview:
Closes#30260 from gengliangwang/ansiCanCast.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This adds support for metadata columns to DataSourceV2. If a source implements `SupportsMetadataColumns` it must also implement `SupportsPushDownRequiredColumns` to support projecting those columns.
The analyzer is updated to resolve metadata columns from `LogicalPlan.metadataOutput`, and this adds a rule that will add metadata columns to the output of `DataSourceV2Relation` if one is used.
### Why are the changes needed?
This is the solution discussed for exposing additional data in the Kafka source. It is also needed for a generic `MERGE INTO` plan.
### Does this PR introduce any user-facing change?
Yes. Users can project additional columns from sources that implement the new API. This also updates `DescribeTableExec` to show metadata columns.
### How was this patch tested?
Will include new unit tests.
Closes#28027 from rdblue/add-dsv2-metadata-columns.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
### What changes were proposed in this pull request?
This change adds MapType support for PySpark with Arrow, if using pyarrow >= 2.0.0.
### Why are the changes needed?
MapType was previous unsupported with Arrow.
### Does this PR introduce _any_ user-facing change?
User can now enable MapType for `createDataFrame()`, `toPandas()` with Arrow optimization, and with Pandas UDFs.
### How was this patch tested?
Added new PySpark tests for createDataFrame(), toPandas() and Scalar Pandas UDFs.
Closes#30393 from BryanCutler/arrow-add-MapType-SPARK-24554.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch proposes to add subexpression elimination for interpreted expression evaluation. Interpreted expression evaluation is used when codegen was not able to work, for example complex schema.
### Why are the changes needed?
Currently we only do subexpression elimination for codegen. For some reasons, we may need to run interpreted expression evaluation. For example, codegen fails to compile and fallbacks to interpreted mode, or complex input/output schema of expressions. It is commonly seen for complex schema from expressions that is possibly caused by the query optimizer too, e.g. SPARK-32945.
We should also support subexpression elimination for interpreted evaluation. That could reduce performance difference when Spark fallbacks from codegen to interpreted expression evaluation, and improve Spark usability.
#### Benchmark
Update `SubExprEliminationBenchmark`:
Before:
```
OpenJDK 64-Bit Server VM 1.8.0_265-b01 on Mac OS X 10.15.6
Intel(R) Core(TM) i7-9750H CPU 2.60GHz
from_json as subExpr: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------------------------------------
subexpressionElimination on, codegen off 24707 25688 903 0.0 247068775.9 1.0X
```
After:
```
OpenJDK 64-Bit Server VM 1.8.0_265-b01 on Mac OS X 10.15.6
Intel(R) Core(TM) i7-9750H CPU 2.60GHz
from_json as subExpr: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------------------------------------
subexpressionElimination on, codegen off 2360 2435 87 0.0 23604320.7 11.2X
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test. Benchmark manually.
Closes#30341 from viirya/SPARK-33427.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
We [rewrite](5197c5d2e7/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala (L722-L724)) `In`/`InSet` predicate to `or` expressions when pruning Hive partitions. That will cause Hive metastore stack over flow if there are a lot of values.
This pr rewrite `InSet` predicate to `GreaterThanOrEqual` min value and `LessThanOrEqual ` max value when pruning Hive partitions to avoid Hive metastore stack overflow.
From our experience, `spark.sql.hive.metastorePartitionPruningInSetThreshold` should be less than 10000.
### Why are the changes needed?
Avoid Hive metastore stack overflow when `InSet` predicate have many values.
Especially DPP, it may generate many values.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manual test.
Closes#30325 from wangyum/SPARK-33416.
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 proposes to simplify the exception messages from Python UDFS.
Currently, the exception message from Python UDFs is as below:
```python
from pyspark.sql.functions import udf; spark.range(10).select(udf(lambda x: x/0)("id")).collect()
```
```python
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../python/pyspark/sql/dataframe.py", line 427, in show
print(self._jdf.showString(n, 20, vertical))
File "/.../python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
File "/.../python/pyspark/sql/utils.py", line 127, in deco
raise_from(converted)
File "<string>", line 3, in raise_from
pyspark.sql.utils.PythonException:
An exception was thrown from Python worker in the executor:
Traceback (most recent call last):
File "/.../python/lib/pyspark.zip/pyspark/worker.py", line 605, in main
process()
File "/.../python/lib/pyspark.zip/pyspark/worker.py", line 597, in process
serializer.dump_stream(out_iter, outfile)
File "/.../python/lib/pyspark.zip/pyspark/serializers.py", line 223, in dump_stream
self.serializer.dump_stream(self._batched(iterator), stream)
File "/.../python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
for obj in iterator:
File "/.../python/lib/pyspark.zip/pyspark/serializers.py", line 212, in _batched
for item in iterator:
File "/.../python/lib/pyspark.zip/pyspark/worker.py", line 450, in mapper
result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
File "/.../python/lib/pyspark.zip/pyspark/worker.py", line 450, in <genexpr>
result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
File "/.../python/lib/pyspark.zip/pyspark/worker.py", line 90, in <lambda>
return lambda *a: f(*a)
File "/.../python/lib/pyspark.zip/pyspark/util.py", line 107, in wrapper
return f(*args, **kwargs)
File "<stdin>", line 1, in <lambda>
ZeroDivisionError: division by zero
```
Actually, almost all cases, users only care about `ZeroDivisionError: division by zero`. We don't really have to show the internal stuff in 99% cases.
This PR adds a configuration `spark.sql.execution.pyspark.udf.simplifiedException.enabled` (disabled by default) that hides the internal tracebacks related to Python worker, (de)serialization, etc.
```python
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../python/pyspark/sql/dataframe.py", line 427, in show
print(self._jdf.showString(n, 20, vertical))
File "/.../python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
File "/.../python/pyspark/sql/utils.py", line 127, in deco
raise_from(converted)
File "<string>", line 3, in raise_from
pyspark.sql.utils.PythonException:
An exception was thrown from Python worker in the executor:
Traceback (most recent call last):
File "<stdin>", line 1, in <lambda>
ZeroDivisionError: division by zero
```
The trackback will be shown from the point when any non-PySpark file is seen in the traceback.
### Why are the changes needed?
Without this configuration. such internal tracebacks are exposed to users directly especially for shall or notebook users in PySpark. 99% cases people don't care about the internal Python worker, (de)serialization and related tracebacks. It just makes the exception more difficult to read. For example, one statement of `x/0` above shows a very long traceback and most of them are unnecessary.
This configuration enables the ability to show simplified tracebacks which users will likely be most interested in.
### Does this PR introduce _any_ user-facing change?
By default, no. It adds one configuration that simplifies the exception message. See the example above.
### How was this patch tested?
Manually tested:
```bash
$ pyspark --conf spark.sql.execution.pyspark.udf.simplifiedException.enabled=true
```
```python
from pyspark.sql.functions import udf; spark.sparkContext.setLogLevel("FATAL"); spark.range(10).select(udf(lambda x: x/0)("id")).collect()
```
and unittests were also added.
Closes#30309 from HyukjinKwon/SPARK-33407.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR is a followup from https://github.com/apache/spark/pull/30076 to refactor unit test of stream-stream join in `UnsupportedOperationsSuite`, where we had a lot of duplicated code for stream-stream join unit test, for each join type.
### Why are the changes needed?
Help reduce duplicated code and make it easier for developers to read and add code in the future.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit test in `UnsupportedOperationsSuite.scala` (pure refactoring).
Closes#30347 from c21/stream-test.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Instead of returning NULL, throws runtime NoSuchElementException towards invalid key accessing in map-like functions, such as element_at, GetMapValue, when ANSI mode is on.
### Why are the changes needed?
For ANSI mode.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added UT and Existing UT.
Closes#30386 from leanken/leanken-SPARK-33460.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Move `SHOW PARTITIONS` parsing tests to `ShowPartitionsParserSuite`
2. Place Hive tests for `SHOW PARTITIONS` from `HiveCommandSuite` to the base test suite `v1.ShowPartitionsSuiteBase`. This will allow to run the tests w/ and w/o Hive.
The changes follow the approach of https://github.com/apache/spark/pull/30287.
### Why are the changes needed?
- The unification will allow to run common `SHOW PARTITIONS` tests for both DSv1 and Hive DSv1, DSv2
- We can detect missing features and differences between DSv1 and DSv2 implementations.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running:
- new test suites `build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *ShowPartitionsSuite"`
- and old one `build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly org.apache.spark.sql.hive.execution.HiveCommandSuite"`
Closes#30377 from MaxGekk/unify-dsv1_v2-show-partitions-tests.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR makes internal classes of SparkSession always using active SQLConf. We should remove all `conf: SQLConf`s from ctor-parameters of this classes (`Analyzer`, `SparkPlanner`, `SessionCatalog`, `CatalogManager` `SparkSqlParser` and etc.) and use `SQLConf.get` instead.
### Why are the changes needed?
Code refine.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing test
Closes#30299 from luluorta/SPARK-33389.
Authored-by: luluorta <luluorta@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Replace `spark.sql.sources.write.useV1SourceList` by `spark.sql.sources.useV1SourceList` in the comment for `CatalogManager.v2SessionCatalog()`.
### Why are the changes needed?
To have correct comments.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running `./dev/scalastyle`.
Closes#30385 from MaxGekk/fix-comment-useV1SourceList.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Throw `NoSuchNamespaceException` in `listTables()` of the custom test catalog `InMemoryTableCatalog` if the passed namespace doesn't exist.
### Why are the changes needed?
1. To align behavior of V2 `InMemoryTableCatalog` to V1 session catalog.
2. To distinguish two situations:
1. A namespace **does exist** but does not contain any tables. In that case, `listTables()` returns empty result.
2. A namespace **does not exist**. `listTables()` throws `NoSuchNamespaceException` in this case.
### Does this PR introduce _any_ user-facing change?
Yes. For example, `SHOW TABLES` returns empty result before the changes.
### How was this patch tested?
By running V1/V2 ShowTablesSuites.
Closes#30358 from MaxGekk/show-tables-in-not-existing-namespace.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR makes SQL parser using active SQLConf instead of the one in ctor-parameters.
### Why are the changes needed?
In ANSI mode, schema string parsing should fail if the schema uses ANSI reserved keyword as attribute name:
```scala
spark.conf.set("spark.sql.ansi.enabled", "true")
spark.sql("""select from_json('{"time":"26/10/2015"}', 'time Timestamp', map('timestampFormat', 'dd/MM/yyyy'));""").show
```
output:
> Cannot parse the data type:
> no viable alternative at input 'time'(line 1, pos 0)
>
> == SQL ==
> time Timestamp
> ^^^
But this query may accidentally succeed in certain cases cause the DataType parser sticks to the configs of the first created session in the current thread:
```scala
DataType.fromDDL("time Timestamp")
val newSpark = spark.newSession()
newSpark.conf.set("spark.sql.ansi.enabled", "true")
newSpark.sql("""select from_json('{"time":"26/10/2015"}', 'time Timestamp', map('timestampFormat', 'dd/MM/yyyy'));""").show
```
output:
> +--------------------------------+
> |from_json({"time":"26/10/2015"})|
> +--------------------------------+
> | {2015-10-26 00:00...|
> +--------------------------------+
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Newly and updated UTs
Closes#30357 from luluorta/SPARK-33432.
Authored-by: luluorta <luluorta@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This is a simple followup to prevent test flakyness in SubexpressionEliminationSuite. If `getAllEquivalentExprs` returns more than 1 sequences, due to HashMap, we should use `contains` instead of assuming the order of results.
### Why are the changes needed?
Prevent test flakyness in SubexpressionEliminationSuite.
### Does this PR introduce _any_ user-facing change?
No, dev only.
### How was this patch tested?
Unit test.
Closes#30371 from viirya/SPARK-33337-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
In [SPARK-33139] we defined `setActionSession` and `clearActiveSession` as deprecated API, it turns out it is widely used, and after discussion, even if without this PR, it should work with unify view feature, it might only be a risk if user really abuse using these two API. So revert the PR is needed.
[SPARK-33139] has two commit, include a follow up. Revert them both.
### Why are the changes needed?
Revert.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT.
Closes#30367 from leanken/leanken-revert-SPARK-33139.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
SparkSession.sql converts a string value to a DataFrame, and the string value should be one single SQL statement ending up w/ or w/o one or more semicolons. e.g.
```sql
scala> spark.sql(" select 2").show
+---+
| 2|
+---+
| 2|
+---+
scala> spark.sql(" select 2;").show
+---+
| 2|
+---+
| 2|
+---+
scala> spark.sql(" select 2;;;;").show
+---+
| 2|
+---+
| 2|
+---+
```
If we put 2 or more statements in, it fails in the parser as expected, e.g.
```sql
scala> spark.sql(" select 2; select 1;").show
org.apache.spark.sql.catalyst.parser.ParseException:
extraneous input 'select' expecting {<EOF>, ';'}(line 1, pos 11)
== SQL ==
select 2; select 1;
-----------^^^
at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:263)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:130)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:51)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:81)
at org.apache.spark.sql.SparkSession.$anonfun$sql$2(SparkSession.scala:610)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:610)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:769)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:607)
... 47 elided
```
As a very generic user scenario, users may want to change some settings before they execute
the queries. They may pass a string value like `set spark.sql.abc=2; select 1;` into this API, which creates a confusing gap between the actual effect and the user's expectations.
The user may want the query to be executed with spark.sql.abc=2, but Spark actually treats the whole part of `2; select 1;` as the value of the property 'spark.sql.abc',
e.g.
```
scala> spark.sql("set spark.sql.abc=2; select 1;").show
+-------------+------------+
| key| value|
+-------------+------------+
|spark.sql.abc|2; select 1;|
+-------------+------------+
```
What's more, the SET symbol could digest everything behind it, which makes it unstable from version to version, e.g.
#### 3.1
```sql
scala> spark.sql("set;").show
org.apache.spark.sql.catalyst.parser.ParseException:
Expected format is 'SET', 'SET key', or 'SET key=value'. If you want to include special characters in key, please use quotes, e.g., SET `ke y`=value.(line 1, pos 0)
== SQL ==
set;
^^^
at org.apache.spark.sql.execution.SparkSqlAstBuilder.$anonfun$visitSetConfiguration$1(SparkSqlParser.scala:83)
at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:113)
at org.apache.spark.sql.execution.SparkSqlAstBuilder.visitSetConfiguration(SparkSqlParser.scala:72)
at org.apache.spark.sql.execution.SparkSqlAstBuilder.visitSetConfiguration(SparkSqlParser.scala:58)
at org.apache.spark.sql.catalyst.parser.SqlBaseParser$SetConfigurationContext.accept(SqlBaseParser.java:2161)
at org.antlr.v4.runtime.tree.AbstractParseTreeVisitor.visit(AbstractParseTreeVisitor.java:18)
at org.apache.spark.sql.catalyst.parser.AstBuilder.$anonfun$visitSingleStatement$1(AstBuilder.scala:77)
at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:113)
at org.apache.spark.sql.catalyst.parser.AstBuilder.visitSingleStatement(AstBuilder.scala:77)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.$anonfun$parsePlan$1(ParseDriver.scala:82)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:113)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:51)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:81)
at org.apache.spark.sql.SparkSession.$anonfun$sql$2(SparkSession.scala:610)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:610)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:769)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:607)
... 47 elided
scala> spark.sql("set a;").show
org.apache.spark.sql.catalyst.parser.ParseException:
Expected format is 'SET', 'SET key', or 'SET key=value'. If you want to include special characters in key, please use quotes, e.g., SET `ke y`=value.(line 1, pos 0)
== SQL ==
set a;
^^^
at org.apache.spark.sql.execution.SparkSqlAstBuilder.$anonfun$visitSetConfiguration$1(SparkSqlParser.scala:83)
at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:113)
at org.apache.spark.sql.execution.SparkSqlAstBuilder.visitSetConfiguration(SparkSqlParser.scala:72)
at org.apache.spark.sql.execution.SparkSqlAstBuilder.visitSetConfiguration(SparkSqlParser.scala:58)
at org.apache.spark.sql.catalyst.parser.SqlBaseParser$SetConfigurationContext.accept(SqlBaseParser.java:2161)
at org.antlr.v4.runtime.tree.AbstractParseTreeVisitor.visit(AbstractParseTreeVisitor.java:18)
at org.apache.spark.sql.catalyst.parser.AstBuilder.$anonfun$visitSingleStatement$1(AstBuilder.scala:77)
at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:113)
at org.apache.spark.sql.catalyst.parser.AstBuilder.visitSingleStatement(AstBuilder.scala:77)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.$anonfun$parsePlan$1(ParseDriver.scala:82)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:113)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:51)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:81)
at org.apache.spark.sql.SparkSession.$anonfun$sql$2(SparkSession.scala:610)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:610)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:769)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:607)
... 47 elided
```
#### 2.4
```sql
scala> spark.sql("set;").show
+---+-----------+
|key| value|
+---+-----------+
| ;|<undefined>|
+---+-----------+
scala> spark.sql("set a;").show
+---+-----------+
|key| value|
+---+-----------+
| a;|<undefined>|
+---+-----------+
```
In this PR,
1. make `set spark.sql.abc=2; select 1;` in `SparkSession.sql` fail directly, user should call `.sql` for each statement separately.
2. make the semicolon as the separator of statements, and if users want to use it as part of the property value, shall use quotes too.
### Why are the changes needed?
1. disambiguation for `SparkSession.sql`
2. make semicolon work same both w/ `SET` and other statements
### Does this PR introduce _any_ user-facing change?
yes,
the semicolon works as a separator of statements now, it will be trimmed if it is at the end of the statement and fail the statement if it is in the middle. you need to use quotes if you want it to be part of the property value
### How was this patch tested?
new tests
Closes#30332 from yaooqinn/SPARK-33419.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Change `Aggregate` max rows to 1 if grouping is empty.
### Why are the changes needed?
If `Aggregate` grouping is empty, the result is always one row.
Then we don't need push down limit in `LimitPushDown` with such case
```
select count(*) from t1
union
select count(*) from t2
limit 1
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add test.
Closes#30356 from ulysses-you/SPARK-33433.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This patch proposes to disable the streaming query with possible correctness issue in chained stateful operators. The behavior can be controlled by a SQL config, so if users understand the risk and still want to run the query, they can disable the check.
### Why are the changes needed?
The possible correctness in chained stateful operators in streaming query is not straightforward for users. From users perspective, it will be considered as a Spark bug. It is also possible the worse case, users are not aware of the correctness issue and use wrong results.
A better approach should be to disable such queries and let users choose to run the query if they understand there is such risk, instead of implicitly running the query and let users to find out correctness issue by themselves and report this known to Spark community.
### Does this PR introduce _any_ user-facing change?
Yes. Streaming query with possible correctness issue will be blocked to run, except for users explicitly disable the SQL config.
### How was this patch tested?
Unit test.
Closes#30210 from viirya/SPARK-33259.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
In #30097, many rules changed from case class to object, but if the rule
is stateful, there will be a problem. For example, if an object rule uses a
`val` to refer to a config, it will be unchanged after initialization even if
other spark session uses a different config value.
### Why are the changes needed?
Avoid potential bug
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing UT
Closes#30354 from linhongliu-db/SPARK-33140-followup-2.
Lead-authored-by: Linhong Liu <67896261+linhongliu-db@users.noreply.github.com>
Co-authored-by: Linhong Liu <linhong.liu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
https://github.com/apache/spark/pull/29800 provides a performance improvement for `NTH_VALUE`.
`FIRST_VALUE` also could use the `UnboundedOffsetWindowFunctionFrame` and `UnboundedPrecedingOffsetWindowFunctionFrame`.
### Why are the changes needed?
Improve the performance for `FIRST_VALUE`.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#30178 from beliefer/SPARK-33278.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add `Greatest` and `Least` check in `Canonicalize`.
### Why are the changes needed?
The children of both `Greatest` and `Least` are order Irrelevant.
Let's say we have `greatest(1, 2)` and `greatest(2, 1)`. We can get the same canonicalized expression in this case.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add test.
Closes#30330 from ulysses-you/SPARK-33421.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
