### What changes were proposed in this pull request?
Avro add zstandard codec since AVRO-2195. This pr add zstandard codec to Avro compression codec list.
### Why are the changes needed?
To make Avro support zstandard codec.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#31673 from wangyum/SPARK-34479.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
To support +8:00 in Spark3 when execute sql
`select to_utc_timestamp("2020-02-07 16:00:00", "GMT+8:00")`
### Why are the changes needed?
+8:00 this format is supported in PostgreSQL,hive, presto, but not supported in Spark3
https://issues.apache.org/jira/browse/SPARK-34392
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
unit test
Closes#31624 from Karl-WangSK/zone.
Lead-authored-by: ShiKai Wang <wskqing@gmail.com>
Co-authored-by: Karl-WangSK <shikai.wang@linkflowtech.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Add more aggregate expressions to `EliminateDistinct` rule.
### Why are the changes needed?
Distinct aggregation can add a significant overhead. It's better to remove distinct whenever possible.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
UT
Closes#30999 from tanelk/SPARK-33971_eliminate_distinct.
Authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Implement `ColumnarMap.copy()` by using the `copy()` method of `ColumnarArray`.
### Why are the changes needed?
To eliminate `java.lang.UnsupportedOperationException` while using `ColumnarMap`.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By running new tests in `ColumnarBatchSuite`.
Closes#31663 from MaxGekk/columnar-map-copy.
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 simplifies the resolution of v2 partition commands:
1. Add a common trait for v2 partition commands, so that we don't need to match them one by one in the rules.
2. Make partition spec an expression, so that it's easier to resolve them via tree node transformation.
3. Add `TruncatePartition` so that `TruncateTable` doesn't need to be a v2 partition command.
4. Simplify `CheckAnalysis` to only check if the table is partitioned. For partitioned tables, partition spec is always resolved, so we don't need to check it. The `SupportsAtomicPartitionManagement` check is also done in the runtime. Since Spark eagerly executes commands, exception in runtime will also be thrown at analysis time.
### Why are the changes needed?
code cleanup
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing tests
Closes#31637 from cloud-fan/simplify.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch proposes to let optimizer to remove unnecessary `Union` under `Distinct`/`Deduplicate`.
### Why are the changes needed?
For an `Union` under `Distinct`/`Deduplicate`, if its children are all the same, we can just keep one among them and remove the `Union`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit tests.
Closes#31595 from viirya/remove-union.
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?
I found out during code review of https://github.com/apache/spark/pull/31567#discussion_r577379572, where we can push down limit to the left side of LEFT SEMI and LEFT ANTI join, if the join condition is empty.
Why it's safe to push down limit:
The semantics of LEFT SEMI join without condition:
(1). if right side is non-empty, output all rows from left side.
(2). if right side is empty, output nothing.
The semantics of LEFT ANTI join without condition:
(1). if right side is non-empty, output nothing.
(2). if right side is empty, output all rows from left side.
With the semantics of output all rows from left side or nothing (all or nothing), it's safe to push down limit to left side.
NOTE: LEFT SEMI / LEFT ANTI join with non-empty condition is not safe for limit push down, because output can be a portion of left side rows.
Reference: physical operator implementation for LEFT SEMI / LEFT ANTI join without condition - https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoinExec.scala#L200-L204 .
### Why are the changes needed?
Better performance. Save CPU and IO for these joins, as limit being pushed down before join.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added unit test in `LimitPushdownSuite.scala` and `SQLQuerySuite.scala`.
Closes#31630 from c21/limit-pushdown.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR follows up https://github.com/apache/spark/pull/30717
Maybe some contributors don't know the job and added some exception by the old way.
### 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#31316 from beliefer/SPARK-33599-followup.
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?
This PR proposes to make `CreateViewStatement.child` to be `LogicalPlan`'s `children` so that it's resolved in the analyze phase.
### Why are the changes needed?
Currently, the `CreateViewStatement.child` is resolved when the create view command runs, which is inconsistent with other plan resolutions. For example, you may see the following in the physical plan:
```
== Physical Plan ==
Execute CreateViewCommand (1)
+- CreateViewCommand (2)
+- Project (4)
+- UnresolvedRelation (3)
```
### Does this PR introduce _any_ user-facing change?
Yes. For the example, you will now see the resolved plan:
```
== Physical Plan ==
Execute CreateViewCommand (1)
+- CreateViewCommand (2)
+- Project (5)
+- SubqueryAlias (4)
+- LogicalRelation (3)
```
### How was this patch tested?
Updated existing tests.
Closes#31273 from imback82/spark-34152.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In Spark ANSI mode, the type coercion rules are based on the type precedence lists of the input data types.
As per the section "Type precedence list determination" of "ISO/IEC 9075-2:2011
Information technology — Database languages - SQL — Part 2: Foundation (SQL/Foundation)", the type precedence lists of primitive data types are as following:
- Byte: Byte, Short, Int, Long, Decimal, Float, Double
- Short: Short, Int, Long, Decimal, Float, Double
- Int: Int, Long, Decimal, Float, Double
- Long: Long, Decimal, Float, Double
- Decimal: Any wider Numeric type
- Float: Float, Double
- Double: Double
- String: String
- Date: Date, Timestamp
- Timestamp: Timestamp
- Binary: Binary
- Boolean: Boolean
- Interval: Interval
As for complex data types, Spark will determine the precedent list recursively based on their sub-types.
With the definition of type precedent list, the general type coercion rules are as following:
- Data type S is allowed to be implicitly cast as type T iff T is in the precedence list of S
- Comparison is allowed iff the data type precedence list of both sides has at least one common element. When evaluating the comparison, Spark casts both sides as the tightest common data type of their precedent lists.
- There should be at least one common data type among all the children's precedence lists for the following operators. The data type of the operator is the tightest common precedent data type.
```
In, Except(odd), Intersect, Greatest, Least, Union, If, CaseWhen, CreateArray, Array Concat,Sequence, MapConcat, CreateMap
```
- For complex types (struct, array, map), Spark recursively looks into the element type and applies the rules above. If the element nullability is converted from true to false, add runtime null check to the elements.
Note: this new type coercion system will allow implicit converting String type literals as other primitive types, in case of breaking too many existing Spark SQL queries. This is a special rule and it is not from the ANSI SQL standard.
### Why are the changes needed?
The current type coercion rules are complex. Also, they are very hard to describe and understand. For details please refer the attached documentation "Default Type coercion rules of Spark"
[Default Type coercion rules of Spark.pdf](https://github.com/apache/spark/files/5874362/Default.Type.coercion.rules.of.Spark.pdf)
This PR is to create a new and strict type coercion system under ANSI mode. The rules are simple and clean, so that users can follow them easily
### Does this PR introduce _any_ user-facing change?
Yes, new implicit cast syntax rules in ANSI mode. All the details are in the first section of this description.
### How was this patch tested?
Unit tests
Closes#31349 from gengliangwang/ansiImplicitConversion.
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?
Implement the v2 execution node for the `TRUNCATE TABLE` command.
### Why are the changes needed?
To have feature parity with DS v1, and support truncation of v2 tables.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By running the unified tests for v1 and v2 tables:
```
$ build/sbt -Phive -Phive-thriftserver "test:testOnly *TruncateTableSuite"
```
Closes#31605 from MaxGekk/truncate-table-v2.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to extend the `MSCK REPAIR TABLE` command, and support new options `{ADD|DROP|SYNC} PARTITIONS`. In particular:
1. Extend the logical node `RepairTable`, and add two new flags `enableAddPartitions` and `enableDropPartitions`.
2. Add similar flags to the v1 execution node `AlterTableRecoverPartitionsCommand`
3. Add new method `dropPartitions()` to `AlterTableRecoverPartitionsCommand` which drops partitions from the catalog if their locations in the file system don't exist.
4. Updated public docs about the `MSCK REPAIR TABLE` command:
<img width="1037" alt="Screenshot 2021-02-16 at 13 46 39" src="https://user-images.githubusercontent.com/1580697/108052607-7446d280-705d-11eb-8e25-7398254787a4.png">
Closes#31097
### Why are the changes needed?
- The changes allow to recover tables with removed partitions. The example below portraits the problem:
```sql
spark-sql> create table tbl2 (col int, part int) partitioned by (part);
spark-sql> insert into tbl2 partition (part=1) select 1;
spark-sql> insert into tbl2 partition (part=0) select 0;
spark-sql> show table extended like 'tbl2' partition (part = 0);
default tbl2 false Partition Values: [part=0]
Location: file:/Users/maximgekk/proj/apache-spark/spark-warehouse/tbl2/part=0
...
```
Remove the partition (part = 0) from the filesystem:
```
$ rm -rf /Users/maximgekk/proj/apache-spark/spark-warehouse/tbl2/part=0
```
Even after recovering, we cannot query the table:
```sql
spark-sql> msck repair table tbl2;
spark-sql> select * from tbl2;
21/01/08 22:49:13 ERROR SparkSQLDriver: Failed in [select * from tbl2]
org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: file:/Users/maximgekk/proj/apache-spark/spark-warehouse/tbl2/part=0
```
- To have feature parity with Hive: https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-RecoverPartitions(MSCKREPAIRTABLE)
### Does this PR introduce _any_ user-facing change?
Yes. After the changes, we can query recovered table:
```sql
spark-sql> msck repair table tbl2 sync partitions;
spark-sql> select * from tbl2;
1 1
spark-sql> show partitions tbl2;
part=1
```
### How was this patch tested?
- By running the modified test suite:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *MsckRepairTableParserSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *PlanResolutionSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableRecoverPartitionsSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableRecoverPartitionsParallelSuite"
```
- Added unified v1 and v2 tests for `MSCK REPAIR TABLE`:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *MsckRepairTableSuite"
```
Closes#31499 from MaxGekk/repair-table-drop-partitions.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In the PR, I propose to rename logical nodes of v2 commands in the form: `<verb> + <object>` like:
- AlterTableAddPartition -> AddPartition
- AlterTableSetLocation -> SetTableLocation
### Why are the changes needed?
1. For simplicity and readability of logical plans
2. For consistency with other logical nodes. For example, the logical node `RenameTable` for `ALTER TABLE .. RENAME TO` was added before `AlterTableRenamePartition`.
### Does this PR introduce _any_ user-facing change?
Should not since this is non-public APIs.
### How was this patch tested?
1. Check scala style: `./dev/scalastyle`
2. Affected test suites:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableRenamePartitionSuite"
```
Closes#31596 from MaxGekk/rename-alter-table-logic-nodes.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
When resolving a view, we use the captured view name in `AnalysisContext` to
distinguish whether a relation name is a view or a table. But if the resolution failed,
other rules (e.g. `ResolveTables`) will try to resolve the relation again but without
`AnalysisContext`. So, in this case, the resolution may be incorrect. For example,
if the view refers to a dropped table while a view with the same name exists, the
dropped table will be resolved as a view rather than an unresolved exception.
### Why are the changes needed?
bugfix
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
newly added test cases
Closes#31606 from linhongliu-db/fix-temp-view-master.
Lead-authored-by: Linhong Liu <linhong.liu@databricks.com>
Co-authored-by: Linhong Liu <67896261+linhongliu-db@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to revert https://github.com/apache/spark/pull/26659 partially regarding to comparability of interval values. The comment became incorrect after https://github.com/apache/spark/pull/27262.
### Why are the changes needed?
The comment is incorrect, and it might confuse Spark's devs/users.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By checking scala coding style `./dev/scalastyle`.
Closes#31610 from MaxGekk/doc-interval-not-comparable.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Move parser tests from `DDLParserSuite` to `AlterTableRenameParserSuite`.
2. Port DS v1 tests from `DDLSuite` and other test suites to `v1.AlterTableRenameBase` and to `v1.AlterTableRenameSuite`.
3. Add a test for DSv2 `ALTER TABLE .. RENAME` to `v2.AlterTableRenameSuite`.
### Why are the changes needed?
To improve test coverage.
### 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 *AlterTableRenameSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableRenameParserSuite"
```
Closes#31575 from MaxGekk/unify-rename-table-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?
1. Add new interface `TruncatableTable` which represents tables that allow atomic truncation.
2. Implement new method in `InMemoryTable` and in `InMemoryPartitionTable`.
### Why are the changes needed?
To support `TRUNCATE TABLE` for v2 tables.
### Does this PR introduce _any_ user-facing change?
Should not.
### How was this patch tested?
Added new tests to `TableCatalogSuite` that check truncation of non-partitioned and partitioned tables:
```
$ build/sbt "test:testOnly *TableCatalogSuite"
```
Closes#31475 from MaxGekk/dsv2-truncate-table.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
UserDefinedType and UDTRegistration become public Developer APIs, not package-private to Spark.
### Why are the changes needed?
This proposes to simply open up the UserDefinedType class as a developer API. It was public in 1.x, but closed in 2.x for some possible redesign that does not seem to have happened.
Other libraries have managed to define UDTs anyway by inserting shims into the Spark namespace, and this evidently has worked OK. But package isolation in Java 9+ breaks this.
The logic here is mostly: this is de facto a stable API, so can at least be open to developers with the usual caveats about developer APIs.
Open questions:
- Is there in fact some important redesign that's needed before opening it? The comment to this effect is from 2016
- Is this all that needs to be opened up? Like PythonUserDefinedType?
- Should any of this be kept package-private?
This was first proposed in https://github.com/apache/spark/pull/16478 though it was a larger change, but, the other API issues it was fixing seem to have been addressed already (e.g. no need to return internal Spark types). It was never really reviewed.
My hunch is that there isn't much downside, and some upside, to just opening this as-is now.
### Does this PR introduce _any_ user-facing change?
UserDefinedType becomes visible to developers to subclass.
### How was this patch tested?
Existing tests; there is no change to the existing logic.
Closes#31461 from srowen/SPARK-7768.
Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Handled 'Deduplicate(Keys, Union)' operation in rule 'CombineUnions' to combine adjacent 'Union' operators into a single 'Union' if necessary when using 'Dataset.union.distinct.union.distinct'.
Currently only handle distinct-like 'Deduplicate', where the keys == output, for example:
```
val df1 = Seq((1, 2, 3)).toDF("a", "b", "c")
val df2 = Seq((6, 2, 5)).toDF("a", "b", "c")
val df3 = Seq((2, 4, 3)).toDF("c", "a", "b")
val df4 = Seq((1, 4, 5)).toDF("b", "a", "c")
val unionDF1 = df1.unionByName(df2).dropDuplicates(Seq("b", "a", "c"))
.unionByName(df3).dropDuplicates().unionByName(df4)
.dropDuplicates("a")
```
In this case, **all Union operators will be combined**.
but,
```
val df1 = Seq((1, 2, 3)).toDF("a", "b", "c")
val df2 = Seq((6, 2, 5)).toDF("a", "b", "c")
val df3 = Seq((2, 4, 3)).toDF("c", "a", "b")
val df4 = Seq((1, 4, 5)).toDF("b", "a", "c")
val unionDF = df1.unionByName(df2).dropDuplicates(Seq("a"))
.unionByName(df3).dropDuplicates("c").unionByName(df4)
.dropDuplicates("b")
```
In this case, **all unions will not be combined, because the Deduplicate.keys doesn't equal to Union.output**.
### Why are the changes needed?
When using 'Dataset.union.distinct.union.distinct', the operator is 'Deduplicate(Keys, Union)', but AstBuilder transform sql-style 'Union' to operator 'Distinct(Union)', the rule 'CombineUnions' in Optimizer only handle 'Distinct(Union)' operator but not Deduplicate(Keys, Union).
Please see the detailed description in [SPARK-34283](https://issues.apache.org/jira/browse/SPARK-34283).
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit tests.
Closes#31404 from zzcclp/SPARK-34283.
Authored-by: Zhichao Zhang <441586683@qq.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Make the following SQL configs as internal:
- spark.sql.legacy.allowHashOnMapType
- spark.sql.legacy.sessionInitWithConfigDefaults
2. Add a test to check that all SQL configs from the `legacy` namespace are marked as internal configs.
### Why are the changes needed?
Assuming that legacy SQL configs shouldn't be set by users in common cases. The purpose of such configs is to allow switching to old behavior in corner cases. So, the configs should be marked as internals.
### Does this PR introduce _any_ user-facing change?
Should not.
### How was this patch tested?
By running new test:
```
$ build/sbt "test:testOnly *SQLConfSuite"
```
Closes#31577 from MaxGekk/mark-legacy-configs-as-internal.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
The current implement of some DDL not unify the output and not pass the output properly to physical command.
Such as: The output attributes of `ShowFunctions` does't pass to `ShowFunctionsCommand` properly.
As the query plan, this PR pass the output attributes from `ShowFunctions` to `ShowFunctionsCommand`.
### Why are the changes needed?
This PR pass the output attributes could keep the expr ID unchanged, so that avoid bugs when we apply more operators above the command output dataframe.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#31519 from beliefer/SPARK-34394.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The current implement of some DDL not unify the output and not pass the output properly to physical command.
Such as: The output attributes of `ShowViews` does't pass to `ShowViewsCommand` properly.
As the query plan, this PR pass the output attributes from `ShowViews` to `ShowViewsCommand`.
### Why are the changes needed?
This PR pass the output attributes could keep the expr ID unchanged, so that avoid bugs when we apply more operators above the command output dataframe.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#31508 from beliefer/SPARK-34393.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Put the SQL config `spark.sql.legacy.replaceDatabricksSparkAvro.enabled` to the list of deprecated configs `deprecatedSQLConfigs`
2. Update docs for the Avro datasource
<img width="982" alt="Screenshot 2021-02-17 at 21 04 26" src="https://user-images.githubusercontent.com/1580697/108249890-abed7180-7166-11eb-8cb7-0c246d2a34fc.png">
### Why are the changes needed?
The config exists for enough time. We can deprecate it, and recommend users to use `.format("avro")` instead.
### Does this PR introduce _any_ user-facing change?
Should not except of the warning with the recommendation to use the `avro` format.
### How was this patch tested?
1. By generating docs via:
```
$ SKIP_API=1 SKIP_SCALADOC=1 SKIP_PYTHONDOC=1 SKIP_RDOC=1 jekyll serve --watch
```
2. Manually checking the warning:
```
scala> spark.conf.set("spark.sql.legacy.replaceDatabricksSparkAvro.enabled", false)
21/02/17 21:20:18 WARN SQLConf: The SQL config 'spark.sql.legacy.replaceDatabricksSparkAvro.enabled' has been deprecated in Spark v3.2 and may be removed in the future. Use `.format("avro")` in `DataFrameWriter` or `DataFrameReader` instead.
```
Closes#31578 from MaxGekk/deprecate-replaceDatabricksSparkAvro.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR handles merge operations in `ReplaceNullWithFalseInPredicate`.
### Why are the changes needed?
These changes are needed to match what we already do for delete and update operations.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
This PR extends existing tests to cover merge operations.
Closes#31579 from aokolnychyi/spark-33736.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Move the datetime rebase SQL configs from the `legacy` namespace by:
1. Renaming of the existing rebase configs like `spark.sql.legacy.parquet.datetimeRebaseModeInRead` -> `spark.sql.parquet.datetimeRebaseModeInRead`.
2. Add the legacy configs as alternatives
3. Deprecate the legacy rebase configs.
### Why are the changes needed?
The rebasing SQL configs like `spark.sql.legacy.parquet.datetimeRebaseModeInRead` can be used not only for migration from previous Spark versions but also to read/write datatime columns saved by other systems/frameworks/libs. So, the configs shouldn't be considered as legacy configs.
### Does this PR introduce _any_ user-facing change?
Should not. Users will see a warning if they still use one of the legacy configs.
### How was this patch tested?
1. Manually checking new configs:
```scala
scala> spark.conf.get("spark.sql.parquet.datetimeRebaseModeInRead")
res0: String = EXCEPTION
scala> spark.conf.set("spark.sql.legacy.parquet.datetimeRebaseModeInRead", "LEGACY")
21/02/17 14:57:10 WARN SQLConf: The SQL config 'spark.sql.legacy.parquet.datetimeRebaseModeInRead' has been deprecated in Spark v3.2 and may be removed in the future. Use 'spark.sql.parquet.datetimeRebaseModeInRead' instead.
scala> spark.conf.get("spark.sql.parquet.datetimeRebaseModeInRead")
res2: String = LEGACY
```
2. By running a datetime rebasing test suite:
```
$ build/sbt "test:testOnly *ParquetRebaseDatetimeV1Suite"
```
Closes#31576 from MaxGekk/rebase-confs-alternatives.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Fix descriptions of the SQL configs `spark.sql.legacy.parquet.int96RebaseModeInRead` and `spark.sql.legacy.parquet.int96RebaseModeInWrite`, and mention `EXCEPTION` as the default value.
### Why are the changes needed?
This fixes incorrect descriptions that can mislead users.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running `./dev/scalastyle`.
Closes#31557 from MaxGekk/int96-exception-by-default-followup.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Modify `RandomDataGenerator.forType()` to allow generation of dates/timestamps that are valid in both Julian and Proleptic Gregorian calendars. Currently, the function can produce a date (for example `1582-10-06`) which is valid in the Proleptic Gregorian calendar. Though it cannot be saved to ORC files AS IS since ORC format (ORC libs in fact) assumes Julian calendar. So, Spark shifts `1582-10-06` to the next valid date `1582-10-15` while saving it to ORC files. And as a consequence of that, the test fails because it compares original date `1582-10-06` and the date `1582-10-15` loaded back from the ORC files.
In this PR, I propose to generate valid dates/timestamps in both calendars for ORC datasource till SPARK-34440 is resolved.
### Why are the changes needed?
The changes fix failures of `HiveOrcHadoopFsRelationSuite`. For instance, the test "test all data types" fails with the seed **610710213676**:
```
== Results ==
!== Correct Answer - 20 == == Spark Answer - 20 ==
struct<index:int,col:date> struct<index:int,col:date>
...
![9,1582-10-06] [9,1582-10-15]
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the modified test suite:
```
$ build/sbt -Phive -Phive-thriftserver "test:testOnly *HiveOrcHadoopFsRelationSuite"
```
Closes#31552 from MaxGekk/fix-HiveOrcHadoopFsRelationSuite.
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 support `ifExists` flag for v2 `ALTER TABLE ... UNSET TBLPROPERTIES` command. Currently, the flag is not respected and the command behaves as `ifExists = true` where the command always succeeds when the properties do not exist.
### Why are the changes needed?
To support `ifExists` flag and align with v1 command behavior.
### Does this PR introduce _any_ user-facing change?
Yes, now if the property does not exist and `IF EXISTS` is not specified, the command will fail:
```
ALTER TABLE t UNSET TBLPROPERTIES ('unknown') // Fails with "Attempted to unset non-existent property 'unknown'"
ALTER TABLE t UNSET TBLPROPERTIES IF EXISTS ('unknown') // OK
```
### How was this patch tested?
Added new test
Closes#31494 from imback82/AlterTableUnsetPropertiesIfExists.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Move `PartitionTransforms.scala` from `sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions` to `sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions`.
### Why are the changes needed?
We should put java/scala files to their corresponding directories.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
N/A
Closes#31546 from sunchao/SPARK-34419.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Creating a Pandas dataframe via Apache Arrow currently can use twice as much memory as the final result, because during the conversion, both Pandas and Arrow retain a copy of the data. Arrow has a "self-destruct" mode now (Arrow >= 0.16) to avoid this, by freeing each column after conversion. This PR integrates support for this in toPandas, handling a couple of edge cases:
self_destruct has no effect unless the memory is allocated appropriately, which is handled in the Arrow serializer here. Essentially, the issue is that self_destruct frees memory column-wise, but Arrow record batches are oriented row-wise:
```
Record batch 0: allocation 0: column 0 chunk 0, column 1 chunk 0, ...
Record batch 1: allocation 1: column 0 chunk 1, column 1 chunk 1, ...
```
In this scenario, Arrow will drop references to all of column 0's chunks, but no memory will actually be freed, as the chunks were just slices of an underlying allocation. The PR copies each column into its own allocation so that memory is instead arranged as so:
```
Record batch 0: allocation 0 column 0 chunk 0, allocation 1 column 1 chunk 0, ...
Record batch 1: allocation 2 column 0 chunk 1, allocation 3 column 1 chunk 1, ...
```
The optimization is disabled by default, and can be enabled with the Spark SQL conf "spark.sql.execution.arrow.pyspark.selfDestruct.enabled" set to "true". We can't always apply this optimization because it's more likely to generate a dataframe with immutable buffers, which Pandas doesn't always handle well, and because it is slower overall (since it only converts one column at a time instead of in parallel).
### Why are the changes needed?
This lets us load larger datasets - in particular, with N bytes of memory, before we could never load a dataset bigger than N/2 bytes; now the overhead is more like N/1.25 or so.
### Does this PR introduce _any_ user-facing change?
Yes - it adds a new SQL conf "spark.sql.execution.arrow.pyspark.selfDestruct.enabled"
### How was this patch tested?
See the [mailing list](http://apache-spark-developers-list.1001551.n3.nabble.com/DISCUSS-Reducing-memory-usage-of-toPandas-with-Arrow-quot-self-destruct-quot-option-td30149.html) - it was tested with Python memory_profiler. Unit tests added to check memory within certain bounds and correctness with the option enabled.
Closes#29818 from lidavidm/spark-32953.
Authored-by: David Li <li.davidm96@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
### What changes were proposed in this pull request?
`TreeNodeException` causes the error msg not clear and it didn't work well.
Because the `TreeNodeException` looks redundancy, we could remove it.
There are show a case:
```
val df = Seq(("1", 1), ("1", 2), ("2", 3), ("2", 4)).toDF("x", "y")
val hashAggDF = df.groupBy("x").agg(c, sum("y"))
```
The above code will use `HashAggregateExec`. In order to ensure that an exception will be thrown when executing `HashAggregateExec`, I added `throw new RuntimeException("calculate error")` into 72b7f8abfb/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala (L85)
So, if the above code is executed, `RuntimeException("calculate error")` will be thrown.
Before this PR, the error is:
```
execute, tree:
ShuffleQueryStage 0
+- Exchange hashpartitioning(x#105, 5), ENSURE_REQUIREMENTS, [id=#168]
+- HashAggregate(keys=[x#105], functions=[partial_sum(y#106)], output=[x#105, sum#118L])
+- Project [_1#100 AS x#105, _2#101 AS y#106]
+- LocalTableScan [_1#100, _2#101]
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: execute, tree:
ShuffleQueryStage 0
+- Exchange hashpartitioning(x#105, 5), ENSURE_REQUIREMENTS, [id=#168]
+- HashAggregate(keys=[x#105], functions=[partial_sum(y#106)], output=[x#105, sum#118L])
+- Project [_1#100 AS x#105, _2#101 AS y#106]
+- LocalTableScan [_1#100, _2#101]
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
at org.apache.spark.sql.execution.adaptive.ShuffleQueryStageExec.doMaterialize(QueryStageExec.scala:163)
at org.apache.spark.sql.execution.adaptive.QueryStageExec.$anonfun$materialize$1(QueryStageExec.scala:81)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:218)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:215)
at org.apache.spark.sql.execution.adaptive.QueryStageExec.materialize(QueryStageExec.scala:79)
at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.$anonfun$getFinalPhysicalPlan$5(AdaptiveSparkPlanExec.scala:207)
at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.$anonfun$getFinalPhysicalPlan$5$adapted(AdaptiveSparkPlanExec.scala:205)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.$anonfun$getFinalPhysicalPlan$1(AdaptiveSparkPlanExec.scala:205)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:772)
at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.getFinalPhysicalPlan(AdaptiveSparkPlanExec.scala:179)
at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.executeCollect(AdaptiveSparkPlanExec.scala:289)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3708)
at org.apache.spark.sql.Dataset.$anonfun$collect$1(Dataset.scala:2977)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3699)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:772)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3697)
at org.apache.spark.sql.Dataset.collect(Dataset.scala:2977)
at org.apache.spark.sql.DataFrameAggregateSuite.$anonfun$assertNoExceptions$3(DataFrameAggregateSuite.scala:665)
at org.apache.spark.sql.catalyst.plans.SQLHelper.withSQLConf(SQLHelper.scala:54)
at org.apache.spark.sql.catalyst.plans.SQLHelper.withSQLConf$(SQLHelper.scala:38)
at org.apache.spark.sql.DataFrameAggregateSuite.org$apache$spark$sql$test$SQLTestUtilsBase$$super$withSQLConf(DataFrameAggregateSuite.scala:37)
at org.apache.spark.sql.test.SQLTestUtilsBase.withSQLConf(SQLTestUtils.scala:246)
at org.apache.spark.sql.test.SQLTestUtilsBase.withSQLConf$(SQLTestUtils.scala:244)
at org.apache.spark.sql.DataFrameAggregateSuite.withSQLConf(DataFrameAggregateSuite.scala:37)
at org.apache.spark.sql.DataFrameAggregateSuite.$anonfun$assertNoExceptions$2(DataFrameAggregateSuite.scala:659)
at org.apache.spark.sql.DataFrameAggregateSuite.$anonfun$assertNoExceptions$2$adapted(DataFrameAggregateSuite.scala:655)
at scala.collection.TraversableLike$WithFilter.$anonfun$foreach$1(TraversableLike.scala:877)
at scala.collection.immutable.List.foreach(List.scala:392)
at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:876)
at org.apache.spark.sql.DataFrameAggregateSuite.assertNoExceptions(DataFrameAggregateSuite.scala:655)
at org.apache.spark.sql.DataFrameAggregateSuite.$anonfun$new$126(DataFrameAggregateSuite.scala:695)
at org.apache.spark.sql.DataFrameAggregateSuite.$anonfun$new$126$adapted(DataFrameAggregateSuite.scala:695)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.sql.DataFrameAggregateSuite.$anonfun$new$125(DataFrameAggregateSuite.scala:695)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.scalatest.OutcomeOf.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf.outcomeOf$(OutcomeOf.scala:83)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
at org.scalatest.funsuite.AnyFunSuiteLike$$anon$1.apply(AnyFunSuiteLike.scala:190)
at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:176)
at org.scalatest.funsuite.AnyFunSuiteLike.invokeWithFixture$1(AnyFunSuiteLike.scala:188)
at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$runTest$1(AnyFunSuiteLike.scala:200)
at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306)
at org.scalatest.funsuite.AnyFunSuiteLike.runTest(AnyFunSuiteLike.scala:200)
at org.scalatest.funsuite.AnyFunSuiteLike.runTest$(AnyFunSuiteLike.scala:182)
at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterEach$$super$runTest(SparkFunSuite.scala:61)
at org.scalatest.BeforeAndAfterEach.runTest(BeforeAndAfterEach.scala:234)
at org.scalatest.BeforeAndAfterEach.runTest$(BeforeAndAfterEach.scala:227)
at org.apache.spark.SparkFunSuite.runTest(SparkFunSuite.scala:61)
at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$runTests$1(AnyFunSuiteLike.scala:233)
at org.scalatest.SuperEngine.$anonfun$runTestsInBranch$1(Engine.scala:413)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401)
at org.scalatest.SuperEngine.runTestsInBranch(Engine.scala:396)
at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:475)
at org.scalatest.funsuite.AnyFunSuiteLike.runTests(AnyFunSuiteLike.scala:233)
at org.scalatest.funsuite.AnyFunSuiteLike.runTests$(AnyFunSuiteLike.scala:232)
at org.scalatest.funsuite.AnyFunSuite.runTests(AnyFunSuite.scala:1563)
at org.scalatest.Suite.run(Suite.scala:1112)
at org.scalatest.Suite.run$(Suite.scala:1094)
at org.scalatest.funsuite.AnyFunSuite.org$scalatest$funsuite$AnyFunSuiteLike$$super$run(AnyFunSuite.scala:1563)
at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$run$1(AnyFunSuiteLike.scala:237)
at org.scalatest.SuperEngine.runImpl(Engine.scala:535)
at org.scalatest.funsuite.AnyFunSuiteLike.run(AnyFunSuiteLike.scala:237)
at org.scalatest.funsuite.AnyFunSuiteLike.run$(AnyFunSuiteLike.scala:236)
at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterAll$$super$run(SparkFunSuite.scala:61)
at org.scalatest.BeforeAndAfterAll.liftedTree1$1(BeforeAndAfterAll.scala:213)
at org.scalatest.BeforeAndAfterAll.run(BeforeAndAfterAll.scala:210)
at org.scalatest.BeforeAndAfterAll.run$(BeforeAndAfterAll.scala:208)
at org.apache.spark.SparkFunSuite.run(SparkFunSuite.scala:61)
at org.scalatest.tools.SuiteRunner.run(SuiteRunner.scala:45)
at org.scalatest.tools.Runner$.$anonfun$doRunRunRunDaDoRunRun$13(Runner.scala:1320)
at org.scalatest.tools.Runner$.$anonfun$doRunRunRunDaDoRunRun$13$adapted(Runner.scala:1314)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.scalatest.tools.Runner$.doRunRunRunDaDoRunRun(Runner.scala:1314)
at org.scalatest.tools.Runner$.$anonfun$runOptionallyWithPassFailReporter$24(Runner.scala:993)
at org.scalatest.tools.Runner$.$anonfun$runOptionallyWithPassFailReporter$24$adapted(Runner.scala:971)
at org.scalatest.tools.Runner$.withClassLoaderAndDispatchReporter(Runner.scala:1480)
at org.scalatest.tools.Runner$.runOptionallyWithPassFailReporter(Runner.scala:971)
at org.scalatest.tools.Runner$.run(Runner.scala:798)
at org.scalatest.tools.Runner.run(Runner.scala)
at org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.runScalaTest2(ScalaTestRunner.java:131)
at org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.main(ScalaTestRunner.java:28)
Caused by: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: execute, tree:
HashAggregate(keys=[x#105], functions=[partial_sum(y#106)], output=[x#105, sum#118L])
+- Project [_1#100 AS x#105, _2#101 AS y#106]
+- LocalTableScan [_1#100, _2#101]
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
at org.apache.spark.sql.execution.aggregate.HashAggregateExec.doExecute(HashAggregateExec.scala:84)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:180)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:218)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:215)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:176)
at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.inputRDD$lzycompute(ShuffleExchangeExec.scala:118)
at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.inputRDD(ShuffleExchangeExec.scala:118)
at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.mapOutputStatisticsFuture$lzycompute(ShuffleExchangeExec.scala:122)
at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.mapOutputStatisticsFuture(ShuffleExchangeExec.scala:121)
at org.apache.spark.sql.execution.adaptive.ShuffleQueryStageExec.$anonfun$doMaterialize$1(QueryStageExec.scala:163)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52)
... 91 more
Caused by: java.lang.RuntimeException: calculate error
at org.apache.spark.sql.execution.aggregate.HashAggregateExec.$anonfun$doExecute$1(HashAggregateExec.scala:85)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52)
... 103 more
```
After this PR, the error is:
```
calculate error
java.lang.RuntimeException: calculate error
at org.apache.spark.sql.execution.aggregate.HashAggregateExec.doExecute(HashAggregateExec.scala:84)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:180)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:218)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:215)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:176)
at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.inputRDD$lzycompute(ShuffleExchangeExec.scala:117)
at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.inputRDD(ShuffleExchangeExec.scala:117)
at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.mapOutputStatisticsFuture$lzycompute(ShuffleExchangeExec.scala:121)
at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.mapOutputStatisticsFuture(ShuffleExchangeExec.scala:120)
at org.apache.spark.sql.execution.adaptive.ShuffleQueryStageExec.doMaterialize(QueryStageExec.scala:161)
at org.apache.spark.sql.execution.adaptive.QueryStageExec.$anonfun$materialize$1(QueryStageExec.scala:80)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:218)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:215)
at org.apache.spark.sql.execution.adaptive.QueryStageExec.materialize(QueryStageExec.scala:78)
at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.$anonfun$getFinalPhysicalPlan$5(AdaptiveSparkPlanExec.scala:207)
at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.$anonfun$getFinalPhysicalPlan$5$adapted(AdaptiveSparkPlanExec.scala:205)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.$anonfun$getFinalPhysicalPlan$1(AdaptiveSparkPlanExec.scala:205)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:772)
at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.getFinalPhysicalPlan(AdaptiveSparkPlanExec.scala:179)
at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.executeCollect(AdaptiveSparkPlanExec.scala:289)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3708)
at org.apache.spark.sql.Dataset.$anonfun$collect$1(Dataset.scala:2977)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3699)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:772)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3697)
at org.apache.spark.sql.Dataset.collect(Dataset.scala:2977)
at org.apache.spark.sql.DataFrameAggregateSuite.$anonfun$assertNoExceptions$3(DataFrameAggregateSuite.scala:665)
at org.apache.spark.sql.catalyst.plans.SQLHelper.withSQLConf(SQLHelper.scala:54)
at org.apache.spark.sql.catalyst.plans.SQLHelper.withSQLConf$(SQLHelper.scala:38)
at org.apache.spark.sql.DataFrameAggregateSuite.org$apache$spark$sql$test$SQLTestUtilsBase$$super$withSQLConf(DataFrameAggregateSuite.scala:37)
at org.apache.spark.sql.test.SQLTestUtilsBase.withSQLConf(SQLTestUtils.scala:246)
at org.apache.spark.sql.test.SQLTestUtilsBase.withSQLConf$(SQLTestUtils.scala:244)
at org.apache.spark.sql.DataFrameAggregateSuite.withSQLConf(DataFrameAggregateSuite.scala:37)
at org.apache.spark.sql.DataFrameAggregateSuite.$anonfun$assertNoExceptions$2(DataFrameAggregateSuite.scala:659)
at org.apache.spark.sql.DataFrameAggregateSuite.$anonfun$assertNoExceptions$2$adapted(DataFrameAggregateSuite.scala:655)
at scala.collection.TraversableLike$WithFilter.$anonfun$foreach$1(TraversableLike.scala:877)
at scala.collection.immutable.List.foreach(List.scala:392)
at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:876)
at org.apache.spark.sql.DataFrameAggregateSuite.assertNoExceptions(DataFrameAggregateSuite.scala:655)
at org.apache.spark.sql.DataFrameAggregateSuite.$anonfun$new$126(DataFrameAggregateSuite.scala:695)
at org.apache.spark.sql.DataFrameAggregateSuite.$anonfun$new$126$adapted(DataFrameAggregateSuite.scala:695)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.sql.DataFrameAggregateSuite.$anonfun$new$125(DataFrameAggregateSuite.scala:695)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.scalatest.OutcomeOf.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf.outcomeOf$(OutcomeOf.scala:83)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
at org.scalatest.funsuite.AnyFunSuiteLike$$anon$1.apply(AnyFunSuiteLike.scala:190)
at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:176)
at org.scalatest.funsuite.AnyFunSuiteLike.invokeWithFixture$1(AnyFunSuiteLike.scala:188)
at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$runTest$1(AnyFunSuiteLike.scala:200)
at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306)
at org.scalatest.funsuite.AnyFunSuiteLike.runTest(AnyFunSuiteLike.scala:200)
at org.scalatest.funsuite.AnyFunSuiteLike.runTest$(AnyFunSuiteLike.scala:182)
at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterEach$$super$runTest(SparkFunSuite.scala:61)
at org.scalatest.BeforeAndAfterEach.runTest(BeforeAndAfterEach.scala:234)
at org.scalatest.BeforeAndAfterEach.runTest$(BeforeAndAfterEach.scala:227)
at org.apache.spark.SparkFunSuite.runTest(SparkFunSuite.scala:61)
at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$runTests$1(AnyFunSuiteLike.scala:233)
at org.scalatest.SuperEngine.$anonfun$runTestsInBranch$1(Engine.scala:413)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401)
at org.scalatest.SuperEngine.runTestsInBranch(Engine.scala:396)
at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:475)
at org.scalatest.funsuite.AnyFunSuiteLike.runTests(AnyFunSuiteLike.scala:233)
at org.scalatest.funsuite.AnyFunSuiteLike.runTests$(AnyFunSuiteLike.scala:232)
at org.scalatest.funsuite.AnyFunSuite.runTests(AnyFunSuite.scala:1563)
at org.scalatest.Suite.run(Suite.scala:1112)
at org.scalatest.Suite.run$(Suite.scala:1094)
at org.scalatest.funsuite.AnyFunSuite.org$scalatest$funsuite$AnyFunSuiteLike$$super$run(AnyFunSuite.scala:1563)
at org.scalatest.funsuite.AnyFunSuiteLike.$anonfun$run$1(AnyFunSuiteLike.scala:237)
at org.scalatest.SuperEngine.runImpl(Engine.scala:535)
at org.scalatest.funsuite.AnyFunSuiteLike.run(AnyFunSuiteLike.scala:237)
at org.scalatest.funsuite.AnyFunSuiteLike.run$(AnyFunSuiteLike.scala:236)
at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterAll$$super$run(SparkFunSuite.scala:61)
at org.scalatest.BeforeAndAfterAll.liftedTree1$1(BeforeAndAfterAll.scala:213)
at org.scalatest.BeforeAndAfterAll.run(BeforeAndAfterAll.scala:210)
at org.scalatest.BeforeAndAfterAll.run$(BeforeAndAfterAll.scala:208)
at org.apache.spark.SparkFunSuite.run(SparkFunSuite.scala:61)
at org.scalatest.tools.SuiteRunner.run(SuiteRunner.scala:45)
at org.scalatest.tools.Runner$.$anonfun$doRunRunRunDaDoRunRun$13(Runner.scala:1320)
at org.scalatest.tools.Runner$.$anonfun$doRunRunRunDaDoRunRun$13$adapted(Runner.scala:1314)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.scalatest.tools.Runner$.doRunRunRunDaDoRunRun(Runner.scala:1314)
at org.scalatest.tools.Runner$.$anonfun$runOptionallyWithPassFailReporter$24(Runner.scala:993)
at org.scalatest.tools.Runner$.$anonfun$runOptionallyWithPassFailReporter$24$adapted(Runner.scala:971)
at org.scalatest.tools.Runner$.withClassLoaderAndDispatchReporter(Runner.scala:1480)
at org.scalatest.tools.Runner$.runOptionallyWithPassFailReporter(Runner.scala:971)
at org.scalatest.tools.Runner$.run(Runner.scala:798)
at org.scalatest.tools.Runner.run(Runner.scala)
at org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.runScalaTest2(ScalaTestRunner.java:131)
at org.jetbrains.plugins.scala.testingSupport.scalaTest.ScalaTestRunner.main(ScalaTestRunner.java:28)
```
### Why are the changes needed?
`TreeNodeException` didn't work well.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#31337 from beliefer/SPARK-34234.
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?
This PR includes the following changes:
1. in `CatalogImpl.uncacheTable`, invalidate caches in cascade when the target table is
a temp view, and `spark.sql.legacy.storeAnalyzedPlanForView` is false (default value).
2. make `SessionCatalog.lookupTempView` public and return processed temp view plan (i.e., with `View` op).
### Why are the changes needed?
Following [SPARK-34052](https://issues.apache.org/jira/browse/SPARK-34052) (#31107), we should invalidate in cascade for `CatalogImpl.uncacheTable` when the table is a temp view, so that the behavior is consistent.
### Does this PR introduce _any_ user-facing change?
Yes, now `SQLContext.uncacheTable` will drop temp view in cascade by default.
### How was this patch tested?
Added a UT
Closes#31462 from sunchao/SPARK-34347.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Passing around the output attributes should have more benefits like keeping the exprID unchanged to avoid bugs when we apply more operators above the command output DataFrame.
This PR did 2 things ：
1. After this pr, a `SHOW TBLPROPERTIES` clause's output shows `key` and `value` columns whether you specify the table property `key`. Before this pr, a `SHOW TBLPROPERTIES` clause's output only show a `value` column when you specify the table property `key`..
2. Keep `SHOW TBLPROPERTIES` command's output attribute exprId unchanged.
### Why are the changes needed?
1. Keep `SHOW TBLPROPERTIES`'s output schema consistence
2. Keep `SHOW TBLPROPERTIES` command's output attribute exprId unchanged.
### Does this PR introduce _any_ user-facing change?
After this pr, a `SHOW TBLPROPERTIES` clause's output shows `key` and `value` columns whether you specify the table property `key`. Before this pr, a `SHOW TBLPROPERTIES` clause's output only show a `value` column when you specify the table property `key`.
Before this PR:
```
sql > SHOW TBLPROPERTIES tabe_name('key')
value
value_of_key
```
After this PR
```
sql > SHOW TBLPROPERTIES tabe_name('key')
key value
key value_of_key
```
### How was this patch tested?
Added UT
Closes#31378 from AngersZhuuuu/SPARK-34240.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Keep consistence with other `SHOW` command according to https://github.com/apache/spark/pull/31341#issuecomment-774613080
### Why are the changes needed?
Keep consistence
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Not need
Closes#31516 from AngersZhuuuu/SPARK-34238-follow-up.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Delegate table name validation to the session catalog
### Why are the changes needed?
Queerying of tables with nested namespaces.
### Does this PR introduce _any_ user-facing change?
SQL queries of nested namespace queries
### How was this patch tested?
Unit tests updated.
Closes#31427 from holdenk/SPARK-34209-delegate-table-name-validation-to-the-catalog.
Authored-by: Holden Karau <hkarau@apple.com>
Signed-off-by: Holden Karau <hkarau@apple.com>
### What changes were proposed in this pull request?
Currently, we pass the default value `EmptyRow` to method `checkEvaluation` in the `StringExpressionsSuite`, but the default value of the 'checkEvaluation' method parameter is the `emptyRow`.
We can clean the parameter for Code Simplifications.
### Why are the changes needed?
for Code Simplifications
**before**:
```
def testConcat(inputs: String*): Unit = {
val expected = if (inputs.contains(null)) null else inputs.mkString
checkEvaluation(Concat(inputs.map(Literal.create(_, StringType))), expected, EmptyRow)
}
```
**after**:
```
def testConcat(inputs: String*): Unit = {
val expected = if (inputs.contains(null)) null else inputs.mkString
checkEvaluation(Concat(inputs.map(Literal.create(_, StringType))), expected)
}
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or Github action.
Closes#31510 from yikf/master.
Authored-by: yikf <13468507104@163.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR proposes to propagate the name used for registering UDFs to `ScalaUDF`, `ScalaUDAF` and `ScaalAggregator`.
Note that `PythonUDF` gets the name correctly: 466c045bfa/python/pyspark/sql/udf.py (L358-L359)
, and same for Hive UDFs:
466c045bfa/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveSessionCatalog.scala (L67)
### Why are the changes needed?
This PR can help in the following scenarios:
1) Better EXPLAIN output
2) By adding `def name: String` to `UserDefinedExpression`, we can match an expression by `UserDefinedExpression` and look up the catalog, an use case needed for #31273.
### Does this PR introduce _any_ user-facing change?
The EXPLAIN output involving udfs will be changed to use the name used for UDF registration.
For example, for the following:
```
sql("CREATE TEMPORARY FUNCTION test_udf AS 'org.apache.spark.examples.sql.Spark33084'")
sql("SELECT test_udf(col1) FROM VALUES (1), (2), (3)").explain(true)
```
The output of the optimized plan will change from:
```
Aggregate [spark33084(cast(col1#223 as bigint), org.apache.spark.examples.sql.Spark330846906be0f, 1, 1) AS spark33084(col1)#237]
+- LocalRelation [col1#223]
```
to
```
Aggregate [test_udf(cast(col1#223 as bigint), org.apache.spark.examples.sql.Spark330847a62d697, 1, 1, Some(test_udf)) AS test_udf(col1)#237]
+- LocalRelation [col1#223]
```
### How was this patch tested?
Added new tests.
Closes#31500 from imback82/udaf_name.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Adds an assert to `FixedLengthRowBasedKeyValueBatch#appendRow` method to check the incoming vlen and klen by comparing them with the lengths stored as member variables as followup to https://github.com/apache/spark/pull/30788
### Why are the changes needed?
Add assert statement to catch similar bugs in future.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Ran some tests locally, though not easy to test.
Closes#31447 from yliou/SPARK-33726-Assert.
Authored-by: yliou <yliou@berkeley.edu>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
In Spark, `set -v` is defined as "Queries all properties that are defined in the SQLConf of the sparkSession".
But there are other external modules that also define properties and register them to SQLConf. In this case,
it can't be displayed by `set -v` until the conf object is initiated (i.e. calling the object at least once).
In this PR, I propose to eagerly initiate all the objects registered to SQLConf, so that `set -v` will always output
the completed properties.
### Why are the changes needed?
Improve the `set -v` command to produces completed and deterministic results
### Does this PR introduce _any_ user-facing change?
`set -v` command will dump more configs
### How was this patch tested?
existing tests
Closes#30363 from linhongliu-db/set-v.
Authored-by: Linhong Liu <linhong.liu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Keep consistence with other `SHOW` command according to https://github.com/apache/spark/pull/31341#issuecomment-774613080
### Why are the changes needed?
Keep consistence
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Not need
Closes#31518 from AngersZhuuuu/SPARK-34239-followup.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The current implement of some DDL not unify the output and not pass the output properly to physical command.
Such as: The `ShowTables` output attributes `namespace`, but `ShowTablesCommand` output attributes `database`.
As the query plan, this PR pass the output attributes from `ShowTables` to `ShowTablesCommand`, `ShowTableExtended ` to `ShowTablesCommand`.
Take `show tables` and `show table extended like 'tbl'` as example.
The output before this PR:
`show tables`
|database|tableName|isTemporary|
-- | -- | --
| default| tbl| false|
If catalog is v2 session catalog, the output before this PR:
|namespace|tableName|
-- | --
| default| tbl
`show table extended like 'tbl'`
|database|tableName|isTemporary| information|
-- | -- | -- | --
| default| tbl| false|Database: default...|
The output after this PR:
`show tables`
|namespace|tableName|isTemporary|
-- | -- | --
| default| tbl| false|
`show table extended like 'tbl'`
|namespace|tableName|isTemporary| information|
-- | -- | -- | --
| default| tbl| false|Database: default...|
### Why are the changes needed?
This PR have benefits as follows:
First, Unify schema for the output of SHOW TABLES.
Second, pass the output attributes could keep the expr ID unchanged, so that avoid bugs when we apply more operators above the command output dataframe.
### Does this PR introduce _any_ user-facing change?
Yes.
The output schema of `SHOW TABLES` replace `database` by `namespace`.
### How was this patch tested?
Jenkins test.
Closes#31245 from beliefer/SPARK-34157.
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 pr format DateLiteral and TimestampLiteral toString. For example:
```sql
SELECT * FROM date_dim WHERE d_date BETWEEN (cast('2000-03-11' AS DATE) - INTERVAL 30 days) AND (cast('2000-03-11' AS DATE) + INTERVAL 30 days)
```
Before this pr:
```
Condition : (((isnotnull(d_date#18) AND (d_date#18 >= 10997)) AND (d_date#18 <= 11057))
```
After this pr:
```
Condition : (((isnotnull(d_date#14) AND (d_date#14 >= 2000-02-10)) AND (d_date#14 <= 2000-04-10))
```
### Why are the changes needed?
Make the plan more readable.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#31455 from wangyum/SPARK-34342.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Rewrote one `ExtractGenerator` case such that it would not rely on a side effect of the flatmap function.
### Why are the changes needed?
With the dataframe api it is possible to have a lazy sequence as the `output` of a `LogicalPlan`. When exploding a column on this dataframe using the `withColumn("newName", explode(col("name")))` method, the `ExtractGenerator` does not extract the generator and `CheckAnalysis` would throw an exception.
### Does this PR introduce _any_ user-facing change?
Bugfix
Before this, the work around was to put `.select("*")` before the explode.
### How was this patch tested?
UT
Closes#31213 from tanelk/SPARK-34141_extract_generator.
Authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/28027https://github.com/apache/spark/pull/28027 added a DS v2 API that allows data sources to produce metadata/hidden columns that can only be seen when it's explicitly selected. The way we integrate this API into Spark is:
1. The v2 relation gets normal output and metadata output from the data source, and the metadata output is excluded from the plan output by default.
2. column resolution can resolve `UnresolvedAttribute` with metadata columns, even if the child plan doesn't output metadata columns.
3. An analyzer rule searches the query plan, trying to find a node that has missing inputs. If such node is found, transform the sub-plan of this node, and update the v2 relation to include the metadata output.
The analyzer rule in step 3 brings a perf regression, for queries that do not read v2 tables at all. This rule will calculate `QueryPlan.inputSet` (which builds an `AttributeSet` from outputs of all children) and `QueryPlan.missingInput` (which does a set exclusion and creates a new `AttributeSet`) for every plan node in the query plan. In our benchmark, the TPCDS query compilation time gets increased by more than 10%
This PR proposes a simple way to improve it: we add a special metadata entry to the metadata attribute, which allows us to quickly check if a plan needs to add metadata columns: we just check all the references of this plan, and see if the attribute contains the special metadata entry, instead of calculating `QueryPlan.missingInput`.
This PR also fixes one bug: we should not change the final output schema of the plan, if we only use metadata columns in operators like filter, sort, etc.
### Why are the changes needed?
Fix perf regression in SQL query compilation, and fix a bug.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Run `org.apache.spark.sql.TPCDSQuerySuite`, before this PR, `AddMetadataColumns` is the top 4 rule ranked by running time
```
=== Metrics of Analyzer/Optimizer Rules ===
Total number of runs: 407641
Total time: 47.257239779 seconds
Rule Effective Time / Total Time Effective Runs / Total Runs
OptimizeSubqueries 4157690003 / 8485444626 49 / 2778
Analyzer$ResolveAggregateFunctions 1238968711 / 3369351761 49 / 2141
ColumnPruning 660038236 / 2924755292 338 / 6391
Analyzer$AddMetadataColumns 0 / 2918352992 0 / 2151
```
after this PR:
```
Analyzer$AddMetadataColumns 0 / 122885629 0 / 2151
```
This rule is 20 times faster and is negligible to the total compilation time.
This PR also add new tests to verify the bug fix.
Closes#31440 from cloud-fan/metadata-col.
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 followup of https://github.com/apache/spark/pull/26006
In #26006 , we merged the v1 and v2 SHOW DATABASES/NAMESPACES commands, but we missed a behavior change that the output schema of SHOW DATABASES becomes different.
This PR adds a legacy config to restore the old schema, with a migration guide item to mention this behavior change.
### Why are the changes needed?
Improve backward compatibility
### Does this PR introduce _any_ user-facing change?
No (the legacy config is false by default)
### How was this patch tested?
a new test
Closes#31474 from cloud-fan/command-schema.
Lead-authored-by: Wenchen Fan <cloud0fan@gmail.com>
Co-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 #31424, and proposes to use `UnresolvedTable.relationTypeMismatchHint` when `UnresolvedTable` is resolved to a temp view.
### Why are the changes needed?
This change utilizes the type mismatch hint when a relation is resolved to a temp view when a table is expected.
For example, `ALTER TABLE tmpView SET TBLPROPERTIES ('p' = 'an')` will now include `Please use ALTER VIEW instead.` in the exception message: `tmpView is a temp view. 'ALTER TABLE ... SET TBLPROPERTIES' expects a table. Please use ALTER VIEW instead.`
### Does this PR introduce _any_ user-facing change?
Yes, adds the hint in the exception message.
### How was this patch tested?
Update existing tests to include the hint.
Closes#31452 from imback82/followup_SPARK-34317.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds support for referencing subquery with column aliases by its table alias.
Before
```sql
-- AnalysisException: cannot resolve '`t.c1`' given input columns: [c1, c2];
SELECT t.c1, t.c2 FROM (SELECT 1 AS a, 1 AS b) t(c1, c2)
```
After:
```sql
-- [(1, 1)]
SELECT t.c1, t.c2 FROM (SELECT 1 AS a, 1 AS b) t(c1, c2)
```
### Why are the changes needed?
To allow users to reference subquery with column aliases by its table alias.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
Added parser tests and SQL query tests.
Closes#31444 from allisonwang-db/spark-34335.
Authored-by: allisonwang-db <66282705+allisonwang-db@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to migrate `ALTER TABLE ... SET/UNSET TBLPROPERTIES` 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).
### Why are the changes needed?
This is a part of effort to make the relation lookup behavior consistent: [SPARK-29900](https://issues.apache.org/jira/browse/SPARK-29900).
### Does this PR introduce _any_ user-facing change?
After this PR, `ALTER TABLE SET/UNSET TBLPROPERTIES` will have a consistent resolution behavior.
### How was this patch tested?
Updated existing tests / added new tests.
Closes#31422 from imback82/v2_alter_table_set_unset_properties.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Similar to SPARK-33690, this PR improves the output layout of `printSchema` for the case column names contain meta characters.
Here is an example.
Before:
```
scala> val df1 = spark.sql("SELECT 'aaa\nbbb\tccc\rddd\feee\bfff\u000Bggg\u0007hhh'")
scala> df1.printSchema
root
|-- aaa
ddd ccc
eefff
ggghhh: string (nullable = false)
```
After:
```
scala> df1.printSchema
root
|-- aaa\nbbb\tccc\rddd\feee\bfff\vggg\ahhh: string (nullable = false)
```
### Why are the changes needed?
To avoid breaking the layout of `Dataset#printSchema`
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New test.
Closes#31412 from sarutak/escape-meta-printSchema.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In the current master, the code for treating unicode/octal/escaped characters in string literals is a little bit complex so let's simplify it.
### Why are the changes needed?
To keep it easy to maintain.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
`ParserUtilsSuite` passes.
Closes#31362 from sarutak/refactor-unicode-escapes.
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?
1. Move parser tests from `DDLParserSuite` to `TruncateTableParserSuite`.
2. Port DS v1 tests from `DDLSuite` and other test suites to `v1.TruncateTableSuiteBase` and to `v1.TruncateTableSuite`.
3. Add a test for DSv2 `TRUNCATE TABLE` to `v2.TruncateTableSuite`.
### Why are the changes needed?
To improve test coverage.
### 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 *TruncateTableSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *CatalogedDDLSuite"
```
Closes#31387 from MaxGekk/unify-truncate-table-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?
Correct the version of SQL configuration `spark.sql.legacy.parseNullPartitionSpecAsStringLiteral` from 3.2.0 to 3.0.2.
Also, revise the documentation and test case.
### Why are the changes needed?
The release version in https://github.com/apache/spark/pull/31421 was wrong.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit tests
Closes#31434 from gengliangwang/reviseVersion.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
There exists some `Exception` for assert in fact. Such as:
`throw new IllegalStateException("[BUG] unexpected plan returned by `lookupV2Relation`: " + other)`
This kind `Exception` seems should not put in single dedicated files.
### Why are the changes needed?
Reduce the workload of auditing.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#31395 from beliefer/SPARK-33599-restore-assert.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR fixes an issue that `Dataset.colRegex` doesn't work with column names or qualifiers which contain newlines.
In the current master, if column names or qualifiers passed to `colRegex` contain newlines, it throws exception.
```
val df = Seq(1, 2, 3).toDF("test\n_column").as("test\n_table")
val col1 = df.colRegex("`tes.*\n.*mn`")
org.apache.spark.sql.AnalysisException: Cannot resolve column name "`tes.*
.*mn`" among (test
_column)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$resolveException(Dataset.scala:272)
at org.apache.spark.sql.Dataset.$anonfun$resolve$1(Dataset.scala:263)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.Dataset.resolve(Dataset.scala:263)
at org.apache.spark.sql.Dataset.colRegex(Dataset.scala:1407)
... 47 elided
val col2 = df.colRegex("test\n_table.`tes.*\n.*mn`")
org.apache.spark.sql.AnalysisException: Cannot resolve column name "test
_table.`tes.*
.*mn`" among (test
_column)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$resolveException(Dataset.scala:272)
at org.apache.spark.sql.Dataset.$anonfun$resolve$1(Dataset.scala:263)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.Dataset.resolve(Dataset.scala:263)
at org.apache.spark.sql.Dataset.colRegex(Dataset.scala:1407)
... 47 elided
```
### Why are the changes needed?
Column names and qualifiers can contain newlines but `colRegex` can't work with them, so it's a bug.
### Does this PR introduce _any_ user-facing change?
Yes. users can pass column names and qualifiers even though they contain newlines.
### How was this patch tested?
New test.
Closes#31426 from sarutak/fix-colRegex.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
1. Add new method `truncatePartition()` to the `SupportsPartitionManagement` interface.
2. Add new method `truncatePartitions()` to the `SupportsAtomicPartitionManagement` interface.
3. Default implementation of new methods in `InMemoryPartitionTable`/`InMemoryAtomicPartitionTable`.
### Why are the changes needed?
This is the first step in supporting of v2 `TRUNCATE TABLE .. PARTITION`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running new tests:
```
$ build/sbt "test:testOnly *SupportsPartitionManagementSuite"
$ build/sbt "test:testOnly *SupportsAtomicPartitionManagementSuite"
```
Closes#31420 from MaxGekk/dsv2-truncate-table-partitions.
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 add `relationTypeMismatchHint` to `UnresolvedTable` so that if a relation is resolved to a view when a table is expected, a hint message can be included as a part of the analysis exception message. Note that the same feature is already introduced to `UnresolvedView` in #30636.
This mostly affects `ALTER TABLE` commands where the analysis exception message will now contain `Please use ALTER VIEW as instead`.
### Why are the changes needed?
To give a better error message. (The hint used to exist but got removed for commands that migrated to the new resolution framework)
### Does this PR introduce _any_ user-facing change?
Yes, now `ALTER TABLE` commands include a hint to use `ALTER VIEW` instead.
```
sql("ALTER TABLE v SET SERDE 'whatever'")
```
Before:
```
"v is a view. 'ALTER TABLE ... SET [SERDE|SERDEPROPERTIES]' expects a table.
```
After this PR:
```
"v is a view. 'ALTER TABLE ... SET [SERDE|SERDEPROPERTIES]' expects a table. Please use ALTER VIEW instead.
```
### How was this patch tested?
Updated existing test cases to include the hint.
Closes#31424 from imback82/better_error.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In spark, the `count(table.*)` may cause very weird result, for example:
```
select count(*) from (select 1 as a, null as b) t;
output: 1
select count(t.*) from (select 1 as a, null as b) t;
output: 0
```
This is because spark expands `t.*` while converts `*` to count(1), this will confuse
users. After checking the ANSI standard, `count(*)` should always be `count(1)` while `count(t.*)`
is not allowed. What's more, this is also not allowed by common databases, e.g. MySQL, Oracle.
So, this PR proposes to block the ambiguous behavior and print a clear error message for users.
### Why are the changes needed?
to avoid ambiguous behavior and follow ANSI standard and other SQL engines
### Does this PR introduce _any_ user-facing change?
Yes, `count(table.*)` behavior will be blocked and output an error message.
### How was this patch tested?
newly added and existing tests
Closes#31286 from linhongliu-db/fix-table-star.
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 is a follow up for https://github.com/apache/spark/pull/30538.
It adds a legacy conf `spark.sql.legacy.parseNullPartitionSpecAsStringLiteral` in case users wants the legacy behavior.
It also adds document for the behavior change.
### Why are the changes needed?
In case users want the legacy behavior, they can set `spark.sql.legacy.parseNullPartitionSpecAsStringLiteral` as true.
### Does this PR introduce _any_ user-facing change?
Yes, adding a legacy configuration to restore the old behavior.
### How was this patch tested?
Unit test.
Closes#31421 from gengliangwang/legacyNullStringConstant.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR completes snake_case rule at functions APIs across the languages, see also SPARK-10621.
In more details, this PR:
- Adds `count_distinct` in Scala Python, and R, and document that `count_distinct` is encouraged. This was not deprecated because `countDistinct` is pretty commonly used. We could deprecate in the future releases.
- (Scala-specific) adds `typedlit` but doesn't deprecate `typedLit` which is arguably commonly used. Likewise, we could deprecate in the future releases.
- Deprecates and renames:
- `sumDistinct` -> `sum_distinct`
- `bitwiseNOT` -> `bitwise_not`
- `shiftLeft` -> `shiftleft` (matched with SQL name in `FunctionRegistry`)
- `shiftRight` -> `shiftright` (matched with SQL name in `FunctionRegistry`)
- `shiftRightUnsigned` -> `shiftrightunsigned` (matched with SQL name in `FunctionRegistry`)
- (Scala-specific) `callUDF` -> `call_udf`
### Why are the changes needed?
To keep the consistent naming in APIs.
### Does this PR introduce _any_ user-facing change?
Yes, it deprecates some APIs and add new renamed APIs as described above.
### How was this patch tested?
Unittests were added.
Closes#31408 from HyukjinKwon/SPARK-34306.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Replaces `collection.map(f1).flatten(f2)` with `collection.flatMap` if possible. it's semantically consistent, but looks simpler.
### Why are the changes needed?
Code Simpilefications.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or GitHub Action
Closes#31416 from LuciferYang/SPARK-34310.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Passing around the output attributes should have more benefits like keeping the expr ID unchanged to avoid bugs when we apply more operators above the command output dataframe.
This PR keep SHOW COLUMNS command's output attribute exprId unchanged.
### Why are the changes needed?
Keep SHOW PARTITIONS command's output attribute exprid unchanged.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#31377 from AngersZhuuuu/SPARK-34239.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Remove old statement `AlterTableSetLocationStatement`
2. Introduce new command `AlterTableSetLocation` for `ALTER TABLE .. SET LOCATION`.
### Why are the changes needed?
This is a part of effort to make the relation lookup behavior consistent: SPARK-29900.
### Does this PR introduce _any_ user-facing change?
It can change the error message for views.
### How was this patch tested?
By running `ALTER TABLE .. SET LOCATION` tests:
```
$ build/sbt -Phive -Phive-thriftserver "test:testOnly *DataSourceV2SQLSuite"
$ build/sbt -Phive -Phive-thriftserver "test:testOnly *CatalogedDDLSuite"
```
Closes#31414 from MaxGekk/migrate-set-location-resolv-table.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Describe `SessionCatalog.refreshTable()` and `CatalogImpl.refreshTable()`. what they do and when they are supposed to be used.
### Why are the changes needed?
To improve code maintenance.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running `./dev/scalastyle`
Closes#31364 from MaxGekk/doc-refreshTable.
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 group exception messages in `/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser`.
### 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#31293 from beliefer/SPARK-33601.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The currently SQL (temp or permanent) view resolution is done in 2 steps:
1. In `SessionCatalog`, we get the view metadata, parse the view SQL string, and wrap it with `View`.
2. At the beginning of the optimizer, we run `EliminateView`, which drops the wrapper `View`, and apply some special logic to match the view schema.
Step 2 is tricky, as we need to retain the output attr expr id, while we need to add an extra `Project` to add cast and alias. This PR simplifies the view solution by building a completed plan (with cast and alias added) in `SessionCatalog`, so that we only have 1 step.
### Why are the changes needed?
Code simplification. It also fixes issues like https://github.com/apache/spark/pull/31352
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing tests
Closes#31368 from cloud-fan/try.
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 follows up https://github.com/apache/spark/pull/30870.
Maybe some contributors don't know the job and added some exception by the old way.
### 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#31312 from beliefer/SPARK-33542-followup.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This pr correct the documentation of the `concat_ws` function.
### Why are the changes needed?
`concat_ws` doesn't need any str or array(str) arguments:
```
scala> sql("""select concat_ws("s")""").show
+------------+
|concat_ws(s)|
+------------+
| |
+------------+
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
```
build/sbt "sql/testOnly *.ExpressionInfoSuite"
```
Closes#31370 from wangyum/SPARK-34268.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR changes the column types in the table definitions of `TPCDSBase` from string to char and varchar, with respect to the original definitions for char/varchar columns in the official doc - [TPC-DS_v2.9.0](http://www.tpc.org/tpc_documents_current_versions/pdf/tpc-ds_v2.9.0.pdf).
### Why are the changes needed?
Comply with both TPCDS standard and ANSI, and using string will get wrong results with those TPCDS queries
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
plan stability check
Closes#31012 from yaooqinn/tpcds.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
we need to check whether the `lit` is null before calling `numChars`
### Why are the changes needed?
fix an obvious NPE bug
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
new tests
Closes#31336 from yaooqinn/SPARK-34233.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
For v2 static partitions overwriting, we use `EqualTo ` to generate the `deleteExpr`
This is not right for null partition values, and cause the problem like below because `ConstantFolding` converts it to lit(null)
```scala
SPARK-34223: static partition with null raise NPE *** FAILED *** (19 milliseconds)
[info] org.apache.spark.sql.AnalysisException: Cannot translate expression to source filter: null
[info] at org.apache.spark.sql.execution.datasources.v2.V2Writes$$anonfun$apply$1.$anonfun$applyOrElse$1(V2Writes.scala:50)
[info] at scala.collection.immutable.List.flatMap(List.scala:366)
[info] at org.apache.spark.sql.execution.datasources.v2.V2Writes$$anonfun$apply$1.applyOrElse(V2Writes.scala:47)
[info] at org.apache.spark.sql.execution.datasources.v2.V2Writes$$anonfun$apply$1.applyOrElse(V2Writes.scala:39)
[info] at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$1(TreeNode.scala:317)
[info] at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:73)
```
The right way is to use EqualNullSafe instead to delete the null partitions.
### Why are the changes needed?
bugfix
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
an original test to new place
Closes#31339 from yaooqinn/SPARK-34236.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
When write test about command, when `checkAnswer`,
Always got error as below
```
[info] AttributeSet(partition#607) was not empty The analyzed logical plan has missing inputs:
[info] ShowPartitionsCommand `ns`.`tbl`, [partition#607] (QueryTest.scala:224)
[info] org.scalatest.exceptions.TestFailedException:
[info] at org.scalatest.Assertions.newAssertionFailedException(Assertions.scala:472)
[info] at org.scalatest.Assertions.newAssertionFailedException$(Assertions.scala:471)
```
For Command DDL plan, we can define `producedAttributes` as it's `outputSet` and it's reasonable
### Why are the changes needed?
Add default `producedAttributes` for Command LogicalPlan
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Not need
Closes#31342 from AngersZhuuuu/SPARK-34241.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds repartition and sort nodes to satisfy the required distribution and ordering introduced in SPARK-33779.
Note: This PR contains the final part of changes discussed in PR #29066.
### Why are the changes needed?
These changes are the next step as discussed in the [design doc](https://docs.google.com/document/d/1X0NsQSryvNmXBY9kcvfINeYyKC-AahZarUqg3nS1GQs/edit#) for SPARK-23889.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
This PR comes with a new test suite.
Closes#31083 from aokolnychyi/spark-34026.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
On the read-side, the char length check and padding bring issues to CBO and predicate pushdown and other issues to the catalyst.
This PR reverts 6da5cdf1db that added read side length check) so that we only do length check for the write side, and data sources/vendors are responsible to enforce the char/varchar constraints for data import operations like ADD PARTITION. It doesn't make sense for Spark to report errors on the read-side if the data is already dirty.
This PR also moves the char padding to the write-side, so that it 1) avoids read side issues like CBO and filter pushdown. 2) the data source can preserve char type semantic better even if it's read by systems other than Spark.
### Why are the changes needed?
fix perf regression when tables have char/varchar type columns
closes#31278
### Does this PR introduce _any_ user-facing change?
yes, spark will not raise error for oversized char/varchar values in read side
### How was this patch tested?
modified ut
the dropped read side benchmark
```
================================================================================================
Char Varchar Read Side Perf w/o Tailing Spaces
================================================================================================
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 20: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 20 1564 1573 9 63.9 15.6 1.0X
read char with length 20 1532 1551 18 65.3 15.3 1.0X
read varchar with length 20 1520 1531 13 65.8 15.2 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 40: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 40 1573 1613 41 63.6 15.7 1.0X
read char with length 40 1575 1577 2 63.5 15.7 1.0X
read varchar with length 40 1568 1576 11 63.8 15.7 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 60: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 60 1526 1540 23 65.5 15.3 1.0X
read char with length 60 1514 1539 23 66.0 15.1 1.0X
read varchar with length 60 1486 1497 10 67.3 14.9 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 80: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 80 1531 1542 19 65.3 15.3 1.0X
read char with length 80 1514 1529 15 66.0 15.1 1.0X
read varchar with length 80 1524 1565 42 65.6 15.2 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 100: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 100 1597 1623 25 62.6 16.0 1.0X
read char with length 100 1499 1512 16 66.7 15.0 1.1X
read varchar with length 100 1517 1524 8 65.9 15.2 1.1X
================================================================================================
Char Varchar Read Side Perf w/ Tailing Spaces
================================================================================================
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 20: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 20 1524 1526 1 65.6 15.2 1.0X
read char with length 20 1532 1537 9 65.3 15.3 1.0X
read varchar with length 20 1520 1532 15 65.8 15.2 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 40: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 40 1556 1580 32 64.3 15.6 1.0X
read char with length 40 1600 1611 17 62.5 16.0 1.0X
read varchar with length 40 1648 1716 88 60.7 16.5 0.9X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 60: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 60 1504 1524 20 66.5 15.0 1.0X
read char with length 60 1509 1512 3 66.2 15.1 1.0X
read varchar with length 60 1519 1535 21 65.8 15.2 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 80: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 80 1640 1652 17 61.0 16.4 1.0X
read char with length 80 1625 1666 35 61.5 16.3 1.0X
read varchar with length 80 1590 1605 13 62.9 15.9 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 100: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 100 1622 1628 5 61.6 16.2 1.0X
read char with length 100 1614 1646 30 62.0 16.1 1.0X
read varchar with length 100 1594 1606 11 62.7 15.9 1.0X
```
Closes#31281 from yaooqinn/SPARK-34192.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to convert `null` partition values to `"__HIVE_DEFAULT_PARTITION__"` before storing in the `In-Memory` catalog internally. Currently, the `In-Memory` catalog maintains null partitions as `"__HIVE_DEFAULT_PARTITION__"` in file system but as `null` values in memory that could cause some issues like in SPARK-34203.
### Why are the changes needed?
`InMemoryCatalog` stores partitions in the file system in the Hive compatible form, for instance, it converts the `null` partition value to `"__HIVE_DEFAULT_PARTITION__"` but at the same time it keeps null as is internally. That causes an issue demonstrated by the example below:
```
$ ./bin/spark-shell -c spark.sql.catalogImplementation=in-memory
```
```scala
scala> spark.conf.get("spark.sql.catalogImplementation")
res0: String = in-memory
scala> sql("CREATE TABLE tbl (col1 INT, p1 STRING) USING parquet PARTITIONED BY (p1)")
res1: org.apache.spark.sql.DataFrame = []
scala> sql("INSERT OVERWRITE TABLE tbl VALUES (0, null)")
res2: org.apache.spark.sql.DataFrame = []
scala> sql("ALTER TABLE tbl DROP PARTITION (p1 = null)")
org.apache.spark.sql.catalyst.analysis.NoSuchPartitionsException: The following partitions not found in table 'tbl' database 'default':
Map(p1 -> null)
at org.apache.spark.sql.catalyst.catalog.InMemoryCatalog.dropPartitions(InMemoryCatalog.scala:440)
```
### Does this PR introduce _any_ user-facing change?
Yes. After the changes, `ALTER TABLE .. DROP PARTITION` can drop the `null` partition in `In-Memory` catalog:
```scala
scala> spark.table("tbl").show(false)
+----+----+
|col1|p1 |
+----+----+
|0 |null|
+----+----+
scala> sql("ALTER TABLE tbl DROP PARTITION (p1 = null)")
res4: org.apache.spark.sql.DataFrame = []
scala> spark.table("tbl").show(false)
+----+---+
|col1|p1 |
+----+---+
+----+---+
```
### How was this patch tested?
Added new test to `AlterTableDropPartitionSuiteBase`:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableDropPartitionSuite"
```
Closes#31322 from MaxGekk/insert-overwrite-null-part.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Check the name passed to `SessionCatalog.refreshTable`, and if it belongs to a temporary view, do not invalidate the relation cache.
### Why are the changes needed?
When `SessionCatalog.refreshTable` refreshes a temporary or global temporary view, it should not invalidate an entry in the relation cache associated to a table with the same name.
### Does this PR introduce _any_ user-facing change?
Should not. The change might improve performance slightly.
### How was this patch tested?
By running new UT:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *SessionCatalogSuite"
```
Closes#31265 from MaxGekk/fix-session-catalog-refresh-table.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The `RowBasedKeyValueBatch` has two different implementations depending on whether the aggregation key and value uses only fixed length data types (`FixedLengthRowBasedKeyValueBatch`) or not (`VariableLengthRowBasedKeyValueBatch`).
Before this PR the decision about the used implementation was based on by accessing the schema fields by their name.
But if two fields has the same name and one with variable length and the other with fixed length type (and all the other fields are with fixed length types) a bad decision could be made.
When `FixedLengthRowBasedKeyValueBatch` is chosen but there is a variable length field then an aggregation function could calculate with invalid values. This case is illustrated by the example used in the unit test:
`with T as (select id as a, -id as x from range(3)),
U as (select id as b, cast(id as string) as x from range(3))
select T.x, U.x, min(a) as ma, min(b) as mb from T join U on a=b group by U.x, T.x`
where the 'x' column in the left side of the join is a Long but on the right side is a String.
### Why are the changes needed?
Fixes the issue where duplicate field name aggregation has null values in the dataframe.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT, tested manually on spark shell.
Closes#30788 from yliou/SPARK-33726.
Authored-by: yliou <yliou@berkeley.edu>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Compare the 3.1.1 API doc with the latest release version 3.0.1. Fix the following issues:
- Add missing `Since` annotation for new APIs
- Remove the leaking class/object in API doc
### Why are the changes needed?
Fix the issues in the Spark 3.1.1 release API docs.
### Does this PR introduce _any_ user-facing change?
Yes, API doc changes.
### How was this patch tested?
Manually test.
Closes#31271 from xuanyuanking/SPARK-34185.
Lead-authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Adds `NullType` support for Arrow executions.
### Why are the changes needed?
As Arrow supports null type, we can convert `NullType` between PySpark and pandas with Arrow enabled.
### Does this PR introduce _any_ user-facing change?
Yes, if a user has a DataFrame including `NullType`, it will be able to convert with Arrow enabled.
### How was this patch tested?
Added tests.
Closes#31285 from ueshin/issues/SPARK-33489/arrow_nulltype.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Rename `SessionCatalog.isTemporaryTable()` to `SessionCatalog.isTempView()`.
### Why are the changes needed?
To improve code maintenance. Currently, there are two methods that do the same but have different names:
```scala
def isTempView(nameParts: Seq[String]): Boolean
```
and
```scala
def isTemporaryTable(name: TableIdentifier): Boolean
```
### Does this PR introduce _any_ user-facing change?
Should not since `SessionCatalog` is not public API.
### How was this patch tested?
By running the existing tests:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *SessionCatalogSuite"
```
Closes#31295 from MaxGekk/replace-isTemporaryTable-by-isTempView.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR group exception messages in `/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions`.
### 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#31228 from beliefer/SPARK-33541.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR extends `StringTranslate` to support unicode characters whose code point >= `U+10000`.
### Why are the changes needed?
To make it work with wide variety of characters.
### Does this PR introduce _any_ user-facing change?
Yes. Users can use `StringTranslate` with unicode characters whose code point >= `U+10000`.
### How was this patch tested?
New assertion added to the existing test.
Closes#31164 from sarutak/extends-translate.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR changes cache refreshing of v1 tables in v1 commands. In particular, v1 table dependents are not removed from the cache after this PR. Comparing to current implementation, we just clear cached data of all dependents and keep them in the cache. So, the next actions will fill in the cached data of the original v1 table and its dependents. In more details:
1. Modified the `CatalogImpl.refreshTable()` method to use `recacheByPlan()` instead of `lookupCachedData()`, `uncacheQuery()` and `cacheQuery()`. Users can call this method via public API like `spark.catalog.refreshTable()`.
2. Rewritten the part in `CatalogImpl.refreshTable()` which was responsible for table meta-data refreshing because this code stopped to work properly after removing of the second `sparkSession.table(tableIdent)`.
3. Added new private method `invalidateCachedTable()` to `SessionCatalog`. Comparing to the existing `SessionCatalog.refreshTable`, it invalidates the relation cache only. If we called `SessionCatalog.refreshTable` from `CatalogImpl.refreshTable()`, we would refresh temporary and global temporary views twice (that could lead to refreshing file index twice).
### Why are the changes needed?
1. This should improve user experience with table/view caching. For example, let's imagine that an user has cached v1 table and cached view based on the table. And the user passed the table to external library which drops/renames/adds partitions in the v1 table. Unfortunately, the user gets the view uncached after that even he/she hasn't uncached the view explicitly.
2. To improve code maintenance.
3. To reduce the amount of calls to Hive external catalog.
4. Also this should speed up table recaching.
5. To have the same behavior as for v2 tables supported by https://github.com/apache/spark/pull/31172
### Does this PR introduce _any_ user-facing change?
From the view of the correctness of query results, there are no behavior changes but the changes might influence on consuming memory and query execution time. For example:
Before:
```scala
scala> sql("CREATE TABLE tbl (c int)")
scala> sql("CACHE TABLE tbl")
scala> sql("CREATE VIEW v AS SELECT * FROM tbl")
scala> sql("CACHE TABLE v")
scala> spark.catalog.isCached("v")
res6: Boolean = true
scala> spark.catalog.refreshTable("tbl")
scala> spark.catalog.isCached("v")
res8: Boolean = false
```
After:
```scala
scala> spark.catalog.refreshTable("tbl")
scala> spark.catalog.isCached("v")
res8: Boolean = true
```
### How was this patch tested?
1. Added new unit tests that create a view, a temporary view and a global temporary view on top of v1/v2 tables, and refresh the base table via `ALTER TABLE .. ADD/DROP/RENAME PARTITION`.
2. By running the unified test suites:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableAddPartitionSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableDropPartitionSuite"
# build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableRenamePartitionSuite"
```
Closes#31206 from MaxGekk/refreshTable-recache-by-plan.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
For varchar(N), we currently trim all spaces first to check whether the remained length exceeds, it not necessary to visit them all but at most to those after N.
### Why are the changes needed?
improve varchar performance for write side
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
benchmark and existing ut
Closes#31253 from yaooqinn/SPARK-34164.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Call `copyTagsFrom` for the new node created by `MultiInstanceRelation.newInstance()`.
### Why are the changes needed?
```scala
val df = spark.range(2)
df.join(df, df("id") <=> df("id")).show()
```
For this query, it's supposed to be non-ambiguous join by the rule `DetectAmbiguousSelfJoin` because of the same attribute reference in the condition:
537a49fc09/sql/core/src/main/scala/org/apache/spark/sql/execution/analysis/DetectAmbiguousSelfJoin.scala (L125)
However, `DetectAmbiguousSelfJoin` can not apply this prediction due to the right side plan doesn't contain the dataset_id TreeNodeTag, which is missing after `MultiInstanceRelation.newInstance`. That's why we should preserve the tags info for the copied node.
Fortunately, the query is still considered as non-ambiguous join because `DetectAmbiguousSelfJoin` only checks the left side plan and the reference is the same as the left side plan. However, this's not the expected behavior but only a coincidence.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Updated a unit test
Closes#31260 from Ngone51/fix-missing-tags.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This passes original SQL text to `CacheTableAsSelect` command in DSv1 and v2 so that it will be stored instead of the analyzed logical plan, similar to `CREATE VIEW` command.
In addition, this changes the behavior of dropping temporary view to also invalidate dependent caches in a cascade, when the config `SQLConf.STORE_ANALYZED_PLAN_FOR_VIEW` is false (which is the default value).
### Why are the changes needed?
Currently, after creating a temporary view with `CACHE TABLE ... AS SELECT` command, the view can still be queried even after the source table is dropped or replaced (in v2). This can cause correctness issue.
For instance, in the following:
```sql
> CREATE TABLE t ...;
> CACHE TABLE v AS SELECT * FROM t;
> DROP TABLE t;
> SELECT * FROM v;
```
The last select query still returns the old (and stale) result instead of fail. Note that the cache is already invalidated as part of dropping table `t`, but the temporary view `v` still exist.
On the other hand, the following:
```sql
> CREATE TABLE t ...;
> CREATE TEMPORARY VIEW v AS SELECT * FROM t;
> CACHE TABLE v;
> DROP TABLE t;
> SELECT * FROM v;
```
will throw "Table or view not found" error in the last select query.
This is related to #30567 which aligns the behavior of temporary view and global view by storing the original SQL text for temporary view, as opposed to the analyzed logical plan. However, the PR only handles `CreateView` case but not the `CacheTableAsSelect` case.
This also changes uncache logic and use cascade invalidation for temporary views created above. This is to align its behavior to how a permanent view is handled as of today, and also to avoid potential issues where a dependent view becomes invalid while its data is still kept in cache.
### Does this PR introduce _any_ user-facing change?
Yes, now when `SQLConf.STORE_ANALYZED_PLAN_FOR_VIEW` is set to false (the default value), whenever a table/permanent view/temp view that a cached view depends on is dropped, the cached view itself will become invalid during analysis, i.e., user will get "Table or view not found" error. In addition, when the dependent is a temp view in the previous case, the cache itself will also be invalidated.
### How was this patch tested?
Modified/Enhanced some existing tests.
Closes#31107 from sunchao/SPARK-34052.
Lead-authored-by: Chao Sun <sunchao@apple.com>
Co-authored-by: Chao Sun <sunchao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Port DS V2 tests from `AlterTablePartitionV2SQLSuite ` to the test suite `v2.AlterTableRecoverPartitionsSuite`.
2. Port DS v1 tests from `DDLSuite` to `v1.AlterTableRecoverPartitionsSuiteBase`.
### Why are the changes needed?
To improve test coverage.
### 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 *AlterTableRecoverPartitionsParserSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableRecoverPartitionsSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *CatalogedDDLSuite"
```
Closes#31105 from MaxGekk/unify-recover-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 add row count to `Intersect` operator when CBO enabled.
### Why are the changes needed?
Improve query performance, [JoinEstimation.estimateInnerOuterJoin](d6a68e0b67/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/JoinEstimation.scala (L55-L156)) need the row count.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added
Closes#31240 from AngersZhuuuu/SPARK-34121.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This could reduce the `generate.java` size to prevent codegen fallback which causes performance regression.
here is a case from tpcds that could be fixed by this improvement
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/133964/testReport/org.apache.spark.sql.execution/LogicalPlanTagInSparkPlanSuite/q41/
The original case generate 20K bytes, we are trying to reduce it to less than 8k
### Why are the changes needed?
performance improvement as in the PR benchmark test, the performance w/ codegen is 2~3x better than w/o codegen.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
yes, it's a code reflect so the existing ut should be enough
cross-check with https://github.com/apache/spark/pull/31012 where the tpcds shall all pass
benchmark compared with master
```logtalk
================================================================================================
Char Varchar Read Side Perf
================================================================================================
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 20, hasSpaces: false: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 20 1571 1667 83 63.6 15.7 1.0X
read char with length 20 1710 1764 58 58.5 17.1 0.9X
read varchar with length 20 1774 1792 16 56.4 17.7 0.9X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 40, hasSpaces: false: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 40 1824 1927 91 54.8 18.2 1.0X
read char with length 40 1788 1928 137 55.9 17.9 1.0X
read varchar with length 40 1676 1700 40 59.7 16.8 1.1X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 60, hasSpaces: false: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 60 1727 1762 30 57.9 17.3 1.0X
read char with length 60 1628 1674 43 61.4 16.3 1.1X
read varchar with length 60 1651 1665 13 60.6 16.5 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 80, hasSpaces: true: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 80 1748 1778 28 57.2 17.5 1.0X
read char with length 80 1673 1678 9 59.8 16.7 1.0X
read varchar with length 80 1667 1684 27 60.0 16.7 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 100, hasSpaces: true: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 100 1709 1743 48 58.5 17.1 1.0X
read char with length 100 1610 1664 67 62.1 16.1 1.1X
read varchar with length 100 1614 1673 53 61.9 16.1 1.1X
================================================================================================
Char Varchar Write Side Perf
================================================================================================
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Write with length 20, hasSpaces: false: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
write string with length 20 2277 2327 67 4.4 227.7 1.0X
write char with length 20 2421 2443 19 4.1 242.1 0.9X
write varchar with length 20 2393 2419 27 4.2 239.3 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Write with length 40, hasSpaces: false: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
write string with length 40 2249 2290 38 4.4 224.9 1.0X
write char with length 40 2386 2444 57 4.2 238.6 0.9X
write varchar with length 40 2397 2405 12 4.2 239.7 0.9X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Write with length 60, hasSpaces: false: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
write string with length 60 2326 2367 41 4.3 232.6 1.0X
write char with length 60 2478 2501 37 4.0 247.8 0.9X
write varchar with length 60 2475 2503 24 4.0 247.5 0.9X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Write with length 80, hasSpaces: true: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
write string with length 80 9367 9773 354 1.1 936.7 1.0X
write char with length 80 10454 10621 238 1.0 1045.4 0.9X
write varchar with length 80 18943 19503 571 0.5 1894.3 0.5X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Write with length 100, hasSpaces: true: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
write string with length 100 11055 11104 59 0.9 1105.5 1.0X
write char with length 100 12204 12275 63 0.8 1220.4 0.9X
write varchar with length 100 21737 22275 574 0.5 2173.7 0.5X
```
Closes#31199 from yaooqinn/SPARK-34130.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
While adding new partition to v2 `InMemoryAtomicPartitionTable`/`InMemoryPartitionTable`, add single row to the table content when the table is fully partitioned.
### Why are the changes needed?
The `ALTER TABLE .. ADD PARTITION` command does not change content of fully partitioned v2 table. For instance, `INSERT INTO` changes table content:
```scala
sql(s"CREATE TABLE t (p0 INT, p1 STRING) USING _ PARTITIONED BY (p0, p1)")
sql(s"INSERT INTO t SELECT 1, 'def'")
sql(s"SELECT * FROM t").show(false)
+---+---+
|p0 |p1 |
+---+---+
|1 |def|
+---+---+
```
but `ALTER TABLE .. ADD PARTITION` doesn't change v2 table content:
```scala
sql(s"ALTER TABLE t ADD PARTITION (p0 = 0, p1 = 'abc')")
sql(s"SELECT * FROM t").show(false)
+---+---+
|p0 |p1 |
+---+---+
+---+---+
```
### Does this PR introduce _any_ user-facing change?
No, the changes impact only on tests but for the example above in tests:
```scala
sql(s"ALTER TABLE t ADD PARTITION (p0 = 0, p1 = 'abc')")
sql(s"SELECT * FROM t").show(false)
+---+---+
|p0 |p1 |
+---+---+
|0 |abc|
+---+---+
```
### How was this patch tested?
By running the unified tests for `ALTER TABLE .. ADD PARTITION`.
Closes#31216 from MaxGekk/add-partition-by-all-columns.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Change null Literal to PrettyAttribute during ResolveAlias.
### Why are the changes needed?
We will convert `Literal(null)` to target data type during analysis. Then the generated alias name will include something like `CAST(NULL AS String)` instead of `NULL`.
```
spark.sql("SELECT RAND(null)").columns
-- before
rand(CAST(NULL AS INT))
-- after
rand(NULL)
```
### Does this PR introduce _any_ user-facing change?
Yes, the default column name maybe changed.
### How was this patch tested?
Add test and pass exists test.
Closes#31233 from ulysses-you/SPARK-34150.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, there are many DDL commands where the position of the unresolved identifiers are incorrect:
```
scala> sql("CACHE TABLE unknown")
org.apache.spark.sql.AnalysisException: Table or view not found: unknown; line 1 pos 0;
```
, whereas the `pos` should be `12`.
This PR proposes to fix this issue for commands using `UnresolvedRelation`:
```
CACHE TABLE unknown
UNCACHE TABLE unknown
DELETE FROM unknown
UPDATE unknown SET name='abc'
MERGE INTO unknown1 AS target USING unknown2 AS source ON target.col = source.col WHEN MATCHED THEN DELETE
INSERT INTO TABLE unknown SELECT 1
INSERT OVERWRITE TABLE unknown VALUES (1, 'a')
```
### Why are the changes needed?
To fix a bug.
### Does this PR introduce _any_ user-facing change?
Yes, now the above example will print the following:
```
org.apache.spark.sql.AnalysisException: Table or view not found: unknown; line 1 pos 12;
```
### How was this patch tested?
Add a new test.
Closes#31209 from imback82/unresolved_relation_message.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`QueryCompilationErrors.scala` and `QueryExecutionErrors.scala` use the `org.apache.spark.sql.errors` package, but these files are reside in `org/apache/spark/sql` directory. This PR proposes to move these files to `org/apache/spark/sql/errors`.
### Why are the changes needed?
To match the package name with the directory structure.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests
Closes#31211 from imback82/error_package.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Replace `toMap` by `map(identity).toMap` while getting canonicalized representation of `CatalogTable`. `CatalogTable` became not serializable after https://github.com/apache/spark/pull/31112 due to usage of `filterKeys`. The workaround was taken from https://github.com/scala/bug/issues/7005.
### Why are the changes needed?
This prevents the errors like:
```
[info] org.apache.spark.SparkException: Job aborted due to stage failure: Task not serializable: java.io.NotSerializableException: scala.collection.immutable.MapLike$$anon$1
[info] Cause: java.io.NotSerializableException: scala.collection.immutable.MapLike$$anon$1
```
### Does this PR introduce _any_ user-facing change?
Should not.
### How was this patch tested?
By running the test suite affected by https://github.com/apache/spark/pull/31112:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableDropPartitionSuite"
```
Closes#31197 from MaxGekk/fix-caching-hive-table-2-followup.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This:
1. switches Spark to use shaded Hadoop clients, namely hadoop-client-api and hadoop-client-runtime, for Hadoop 3.x.
2. upgrade built-in version for Hadoop 3.x to Hadoop 3.2.2
Note that for Hadoop 2.7, we'll still use the same modules such as hadoop-client.
In order to still keep default Hadoop profile to be hadoop-3.2, this defines the following Maven properties:
```
hadoop-client-api.artifact
hadoop-client-runtime.artifact
hadoop-client-minicluster.artifact
```
which default to:
```
hadoop-client-api
hadoop-client-runtime
hadoop-client-minicluster
```
but all switch to `hadoop-client` when the Hadoop profile is hadoop-2.7. A side affect from this is we'll import the same dependency multiple times. For this I have to disable Maven enforcer `banDuplicatePomDependencyVersions`.
Besides above, there are the following changes:
- explicitly add a few dependencies which are imported via transitive dependencies from Hadoop jars, but are removed from the shaded client jars.
- removed the use of `ProxyUriUtils.getPath` from `ApplicationMaster` which is a server-side/private API.
- modified `IsolatedClientLoader` to exclude `hadoop-auth` jars when Hadoop version is 3.x. This change should only matter when we're not sharing Hadoop classes with Spark (which is _mostly_ used in tests).
### Why are the changes needed?
Hadoop 3.2.2 is released with new features and bug fixes, so it's good for the Spark community to adopt it. However, latest Hadoop versions starting from Hadoop 3.2.1 have upgraded to use Guava 27+. In order to resolve Guava conflicts, this takes the approach by switching to shaded client jars provided by Hadoop. This also has the benefits of avoid pulling other 3rd party dependencies from Hadoop side so as to avoid more potential future conflicts.
### Does this PR introduce _any_ user-facing change?
When people use Spark with `hadoop-provided` option, they should make sure class path contains `hadoop-client-api` and `hadoop-client-runtime` jars. In addition, they may need to make sure these jars appear before other Hadoop jars in the order. Otherwise, classes may be loaded from the other non-shaded Hadoop jars and cause potential conflicts.
### How was this patch tested?
Relying on existing tests.
Closes#30701 from sunchao/test-hadoop-3.2.2.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
The type-coercion for numeric types of average and sum is not necessary at all, as the resultType and sumType can prevent the overflow.
### Why are the changes needed?
rm unnecessary logic which may cause potential performance regressions
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
tpcds tests for plan
Closes#31079 from yaooqinn/SPARK-34037.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
On the read-side, we should respect the original data instead of trimming it first.
It brings extra overhead on the code-gen code side, trimming and padding for the same field, and it's also unnecessary and a bug
### Why are the changes needed?
bugfix and perf regression
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
new tests
Closes#31181 from yaooqinn/SPARK-34114.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR introduces a new property `spark.sql.cli.print.header` to let users change the behavior of printing header for spark-sql CLI by SET command.
### Why are the changes needed?
Like Hive CLI, spark-sql CLI accepts `hive.cli.print.header` property and we can change the behavior of printing header.
But spark-sql CLI doesn't allow users to change Hive specific configurations dynamically by SET command.
So, it's better to support the way to change the behavior by SET command.
### Does this PR introduce _any_ user-facing change?
Yes. Users can dynamically change the behavior by SET command.
### How was this patch tested?
I confirmed with the following commands/queries.
```
spark-sql> select (1) as a, (2) as b, (3) as c, (4) as d;
1 2 3 4
Time taken: 3.218 seconds, Fetched 1 row(s)
spark-sql> set spark.sql.cli.print.header=true;
key value
spark.sql.cli.print.header true
Time taken: 1.506 seconds, Fetched 1 row(s)
spark-sql> select (1) as a, (2) as b, (3) as c, (4) as d;
a b c d
1 2 3 4
Time taken: 0.79 seconds, Fetched 1 row(s)
```
Closes#31173 from sarutak/spark-sql-print-header.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to strip auto-generated cast. The main logic is:
1. Add tag if Cast is specified by user.
2. Wrap `PrettyAttribute` in usePrettyExpression.
### Why are the changes needed?
Make sql consistent with dsl. Here is an inconsistent example before this PR:
```
-- output field name: FLOOR(1)
spark.emptyDataFrame.select(floor(lit(1)))
-- output field name: FLOOR(CAST(1 AS DOUBLE))
spark.sql("select floor(1)")
```
Note that, we don't remove the `Cast` so the auto-generated `Cast` can still work. The only changed place is `usePrettyExpression`, we use `PrettyAttribute` replace `Cast` to give a better sql string.
### Does this PR introduce _any_ user-facing change?
Yes, the default field name may change.
### How was this patch tested?
Add test and pass exists test.
Closes#31034 from ulysses-you/SPARK-33989.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
<!--
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This PR adds a feature which supports 32-bit unicode escape in string literals like PostgreSQL or some modern programming languages do (e.g, Python3, C++11 and Rust).
In addition to the feature which supports 16-bit unicode escape like `"\u0041"`, users can express unicode characters like `"\U00020BB7"` with this change.
### Why are the changes needed?
<!--
Please clarify why the changes are needed. For instance,
1. If you propose a new API, clarify the use case for a new API.
2. If you fix a bug, you can clarify why it is a bug.
-->
Users can express unicode characters straightly without surrogate pair.
### Does this PR introduce _any_ user-facing change?
<!--
Note that it means *any* user-facing change including all aspects such as the documentation fix.
If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible.
If possible, please also clarify if this is a user-facing change compared to the released Spark versions or within the unreleased branches such as master.
If no, write 'No'.
-->
Yes. Users an express all the unicode characters straightly.
### How was this patch tested?
<!--
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Added new assertions to the existing test case.
Closes#31096 from sarutak/32-bit-unicode-escape.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
There are some redundant collection conversion can be removed, for version compatibility, clean up these with Scala-2.13 profile.
### Why are the changes needed?
Remove redundant collection conversion
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Pass the Jenkins or GitHub Action
- Manual test `core`, `graphx`, `mllib`, `mllib-local`, `sql`, `yarn`,`kafka-0-10` in Scala 2.13 passed
Closes#31125 from LuciferYang/SPARK-34068.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This pr use `exists` to simplify `find + emptiness check`, it's semantically consistent, but looks simpler.
**Before**
```
seq.find(p).isDefined
or
seq.find(p).isEmpty
```
**After**
```
seq.exists(p)
or
!seq.exists(p)
```
### Why are the changes needed?
Code Simpilefications.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or GitHub Action
Closes#31130 from LuciferYang/SPARK-34070.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/133928/testReport/org.apache.spark.sql.execution/LogicalPlanTagInSparkPlanSuite/q41/
We can reduce more than 8000 bytes by removing the unnecessary CONCAT expression.
W/ this fix, for q41 in TPCDS with [Using TPCDS original definitions for char/varchar columns](https://github.com/apache/spark/pull/31012) applied, we can reduce the stage code-gen size from 22523 to 14369
```
14369 - 22523 = - 8154
```
### Why are the changes needed?
fix the perf regression(we need other improvements for q41 works), there will be a huge performance regression if codegen fails
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
modified uts
Closes#31150 from yaooqinn/SPARK-34086.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In 0f8e5dd445, we partially fix the rule conflicts between `PaddingAndLengthCheckForCharVarchar` and `ResolveAggregateFunctions`, as error still exists in
sql like ```SELECT substr(v, 1, 2), sum(i) FROM t GROUP BY v ORDER BY substr(v, 1, 2)```
```sql
[info] Failed to analyze query: org.apache.spark.sql.AnalysisException: expression 'spark_catalog.default.t.`v`' is neither present in the group by, nor is it an aggregate function. Add to group by or wrap in first() (or first_value) if you don't care which value you get.;
[info] Project [substr(v, 1, 2)#100, sum(i)#101L]
[info] +- Sort [aggOrder#102 ASC NULLS FIRST], true
[info] +- !Aggregate [v#106], [substr(v#106, 1, 2) AS substr(v, 1, 2)#100, sum(cast(i#98 as bigint)) AS sum(i)#101L, substr(v#103, 1, 2) AS aggOrder#102
[info] +- SubqueryAlias spark_catalog.default.t
[info] +- Project [if ((length(v#97) <= 3)) v#97 else if ((length(rtrim(v#97, None)) > 3)) cast(raise_error(concat(input string of length , cast(length(v#97) as string), exceeds varchar type length limitation: 3)) as string) else rpad(rtrim(v#97, None), 3, ) AS v#106, i#98]
[info] +- Relation[v#97,i#98] parquet
[info]
[info] Project [substr(v, 1, 2)#100, sum(i)#101L]
[info] +- Sort [aggOrder#102 ASC NULLS FIRST], true
[info] +- !Aggregate [v#106], [substr(v#106, 1, 2) AS substr(v, 1, 2)#100, sum(cast(i#98 as bigint)) AS sum(i)#101L, substr(v#103, 1, 2) AS aggOrder#102
[info] +- SubqueryAlias spark_catalog.default.t
[info] +- Project [if ((length(v#97) <= 3)) v#97 else if ((length(rtrim(v#97, None)) > 3)) cast(raise_error(concat(input string of length , cast(length(v#97) as string), exceeds varchar type length limitation: 3)) as string) else rpad(rtrim(v#97, None), 3, ) AS v#106, i#98]
[info] +- Relation[v#97,i#98] parquet
```
We need to look recursively into children to find char/varchars.
In this PR, we try to resolve the full attributes including the original `Aggregate` expressions and the candidates in `SortOrder` together, then use the new re-resolved `Aggregate` expressions to determine which candidate in the `SortOrder` shall be pushed. This can avoid mismatch for the same attributes w/o this change, as the expressions returned by `executeSameContext` will change when `PaddingAndLengthCheckForCharVarchar` takes effects. W/ this change, the expressions can be matched correctly.
For those unmatched, w need to look recursively into children to find char/varchars instead of the expression itself only.
### Why are the changes needed?
bugfix
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
add new tests
Closes#31129 from yaooqinn/SPARK-34003-F.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add a legacy configuration `spark.sql.legacy.allowParameterlessCount` in case users need the parameterless count.
This is a follow-up for https://github.com/apache/spark/pull/30541.
### Why are the changes needed?
There can be some users depends on the legacy behavior. We need a legacy flag for it.
### Does this PR introduce _any_ user-facing change?
Yes, adding a legacy flag `spark.sql.legacy.allowParameterlessCount`.
### How was this patch tested?
Unit tests
Closes#31143 from gengliangwang/countLegacy.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch fixes few issues when using encoders to serialize input/output in `ScalaUDF`.
### Why are the changes needed?
This fixes a bug when using encoders in Scala UDF. First, the output data type should be corrected to the corresponding data type of the object serializer. Second, `catalystConverter` should not serialize `Option[_]` as the ordinary row because in `ScalaUDF` case it is serialized to a column, not the top-level row. Otherwise, there will be a redundant `value` struct wrapping the serialized `Option[_]` object.
### Does this PR introduce _any_ user-facing change?
Yes, fixing a bug of `ScalaUDF`.
### How was this patch tested?
Unit test.
Closes#31103 from viirya/SPARK-34002.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Fix canonicalisation of `HiveTableRelation` by normalisation of `CatalogTable`, and exclude table stats and temporary fields from the canonicalized plan.
### Why are the changes needed?
This fixes the issue demonstrated by the example below:
```scala
scala> spark.conf.set("spark.sql.statistics.size.autoUpdate.enabled", true)
scala> sql(s"CREATE TABLE tbl (id int, part int) USING hive PARTITIONED BY (part)")
scala> sql("INSERT INTO tbl PARTITION (part=0) SELECT 0")
scala> sql("INSERT INTO tbl PARTITION (part=1) SELECT 1")
scala> sql("CACHE TABLE tbl")
scala> sql("SELECT * FROM tbl").show(false)
+---+----+
|id |part|
+---+----+
|0 |0 |
|1 |1 |
+---+----+
scala> spark.catalog.isCached("tbl")
scala> sql("ALTER TABLE tbl DROP PARTITION (part=0)")
scala> spark.catalog.isCached("tbl")
res19: Boolean = false
```
`ALTER TABLE .. DROP PARTITION` must keep the table in the cache.
### Does this PR introduce _any_ user-facing change?
Yes. After the changes, the drop partition command keeps the table in the cache while updating table stats:
```scala
scala> sql("ALTER TABLE tbl DROP PARTITION (part=0)")
scala> spark.catalog.isCached("tbl")
res19: Boolean = true
```
### How was this patch tested?
By running new UT in `AlterTableDropPartitionSuite`.
Closes#31112 from MaxGekk/fix-caching-hive-table-2.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, there are many DDL commands where the position of the unresolved identifiers are incorrect:
```
scala> sql("DROP TABLE unknown")
org.apache.spark.sql.AnalysisException: Table or view not found: unknown; line 1 pos 0;
```
, whereas the `pos` should be `11`.
This PR proposes to fix this issue for commands using `UnresolvedTableOrView`:
```
DROP TABLE unknown
DESCRIBE TABLE unknown
ANALYZE TABLE unknown COMPUTE STATISTICS
ANALYZE TABLE unknown COMPUTE STATISTICS FOR COLUMNS col
ANALYZE TABLE unknown COMPUTE STATISTICS FOR ALL COLUMNS
SHOW CREATE TABLE unknown
REFRESH TABLE unknown
SHOW COLUMNS FROM unknown
SHOW COLUMNS FROM unknown IN db
ALTER TABLE unknown RENAME TO t
ALTER VIEW unknown RENAME TO v
```
### Why are the changes needed?
To fix a bug.
### Does this PR introduce _any_ user-facing change?
Yes, now the above example will print the following:
```
org.apache.spark.sql.AnalysisException: Table or view not found: unknown; line 1 pos 11;
```
### How was this patch tested?
Add a new test.
Closes#31106 from imback82/unresolved_table_or_view_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 PR is basically a followup of https://github.com/apache/spark/pull/14332.
Calling `map` alone might leave it not executed due to lazy evaluation, e.g.)
```
scala> val foo = Seq(1,2,3)
foo: Seq[Int] = List(1, 2, 3)
scala> foo.map(println)
1
2
3
res0: Seq[Unit] = List((), (), ())
scala> foo.view.map(println)
res1: scala.collection.SeqView[Unit,Seq[_]] = SeqViewM(...)
scala> foo.view.foreach(println)
1
2
3
```
We should better use `foreach` to make sure it's executed where the output is unused or `Unit`.
### Why are the changes needed?
To prevent the potential issues by not executing `map`.
### Does this PR introduce _any_ user-facing change?
No, the current codes look not causing any problem for now.
### How was this patch tested?
I found these item by running IntelliJ inspection, double checked one by one, and fixed them. These should be all instances across the codebase ideally.
Closes#31110 from HyukjinKwon/SPARK-34059.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Move `RepartitionByExpression` fold partition number code to a new rule at `Optimizer`.
### Why are the changes needed?
We meet some ploblem when backport SPARK-33806. It is because the UnresolvedFunction.foldable will throw a exception. It's ok with master branch, but it's better to do it at Optimizer. Some reason:
1. It's not always safe to call Expression.foldable before analysis.
2. fold num partition to 1 more like a optimize behavior.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add test.
Closes#31077 from ulysses-you/SPARK-34030.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR makes `StreamExecution` use the `Write` abstraction introduced in SPARK-33779.
Note: we will need separate plans for streaming writes in order to support the required distribution and ordering in SS. This change only migrates to the `Write` abstraction.
### Why are the changes needed?
These changes prevent exceptions from data sources that implement only the `build` method in `WriteBuilder`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#31093 from aokolnychyi/spark-34049.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR fixes an incorrect unicode literal test in `ParserUtilsSuite`.
In that suite, string literals in queries have unicode escape characters like `\u7328` but the backslash should be escaped because
the queriy strings are given as Java strings.
### Why are the changes needed?
Correct the test.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Run `ParserUtilsSuite` and it passed.
Closes#31088 from sarutak/fix-incorrect-unicode-test.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
1. Recognize `null` while parsing partition specs, and put `null` instead of `"null"` as partition values.
2. For V1 catalog: replace `null` by `__HIVE_DEFAULT_PARTITION__`.
3. For V2 catalogs: pass `null` AS IS, and let catalog implementations to decide how to handle `null`s as partition values in spec.
### Why are the changes needed?
Currently, `null` in partition specs is recognized as the `"null"` string which could lead to incorrect results, for example:
```sql
spark-sql> CREATE TABLE tbl5 (col1 INT, p1 STRING) USING PARQUET PARTITIONED BY (p1);
spark-sql> INSERT INTO TABLE tbl5 PARTITION (p1 = null) SELECT 0;
spark-sql> SELECT isnull(p1) FROM tbl5;
false
```
Even we inserted a row to the partition with the `null` value, **the resulted table doesn't contain `null`**.
### Does this PR introduce _any_ user-facing change?
Yes. After the changes, the example above works as expected:
```sql
spark-sql> SELECT isnull(p1) FROM tbl5;
true
```
### How was this patch tested?
1. By running the affected test suites `SQLQuerySuite`, `AlterTablePartitionV2SQLSuite` and `v1/ShowPartitionsSuite`.
2. Compiling by Scala 2.13:
```
$ ./dev/change-scala-version.sh 2.13
$ ./build/sbt -Pscala-2.13 compile
```
Closes#30538 from MaxGekk/partition-spec-value-null.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
ResolveAggregateFunctions is a hacky rule and it calls `executeSameContext` to generate a `resolved agg` to determine which unresolved sort attribute should be pushed into the agg. However, after we add the PaddingAndLengthCheckForCharVarchar rule which will rewrite the query output, thus, the `resolved agg` cannot match original attributes anymore.
It causes some dissociative sort attribute to be pushed in and fails the query
``` logtalk
[info] Failed to analyze query: org.apache.spark.sql.AnalysisException: expression 'testcat.t1.`v`' is neither present in the group by, nor is it an aggregate function. Add to group by or wrap in first() (or first_value) if you don't care which value you get.;
[info] Project [v#14, sum(i)#11L]
[info] +- Sort [aggOrder#12 ASC NULLS FIRST], true
[info] +- !Aggregate [v#14], [v#14, sum(cast(i#7 as bigint)) AS sum(i)#11L, v#13 AS aggOrder#12]
[info] +- SubqueryAlias testcat.t1
[info] +- Project [if ((length(v#6) <= 3)) v#6 else if ((length(rtrim(v#6, None)) > 3)) cast(raise_error(concat(input string of length , cast(length(v#6) as string), exceeds varchar type length limitation: 3)) as string) else rpad(rtrim(v#6, None), 3, ) AS v#14, i#7]
[info] +- RelationV2[v#6, i#7, index#15, _partition#16] testcat.t1
[info]
[info] Project [v#14, sum(i)#11L]
[info] +- Sort [aggOrder#12 ASC NULLS FIRST], true
[info] +- !Aggregate [v#14], [v#14, sum(cast(i#7 as bigint)) AS sum(i)#11L, v#13 AS aggOrder#12]
[info] +- SubqueryAlias testcat.t1
[info] +- Project [if ((length(v#6) <= 3)) v#6 else if ((length(rtrim(v#6, None)) > 3)) cast(raise_error(concat(input string of length , cast(length(v#6) as string), exceeds varchar type length limitation: 3)) as string) else rpad(rtrim(v#6, None), 3, ) AS v#14, i#7]
[info] +- RelationV2[v#6, i#7, index#15, _partition#16] testcat.t1
```
### Why are the changes needed?
bugfix
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
new tests
Closes#31027 from yaooqinn/SPARK-34003.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This pr add row count to `Union` operator when CBO enabled.
```scala
spark.sql("CREATE TABLE t1 USING parquet AS SELECT id FROM RANGE(10)")
spark.sql("CREATE TABLE t2 USING parquet AS SELECT id FROM RANGE(10)")
spark.sql("ANALYZE TABLE t1 COMPUTE STATISTICS FOR ALL COLUMNS")
spark.sql("ANALYZE TABLE t2 COMPUTE STATISTICS FOR ALL COLUMNS")
spark.sql("set spark.sql.cbo.enabled=true")
spark.sql("SELECT * FROM t1 UNION ALL SELECT * FROM t2").explain("cost")
```
Before this pr:
```
== Optimized Logical Plan ==
Union false, false, Statistics(sizeInBytes=320.0 B)
:- Relation[id#5880L] parquet, Statistics(sizeInBytes=160.0 B, rowCount=10)
+- Relation[id#5881L] parquet, Statistics(sizeInBytes=160.0 B, rowCount=10)
```
After this pr:
```
== Optimized Logical Plan ==
Union false, false, Statistics(sizeInBytes=320.0 B, rowCount=20)
:- Relation[id#2138L] parquet, Statistics(sizeInBytes=160.0 B, rowCount=10)
+- Relation[id#2139L] parquet, Statistics(sizeInBytes=160.0 B, rowCount=10)
```
### Why are the changes needed?
Improve query performance, [`JoinEstimation.estimateInnerOuterJoin`](d6a68e0b67/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/JoinEstimation.scala (L55-L156)) need the row count.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#31068 from wangyum/SPARK-34031.
Lead-authored-by: Yuming Wang <yumwang@ebay.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This pr address https://github.com/apache/spark/pull/30865#pullrequestreview-562344089 to fix simplify conditional in predicate should consider deterministic.
### Why are the changes needed?
Fix bug.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#31067 from wangyum/SPARK-33861-2.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
There is one compilation warning as follow:
```
[WARNING] [Warn] /spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala:1555: [other-match-analysis org.apache.spark.sql.catalyst.catalog.SessionCatalog.lookupFunction.catalogFunction] unreachable code
```
This compilation warning is due to `NoSuchPermanentFunctionException` is sub-class of `AnalysisException` and if there is `NoSuchPermanentFunctionException` be thrown out, it will be catch by `case _: AnalysisException => failFunctionLookup(name)`, so `case _: NoSuchPermanentFunctionException => failFunctionLookup(name)` is `unreachable code`.
This pr remove `case _: NoSuchPermanentFunctionException => failFunctionLookup(name)` directly because both these 2 branches handle exceptions in the same way: `failFunctionLookup(name)`
### Why are the changes needed?
Cleanup "unreachable code" compilation warnings.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or GitHub Action
Closes#31064 from LuciferYang/SPARK-34028.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add check partition expressions is empty.
### Why are the changes needed?
We should keep `spark.range(1).hint("REPARTITION_BY_RANGE")` has default shuffle number instead of 1.
### Does this PR introduce _any_ user-facing change?
Yes.
### How was this patch tested?
Add test.
Closes#31074 from ulysses-you/SPARK-33806-FOLLOWUP.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
We should optimize Like Any/All by LikeSimplification to improve performance.
### Why are the changes needed?
Optimize Like Any/All
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#30975 from beliefer/SPARK-33938.
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 `FrameLessOffsetWindowFunction` as sealed abstract class so that simplify pattern match.
### Why are the changes needed?
Simplify pattern match
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
Jenkins test
Closes#31026 from beliefer/SPARK-30789-followup.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/22696 we support HAVING without GROUP BY means global aggregate
But since we treat having as Filter before, in this way will cause a lot of analyze error, after https://github.com/apache/spark/pull/28294 we use `UnresolvedHaving` to instead `Filter` to solve such problem, but break origin logical about treat `SELECT 1 FROM range(10) HAVING true` as `SELECT 1 FROM range(10) WHERE true` .
This PR fix this issue and add UT.
### Why are the changes needed?
Keep consistent behavior of migration guide.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
added UT
Closes#31039 from AngersZhuuuu/SPARK-25780-Follow-up.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR group exception messages in `/catalyst/src/main/scala/org/apache/spark/sql/catalyst/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#30870 from beliefer/SPARK-33542.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Port DS V2 tests from `DataSourceV2SQLSuite` to the base test suite `ShowNamespacesSuiteBase` to run those tests for v1 catalogs.
2. Port DS v1 tests from `DDLSuite` to `ShowNamespacesSuiteBase` to run the tests for v2 catalogs too.
### Why are the changes needed?
To improve test coverage.
### 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 *ShowNamespacesSuite"
```
Closes#30937 from MaxGekk/unify-show-namespaces-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?
Changed the cost function in CBO to match documentation.
### Why are the changes needed?
The parameter `spark.sql.cbo.joinReorder.card.weight` is documented as:
```
The weight of cardinality (number of rows) for plan cost comparison in join reorder: rows * weight + size * (1 - weight).
```
The implementation in `JoinReorderDP.betterThan` does not match this documentaiton:
```
def betterThan(other: JoinPlan, conf: SQLConf): Boolean = {
if (other.planCost.card == 0 || other.planCost.size == 0) {
false
} else {
val relativeRows = BigDecimal(this.planCost.card) / BigDecimal(other.planCost.card)
val relativeSize = BigDecimal(this.planCost.size) / BigDecimal(other.planCost.size)
relativeRows * conf.joinReorderCardWeight +
relativeSize * (1 - conf.joinReorderCardWeight) < 1
}
}
```
This different implementation has an unfortunate consequence:
given two plans A and B, both A betterThan B and B betterThan A might give the same results. This happes when one has many rows with small sizes and other has few rows with large sizes.
A example values, that have this fenomen with the default weight value (0.7):
A.card = 500, B.card = 300
A.size = 30, B.size = 80
Both A betterThan B and B betterThan A would have score above 1 and would return false.
This happens with several of the TPCDS queries.
The new implementation does not have this behavior.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
New and existing UTs
Closes#30965 from tanelk/SPARK-33935_cbo_cost_function.
Authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/29643, we move the plan rewriting methods to QueryPlan. we need to override transformUpWithNewOutput to add allowInvokingTransformsInAnalyzer
because it and resolveOperatorsUpWithNewOutput are called in the analyzer.
For example,
PaddingAndLengthCheckForCharVarchar could fail query when resolveOperatorsUpWithNewOutput
with
```logtalk
[info] - char/varchar resolution in sub query *** FAILED *** (367 milliseconds)
[info] java.lang.RuntimeException: This method should not be called in the analyzer
[info] at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.assertNotAnalysisRule(AnalysisHelper.scala:150)
[info] at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.assertNotAnalysisRule$(AnalysisHelper.scala:146)
[info] at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.assertNotAnalysisRule(LogicalPlan.scala:29)
[info] at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDown(AnalysisHelper.scala:161)
[info] at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDown$(AnalysisHelper.scala:160)
[info] at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDown(LogicalPlan.scala:29)
[info] at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDown(LogicalPlan.scala:29)
[info] at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$updateOuterReferencesInSubquery(QueryPlan.scala:267)
```
### Why are the changes needed?
trivial bugfix
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
new tests
Closes#31013 from yaooqinn/SPARK-33992.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Instead of returning NULL, the next_day function throws runtime IllegalArgumentException when ansiMode is enable and receiving invalid input of the dayOfWeek parameter.
### Why are the changes needed?
For ansiMode.
### Does this PR introduce _any_ user-facing change?
Yes.
When spark.sql.ansi.enabled = true, the next_day function will throw IllegalArgumentException when receiving invalid input of the dayOfWeek parameter.
When spark.sql.ansi.enabled = false, same behaviour as before.
### How was this patch tested?
Ansi mode is tested with existing tests.
End-to-end tests have been added.
Closes#30807 from chongguang/SPARK-33794.
Authored-by: Chongguang LIU <chongguang.liu@laposte.fr>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Added the `RemoveNoopOperators` rule to optimization batch `Union`. Also made sure that the `RemoveNoopOperators` would be idempotent.
### Why are the changes needed?
In several TPCDS queries the `CombineUnions` rule does not manage to combine unions, because they have noop `Project`s between them.
The `Project`s will be removed by `RemoveNoopOperators`, but by then `ReplaceDistinctWithAggregate` has been applied and there are aggregates between the unions. Adding a copy of `RemoveNoopOperators` earlier in the optimization chain allows `CombineUnions` to work on more queries.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
New UTs and the output of `PlanStabilitySuite`
Closes#30996 from tanelk/SPARK-33964_combine_unions.
Authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Remove partition data by `ALTER TABLE .. DROP PARTITION` in V2 table catalog used in tests.
### Why are the changes needed?
This is a bug fix. Before the fix, `ALTER TABLE .. DROP PARTITION` does not remove the data belongs to the dropped partition. As a consequence of that, the `select` query returns removed data.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running tests suites for v1 and v2 catalogs:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableDropPartitionSuite"
```
Closes#31014 from MaxGekk/fix-drop-partition-v2.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR proposes to implement `DESCRIBE COLUMN` for v2 tables.
Note that `isExnteded` option is not implemented in this PR.
### Why are the changes needed?
Parity with v1 tables.
### Does this PR introduce _any_ user-facing change?
Yes, now, `DESCRIBE COLUMN` works for v2 tables.
```scala
sql("CREATE TABLE testcat.tbl (id bigint, data string COMMENT 'hello') USING foo")
sql("DESCRIBE testcat.tbl data").show
```
```
+---------+----------+
|info_name|info_value|
+---------+----------+
| col_name| data|
|data_type| string|
| comment| hello|
+---------+----------+
```
Before this PR, the command would fail with: `Describing columns is not supported for v2 tables.`
### How was this patch tested?
Added new test.
Closes#30881 from imback82/describe_col_v2.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This pr fix some operator missing rowCount when enable CBO, e.g.:
```scala
spark.range(1000).selectExpr("id as a", "id as b").write.saveAsTable("t1")
spark.sql("ANALYZE TABLE t1 COMPUTE STATISTICS FOR ALL COLUMNS")
spark.sql("set spark.sql.cbo.enabled=true")
spark.sql("set spark.sql.cbo.planStats.enabled=true")
spark.sql("select * from (select * from t1 distribute by a limit 100) distribute by b").explain("cost")
```
Before this pr:
```
== Optimized Logical Plan ==
RepartitionByExpression [b#2129L], Statistics(sizeInBytes=2.3 KiB)
+- GlobalLimit 100, Statistics(sizeInBytes=2.3 KiB, rowCount=100)
+- LocalLimit 100, Statistics(sizeInBytes=23.4 KiB)
+- RepartitionByExpression [a#2128L], Statistics(sizeInBytes=23.4 KiB)
+- Relation[a#2128L,b#2129L] parquet, Statistics(sizeInBytes=23.4 KiB, rowCount=1.00E+3)
```
After this pr:
```
== Optimized Logical Plan ==
RepartitionByExpression [b#2129L], Statistics(sizeInBytes=2.3 KiB, rowCount=100)
+- GlobalLimit 100, Statistics(sizeInBytes=2.3 KiB, rowCount=100)
+- LocalLimit 100, Statistics(sizeInBytes=23.4 KiB, rowCount=1.00E+3)
+- RepartitionByExpression [a#2128L], Statistics(sizeInBytes=23.4 KiB, rowCount=1.00E+3)
+- Relation[a#2128L,b#2129L] parquet, Statistics(sizeInBytes=23.4 KiB, rowCount=1.00E+3)
```
### Why are the changes needed?
[`JoinEstimation.estimateInnerOuterJoin`](d6a68e0b67/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/JoinEstimation.scala (L55-L156)) need the row count.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#30987 from wangyum/SPARK-33954.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The error messages for specifying filter and distinct for the aggregate function are mixed together and should be separated. This can increase readability and ease of use.
### Why are the changes needed?
increase readability and ease of use.
### Does this PR introduce _any_ user-facing change?
'Yes'.
### How was this patch tested?
Jenkins test
Closes#30982 from beliefer/SPARK-33951.
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 patch proposes to add latest offset to source progress for streaming queries.
### Why are the changes needed?
Currently we record start and end offsets per source in streaming process. Latest offset is an important information for streaming process but the progress lacks of this info. We can use it to track the process lag and adjust streaming queries. We should add latest offset to source progress.
### Does this PR introduce _any_ user-facing change?
Yes, for new metric about latest source offset in source progress.
### How was this patch tested?
Unit test. Manually test in Spark cluster:
```
"description" : "KafkaV2[Subscribe[page_view_events]]",
"startOffset" : {
"page_view_events" : {
"2" : 582370921,
"4" : 391910836,
"1" : 631009201,
"3" : 406601346,
"0" : 195799112
}
},
"endOffset" : {
"page_view_events" : {
"2" : 583764414,
"4" : 392338002,
"1" : 632183480,
"3" : 407101489,
"0" : 197304028
}
},
"latestOffset" : {
"page_view_events" : {
"2" : 589852545,
"4" : 394204277,
"1" : 637313869,
"3" : 409286602,
"0" : 203878962
}
},
"numInputRows" : 4999997,
"inputRowsPerSecond" : 29287.70501405811,
```
Closes#30988 from viirya/latest-offset.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Skip table stats in canonicalizing of `HiveTableRelation`.
### Why are the changes needed?
The changes fix a regression comparing to Spark 3.0, see SPARK-33963.
### Does this PR introduce _any_ user-facing change?
Yes. After changes Spark behaves as in the version 3.0.1.
### How was this patch tested?
By running new UT:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *CachedTableSuite"
```
Closes#30995 from MaxGekk/fix-caching-hive-table.
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 improve the statistics estimation of the `Tail`:
```scala
spark.sql("set spark.sql.cbo.enabled=true")
spark.range(100).selectExpr("id as a", "id as b", "id as c", "id as e").write.saveAsTable("t1")
println(Tail(Literal(5), spark.sql("SELECT * FROM t1").queryExecution.logical).queryExecution.stringWithStats)
```
Before this pr:
```
== Optimized Logical Plan ==
Tail 5, Statistics(sizeInBytes=3.8 KiB)
+- Relation[a#24L,b#25L,c#26L,e#27L] parquet, Statistics(sizeInBytes=3.8 KiB)
```
After this pr:
```
== Optimized Logical Plan ==
Tail 5, Statistics(sizeInBytes=200.0 B, rowCount=5)
+- Relation[a#24L,b#25L,c#26L,e#27L] parquet, Statistics(sizeInBytes=3.8 KiB)
```
### Why are the changes needed?
Import statistics estimation.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#30991 from wangyum/SPARK-33959.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This pr add rowCount for `Range` operator:
```scala
spark.sql("set spark.sql.cbo.enabled=true")
spark.sql("select id from range(100)").explain("cost")
```
Before this pr:
```
== Optimized Logical Plan ==
Range (0, 100, step=1, splits=None), Statistics(sizeInBytes=800.0 B)
```
After this pr:
```
== Optimized Logical Plan ==
Range (0, 100, step=1, splits=None), Statistics(sizeInBytes=800.0 B, rowCount=100)
```
### Why are the changes needed?
[`JoinEstimation.estimateInnerOuterJoin`](d6a68e0b67/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/JoinEstimation.scala (L55-L156)) need the row count.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#30989 from wangyum/SPARK-33956.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
As a follow-up task to SPARK-32958, this patch takes safer approach to only prune columns from JsonToStructs if the parsing option is empty. It is to avoid unexpected behavior change regarding parsing.
This patch also adds a few e2e tests to make sure failfast parsing behavior is not changed.
### Why are the changes needed?
It is to avoid unexpected behavior change regarding parsing.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Closes#30970 from viirya/SPARK-33907-3.2.
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 the `saveAsTable()` and `insertInto()` methods of `DataFrameWriter`, recognize `spark_catalog` as the default session catalog in table names.
### Why are the changes needed?
1. To simplify writing of unified v1 and v2 tests
2. To improve Spark SQL user experience. `insertInto()` should have feature parity with the `INSERT INTO` sql command. Currently, `insertInto()` fails on a table from a namespace in `spark_catalog`:
```scala
scala> sql("CREATE NAMESPACE spark_catalog.ns")
scala> Seq(0).toDF().write.saveAsTable("spark_catalog.ns.tbl")
org.apache.spark.sql.AnalysisException: Couldn't find a catalog to handle the identifier spark_catalog.ns.tbl.
at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:629)
... 47 elided
scala> Seq(0).toDF().write.insertInto("spark_catalog.ns.tbl")
org.apache.spark.sql.AnalysisException: Couldn't find a catalog to handle the identifier spark_catalog.ns.tbl.
at org.apache.spark.sql.DataFrameWriter.insertInto(DataFrameWriter.scala:498)
... 47 elided
```
but `INSERT INTO` succeed:
```sql
spark-sql> create table spark_catalog.ns.tbl (c int);
spark-sql> insert into spark_catalog.ns.tbl select 0;
spark-sql> select * from spark_catalog.ns.tbl;
0
```
### Does this PR introduce _any_ user-facing change?
Yes. After the changes for the example above:
```scala
scala> Seq(0).toDF().write.saveAsTable("spark_catalog.ns.tbl")
scala> Seq(1).toDF().write.insertInto("spark_catalog.ns.tbl")
scala> spark.table("spark_catalog.ns.tbl").show(false)
+-----+
|value|
+-----+
|0 |
|1 |
+-----+
```
### How was this patch tested?
By running the affected test suites:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *.ShowPartitionsSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *.FileFormatWriterSuite"
```
Closes#30919 from MaxGekk/insert-into-spark_catalog.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The current implement of trim/trimleft/trimright have somewhat redundant.
### Why are the changes needed?
Improve the implement of trim/trimleft/trimright
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test
Closes#30905 from beliefer/SPARK-33890.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Co-authored-by: Wenchen Fan <cloud0fan@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add the `since` tag to methods and interfaces added recently.
### Why are the changes needed?
1. To follow the existing convention for Spark API.
2. To inform devs when Spark API was changed.
### Does this PR introduce _any_ user-facing change?
Should not.
### How was this patch tested?
`dev/scalastyle`
Closes#30966 from MaxGekk/spark-23889-interfaces-followup.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This pr fix remove the `CaseWhen` if elseValue is empty and other outputs are null because of we should consider deterministic.
### Why are the changes needed?
Fix bug.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#30960 from wangyum/SPARK-33847-2.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add the version 3.2.0 to new method `renamePartition()` in the `SupportsPartitionManagement` interface.
### Why are the changes needed?
To inform Spark devs when the method appears in the interface.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
`./dev/scalastyle`
Closes#30964 from MaxGekk/alter-table-rename-partition-v2-followup.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Introduce allowList push into (if / case) branches to fix potential bug.
### Why are the changes needed?
Fix potential bug.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing test.
Closes#30955 from wangyum/SPARK-33848-2.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Move seed is legal check to `CheckAnalysis`.
### Why are the changes needed?
It's better to check seed expression is legal at analyzer side instead of execution, and user can get exception as soon as possible.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add test.
Closes#30923 from ulysses-you/SPARK-33909.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Add `renamePartition()` to the `SupportsPartitionManagement`
2. Implement `renamePartition()` in `InMemoryPartitionTable`
3. Add v2 execution node `AlterTableRenamePartitionExec`
4. Resolve the logical node `AlterTableRenamePartition` to `AlterTableRenamePartitionExec` for v2 tables that support `SupportsPartitionManagement`
5. Move v1 tests to the base suite `org.apache.spark.sql.execution.command.AlterTableRenamePartitionSuiteBase` to run them for v2 table catalogs.
### Why are the changes needed?
To have feature parity with Datasource V1.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By running the unified tests:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableRenamePartitionSuite"
```
Closes#30935 from MaxGekk/alter-table-rename-partition-v2.
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 patch proposes to do column pruning for CsvToStructs expression if we only require some fields from it.
### Why are the changes needed?
`CsvToStructs` takes a schema parameter used to tell CSV Parser what fields are needed to parse. If `CsvToStructs` is followed by GetStructField. We can prune the schema to only parse certain field.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test
Closes#30912 from viirya/SPARK-32968.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This pr simplify `CaseWhen`clauses with (true and false) and (false and true):
Expression | cond.nullable | After simplify
-- | -- | --
case when cond then true else false end | true | cond <=> true
case when cond then true else false end | false | cond
case when cond then false else true end | true | !(cond <=> true)
case when cond then false else true end | false | !cond
### 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#30898 from wangyum/SPARK-33884.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
For `InMemoryPartitionTable` used in tests, set empty partition metadata only when a partition doesn't exists.
### Why are the changes needed?
This bug fix is needed to use `INSERT INTO .. PARTITION` in other tests.
### Does this PR introduce _any_ user-facing change?
No. It affects only the v2 table catalog used in tests.
### How was this patch tested?
Added new UT to `DataSourceV2SQLSuite`, and run the affected test suite by:
```
$ build/sbt -Phive -Phive-thriftserver "test:testOnly org.apache.spark.sql.connector.DataSourceV2SQLSuite"
```
Closes#30952 from MaxGekk/fix-insert-into-partition-v2.
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 is a followup of https://github.com/apache/spark/pull/30849, to fix a correctness issue caused by null value handling.
### Why are the changes needed?
Fix a correctness issue. `If(null, true, false)` should return false, not true.
### Does this PR introduce _any_ user-facing change?
Yes, but the bug only exist in the master branch.
### How was this patch tested?
updated tests.
Closes#30953 from cloud-fan/bug.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
After CTAS / CREATE TABLE LIKE / CVAS/ alter table add columns, the target tables will display string instead of char/varchar
### Why are the changes needed?
bugfix
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
new tests
Closes#30918 from yaooqinn/SPARK-33901.
Lead-authored-by: Kent Yao <yao@apache.org>
Co-authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
There are total 15 compilation warnings about `Unicode escapes in triple quoted strings are deprecated` in Spark code now:
```
[WARNING] /spark-source/core/src/main/scala/org/apache/spark/util/Utils.scala:2930: Unicode escapes in triple quoted strings are deprecated, use the literal character instead
[WARNING] /spark-source/core/src/main/scala/org/apache/spark/util/Utils.scala:2931: Unicode escapes in triple quoted strings are deprecated, use the literal character instead
[WARNING] /spark-source/core/src/main/scala/org/apache/spark/util/Utils.scala:2932: Unicode escapes in triple quoted strings are deprecated, use the literal character instead
[WARNING] /spark-source/core/src/main/scala/org/apache/spark/util/Utils.scala:2933: Unicode escapes in triple quoted strings are deprecated, use the literal character instead
[WARNING] /spark-source/core/src/main/scala/org/apache/spark/util/Utils.scala:2934: Unicode escapes in triple quoted strings are deprecated, use the literal character instead
[WARNING] /spark-source/core/src/main/scala/org/apache/spark/util/Utils.scala:2935: Unicode escapes in triple quoted strings are deprecated, use the literal character instead
[WARNING] /spark-source/core/src/main/scala/org/apache/spark/util/Utils.scala:2936: Unicode escapes in triple quoted strings are deprecated, use the literal character instead
[WARNING] /spark-source/core/src/main/scala/org/apache/spark/util/Utils.scala:2937: Unicode escapes in triple quoted strings are deprecated, use the literal character instead
[WARNING] /spark-source/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/CSVExprUtils.scala:82: Unicode escapes in triple quoted strings are deprecated, use the literal character instead
[WARNING] /spark-source/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/csv/CSVExprUtilsSuite.scala:32: Unicode escapes in triple quoted strings are deprecated, use the literal character instead
[WARNING] /spark-source/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/csv/CSVExprUtilsSuite.scala:79: Unicode escapes in triple quoted strings are deprecated, use the literal character instead
[WARNING] /spark-source/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/parser/ParserUtilsSuite.scala:97: Unicode escapes in triple quoted strings are deprecated, use the literal character instead
[WARNING] /spark-source/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/parser/ParserUtilsSuite.scala:101: Unicode escapes in triple quoted strings are deprecated, use the literal character instead
[WARNING] /spark-source/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonParsingOptionsSuite.scala:76: Unicode escapes in triple quoted strings are deprecated, use the literal character instead
[WARNING] /spark-source/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonParsingOptionsSuite.scala:83: Unicode escapes in triple quoted strings are deprecated, use the literal character instead
```
This pr try to fix these warnnings.
### Why are the changes needed?
Cleanup compilation warnings about `Unicode escapes in triple quoted strings are deprecated`
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or GitHub Action
Closes#30926 from LuciferYang/SPARK-33801.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Currently, there are many DDL commands where the position of the unresolved identifiers are incorrect:
```
scala> sql("DROP VIEW unknown")
org.apache.spark.sql.AnalysisException: View not found: unknown; line 1 pos 0;
```
, whereas the `pos` should be `10`.
This PR proposes to fix this issue for commands using `UnresolvedTable`:
```
DROP VIEW v
ALTER VIEW v SET TBLPROPERTIES ('k'='v')
ALTER VIEW v UNSET TBLPROPERTIES ('k')
ALTER VIEW v AS SELECT 1
```
### Why are the changes needed?
To fix a bug.
### Does this PR introduce _any_ user-facing change?
Yes, now the above example will print the following:
```
org.apache.spark.sql.AnalysisException: View not found: unknown; line 1 pos 10;
```
### How was this patch tested?
Add a new suite of tests.
Closes#30936 from imback82/position_view_fix.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
[The PySpark documentation](https://spark.apache.org/docs/3.0.1/api/python/pyspark.sql.html#pyspark.sql.DataFrame.join) says "Must be one of: inner, cross, outer, full, fullouter, full_outer, left, leftouter, left_outer, right, rightouter, right_outer, semi, leftsemi, left_semi, anti, leftanti and left_anti."
However, I get the following error when I set the cross option.
```
scala> val df1 = spark.createDataFrame(Seq((1,"a"),(2,"b")))
df1: org.apache.spark.sql.DataFrame = [_1: int, _2: string]
scala> val df2 = spark.createDataFrame(Seq((1,"A"),(2,"B"), (3, "C")))
df2: org.apache.spark.sql.DataFrame = [_1: int, _2: string]
scala> df1.join(right = df2, usingColumns = Seq("_1"), joinType = "cross").show()
java.lang.IllegalArgumentException: requirement failed: Unsupported using join type Cross
at scala.Predef$.require(Predef.scala:281)
at org.apache.spark.sql.catalyst.plans.UsingJoin.<init>(joinTypes.scala:106)
at org.apache.spark.sql.Dataset.join(Dataset.scala:1025)
... 53 elided
```
### Why are the changes needed?
The documentation says cross option can be set, but when I try to set it, I get an java.lang.IllegalArgumentException.
### Does this PR introduce _any_ user-facing change?
Accepting this PR fix will behave the same as the documentation.
### How was this patch tested?
There is already a test for [JoinTypes](1b9fd67904/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/JoinTypesTest.scala), but I can't find a test for the join option itself.
Closes#30803 from kozakana/allow_cross_option.
Authored-by: kozakana <goki727@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Unify the seed of random functions
1. Add a hold place expression `UnresolvedSeed ` as the defualt seed.
2. Change `Rand`,`Randn`,`Uuid`,`Shuffle` default seed to `UnresolvedSeed `.
3. Replace `UnresolvedSeed ` to real seed at `ResolveRandomSeed` rule.
### Why are the changes needed?
`Uuid` and `Shuffle` use the `ResolveRandomSeed` rule to set the seed if user doesn't give a seed value. `Rand` and `Randn` do this at constructing.
It's better to unify the default seed at Analyzer side since we have used `ExpressionWithRandomSeed` at streaming query.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass exists test and add test.
Closes#30864 from ulysses-you/SPARK-33857.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This pr simplify conditional in predicate, after this change we can push down the filter to datasource:
Expression | After simplify
-- | --
IF(cond, trueVal, false) | AND(cond, trueVal)
IF(cond, trueVal, true) | OR(NOT(cond), trueVal)
IF(cond, false, falseVal) | AND(NOT(cond), elseVal)
IF(cond, true, falseVal) | OR(cond, elseVal)
CASE WHEN cond THEN trueVal ELSE false END | AND(cond, trueVal)
CASE WHEN cond THEN trueVal END | AND(cond, trueVal)
CASE WHEN cond THEN trueVal ELSE null END | AND(cond, trueVal)
CASE WHEN cond THEN trueVal ELSE true END | OR(NOT(cond), trueVal)
CASE WHEN cond THEN false ELSE elseVal END | AND(NOT(cond), elseVal)
CASE WHEN cond THEN false END | false
CASE WHEN cond THEN true ELSE elseVal END | OR(cond, elseVal)
CASE WHEN cond THEN true END | cond
### 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#30865 from wangyum/SPARK-33861.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, there are many DDL commands where the position of the unresolved identifiers are incorrect:
```
scala> sql("MSCK REPAIR TABLE unknown")
org.apache.spark.sql.AnalysisException: Table not found: unknown; line 1 pos 0;
```
, whereas the `pos` should be 18.
This PR proposes to fix this issue for commands using `UnresolvedTable`:
```
MSCK REPAIR TABLE t
LOAD DATA LOCAL INPATH 'filepath' INTO TABLE t
TRUNCATE TABLE t
SHOW PARTITIONS t
ALTER TABLE t RECOVER PARTITIONS
ALTER TABLE t ADD PARTITION (p=1)
ALTER TABLE t PARTITION (p=1) RENAME TO PARTITION (p=2)
ALTER TABLE t DROP PARTITION (p=1)
ALTER TABLE t SET SERDEPROPERTIES ('a'='b')
COMMENT ON TABLE t IS 'hello'"
```
### Why are the changes needed?
To fix a bug.
### Does this PR introduce _any_ user-facing change?
Yes, now the above example will print the following:
```
org.apache.spark.sql.AnalysisException: Table not found: unknown; line 1 pos 18;
```
### How was this patch tested?
Add a new suite of tests.
Closes#30900 from imback82/position_Fix.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Enhance `ReplaceNullWithFalseInPredicate` to replace None of elseValue inside `CaseWhen` with `FalseLiteral` if all branches are `FalseLiteral` . The use case is:
```sql
create table t1 using parquet as select id from range(10);
explain select id from t1 where (CASE WHEN id = 1 THEN 'a' WHEN id = 3 THEN 'b' end) = 'c';
```
Before this pr:
```
== Physical Plan ==
*(1) Filter CASE WHEN (id#1L = 1) THEN false WHEN (id#1L = 3) THEN false END
+- *(1) ColumnarToRow
+- FileScan parquet default.t1[id#1L] Batched: true, DataFilters: [CASE WHEN (id#1L = 1) THEN false WHEN (id#1L = 3) THEN false END], Format: Parquet, Location: InMemoryFileIndex[file:/Users/yumwang/opensource/spark/spark-warehouse/org.apache.spark.sql.DataF..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:bigint>
```
After this pr:
```
== Physical Plan ==
LocalTableScan <empty>, [id#1L]
```
2. Enhance `SimplifyConditionals` if elseValue is None and all outputs are null.
### 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#30852 from wangyum/SPARK-33847.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Move the `ALTER TABLE .. RENAME PARTITION` parsing tests to `AlterTableRenamePartitionParserSuite`
2. Place the v1 tests for `ALTER TABLE .. RENAME PARTITION` from `DDLSuite` to `v1.AlterTableRenamePartitionSuite` and v2 tests from `AlterTablePartitionV2SQLSuite` to `v2.AlterTableRenamePartitionSuite`, so, the tests will run for V1, Hive V1 and V2 DS.
### Why are the changes needed?
- The unification will allow to run common `ALTER TABLE .. RENAME PARTITION` 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 *AlterTableRenamePartitionParserSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableRenamePartitionSuite"
```
Closes#30863 from MaxGekk/unify-rename-partition-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 aims to override maxRows method in these follow `LogicalPlan`:
* `ReturnAnswer`
* `Join`
* `Range`
* `Sample`
* `RepartitionOperation`
* `Deduplicate`
* `LocalRelation`
* `Window`
### Why are the changes needed?
1. Logically, we know the max rows info with these `LogicalPlan`.
2. Before this PR, we already have some max rows with `LogicalPlan`, so we can eliminate limit with more case if we expand more.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add test.
Closes#30443 from ulysses-you/SPARK-33497.
Lead-authored-by: ulysses-you <youxiduo@weidian.com>
Co-authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Add new methods `purgePartition()`/`purgePartitions()` to the interfaces `SupportsPartitionManagement`/`SupportsAtomicPartitionManagement`.
2. Default implementation of new methods throw the exception `UnsupportedOperationException`.
3. Add tests for new methods to `SupportsPartitionManagementSuite`/`SupportsAtomicPartitionManagementSuite`.
4. Add `ALTER TABLE .. DROP PARTITION` tests for DS v1 and v2.
Closes#30776Closes#30821
### Why are the changes needed?
Currently, the `PURGE` option that user can set in `ALTER TABLE .. DROP PARTITION` is completely ignored. We should pass this flag to the catalog implementation, so, the catalog should decide how to handle the flag.
### Does this PR introduce _any_ user-facing change?
The changes can impact on behavior of `ALTER TABLE .. DROP PARTITION` for v2 tables.
### How was this patch tested?
By running the affected test suites, for instance:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableDropPartitionSuite"
```
Closes#30886 from MaxGekk/purge-partition.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
```sql
spark-sql> select * from t10 where c0='abcd';
20/12/22 15:43:38 ERROR SparkSQLDriver: Failed in [select * from t10 where c0='abcd']
scala.MatchError: CharType(10) (of class org.apache.spark.sql.types.CharType)
at org.apache.spark.sql.catalyst.expressions.CastBase.cast(Cast.scala:815)
at org.apache.spark.sql.catalyst.expressions.CastBase.cast$lzycompute(Cast.scala:842)
at org.apache.spark.sql.catalyst.expressions.CastBase.cast(Cast.scala:842)
at org.apache.spark.sql.catalyst.expressions.CastBase.nullSafeEval(Cast.scala:844)
at org.apache.spark.sql.catalyst.expressions.UnaryExpression.eval(Expression.scala:476)
at org.apache.spark.sql.catalyst.catalog.CatalogTablePartition.$anonfun$toRow$2(interface.scala:164)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
at scala.collection.Iterator.foreach(Iterator.scala:941)
at scala.collection.Iterator.foreach$(Iterator.scala:941)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
at scala.collection.IterableLike.foreach(IterableLike.scala:74)
at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
at org.apache.spark.sql.types.StructType.foreach(StructType.scala:102)
at scala.collection.TraversableLike.map(TraversableLike.scala:238)
at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
at org.apache.spark.sql.types.StructType.map(StructType.scala:102)
at org.apache.spark.sql.catalyst.catalog.CatalogTablePartition.toRow(interface.scala:158)
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogUtils$.$anonfun$prunePartitionsByFilter$3(ExternalCatalogUtils.scala:157)
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogUtils$.$anonfun$prunePartitionsByFilter$3$adapted(ExternalCatalogUtils.scala:156)
```
c0 is a partition column, it fails in the partition pruning rule
In this PR, we relace char/varchar w/ string type before the CAST happends
### Why are the changes needed?
bugfix, see the case above
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
yes, new tests
Closes#30887 from yaooqinn/SPARK-33879.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Make test stable and fix docs.
### Why are the changes needed?
Query timeout sometime since we set an another config after set query timeout.
```
sbt.ForkMain$ForkError: java.sql.SQLTimeoutException: Query timed out after 0 seconds
at org.apache.hive.jdbc.HiveStatement.waitForOperationToComplete(HiveStatement.java:381)
at org.apache.hive.jdbc.HiveStatement.execute(HiveStatement.java:254)
at org.apache.spark.sql.hive.thriftserver.ThriftServerWithSparkContextSuite.$anonfun$$init$$13(ThriftServerWithSparkContextSuite.scala:107)
at org.apache.spark.sql.hive.thriftserver.ThriftServerWithSparkContextSuite.$anonfun$$init$$13$adapted(ThriftServerWithSparkContextSuite.scala:106)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.sql.hive.thriftserver.ThriftServerWithSparkContextSuite.$anonfun$$init$$12(ThriftServerWithSparkContextSuite.scala:106)
at org.apache.spark.sql.hive.thriftserver.ThriftServerWithSparkContextSuite.$anonfun$$init$$12$adapted(ThriftServerWithSparkContextSuite.scala:89)
at org.apache.spark.sql.hive.thriftserver.SharedThriftServer.$anonfun$withJdbcStatement$4(SharedThriftServer.scala:95)
at org.apache.spark.sql.hive.thriftserver.SharedThriftServer.$anonfun$withJdbcStatement$4$adapted(SharedThriftServer.scala:95)
```
The reason is:
1. we execute `set spark.sql.thriftServer.queryTimeout = 1`, then all the option will be limited in 1s.
2. we execute `set spark.sql.thriftServer.interruptOnCancel = false/true`. This sql will get timeout exception if there is something hung within 1s. It's not our expected.
Reset the timeout before we do the step2 can avoid this problem.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Fix test.
Closes#30897 from ulysses-you/SPARK-33526-followup.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/30267
Inspired by https://github.com/apache/spark/pull/30886, it's better to have 2 methods `def dropTable` and `def purgeTable`, than `def dropTable(ident)` and `def dropTable(ident, purge)`.
### Why are the changes needed?
1. make the APIs orthogonal. Previously, `def dropTable(ident, purge)` calls `def dropTable(ident)` and is a superset.
2. simplifies the catalog implementation a little bit. Now the `if (purge) ... else ...` check is done at the Spark side.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
existing tests
Closes#30890 from cloud-fan/purgeTable.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add support for Java Enums (`java.lang.Enum`) from the Scala typed Dataset APIs. This involves adding an implicit for `Encoder` creation in `SQLImplicits`, and updating `ScalaReflection` to handle Java Enums on the serialization and deserialization pathways.
Enums are mapped to a `StringType` which is just the name of the Enum value.
### Why are the changes needed?
In [SPARK-21255](https://issues.apache.org/jira/browse/SPARK-21255), support for (de)serialization of Java Enums was added, but only when called from Java code. It is common for Scala code to rely on Java libraries that are out of control of the Scala developer. Today, if there is a dependency on some Java code which defines an Enum, it would be necessary to define a corresponding Scala class. This change brings closer feature parity between Scala and Java APIs.
### Does this PR introduce _any_ user-facing change?
Yes, previously something like:
```
val ds = Seq(MyJavaEnum.VALUE1, MyJavaEnum.VALUE2).toDS
// or
val ds = Seq(CaseClass(MyJavaEnum.VALUE1), CaseClass(MyJavaEnum.VALUE2)).toDS
```
would fail. Now, it will succeed.
### How was this patch tested?
Additional unit tests are added in `DatasetSuite`. Tests include validating top-level enums, enums inside of case classes, enums inside of arrays, and validating that the Enum is stored as the expected string.
Closes#30877 from xkrogen/xkrogen-SPARK-23862-scalareflection-java-enums.
Lead-authored-by: Erik Krogen <xkrogen@apache.org>
Co-authored-by: Fangshi Li <fli@linkedin.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR adds the length check to the existing ApplyCharPadding rule. Tables will have external locations when users execute
SET LOCATION or CREATE TABLE ... LOCATION. If the location contains over length values we should FAIL ON READ.
### Why are the changes needed?
```sql
spark-sql> INSERT INTO t2 VALUES ('1', 'b12345');
Time taken: 0.141 seconds
spark-sql> alter table t set location '/tmp/hive_one/t2';
Time taken: 0.095 seconds
spark-sql> select * from t;
1 b1234
```
the above case should fail rather than implicitly applying truncation
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
new tests
Closes#30882 from yaooqinn/SPARK-33876.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
```scala
val nestedStruct = new StructType()
.add(StructField("b", StringType).withComment("Nested comment"))
val struct = new StructType()
.add(StructField("a", nestedStruct).withComment("comment"))
struct.toDDL
```
Currently, returns:
```
`a` STRUCT<`b`: STRING> COMMENT 'comment'`
```
With this PR, the code above returns:
```
`a` STRUCT<`b`: STRING COMMENT 'Nested comment'> COMMENT 'comment'`
```
### Why are the changes needed?
My team is using nested columns as first citizens, and I thought it would be nice to have comments for nested columns.
### Does this PR introduce _any_ user-facing change?
Now, when users call something like this,
```scala
spark.table("foo.bar").schema.fields.map(_.toDDL).mkString(", ")
```
they will get comments for the nested columns.
### How was this patch tested?
I added unit tests under `org.apache.spark.sql.types.StructTypeSuite`. They test if nested StructType's comment is included in the DDL string.
Closes#30851 from jacobhjkim/structtype-toddl.
Authored-by: Jacob Kim <me@jacobkim.io>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR tries to rename `dataSourceRewriteRules` into something more generic.
### Why are the changes needed?
These changes are needed to address the post-review discussion [here](https://github.com/apache/spark/pull/30558#discussion_r533885837).
### Does this PR introduce _any_ user-facing change?
Yes but the changes haven't been released yet.
### How was this patch tested?
Existing tests.
Closes#30808 from aokolnychyi/spark-33784.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds logic to build logical writes introduced in SPARK-33779.
Note: This PR contains a subset of changes discussed in PR #29066.
### Why are the changes needed?
These changes are the next step as discussed in the [design doc](https://docs.google.com/document/d/1X0NsQSryvNmXBY9kcvfINeYyKC-AahZarUqg3nS1GQs/edit#) for SPARK-23889.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#30806 from aokolnychyi/spark-33808.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add some case to match Array whose element type is primitive.
### Why are the changes needed?
We will get exception when use `Literal.create(Array(1, 2, 3), ArrayType(IntegerType))` .
```
Exception in thread "main" java.lang.IllegalArgumentException: requirement failed: Literal must have a corresponding value to array<int>, but class int[] found.
at scala.Predef$.require(Predef.scala:281)
at org.apache.spark.sql.catalyst.expressions.Literal$.validateLiteralValue(literals.scala:215)
at org.apache.spark.sql.catalyst.expressions.Literal.<init>(literals.scala:292)
at org.apache.spark.sql.catalyst.expressions.Literal$.create(literals.scala:140)
```
And same problem with other array whose element is primitive.
### Does this PR introduce _any_ user-facing change?
Yes.
### How was this patch tested?
Add test.
Closes#30868 from ulysses-you/SPARK-33860.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Verify ALTER TABLE CHANGE COLUMN with Char and Varchar and avoid unexpected change
For v1 table, changing type is not allowed, we fix a regression that uses the replaced string instead of the original char/varchar type when altering char/varchar columns
For v2 table,
char/varchar to string,
char(x) to char(x),
char(x)/varchar(x) to varchar(y) if x <=y are valid cases,
other changes are invalid
### Why are the changes needed?
Verify ALTER TABLE CHANGE COLUMN with Char and Varchar and avoid unexpected change
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
new test
Closes#30833 from yaooqinn/SPARK-33834.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
* Implement `SparkScriptTransformationExec` based on `BaseScriptTransformationExec`
* Implement `SparkScriptTransformationWriterThread` based on `BaseScriptTransformationWriterThread` of writing data
* Add rule `SparkScripts` to support convert script LogicalPlan to SparkPlan in Spark SQL (without hive mode)
* Add `SparkScriptTransformationSuite` test spark spec case
* add test in `SQLQueryTestSuite`
And we will close#29085 .
### Why are the changes needed?
Support user use Script Transform without Hive
### Does this PR introduce _any_ user-facing change?
User can use Script Transformation without hive in no serde mode.
Such as :
**default no serde **
```
SELECT TRANSFORM(a, b, c)
USING 'cat' AS (a int, b string, c long)
FROM testData
```
**no serde with spec ROW FORMAT DELIMITED**
```
SELECT TRANSFORM(a, b, c)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
COLLECTION ITEMS TERMINATED BY '\u0002'
MAP KEYS TERMINATED BY '\u0003'
LINES TERMINATED BY '\n'
NULL DEFINED AS 'null'
USING 'cat' AS (a, b, c)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
COLLECTION ITEMS TERMINATED BY '\u0004'
MAP KEYS TERMINATED BY '\u0005'
LINES TERMINATED BY '\n'
NULL DEFINED AS 'NULL'
FROM testData
```
### How was this patch tested?
Added UT
Closes#29414 from AngersZhuuuu/SPARK-32106-MINOR.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This pr push the `UnaryExpression` into (if / case) branches. The use case is:
```sql
create table t1 using parquet as select id from range(10);
explain select id from t1 where (CASE WHEN id = 1 THEN '1' WHEN id = 3 THEN '2' end) > 3;
```
Before this pr:
```
== Physical Plan ==
*(1) Filter (cast(CASE WHEN (id#1L = 1) THEN 1 WHEN (id#1L = 3) THEN 2 END as int) > 3)
+- *(1) ColumnarToRow
+- FileScan parquet default.t1[id#1L] Batched: true, DataFilters: [(cast(CASE WHEN (id#1L = 1) THEN 1 WHEN (id#1L = 3) THEN 2 END as int) > 3)], Format: Parquet, Location: InMemoryFileIndex[file:/Users/yumwang/opensource/spark/spark-warehouse/org.apache.spark.sql.DataF..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:bigint>
```
After this pr:
```
== Physical Plan ==
LocalTableScan <empty>, [id#1L]
```
This change can also improve this case:
a78d6ce376/sql/core/src/test/resources/tpcds/q62.sql (L5-L22)
### 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#30853 from wangyum/SPARK-33848.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add comments for the `PURGE` option to the logical nodes `DropTable` and `AlterTableDropPartition`.
### Why are the changes needed?
To improve code maintenance.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running `./dev/scalastyle`
Closes#30837 from MaxGekk/comment-purge-logical-node.
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 fill missing group tags and re-categorize all the group tags for built-in functions.
New groups below are added in this PR:
- binary_funcs
- bitwise_funcs
- collection_funcs
- predicate_funcs
- conditional_funcs
- conversion_funcs
- csv_funcs
- generator_funcs
- hash_funcs
- lambda_funcs
- math_funcs
- misc_funcs
- string_funcs
- struct_funcs
- xml_funcs
A basic policy to re-categorize functions is that functions in the same file are categorized into the same group. For example, all the functions in `hash.scala` are categorized into `hash_funcs`. But, there are some exceptional/ambiguous cases when categorizing them. Here are some special notes:
- All the aggregate functions are categorized into `agg_funcs`.
- `array_funcs` and `map_funcs` are sub-groups of `collection_funcs`. For example, `array_contains` is used only for arrays, so it is assigned to `array_funcs`. On the other hand, `reverse` is used for both arrays and strings, so it is assigned to `collection_funcs`.
- Some functions logically belong to multiple groups. In this case, these functions are categorized based on the file that they belong to. For example, `schema_of_csv` can be grouped into both `csv_funcs` and `struct_funcs` in terms of input types, but it is assigned to `csv_funcs` because it belongs to the `csvExpressions.scala` file that holds the other CSV-related functions.
- Functions in `nullExpressions.scala`, `complexTypeCreator.scala`, `randomExpressions.scala`, and `regexExpressions.scala` are categorized based on their functionalities. For example:
- `isnull` in `nullExpressions` is assigned to `predicate_funcs` because this is a predicate function.
- `array` in `complexTypeCreator.scala` is assigned to `array_funcs`based on its output type (The other functions in `array_funcs` are categorized based on their input types though).
A category list (after this PR) is as follows (the list below includes the exprs that already have a group tag in the current master):
|group|name|class|
|-----|----|-----|
|agg_funcs|any|org.apache.spark.sql.catalyst.expressions.aggregate.BoolOr|
|agg_funcs|approx_count_distinct|org.apache.spark.sql.catalyst.expressions.aggregate.HyperLogLogPlusPlus|
|agg_funcs|approx_percentile|org.apache.spark.sql.catalyst.expressions.aggregate.ApproximatePercentile|
|agg_funcs|avg|org.apache.spark.sql.catalyst.expressions.aggregate.Average|
|agg_funcs|bit_and|org.apache.spark.sql.catalyst.expressions.aggregate.BitAndAgg|
|agg_funcs|bit_or|org.apache.spark.sql.catalyst.expressions.aggregate.BitOrAgg|
|agg_funcs|bit_xor|org.apache.spark.sql.catalyst.expressions.aggregate.BitXorAgg|
|agg_funcs|bool_and|org.apache.spark.sql.catalyst.expressions.aggregate.BoolAnd|
|agg_funcs|bool_or|org.apache.spark.sql.catalyst.expressions.aggregate.BoolOr|
|agg_funcs|collect_list|org.apache.spark.sql.catalyst.expressions.aggregate.CollectList|
|agg_funcs|collect_set|org.apache.spark.sql.catalyst.expressions.aggregate.CollectSet|
|agg_funcs|corr|org.apache.spark.sql.catalyst.expressions.aggregate.Corr|
|agg_funcs|count_if|org.apache.spark.sql.catalyst.expressions.aggregate.CountIf|
|agg_funcs|count_min_sketch|org.apache.spark.sql.catalyst.expressions.aggregate.CountMinSketchAgg|
|agg_funcs|count|org.apache.spark.sql.catalyst.expressions.aggregate.Count|
|agg_funcs|covar_pop|org.apache.spark.sql.catalyst.expressions.aggregate.CovPopulation|
|agg_funcs|covar_samp|org.apache.spark.sql.catalyst.expressions.aggregate.CovSample|
|agg_funcs|cube|org.apache.spark.sql.catalyst.expressions.Cube|
|agg_funcs|every|org.apache.spark.sql.catalyst.expressions.aggregate.BoolAnd|
|agg_funcs|first_value|org.apache.spark.sql.catalyst.expressions.aggregate.First|
|agg_funcs|first|org.apache.spark.sql.catalyst.expressions.aggregate.First|
|agg_funcs|grouping_id|org.apache.spark.sql.catalyst.expressions.GroupingID|
|agg_funcs|grouping|org.apache.spark.sql.catalyst.expressions.Grouping|
|agg_funcs|kurtosis|org.apache.spark.sql.catalyst.expressions.aggregate.Kurtosis|
|agg_funcs|last_value|org.apache.spark.sql.catalyst.expressions.aggregate.Last|
|agg_funcs|last|org.apache.spark.sql.catalyst.expressions.aggregate.Last|
|agg_funcs|max_by|org.apache.spark.sql.catalyst.expressions.aggregate.MaxBy|
|agg_funcs|max|org.apache.spark.sql.catalyst.expressions.aggregate.Max|
|agg_funcs|mean|org.apache.spark.sql.catalyst.expressions.aggregate.Average|
|agg_funcs|min_by|org.apache.spark.sql.catalyst.expressions.aggregate.MinBy|
|agg_funcs|min|org.apache.spark.sql.catalyst.expressions.aggregate.Min|
|agg_funcs|percentile_approx|org.apache.spark.sql.catalyst.expressions.aggregate.ApproximatePercentile|
|agg_funcs|percentile|org.apache.spark.sql.catalyst.expressions.aggregate.Percentile|
|agg_funcs|rollup|org.apache.spark.sql.catalyst.expressions.Rollup|
|agg_funcs|skewness|org.apache.spark.sql.catalyst.expressions.aggregate.Skewness|
|agg_funcs|some|org.apache.spark.sql.catalyst.expressions.aggregate.BoolOr|
|agg_funcs|stddev_pop|org.apache.spark.sql.catalyst.expressions.aggregate.StddevPop|
|agg_funcs|stddev_samp|org.apache.spark.sql.catalyst.expressions.aggregate.StddevSamp|
|agg_funcs|stddev|org.apache.spark.sql.catalyst.expressions.aggregate.StddevSamp|
|agg_funcs|std|org.apache.spark.sql.catalyst.expressions.aggregate.StddevSamp|
|agg_funcs|sum|org.apache.spark.sql.catalyst.expressions.aggregate.Sum|
|agg_funcs|var_pop|org.apache.spark.sql.catalyst.expressions.aggregate.VariancePop|
|agg_funcs|var_samp|org.apache.spark.sql.catalyst.expressions.aggregate.VarianceSamp|
|agg_funcs|variance|org.apache.spark.sql.catalyst.expressions.aggregate.VarianceSamp|
|array_funcs|array_contains|org.apache.spark.sql.catalyst.expressions.ArrayContains|
|array_funcs|array_distinct|org.apache.spark.sql.catalyst.expressions.ArrayDistinct|
|array_funcs|array_except|org.apache.spark.sql.catalyst.expressions.ArrayExcept|
|array_funcs|array_intersect|org.apache.spark.sql.catalyst.expressions.ArrayIntersect|
|array_funcs|array_join|org.apache.spark.sql.catalyst.expressions.ArrayJoin|
|array_funcs|array_max|org.apache.spark.sql.catalyst.expressions.ArrayMax|
|array_funcs|array_min|org.apache.spark.sql.catalyst.expressions.ArrayMin|
|array_funcs|array_position|org.apache.spark.sql.catalyst.expressions.ArrayPosition|
|array_funcs|array_remove|org.apache.spark.sql.catalyst.expressions.ArrayRemove|
|array_funcs|array_repeat|org.apache.spark.sql.catalyst.expressions.ArrayRepeat|
|array_funcs|array_union|org.apache.spark.sql.catalyst.expressions.ArrayUnion|
|array_funcs|arrays_overlap|org.apache.spark.sql.catalyst.expressions.ArraysOverlap|
|array_funcs|arrays_zip|org.apache.spark.sql.catalyst.expressions.ArraysZip|
|array_funcs|array|org.apache.spark.sql.catalyst.expressions.CreateArray|
|array_funcs|flatten|org.apache.spark.sql.catalyst.expressions.Flatten|
|array_funcs|sequence|org.apache.spark.sql.catalyst.expressions.Sequence|
|array_funcs|shuffle|org.apache.spark.sql.catalyst.expressions.Shuffle|
|array_funcs|slice|org.apache.spark.sql.catalyst.expressions.Slice|
|array_funcs|sort_array|org.apache.spark.sql.catalyst.expressions.SortArray|
|bitwise_funcs|&|org.apache.spark.sql.catalyst.expressions.BitwiseAnd|
|bitwise_funcs|^|org.apache.spark.sql.catalyst.expressions.BitwiseXor|
|bitwise_funcs|bit_count|org.apache.spark.sql.catalyst.expressions.BitwiseCount|
|bitwise_funcs|shiftrightunsigned|org.apache.spark.sql.catalyst.expressions.ShiftRightUnsigned|
|bitwise_funcs|shiftright|org.apache.spark.sql.catalyst.expressions.ShiftRight|
|bitwise_funcs|~|org.apache.spark.sql.catalyst.expressions.BitwiseNot|
|collection_funcs|cardinality|org.apache.spark.sql.catalyst.expressions.Size|
|collection_funcs|concat|org.apache.spark.sql.catalyst.expressions.Concat|
|collection_funcs|reverse|org.apache.spark.sql.catalyst.expressions.Reverse|
|collection_funcs|size|org.apache.spark.sql.catalyst.expressions.Size|
|conditional_funcs|coalesce|org.apache.spark.sql.catalyst.expressions.Coalesce|
|conditional_funcs|ifnull|org.apache.spark.sql.catalyst.expressions.IfNull|
|conditional_funcs|if|org.apache.spark.sql.catalyst.expressions.If|
|conditional_funcs|nanvl|org.apache.spark.sql.catalyst.expressions.NaNvl|
|conditional_funcs|nullif|org.apache.spark.sql.catalyst.expressions.NullIf|
|conditional_funcs|nvl2|org.apache.spark.sql.catalyst.expressions.Nvl2|
|conditional_funcs|nvl|org.apache.spark.sql.catalyst.expressions.Nvl|
|conditional_funcs|when|org.apache.spark.sql.catalyst.expressions.CaseWhen|
|conversion_funcs|bigint|org.apache.spark.sql.catalyst.expressions.Cast|
|conversion_funcs|binary|org.apache.spark.sql.catalyst.expressions.Cast|
|conversion_funcs|boolean|org.apache.spark.sql.catalyst.expressions.Cast|
|conversion_funcs|cast|org.apache.spark.sql.catalyst.expressions.Cast|
|conversion_funcs|date|org.apache.spark.sql.catalyst.expressions.Cast|
|conversion_funcs|decimal|org.apache.spark.sql.catalyst.expressions.Cast|
|conversion_funcs|double|org.apache.spark.sql.catalyst.expressions.Cast|
|conversion_funcs|float|org.apache.spark.sql.catalyst.expressions.Cast|
|conversion_funcs|int|org.apache.spark.sql.catalyst.expressions.Cast|
|conversion_funcs|smallint|org.apache.spark.sql.catalyst.expressions.Cast|
|conversion_funcs|string|org.apache.spark.sql.catalyst.expressions.Cast|
|conversion_funcs|timestamp|org.apache.spark.sql.catalyst.expressions.Cast|
|conversion_funcs|tinyint|org.apache.spark.sql.catalyst.expressions.Cast|
|csv_funcs|from_csv|org.apache.spark.sql.catalyst.expressions.CsvToStructs|
|csv_funcs|schema_of_csv|org.apache.spark.sql.catalyst.expressions.SchemaOfCsv|
|csv_funcs|to_csv|org.apache.spark.sql.catalyst.expressions.StructsToCsv|
|datetime_funcs|add_months|org.apache.spark.sql.catalyst.expressions.AddMonths|
|datetime_funcs|current_date|org.apache.spark.sql.catalyst.expressions.CurrentDate|
|datetime_funcs|current_timestamp|org.apache.spark.sql.catalyst.expressions.CurrentTimestamp|
|datetime_funcs|current_timezone|org.apache.spark.sql.catalyst.expressions.CurrentTimeZone|
|datetime_funcs|date_add|org.apache.spark.sql.catalyst.expressions.DateAdd|
|datetime_funcs|date_format|org.apache.spark.sql.catalyst.expressions.DateFormatClass|
|datetime_funcs|date_from_unix_date|org.apache.spark.sql.catalyst.expressions.DateFromUnixDate|
|datetime_funcs|date_part|org.apache.spark.sql.catalyst.expressions.DatePart|
|datetime_funcs|date_sub|org.apache.spark.sql.catalyst.expressions.DateSub|
|datetime_funcs|date_trunc|org.apache.spark.sql.catalyst.expressions.TruncTimestamp|
|datetime_funcs|datediff|org.apache.spark.sql.catalyst.expressions.DateDiff|
|datetime_funcs|dayofmonth|org.apache.spark.sql.catalyst.expressions.DayOfMonth|
|datetime_funcs|dayofweek|org.apache.spark.sql.catalyst.expressions.DayOfWeek|
|datetime_funcs|dayofyear|org.apache.spark.sql.catalyst.expressions.DayOfYear|
|datetime_funcs|day|org.apache.spark.sql.catalyst.expressions.DayOfMonth|
|datetime_funcs|extract|org.apache.spark.sql.catalyst.expressions.Extract|
|datetime_funcs|from_unixtime|org.apache.spark.sql.catalyst.expressions.FromUnixTime|
|datetime_funcs|from_utc_timestamp|org.apache.spark.sql.catalyst.expressions.FromUTCTimestamp|
|datetime_funcs|hour|org.apache.spark.sql.catalyst.expressions.Hour|
|datetime_funcs|last_day|org.apache.spark.sql.catalyst.expressions.LastDay|
|datetime_funcs|make_date|org.apache.spark.sql.catalyst.expressions.MakeDate|
|datetime_funcs|make_interval|org.apache.spark.sql.catalyst.expressions.MakeInterval|
|datetime_funcs|make_timestamp|org.apache.spark.sql.catalyst.expressions.MakeTimestamp|
|datetime_funcs|minute|org.apache.spark.sql.catalyst.expressions.Minute|
|datetime_funcs|months_between|org.apache.spark.sql.catalyst.expressions.MonthsBetween|
|datetime_funcs|month|org.apache.spark.sql.catalyst.expressions.Month|
|datetime_funcs|next_day|org.apache.spark.sql.catalyst.expressions.NextDay|
|datetime_funcs|now|org.apache.spark.sql.catalyst.expressions.Now|
|datetime_funcs|quarter|org.apache.spark.sql.catalyst.expressions.Quarter|
|datetime_funcs|second|org.apache.spark.sql.catalyst.expressions.Second|
|datetime_funcs|timestamp_micros|org.apache.spark.sql.catalyst.expressions.MicrosToTimestamp|
|datetime_funcs|timestamp_millis|org.apache.spark.sql.catalyst.expressions.MillisToTimestamp|
|datetime_funcs|timestamp_seconds|org.apache.spark.sql.catalyst.expressions.SecondsToTimestamp|
|datetime_funcs|to_date|org.apache.spark.sql.catalyst.expressions.ParseToDate|
|datetime_funcs|to_timestamp|org.apache.spark.sql.catalyst.expressions.ParseToTimestamp|
|datetime_funcs|to_unix_timestamp|org.apache.spark.sql.catalyst.expressions.ToUnixTimestamp|
|datetime_funcs|to_utc_timestamp|org.apache.spark.sql.catalyst.expressions.ToUTCTimestamp|
|datetime_funcs|trunc|org.apache.spark.sql.catalyst.expressions.TruncDate|
|datetime_funcs|unix_date|org.apache.spark.sql.catalyst.expressions.UnixDate|
|datetime_funcs|unix_micros|org.apache.spark.sql.catalyst.expressions.UnixMicros|
|datetime_funcs|unix_millis|org.apache.spark.sql.catalyst.expressions.UnixMillis|
|datetime_funcs|unix_seconds|org.apache.spark.sql.catalyst.expressions.UnixSeconds|
|datetime_funcs|unix_timestamp|org.apache.spark.sql.catalyst.expressions.UnixTimestamp|
|datetime_funcs|weekday|org.apache.spark.sql.catalyst.expressions.WeekDay|
|datetime_funcs|weekofyear|org.apache.spark.sql.catalyst.expressions.WeekOfYear|
|datetime_funcs|year|org.apache.spark.sql.catalyst.expressions.Year|
|generator_funcs|explode_outer|org.apache.spark.sql.catalyst.expressions.Explode|
|generator_funcs|explode|org.apache.spark.sql.catalyst.expressions.Explode|
|generator_funcs|inline_outer|org.apache.spark.sql.catalyst.expressions.Inline|
|generator_funcs|inline|org.apache.spark.sql.catalyst.expressions.Inline|
|generator_funcs|posexplode_outer|org.apache.spark.sql.catalyst.expressions.PosExplode|
|generator_funcs|posexplode|org.apache.spark.sql.catalyst.expressions.PosExplode|
|generator_funcs|stack|org.apache.spark.sql.catalyst.expressions.Stack|
|hash_funcs|crc32|org.apache.spark.sql.catalyst.expressions.Crc32|
|hash_funcs|hash|org.apache.spark.sql.catalyst.expressions.Murmur3Hash|
|hash_funcs|md5|org.apache.spark.sql.catalyst.expressions.Md5|
|hash_funcs|sha1|org.apache.spark.sql.catalyst.expressions.Sha1|
|hash_funcs|sha2|org.apache.spark.sql.catalyst.expressions.Sha2|
|hash_funcs|sha|org.apache.spark.sql.catalyst.expressions.Sha1|
|hash_funcs|xxhash64|org.apache.spark.sql.catalyst.expressions.XxHash64|
|json_funcs|from_json|org.apache.spark.sql.catalyst.expressions.JsonToStructs|
|json_funcs|get_json_object|org.apache.spark.sql.catalyst.expressions.GetJsonObject|
|json_funcs|json_array_length|org.apache.spark.sql.catalyst.expressions.LengthOfJsonArray|
|json_funcs|json_object_keys|org.apache.spark.sql.catalyst.expressions.JsonObjectKeys|
|json_funcs|json_tuple|org.apache.spark.sql.catalyst.expressions.JsonTuple|
|json_funcs|schema_of_json|org.apache.spark.sql.catalyst.expressions.SchemaOfJson|
|json_funcs|to_json|org.apache.spark.sql.catalyst.expressions.StructsToJson|
|lambda_funcs|aggregate|org.apache.spark.sql.catalyst.expressions.ArrayAggregate|
|lambda_funcs|array_sort|org.apache.spark.sql.catalyst.expressions.ArraySort|
|lambda_funcs|exists|org.apache.spark.sql.catalyst.expressions.ArrayExists|
|lambda_funcs|filter|org.apache.spark.sql.catalyst.expressions.ArrayFilter|
|lambda_funcs|forall|org.apache.spark.sql.catalyst.expressions.ArrayForAll|
|lambda_funcs|map_filter|org.apache.spark.sql.catalyst.expressions.MapFilter|
|lambda_funcs|map_zip_with|org.apache.spark.sql.catalyst.expressions.MapZipWith|
|lambda_funcs|transform_keys|org.apache.spark.sql.catalyst.expressions.TransformKeys|
|lambda_funcs|transform_values|org.apache.spark.sql.catalyst.expressions.TransformValues|
|lambda_funcs|transform|org.apache.spark.sql.catalyst.expressions.ArrayTransform|
|lambda_funcs|zip_with|org.apache.spark.sql.catalyst.expressions.ZipWith|
|map_funcs|element_at|org.apache.spark.sql.catalyst.expressions.ElementAt|
|map_funcs|map_concat|org.apache.spark.sql.catalyst.expressions.MapConcat|
|map_funcs|map_entries|org.apache.spark.sql.catalyst.expressions.MapEntries|
|map_funcs|map_from_arrays|org.apache.spark.sql.catalyst.expressions.MapFromArrays|
|map_funcs|map_from_entries|org.apache.spark.sql.catalyst.expressions.MapFromEntries|
|map_funcs|map_keys|org.apache.spark.sql.catalyst.expressions.MapKeys|
|map_funcs|map_values|org.apache.spark.sql.catalyst.expressions.MapValues|
|map_funcs|map|org.apache.spark.sql.catalyst.expressions.CreateMap|
|map_funcs|str_to_map|org.apache.spark.sql.catalyst.expressions.StringToMap|
|math_funcs|%|org.apache.spark.sql.catalyst.expressions.Remainder|
|math_funcs|*|org.apache.spark.sql.catalyst.expressions.Multiply|
|math_funcs|+|org.apache.spark.sql.catalyst.expressions.Add|
|math_funcs|-|org.apache.spark.sql.catalyst.expressions.Subtract|
|math_funcs|/|org.apache.spark.sql.catalyst.expressions.Divide|
|math_funcs|abs|org.apache.spark.sql.catalyst.expressions.Abs|
|math_funcs|acosh|org.apache.spark.sql.catalyst.expressions.Acosh|
|math_funcs|acos|org.apache.spark.sql.catalyst.expressions.Acos|
|math_funcs|asinh|org.apache.spark.sql.catalyst.expressions.Asinh|
|math_funcs|asin|org.apache.spark.sql.catalyst.expressions.Asin|
|math_funcs|atan2|org.apache.spark.sql.catalyst.expressions.Atan2|
|math_funcs|atanh|org.apache.spark.sql.catalyst.expressions.Atanh|
|math_funcs|atan|org.apache.spark.sql.catalyst.expressions.Atan|
|math_funcs|bin|org.apache.spark.sql.catalyst.expressions.Bin|
|math_funcs|bround|org.apache.spark.sql.catalyst.expressions.BRound|
|math_funcs|cbrt|org.apache.spark.sql.catalyst.expressions.Cbrt|
|math_funcs|ceiling|org.apache.spark.sql.catalyst.expressions.Ceil|
|math_funcs|ceil|org.apache.spark.sql.catalyst.expressions.Ceil|
|math_funcs|conv|org.apache.spark.sql.catalyst.expressions.Conv|
|math_funcs|cosh|org.apache.spark.sql.catalyst.expressions.Cosh|
|math_funcs|cos|org.apache.spark.sql.catalyst.expressions.Cos|
|math_funcs|cot|org.apache.spark.sql.catalyst.expressions.Cot|
|math_funcs|degrees|org.apache.spark.sql.catalyst.expressions.ToDegrees|
|math_funcs|div|org.apache.spark.sql.catalyst.expressions.IntegralDivide|
|math_funcs|expm1|org.apache.spark.sql.catalyst.expressions.Expm1|
|math_funcs|exp|org.apache.spark.sql.catalyst.expressions.Exp|
|math_funcs|e|org.apache.spark.sql.catalyst.expressions.EulerNumber|
|math_funcs|factorial|org.apache.spark.sql.catalyst.expressions.Factorial|
|math_funcs|floor|org.apache.spark.sql.catalyst.expressions.Floor|
|math_funcs|greatest|org.apache.spark.sql.catalyst.expressions.Greatest|
|math_funcs|hex|org.apache.spark.sql.catalyst.expressions.Hex|
|math_funcs|hypot|org.apache.spark.sql.catalyst.expressions.Hypot|
|math_funcs|least|org.apache.spark.sql.catalyst.expressions.Least|
|math_funcs|ln|org.apache.spark.sql.catalyst.expressions.Log|
|math_funcs|log10|org.apache.spark.sql.catalyst.expressions.Log10|
|math_funcs|log1p|org.apache.spark.sql.catalyst.expressions.Log1p|
|math_funcs|log2|org.apache.spark.sql.catalyst.expressions.Log2|
|math_funcs|log|org.apache.spark.sql.catalyst.expressions.Logarithm|
|math_funcs|mod|org.apache.spark.sql.catalyst.expressions.Remainder|
|math_funcs|negative|org.apache.spark.sql.catalyst.expressions.UnaryMinus|
|math_funcs|pi|org.apache.spark.sql.catalyst.expressions.Pi|
|math_funcs|pmod|org.apache.spark.sql.catalyst.expressions.Pmod|
|math_funcs|positive|org.apache.spark.sql.catalyst.expressions.UnaryPositive|
|math_funcs|power|org.apache.spark.sql.catalyst.expressions.Pow|
|math_funcs|pow|org.apache.spark.sql.catalyst.expressions.Pow|
|math_funcs|radians|org.apache.spark.sql.catalyst.expressions.ToRadians|
|math_funcs|randn|org.apache.spark.sql.catalyst.expressions.Randn|
|math_funcs|random|org.apache.spark.sql.catalyst.expressions.Rand|
|math_funcs|rand|org.apache.spark.sql.catalyst.expressions.Rand|
|math_funcs|rint|org.apache.spark.sql.catalyst.expressions.Rint|
|math_funcs|round|org.apache.spark.sql.catalyst.expressions.Round|
|math_funcs|shiftleft|org.apache.spark.sql.catalyst.expressions.ShiftLeft|
|math_funcs|signum|org.apache.spark.sql.catalyst.expressions.Signum|
|math_funcs|sign|org.apache.spark.sql.catalyst.expressions.Signum|
|math_funcs|sinh|org.apache.spark.sql.catalyst.expressions.Sinh|
|math_funcs|sin|org.apache.spark.sql.catalyst.expressions.Sin|
|math_funcs|sqrt|org.apache.spark.sql.catalyst.expressions.Sqrt|
|math_funcs|tanh|org.apache.spark.sql.catalyst.expressions.Tanh|
|math_funcs|tan|org.apache.spark.sql.catalyst.expressions.Tan|
|math_funcs|unhex|org.apache.spark.sql.catalyst.expressions.Unhex|
|math_funcs|width_bucket|org.apache.spark.sql.catalyst.expressions.WidthBucket|
|misc_funcs|assert_true|org.apache.spark.sql.catalyst.expressions.AssertTrue|
|misc_funcs|current_catalog|org.apache.spark.sql.catalyst.expressions.CurrentCatalog|
|misc_funcs|current_database|org.apache.spark.sql.catalyst.expressions.CurrentDatabase|
|misc_funcs|input_file_block_length|org.apache.spark.sql.catalyst.expressions.InputFileBlockLength|
|misc_funcs|input_file_block_start|org.apache.spark.sql.catalyst.expressions.InputFileBlockStart|
|misc_funcs|input_file_name|org.apache.spark.sql.catalyst.expressions.InputFileName|
|misc_funcs|java_method|org.apache.spark.sql.catalyst.expressions.CallMethodViaReflection|
|misc_funcs|monotonically_increasing_id|org.apache.spark.sql.catalyst.expressions.MonotonicallyIncreasingID|
|misc_funcs|raise_error|org.apache.spark.sql.catalyst.expressions.RaiseError|
|misc_funcs|reflect|org.apache.spark.sql.catalyst.expressions.CallMethodViaReflection|
|misc_funcs|spark_partition_id|org.apache.spark.sql.catalyst.expressions.SparkPartitionID|
|misc_funcs|typeof|org.apache.spark.sql.catalyst.expressions.TypeOf|
|misc_funcs|uuid|org.apache.spark.sql.catalyst.expressions.Uuid|
|misc_funcs|version|org.apache.spark.sql.catalyst.expressions.SparkVersion|
|predicate_funcs|!|org.apache.spark.sql.catalyst.expressions.Not|
|predicate_funcs|<=>|org.apache.spark.sql.catalyst.expressions.EqualNullSafe|
|predicate_funcs|<=|org.apache.spark.sql.catalyst.expressions.LessThanOrEqual|
|predicate_funcs|<|org.apache.spark.sql.catalyst.expressions.LessThan|
|predicate_funcs|==|org.apache.spark.sql.catalyst.expressions.EqualTo|
|predicate_funcs|=|org.apache.spark.sql.catalyst.expressions.EqualTo|
|predicate_funcs|>=|org.apache.spark.sql.catalyst.expressions.GreaterThanOrEqual|
|predicate_funcs|>|org.apache.spark.sql.catalyst.expressions.GreaterThan|
|predicate_funcs|and|org.apache.spark.sql.catalyst.expressions.And|
|predicate_funcs|in|org.apache.spark.sql.catalyst.expressions.In|
|predicate_funcs|isnan|org.apache.spark.sql.catalyst.expressions.IsNaN|
|predicate_funcs|isnotnull|org.apache.spark.sql.catalyst.expressions.IsNotNull|
|predicate_funcs|isnull|org.apache.spark.sql.catalyst.expressions.IsNull|
|predicate_funcs|like|org.apache.spark.sql.catalyst.expressions.Like|
|predicate_funcs|not|org.apache.spark.sql.catalyst.expressions.Not|
|predicate_funcs|or|org.apache.spark.sql.catalyst.expressions.Or|
|predicate_funcs|regexp_like|org.apache.spark.sql.catalyst.expressions.RLike|
|predicate_funcs|rlike|org.apache.spark.sql.catalyst.expressions.RLike|
|string_funcs|ascii|org.apache.spark.sql.catalyst.expressions.Ascii|
|string_funcs|base64|org.apache.spark.sql.catalyst.expressions.Base64|
|string_funcs|bit_length|org.apache.spark.sql.catalyst.expressions.BitLength|
|string_funcs|char_length|org.apache.spark.sql.catalyst.expressions.Length|
|string_funcs|character_length|org.apache.spark.sql.catalyst.expressions.Length|
|string_funcs|char|org.apache.spark.sql.catalyst.expressions.Chr|
|string_funcs|chr|org.apache.spark.sql.catalyst.expressions.Chr|
|string_funcs|concat_ws|org.apache.spark.sql.catalyst.expressions.ConcatWs|
|string_funcs|decode|org.apache.spark.sql.catalyst.expressions.Decode|
|string_funcs|elt|org.apache.spark.sql.catalyst.expressions.Elt|
|string_funcs|encode|org.apache.spark.sql.catalyst.expressions.Encode|
|string_funcs|find_in_set|org.apache.spark.sql.catalyst.expressions.FindInSet|
|string_funcs|format_number|org.apache.spark.sql.catalyst.expressions.FormatNumber|
|string_funcs|format_string|org.apache.spark.sql.catalyst.expressions.FormatString|
|string_funcs|initcap|org.apache.spark.sql.catalyst.expressions.InitCap|
|string_funcs|instr|org.apache.spark.sql.catalyst.expressions.StringInstr|
|string_funcs|lcase|org.apache.spark.sql.catalyst.expressions.Lower|
|string_funcs|left|org.apache.spark.sql.catalyst.expressions.Left|
|string_funcs|length|org.apache.spark.sql.catalyst.expressions.Length|
|string_funcs|levenshtein|org.apache.spark.sql.catalyst.expressions.Levenshtein|
|string_funcs|locate|org.apache.spark.sql.catalyst.expressions.StringLocate|
|string_funcs|lower|org.apache.spark.sql.catalyst.expressions.Lower|
|string_funcs|lpad|org.apache.spark.sql.catalyst.expressions.StringLPad|
|string_funcs|ltrim|org.apache.spark.sql.catalyst.expressions.StringTrimLeft|
|string_funcs|octet_length|org.apache.spark.sql.catalyst.expressions.OctetLength|
|string_funcs|overlay|org.apache.spark.sql.catalyst.expressions.Overlay|
|string_funcs|parse_url|org.apache.spark.sql.catalyst.expressions.ParseUrl|
|string_funcs|position|org.apache.spark.sql.catalyst.expressions.StringLocate|
|string_funcs|printf|org.apache.spark.sql.catalyst.expressions.FormatString|
|string_funcs|regexp_extract_all|org.apache.spark.sql.catalyst.expressions.RegExpExtractAll|
|string_funcs|regexp_extract|org.apache.spark.sql.catalyst.expressions.RegExpExtract|
|string_funcs|regexp_replace|org.apache.spark.sql.catalyst.expressions.RegExpReplace|
|string_funcs|repeat|org.apache.spark.sql.catalyst.expressions.StringRepeat|
|string_funcs|replace|org.apache.spark.sql.catalyst.expressions.StringReplace|
|string_funcs|right|org.apache.spark.sql.catalyst.expressions.Right|
|string_funcs|rpad|org.apache.spark.sql.catalyst.expressions.StringRPad|
|string_funcs|rtrim|org.apache.spark.sql.catalyst.expressions.StringTrimRight|
|string_funcs|sentences|org.apache.spark.sql.catalyst.expressions.Sentences|
|string_funcs|soundex|org.apache.spark.sql.catalyst.expressions.SoundEx|
|string_funcs|space|org.apache.spark.sql.catalyst.expressions.StringSpace|
|string_funcs|split|org.apache.spark.sql.catalyst.expressions.StringSplit|
|string_funcs|substring_index|org.apache.spark.sql.catalyst.expressions.SubstringIndex|
|string_funcs|substring|org.apache.spark.sql.catalyst.expressions.Substring|
|string_funcs|substr|org.apache.spark.sql.catalyst.expressions.Substring|
|string_funcs|translate|org.apache.spark.sql.catalyst.expressions.StringTranslate|
|string_funcs|trim|org.apache.spark.sql.catalyst.expressions.StringTrim|
|string_funcs|ucase|org.apache.spark.sql.catalyst.expressions.Upper|
|string_funcs|unbase64|org.apache.spark.sql.catalyst.expressions.UnBase64|
|string_funcs|upper|org.apache.spark.sql.catalyst.expressions.Upper|
|struct_funcs|named_struct|org.apache.spark.sql.catalyst.expressions.CreateNamedStruct|
|struct_funcs|struct|org.apache.spark.sql.catalyst.expressions.CreateNamedStruct|
|window_funcs|cume_dist|org.apache.spark.sql.catalyst.expressions.CumeDist|
|window_funcs|dense_rank|org.apache.spark.sql.catalyst.expressions.DenseRank|
|window_funcs|lag|org.apache.spark.sql.catalyst.expressions.Lag|
|window_funcs|lead|org.apache.spark.sql.catalyst.expressions.Lead|
|window_funcs|nth_value|org.apache.spark.sql.catalyst.expressions.NthValue|
|window_funcs|ntile|org.apache.spark.sql.catalyst.expressions.NTile|
|window_funcs|percent_rank|org.apache.spark.sql.catalyst.expressions.PercentRank|
|window_funcs|rank|org.apache.spark.sql.catalyst.expressions.Rank|
|window_funcs|row_number|org.apache.spark.sql.catalyst.expressions.RowNumber|
|xml_funcs|xpath_boolean|org.apache.spark.sql.catalyst.expressions.xml.XPathBoolean|
|xml_funcs|xpath_double|org.apache.spark.sql.catalyst.expressions.xml.XPathDouble|
|xml_funcs|xpath_float|org.apache.spark.sql.catalyst.expressions.xml.XPathFloat|
|xml_funcs|xpath_int|org.apache.spark.sql.catalyst.expressions.xml.XPathInt|
|xml_funcs|xpath_long|org.apache.spark.sql.catalyst.expressions.xml.XPathLong|
|xml_funcs|xpath_number|org.apache.spark.sql.catalyst.expressions.xml.XPathDouble|
|xml_funcs|xpath_short|org.apache.spark.sql.catalyst.expressions.xml.XPathShort|
|xml_funcs|xpath_string|org.apache.spark.sql.catalyst.expressions.xml.XPathString|
|xml_funcs|xpath|org.apache.spark.sql.catalyst.expressions.xml.XPathList|
Closes#30040
NOTE: An original author of this PR is tanelk, so the credit should be given to tanelk.
### Why are the changes needed?
For better documents.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add a test to check if exprs have a group tag in `ExpressionInfoSuite`.
Closes#30867 from maropu/pr30040.
Lead-authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Co-authored-by: tanel.kiis@gmail.com <tanel.kiis@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Improve `SimplifyConditionals`.
Simplify `If(cond, TrueLiteral, FalseLiteral)` to `cond`.
Simplify `If(cond, FalseLiteral, TrueLiteral)` to `Not(cond)`.
The use case is:
```sql
create table t1 using parquet as select id from range(10);
select if (id > 2, false, true) from t1;
```
Before this pr:
```
== Physical Plan ==
*(1) Project [if ((id#1L > 2)) false else true AS (IF((id > CAST(2 AS BIGINT)), false, true))#2]
+- *(1) ColumnarToRow
+- FileScan parquet default.t1[id#1L] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/Users/yumwang/opensource/spark/spark-warehouse/org.apache.spark.sql.DataF..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:bigint>
```
After this pr:
```
== Physical Plan ==
*(1) Project [(id#1L <= 2) AS (IF((id > CAST(2 AS BIGINT)), false, true))#2]
+- *(1) ColumnarToRow
+- FileScan parquet default.t1[id#1L] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex[file:/Users/yumwang/opensource/spark/spark-warehouse/org.apache.spark.sql.DataF..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:bigint>
```
### 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#30849 from wangyum/SPARK-33798-2.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
It's a known issue that re-analyzing an optimized plan can lead to various issues. We made several attempts to avoid it from happening, but the current solution `AlreadyOptimized` is still not 100% safe, as people can inject catalyst rules to call analyzer directly.
This PR proposes a simpler and safer idea: we set the `analyzed` flag to true after optimization, and analyzer will skip processing plans whose `analyzed` flag is true.
### Why are the changes needed?
make the code simpler and safer
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing tests.
Closes#30777 from cloud-fan/ds.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1. Move the `DROP TABLE` parsing tests to `DropTableParserSuite`
2. Place the v1 tests for `DROP TABLE` from `DDLSuite` and v2 tests from `DataSourceV2SQLSuite` to the common trait `DropTableSuiteBase`, so, the tests will run for V1, Hive V1 and V2 DS.
### Why are the changes needed?
- The unification will allow to run common `DROP TABLE` 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 *DropTableParserSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *DropTableSuite"
```
Closes#30854 from MaxGekk/unify-drop-table-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 proposes to migrate `ALTER TABLE ... RENAME TO PARTITION` 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 `ALTER TABLE ... RENAME TO PARTITION` is not supported for v2 tables.
### Why are the changes needed?
The PR makes the resolution consistent behavior consistent. For example,
```
sql("CREATE DATABASE test")
sql("CREATE TABLE spark_catalog.test.t (id bigint, val string) USING csv PARTITIONED BY (id)")
sql("CREATE TEMPORARY VIEW t AS SELECT 2")
sql("USE spark_catalog.test")
sql("ALTER TABLE t PARTITION (id=1) RENAME TO PARTITION (id=2)") // works fine assuming id=1 exists.
```
, but after this PR:
```
sql("ALTER TABLE t PARTITION (id=1) RENAME TO PARTITION (id=2)")
org.apache.spark.sql.AnalysisException: t is a temp view. 'ALTER TABLE ... RENAME TO PARTITION' expects a table; line 1 pos 0
```
, which is the consistent behavior with other commands.
### Does this PR introduce _any_ user-facing change?
After this PR, `ALTER TABLE` in the above example is resolved to a temp view `t` first instead of `spark_catalog.test.t`.
### How was this patch tested?
Updated existing tests.
Closes#30862 from imback82/alter_table_rename_partition_v2.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Hive metastore has a limitation for the table property length. To work around it, Spark split the schema json string into several parts when saving to hive metastore as table properties. We need to do the same for histogram column stats as it can go very big.
This PR refactors the table property splitting code, so that we can share it between the schema json string and histogram column stats.
### Why are the changes needed?
To be able to analyze table when histogram data is big.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
existing test and new tests
Closes#30809 from cloud-fan/cbo.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
FIX Github Action with unidoc
### Why are the changes needed?
FIX Github Action with unidoc
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
Pass GA
Closes#30846 from yaooqinn/SPARK-33599.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR group exception messages in `/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis`.
### 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#30717 from beliefer/SPARK-33599.
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?
This pr add a new rule(`PushFoldableIntoBranches`) to push down the foldable expressions through `CaseWhen/If`. This is a real case from production:
```sql
create table t1 using parquet as select * from range(100);
create table t2 using parquet as select * from range(200);
create temp view v1 as
select 'a' as event_type, * from t1
union all
select CASE WHEN id = 1 THEN 'b' WHEN id = 3 THEN 'c' end as event_type, * from t2
explain select * from v1 where event_type = 'a';
```
Before this PR:
```
== Physical Plan ==
Union
:- *(1) Project [a AS event_type#30533, id#30535L]
: +- *(1) ColumnarToRow
: +- FileScan parquet default.t1[id#30535L] Batched: true, DataFilters: [], Format: Parquet
+- *(2) Project [CASE WHEN (id#30536L = 1) THEN b WHEN (id#30536L = 3) THEN c END AS event_type#30534, id#30536L]
+- *(2) Filter (CASE WHEN (id#30536L = 1) THEN b WHEN (id#30536L = 3) THEN c END = a)
+- *(2) ColumnarToRow
+- FileScan parquet default.t2[id#30536L] Batched: true, DataFilters: [(CASE WHEN (id#30536L = 1) THEN b WHEN (id#30536L = 3) THEN c END = a)], Format: Parquet
```
After this PR:
```
== Physical Plan ==
*(1) Project [a AS event_type#8, id#4L]
+- *(1) ColumnarToRow
+- FileScan parquet default.t1[id#4L] Batched: true, DataFilters: [], Format: Parquet
```
### 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#30790 from wangyum/SPARK-33798.
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 update `CACHE TABLE` to use a `LogicalPlan` when caching a query to avoid creating a `DataFrame` as suggested here: https://github.com/apache/spark/pull/30743#discussion_r543123190
For reference, `UNCACHE TABLE` also uses `LogicalPlan`: 0c12900120/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/CacheTableExec.scala (L91-L98)
### Why are the changes needed?
To avoid creating an unnecessary dataframe and make it consistent with `uncacheQuery` used in `UNCACHE TABLE`.
### Does this PR introduce _any_ user-facing change?
No, just internal changes.
### How was this patch tested?
Existing tests since this is an internal refactoring change.
Closes#30815 from imback82/cache_with_logical_plan.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to migrate `ALTER TABLE ... SET [SERDE|SERDEPROPERTIES` 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 `ALTER TABLE ... SET [SERDE|SERDEPROPERTIES]` is not supported for v2 tables.
### Why are the changes needed?
The PR makes the resolution consistent behavior consistent. For example,
```scala
sql("CREATE DATABASE test")
sql("CREATE TABLE spark_catalog.test.t (id bigint, val string) USING csv PARTITIONED BY (id)")
sql("CREATE TEMPORARY VIEW t AS SELECT 2")
sql("USE spark_catalog.test")
sql("ALTER TABLE t SET SERDE 'serdename'") // works fine
```
, but after this PR:
```
sql("ALTER TABLE t SET SERDE 'serdename'")
org.apache.spark.sql.AnalysisException: t is a temp view. 'ALTER TABLE ... SET [SERDE|SERDEPROPERTIES\' expects a table; line 1 pos 0
```
, which is the consistent behavior with other commands.
### Does this PR introduce _any_ user-facing change?
After this PR, `t` in the above example is resolved to a temp view first instead of `spark_catalog.test.t`.
### How was this patch tested?
Updated existing tests.
Closes#30813 from imback82/alter_table_serde_v2.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR removes unused `TruncateTableStatement`: https://github.com/apache/spark/pull/30457#discussion_r544433820
### Why are the changes needed?
To remove unused `TruncateTableStatement` from #30457.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Not needed.
Closes#30811 from imback82/remove_truncate_table_stmt.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
It seems a very popular way that people use DISTRIBUTE BY clause with a literal to coalesce partition in the pure SQL data processing.
For example
```
insert into table src select * from values (1), (2), (3) t(a) distribute by 1
```
Users may want the final output to be one single data file, but if the reality is not always true. Spark will always create a file for partition 0 whether it contains data or not, so when the data all goes to a partition(IDX >0), there will be always 2 files there and the part-00000 is empty. On the other hand, a lot of empty tasks will be launched too, this is unnecessary.
When users repeat the insert statement daily, hourly, or minutely, it causes small file issues.
```
spark-sql> set spark.sql.shuffle.partitions=3;drop table if exists test2;create table test2 using parquet as select * from values (1), (2), (3) t(a) distribute by 1;
kentyaohulk ~/spark SPARK-33806 tree /Users/kentyao/Downloads/spark/spark-3.1.0-SNAPSHOT-bin-20201202/spark-warehouse/test2/ -s
/Users/kentyao/Downloads/spark/spark-3.1.0-SNAPSHOT-bin-20201202/spark-warehouse/test2/
├── [ 0] _SUCCESS
├── [ 298] part-00000-5dc19733-9405-414b-9681-d25c4d3e9ee6-c000.snappy.parquet
└── [ 426] part-00001-5dc19733-9405-414b-9681-d25c4d3e9ee6-c000.snappy.parquet
```
To avoid this, there are some options you can take.
1. use `distribute by null`, let the data go to the partition 0
2. set spark.sql.adaptive.enabled to true for Spark to automatically coalesce
3. using hints instead of `distribute by`
4. set spark.sql.shuffle.partitions to 1
In this PR, we set the partition number to 1 in this particular case.
### Why are the changes needed?
1. avoid small file issues
2. avoid unnecessary empty tasks when no adaptive execution
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
new test
Closes#30800 from yaooqinn/SPARK-33806.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Based on the discussion https://github.com/apache/spark/pull/30743#discussion_r543124594, this PR proposes to remove the command name in AnalysisException message when a relation is not resolved.
For some of the commands that use `UnresolvedTable`, `UnresolvedView`, and `UnresolvedTableOrView` to resolve an identifier, when the identifier cannot be resolved, the exception will be something like `Table or view not found for 'SHOW TBLPROPERTIES': badtable`. The command name (`SHOW TBLPROPERTIES` in this case) should be dropped to be consistent with other existing commands.
### Why are the changes needed?
To make the exception message consistent.
### Does this PR introduce _any_ user-facing change?
Yes, the exception message will be changed from
```
Table or view not found for 'SHOW TBLPROPERTIES': badtable
```
to
```
Table or view not found: badtable
```
for commands that use `UnresolvedTable`, `UnresolvedView`, and `UnresolvedTableOrView` to resolve an identifier.
### How was this patch tested?
Updated existing tests.
Closes#30794 from imback82/remove_cmd_from_exception_msg.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
As a follow-up of https://github.com/apache/spark/pull/30045, we modify the RESET command here to respect the session initial configs per session first then fall back to the `SharedState` conf, which makes each session could maintain a different copy of initial configs for resetting.
### Why are the changes needed?
to make reset command saner.
### Does this PR introduce _any_ user-facing change?
yes, RESET will respect session initials first not always go to the system defaults
### How was this patch tested?
add new tests
Closes#30642 from yaooqinn/SPARK-32991-F.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to sort table properties in DESCRIBE TABLE command. This is consistent with DSv2 command as well:
e3058ba17c/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DescribeTableExec.scala (L63)
This PR fixes the test case in Scala 2.13 build as well where the table properties have different order in the map.
### Why are the changes needed?
To keep the deterministic and pretty output, and fix the tests in Scala 2.13 build.
See https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-maven-hadoop-3.2-scala-2.13/49/testReport/junit/org.apache.spark.sql/SQLQueryTestSuite/describe_sql/
```
describe.sql
Expected "...spark_catalog, view.[query.out.col.2=c, view.referredTempFunctionsNames=[], view.catalogAndNamespace.part.1=default]]", but got "...spark_catalog, view.[catalogAndNamespace.part.1=default, view.query.out.col.2=c, view.referredTempFunctionsNames=[]]]" Result did not match for query #29
DESC FORMATTED v
```
### Does this PR introduce _any_ user-facing change?
Yes, it will change the text output from `DESCRIBE [EXTENDED|FORMATTED] table_name`.
Now the table properties are sorted by its key.
### How was this patch tested?
Related unittests were fixed accordingly.
Closes#30799 from HyukjinKwon/SPARK-33803.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to migrate `UNCACHE TABLE` to use `UnresolvedRelation` to resolve the table/view identifier in Analyzer as discussed https://github.com/apache/spark/pull/30403/files#r532360022.
### Why are the changes needed?
To resolve the table/view in the analyzer.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Updated existing tests
Closes#30743 from imback82/uncache_v2.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds `UpdateTable` to supported plans in `ReplaceNullWithFalseInPredicate`.
### Why are the changes needed?
This change allows Spark to optimize update conditions like we optimize filters.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
This PR extends the existing test cases to also cover `UpdateTable`.
Closes#30787 from aokolnychyi/spark-33735.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/30559 . The default parallelism config in Spark core is not good, as it's unclear where it applies. To not inherit this problem in Spark SQL, this PR refines the default parallelism SQL config, to make it clear that it only applies to leaf nodes.
### Why are the changes needed?
Make the config clearer.
### Does this PR introduce _any_ user-facing change?
It changes an unreleased config.
### How was this patch tested?
existing tests
Closes#30736 from cloud-fan/follow.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The current `getSimpleMessage` of `AnalysisException` may adds semicolon repeatedly. There show an example below:
`select decode()`
The output will be:
```
org.apache.spark.sql.AnalysisException
Invalid number of arguments for function decode. Expected: 2; Found: 0;; line 1 pos 7
```
### Why are the changes needed?
Fix a bug, because it adds semicolon repeatedly.
### Does this PR introduce _any_ user-facing change?
Yes. the message of AnalysisException will be correct.
### How was this patch tested?
Jenkins test.
Closes#30724 from beliefer/SPARK-33752.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1. Move the `ALTER TABLE .. DROP PARTITION` parsing tests to `AlterTableDropPartitionParserSuite`
2. Place v1 tests for `ALTER TABLE .. DROP PARTITION` from `DDLSuite` and v2 tests from `AlterTablePartitionV2SQLSuite` to the common trait `AlterTableDropPartitionSuiteBase`, so, the tests will run for V1, Hive V1 and V2 DS.
### Why are the changes needed?
- The unification will allow to run common `ALTER TABLE .. DROP PARTITION` 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 -Phive-thriftserver "test:testOnly *AlterTableDropPartitionParserSuite"
$ build/sbt -Phive -Phive-thriftserver "test:testOnly *AlterTableDropPartitionSuite"
```
Closes#30747 from MaxGekk/unify-alter-table-drop-partition-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 proposes to migrate `ALTER TABLE ... RECOVER PARTITIONS` 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 `ALTER TABLE ... RECOVER PARTITIONS` is not supported for v2 tables.
### Why are the changes needed?
The PR makes the resolution consistent behavior consistent. For example,
```scala
sql("CREATE DATABASE test")
sql("CREATE TABLE spark_catalog.test.t (id bigint, val string) USING csv PARTITIONED BY (id)")
sql("CREATE TEMPORARY VIEW t AS SELECT 2")
sql("USE spark_catalog.test")
sql("ALTER TABLE t RECOVER PARTITIONS") // works fine
```
, but after this PR:
```
sql("ALTER TABLE t RECOVER PARTITIONS")
org.apache.spark.sql.AnalysisException: t is a temp view. 'ALTER TABLE ... RECOVER PARTITIONS' expects a table; line 1 pos 0
```
, which is the consistent behavior with other commands.
### Does this PR introduce _any_ user-facing change?
After this PR, `ALTER TABLE t RECOVER PARTITIONS` in the above example is resolved to a temp view `t` first instead of `spark_catalog.test.t`.
### How was this patch tested?
Updated existing tests.
Closes#30773 from imback82/alter_table_recover_part_v2.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This pr fix invalid value for HourOfAmPm when testing on JDK 14.
### Why are the changes needed?
Run test on JDK 14.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
N/A
Closes#30754 from wangyum/SPARK-33771.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR removes unused imports.
### Why are the changes needed?
These changes are required to fix the build.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Via `dev/lint-java`.
Closes#30767 from aokolnychyi/fix-linter.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR adds connector interfaces proposed in the [design doc](https://docs.google.com/document/d/1X0NsQSryvNmXBY9kcvfINeYyKC-AahZarUqg3nS1GQs/edit#) for SPARK-23889.
**Note**: This PR contains a subset of changes discussed in PR #29066.
### Why are the changes needed?
Data sources should be able to request a specific distribution and ordering of data on write. In particular, these scenarios are considered useful:
- global sort
- cluster data and sort within partitions
- local sort within partitions
- no sort
Please see the design doc above for a more detailed explanation of requirements.
### Does this PR introduce _any_ user-facing change?
This PR introduces public changes to the DS V2 by adding a logical write abstraction as we have on the read path as well as additional interfaces to represent distribution and ordering of data (please see the doc for more info).
The existing `Distribution` interface in `read` package is read-specific and not flexible enough like discussed in the design doc. The current proposal is to evolve these interfaces separately until they converge.
### How was this patch tested?
This patch adds only interfaces.
Closes#30706 from aokolnychyi/spark-23889-interfaces.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: Ryan Blue <blue@apache.org>
### What changes were proposed in this pull request?
The deterministic field is wider than `NonDerterministic`, we should keep same range between pull out and check analysis.
### Why are the changes needed?
For example
```
select * from values(1), (4) as t(c1) order by java_method('java.lang.Math', 'abs', c1)
```
We will get exception since `java_method` deterministic field is false but not a `NonDeterministic`
```
Exception in thread "main" org.apache.spark.sql.AnalysisException: nondeterministic expressions are only allowed in
Project, Filter, Aggregate or Window, found:
java_method('java.lang.Math', 'abs', t.`c1`) ASC NULLS FIRST
in operator Sort [java_method(java.lang.Math, abs, c1#1) ASC NULLS FIRST], true
;;
```
### Does this PR introduce _any_ user-facing change?
Yes.
### How was this patch tested?
Add test.
Closes#30703 from ulysses-you/SPARK-33733.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Use Long value store encode value will overflow and return unexpected result, use BigInt to replace Long value and make logical more simple.
### Why are the changes needed?
Fix value overflow issue
### Does this PR introduce _any_ user-facing change?
People can sue `conf` function to convert value big then LONG.MAX_VALUE
### How was this patch tested?
Added UT
#### BenchMark
```
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.sql.execution.benchmark
import scala.util.Random
import org.apache.spark.benchmark.Benchmark
import org.apache.spark.sql.functions._
object ConvFuncBenchMark extends SqlBasedBenchmark {
val charset =
Array[String]("0", "1", "2", "3", "4", "5", "6", "7", "8", "9",
"A", "B", "C", "D", "E", "F", "G",
"H", "I", "J", "K", "L", "M", "N",
"O", "P", "Q", "R", "S", "T",
"U", "V", "W", "X", "Y", "Z")
def constructString(from: Int, length: Int): String = {
val chars = charset.slice(0, from)
(0 to length).map(x => {
val v = Random.nextInt(from)
chars(v)
}).mkString("")
}
private def doBenchmark(cardinality: Long, length: Int, from: Int, toBase: Int): Unit = {
spark.range(cardinality)
.withColumn("str", lit(constructString(from, length)))
.select(conv(col("str"), from, toBase))
.noop()
}
/**
* Main process of the whole benchmark.
* Implementations of this method are supposed to use the wrapper method `runBenchmark`
* for each benchmark scenario.
*/
override def runBenchmarkSuite(mainArgs: Array[String]): Unit = {
val N = 1000000L
val benchmark = new Benchmark("conv", N, output = output)
benchmark.addCase("length 10 from 2 to 16") { _ =>
doBenchmark(N, 10, 2, 16)
}
benchmark.addCase("length 10 from 2 to 10") { _ =>
doBenchmark(N, 10, 2, 10)
}
benchmark.addCase("length 10 from 10 to 16") { _ =>
doBenchmark(N, 10, 10, 16)
}
benchmark.addCase("length 10 from 10 to 36") { _ =>
doBenchmark(N, 10, 10, 36)
}
benchmark.addCase("length 10 from 16 to 10") { _ =>
doBenchmark(N, 10, 10, 10)
}
benchmark.addCase("length 10 from 16 to 36") { _ =>
doBenchmark(N, 10, 16, 36)
}
benchmark.addCase("length 10 from 36 to 10") { _ =>
doBenchmark(N, 10, 36, 10)
}
benchmark.addCase("length 10 from 36 to 16") { _ =>
doBenchmark(N, 10, 36, 16)
}
//
benchmark.addCase("length 20 from 10 to 16") { _ =>
doBenchmark(N, 20, 10, 16)
}
benchmark.addCase("length 20 from 10 to 36") { _ =>
doBenchmark(N, 20, 10, 36)
}
benchmark.addCase("length 30 from 10 to 16") { _ =>
doBenchmark(N, 30, 10, 16)
}
benchmark.addCase("length 30 from 10 to 36") { _ =>
doBenchmark(N, 30, 10, 36)
}
//
benchmark.addCase("length 20 from 16 to 10") { _ =>
doBenchmark(N, 20, 16, 10)
}
benchmark.addCase("length 20 from 16 to 36") { _ =>
doBenchmark(N, 20, 16, 36)
}
benchmark.addCase("length 30 from 16 to 10") { _ =>
doBenchmark(N, 30, 16, 10)
}
benchmark.addCase("length 30 from 16 to 36") { _ =>
doBenchmark(N, 30, 16, 36)
}
benchmark.run()
}
}
```
Result with patch :
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_191-b12 on Mac OS X 10.14.6
Intel(R) Core(TM) i5-8259U CPU 2.30GHz
conv: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
length 10 from 2 to 16 54 73 18 18.7 53.6 1.0X
length 10 from 2 to 10 43 47 5 23.5 42.5 1.3X
length 10 from 10 to 16 39 47 12 25.5 39.2 1.4X
length 10 from 10 to 36 38 42 3 26.5 37.7 1.4X
length 10 from 16 to 10 39 41 3 25.7 38.9 1.4X
length 10 from 16 to 36 36 41 4 27.6 36.3 1.5X
length 10 from 36 to 10 38 40 2 26.3 38.0 1.4X
length 10 from 36 to 16 37 39 2 26.8 37.2 1.4X
length 20 from 10 to 16 36 39 2 27.4 36.5 1.5X
length 20 from 10 to 36 37 39 2 27.2 36.8 1.5X
length 30 from 10 to 16 37 39 2 27.0 37.0 1.4X
length 30 from 10 to 36 36 38 2 27.5 36.3 1.5X
length 20 from 16 to 10 35 38 2 28.3 35.4 1.5X
length 20 from 16 to 36 34 38 3 29.2 34.3 1.6X
length 30 from 16 to 10 38 40 2 26.3 38.1 1.4X
length 30 from 16 to 36 37 38 1 27.2 36.8 1.5X
```
Result without patch:
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_191-b12 on Mac OS X 10.14.6
Intel(R) Core(TM) i5-8259U CPU 2.30GHz
conv: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
length 10 from 2 to 16 66 101 29 15.1 66.1 1.0X
length 10 from 2 to 10 50 55 5 20.2 49.5 1.3X
length 10 from 10 to 16 46 51 5 21.8 45.9 1.4X
length 10 from 10 to 36 43 48 4 23.4 42.7 1.5X
length 10 from 16 to 10 44 47 4 22.9 43.7 1.5X
length 10 from 16 to 36 40 44 2 24.7 40.5 1.6X
length 10 from 36 to 10 40 44 4 25.0 40.1 1.6X
length 10 from 36 to 16 41 43 2 24.3 41.2 1.6X
length 20 from 10 to 16 39 41 2 25.7 38.9 1.7X
length 20 from 10 to 36 40 42 2 24.9 40.2 1.6X
length 30 from 10 to 16 39 40 1 25.9 38.6 1.7X
length 30 from 10 to 36 40 41 1 25.0 40.0 1.7X
length 20 from 16 to 10 40 41 1 25.1 39.8 1.7X
length 20 from 16 to 36 40 42 2 25.2 39.7 1.7X
length 30 from 16 to 10 39 42 2 25.6 39.0 1.7X
length 30 from 16 to 36 39 40 2 25.7 38.8 1.7X
```
Closes#30350 from AngersZhuuuu/SPARK-33428.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR migrates `ALTER VIEW ... AS` to use `UnresolvedView` to resolve the 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).
The `TempViewOrV1Table` extractor in `ResolveSessionCatalog.scala` can now be removed as well.
### Why are the changes needed?
To use `UnresolvedView` for view resolution.
### Does this PR introduce _any_ user-facing change?
The exception message changes if a table is found instead of view:
```
// OLD
`tab1` is not a view"
```
```
// NEW
"tab1 is a table. 'ALTER VIEW ... AS' expects a view."
```
### How was this patch tested?
Updated existing tests.
Closes#30723 from imback82/alter_view_as_statement.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Addressed comments in PR #30567, including:
1. add test case for SPARK-33647 and SPARK-33142
2. add migration guide
3. add `getRawTempView` and `getRawGlobalTempView` to return the raw view info (i.e. TemporaryViewRelation)
4. other minor code clean
### Why are the changes needed?
Code clean and more test cases
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing and newly added test cases
Closes#30666 from linhongliu-db/SPARK-33142-followup.
Lead-authored-by: Linhong Liu <linhong.liu@databricks.com>
Co-authored-by: Linhong Liu <67896261+linhongliu-db@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
[SPARK-33546] stated the there are three inconsistency behaviors for CREATE TABLE LIKE.
1. CREATE TABLE LIKE does not validate the user-specified hive serde. e.g., STORED AS PARQUET can't be used with ROW FORMAT SERDE.
2. CREATE TABLE LIKE requires STORED AS and ROW FORMAT SERDE to be specified together, which is not necessary.
3. CREATE TABLE LIKE does not respect the default hive serde.
This PR fix No.1, and after investigate, No.2 and No.3 turn out not to be issue.
Within Hive.
CREATE TABLE abc ... ROW FORMAT SERDE 'xxx.xxx.SerdeClass' (Without Stored as) will have
following result. Using the user specific SerdeClass and fetch default input/output format from default textfile format.
```
SerDe Library: xxx.xxx.SerdeClass
InputFormat: org.apache.hadoop.mapred.TextInputFormat
OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
```
But for
CREATE TABLE dst LIKE src ROW FORMAT SERDE 'xxx.xxx.SerdeClass' (Without Stored as) will just ignore user specific SerdeClass and using (input, output, serdeClass) from src table.
It's better to just throw an exception on such ambiguous behavior, so No.2 is not an issue, but in the PR, we add some comments.
For No.3, in fact, CreateTableLikeCommand is using following logical to try to follow src table's storageFormat if current fileFormat.inputFormat is empty
```
val newStorage = if (fileFormat.inputFormat.isDefined) {
fileFormat
} else {
sourceTableDesc.storage.copy(locationUri = fileFormat.locationUri)
}
```
If we try to fill the new target table with HiveSerDe.getDefaultStorage if file format and row format is not explicity spefified, it will break the CREATE TABLE LIKE semantic.
### Why are the changes needed?
Bug Fix.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added UT and Existing UT.
Closes#30705 from leanken/leanken-SPARK-33546.
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?
Remove the `retainData` parameter from the logical node `AlterTableDropPartition`.
### Why are the changes needed?
The `AlterTableDropPartition` command reflects the sql statement (see SqlBase.g4):
```
| ALTER (TABLE | VIEW) multipartIdentifier
DROP (IF EXISTS)? partitionSpec (',' partitionSpec)* PURGE? #dropTablePartitions
```
but Spark doesn't allow to specify data retention. So, the parameter can be removed to improve code maintenance.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the test suite `DDLParserSuite`.
Closes#30748 from MaxGekk/remove-retainData.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Modify the tests that add partitions with `LOCATION`, and where the number of nested folders in `LOCATION` doesn't match to the number of partitioned columns. In that case, `ALTER TABLE .. DROP PARTITION` tries to access (delete) folder out of the "base" path in `LOCATION`.
The problem belongs to Hive's MetaStore method `drop_partition_common`:
8696c82d07/standalone-metastore/metastore-server/src/main/java/org/apache/hadoop/hive/metastore/HiveMetaStore.java (L4876)
which tries to delete empty partition sub-folders recursively starting from the most deeper partition sub-folder up to the base folder. In the case when the number of sub-folder is not equal to the number of partitioned columns `part_vals.size()`, the method will try to list and delete folders out of the base path.
### Why are the changes needed?
To fix test failures like https://github.com/apache/spark/pull/30643#issuecomment-743774733:
```
org.apache.spark.sql.hive.execution.command.AlterTableAddPartitionSuite.ALTER TABLE .. ADD PARTITION Hive V1: SPARK-33521: universal type conversions of partition values
sbt.ForkMain$ForkError: org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: File file:/home/jenkins/workspace/SparkPullRequestBuilder/target/tmp/spark-832cb19c-65fd-41f3-ae0b-937d76c07897 does not exist;
at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:112)
at org.apache.spark.sql.hive.HiveExternalCatalog.dropPartitions(HiveExternalCatalog.scala:1014)
...
Caused by: sbt.ForkMain$ForkError: org.apache.hadoop.hive.metastore.api.MetaException: File file:/home/jenkins/workspace/SparkPullRequestBuilder/target/tmp/spark-832cb19c-65fd-41f3-ae0b-937d76c07897 does not exist
at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.drop_partition_with_environment_context(HiveMetaStore.java:3381)
at sun.reflect.GeneratedMethodAccessor304.invoke(Unknown Source)
```
The issue can be reproduced by the following steps:
1. Create a base folder, for example: `/Users/maximgekk/tmp/part-location`
2. Create a sub-folder in the base folder and drop permissions for it:
```
$ mkdir /Users/maximgekk/tmp/part-location/aaa
$ chmod a-rwx chmod a-rwx /Users/maximgekk/tmp/part-location/aaa
$ ls -al /Users/maximgekk/tmp/part-location
total 0
drwxr-xr-x 3 maximgekk staff 96 Dec 13 18:42 .
drwxr-xr-x 33 maximgekk staff 1056 Dec 13 18:32 ..
d--------- 2 maximgekk staff 64 Dec 13 18:42 aaa
```
3. Create a table with a partition folder in the base folder:
```sql
spark-sql> create table tbl (id int) partitioned by (part0 int, part1 int);
spark-sql> alter table tbl add partition (part0=1,part1=2) location '/Users/maximgekk/tmp/part-location/tbl';
```
4. Try to drop this partition:
```
spark-sql> alter table tbl drop partition (part0=1,part1=2);
20/12/13 18:46:07 ERROR HiveClientImpl:
======================
Attempt to drop the partition specs in table 'tbl' database 'default':
Map(part0 -> 1, part1 -> 2)
In this attempt, the following partitions have been dropped successfully:
The remaining partitions have not been dropped:
[1, 2]
======================
Error in query: org.apache.hadoop.hive.ql.metadata.HiveException: Error accessing file:/Users/maximgekk/tmp/part-location/aaa;
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: Error accessing file:/Users/maximgekk/tmp/part-location/aaa;
```
The command fails because it tries to access to the sub-folder `aaa` that is out of the partition path `/Users/maximgekk/tmp/part-location/tbl`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the affected tests from local IDEA which does not have access to folders out of partition paths.
Closes#30752 from MaxGekk/fix-drop-partition-location.
Lead-authored-by: Max Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Currently, when casting a string as timestamp type in ANSI mode, Spark throws a runtime exception on parsing error.
However, the result for casting a string to date is always null. We should throw an exception on parsing error as well.
### Why are the changes needed?
Add missing feature for ANSI mode
### Does this PR introduce _any_ user-facing change?
Yes for ANSI mode, Casting string to date will throw an exception on parsing error
### How was this patch tested?
Unit test
Closes#30687 from gengliangwang/castDate.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Currently the maintenance interval is hard-coded in `StateStore`. This patch proposes to make it as SQL config.
### Why are the changes needed?
Currently the maintenance interval is hard-coded in `StateStore`. For consistency reason, it should be placed together with other SS configs together. SQLConf also has a better way to have doc and default value setting.
### Does this PR introduce _any_ user-facing change?
Yes. Previously users use Spark config to set the maintenance interval. Now they could use SQL config to set it.
### How was this patch tested?
Unit test.
Closes#30741 from viirya/maintenance-interval-sqlconfig.
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 add a new config `spark.sql.thriftServer.forceCancel` to give user a way to interrupt task when cancel statement.
### Why are the changes needed?
After [#29933](https://github.com/apache/spark/pull/29933), we support cancel query if timeout, but the default behavior of `SparkContext.cancelJobGroups` won't interrupt task and just let task finish by itself. In some case it's dangerous, e.g., data skew or exists a heavily shuffle. A task will hold in a long time after do cancel and the resource will not release.
### Does this PR introduce _any_ user-facing change?
Yes, a new config.
### How was this patch tested?
Add test.
Closes#30481 from ulysses-you/SPARK-33526.
Lead-authored-by: ulysses-you <ulyssesyou18@gmail.com>
Co-authored-by: ulysses-you <youxiduo@weidian.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1. Check that the partition identifier passed to `SupportsPartitionManagement.partitionExists()` is fully specified (specifies all values of partition fields).
2. Remove the custom implementation of `partitionExists()` from `InMemoryPartitionTable`, and re-use the default implementation from `SupportsPartitionManagement`.
### Why are the changes needed?
The method is supposed to check existence of one partition but currently it can return `true` for partially specified partition. This can lead to incorrect commands behavior, for instance the commands could modify or place data in the middle of partition path.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running existing test suites:
```
$ build/sbt "test:testOnly *AlterTablePartitionV2SQLSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *SupportsPartitionManagementSuite"
```
Closes#30667 from MaxGekk/check-len-partitionExists.
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 migrate `CACHE TABLE` to use `UnresolvedRelation` to resolve the table/view identifier in Analyzer as discussed https://github.com/apache/spark/pull/30403/files#r532360022.
### Why are the changes needed?
To resolve the table in the analyzer.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests
Closes#30598 from imback82/cache_v2.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR fixes the Scala 2.13 build failure brought by #30479 .
### Why are the changes needed?
To pass Scala 2.13 build.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Should be done byGitHub Actions.
Closes#30727 from sarutak/fix-scala213-build-failure.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Replace `legacy_setops_precedence_enbled` with `legacy_setops_precedence_enabled`
Alternatively, `legacy_setops_precedence_enabled` could be added, and `legacy_setops_precedence_enbled` retained, and if set the code could honor it and warn about the deprecated spelling.
### Why are the changes needed?
`enabled` is misspelled in `legacy_setops_precedence_enbled`
### Does this PR introduce _any_ user-facing change?
Yes.
It would break current consumers.
Examples include:
* https://www.programmersought.com/article/87752082924/
* 125d873c38/fugue_sql/_antlr/fugue_sqlLexer.py
* https://github.com/search?q=legacy_setops_precedence_enbled&type=code
### How was this patch tested?
It's been included in #30323 for a while (and is now split out here)
Closes#30677 from jsoref/spelling-enabled.
Authored-by: Josh Soref <jsoref@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Using the view captured catalog and namespace to lookup function, so the view
referred functions won't be overridden by newly created function with the same name,
but different database or function type (i.e. temporary function)
### Why are the changes needed?
bug fix, without this PR, changing database or create a temporary function with
the same name may cause failure when querying a view.
### Does this PR introduce _any_ user-facing change?
Yes, bug fix.
### How was this patch tested?
newly added and existing test cases.
Closes#30662 from linhongliu-db/SPARK-33692.
Lead-authored-by: Linhong Liu <linhong.liu@databricks.com>
Co-authored-by: Linhong Liu <67896261+linhongliu-db@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR follows up https://github.com/apache/spark/pull/29497.
Because https://github.com/apache/spark/pull/29497 just give us an example to group all `AnalysisExcpetion` in Analyzer into QueryCompilationErrors.
This PR group other `AnalysisExcpetion` into QueryCompilationErrors.
### 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#30564 from beliefer/SPARK-32670-followup.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds `allowTemp` flag to `UnresolvedView` so that `Analyzer` can check whether to resolve temp views or not.
This PR also migrates `ALTER VIEW ... SET/UNSET TBLPROPERTIES` to use `UnresolvedView` 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).
### Why are the changes needed?
To use `UnresolvedView` for view resolution.
One benefit is that the exception message is better for `ALTER VIEW ... SET/UNSET TBLPROPERTIES`. Before, if a temp view is passed, you will just get `NoSuchTableException` with `Table or view 'tmpView' not found in database 'default'`. But with this PR, you will get more description exception message: `tmpView is a temp view. ALTER VIEW ... SET TBLPROPERTIES expects a permanent view`.
### Does this PR introduce _any_ user-facing change?
The exception message changes as describe above.
### How was this patch tested?
Updated existing tests.
Closes#30676 from imback82/alter_view_set_unset_properties.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Move the `ALTER TABLE .. ADD PARTITION` parsing tests to `AlterTableAddPartitionParserSuite`
2. Place v1 tests for `ALTER TABLE .. ADD PARTITION` from `DDLSuite` and v2 tests from `AlterTablePartitionV2SQLSuite` to the common trait `AlterTableAddPartitionSuiteBase`, so, the tests will run for V1, Hive V1 and V2 DS.
### Why are the changes needed?
- The unification will allow to run common `ALTER TABLE .. ADD PARTITION` 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 *AlterTableAddPartitionSuite"
```
Closes#30685 from MaxGekk/unify-alter-table-add-partition-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 adds `DeleteFromTable` to supported plans in `ReplaceNullWithFalseInPredicate`.
### Why are the changes needed?
This change allows Spark to optimize delete conditions like we optimize filters.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
This PR extends the existing test cases to also cover `DeleteFromTable`.
Closes#30688 from aokolnychyi/spark-33722.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR is a followup of https://github.com/apache/spark/pull/30488. This PR proposes to rename `Alias.deniedMetadataKeys` to `Alias.nonInheritableMetadataKeys` to make it less confusing.
### Why are the changes needed?
To make it easier to maintain and read.
### Does this PR introduce _any_ user-facing change?
No. This is rather a code cleanup.
### How was this patch tested?
Ran the unittests written in the previous PR manually. Jenkins and GitHub Actions in this PR should also test them.
Closes#30682 from HyukjinKwon/SPARK-33071-SPARK-33536.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to migrate `MSCK REPAIR 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 `MSCK REPAIR TABLE` is not supported for v2 tables.
### Why are the changes needed?
The PR makes the resolution consistent behavior consistent. For example,
```scala
sql("CREATE DATABASE test")
sql("CREATE TABLE spark_catalog.test.t (id bigint, val string) USING csv PARTITIONED BY (id)")
sql("CREATE TEMPORARY VIEW t AS SELECT 2")
sql("USE spark_catalog.test")
sql("MSCK REPAIR TABLE t") // works fine
```
, but after this PR:
```
sql("MSCK REPAIR TABLE t")
org.apache.spark.sql.AnalysisException: t is a temp view. 'MSCK REPAIR TABLE' expects a table; line 1 pos 0
```
, which is the consistent behavior with other commands.
### Does this PR introduce _any_ user-facing change?
After this PR, `MSCK REPAIR TABLE t` in the above example is resolved to a temp view `t` first instead of `spark_catalog.test.t`.
### How was this patch tested?
Updated existing tests.
Closes#30664 from imback82/repair_table_V2.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, Spark treats 0.0 and -0.0 semantically equal, while it still retains the difference between them so that users can see -0.0 when displaying the data set.
The comparison expressions in Spark take care of the special floating numbers and implement the correct semantic. However, Spark doesn't always use these comparison expressions to compare values, and we need to normalize the special floating numbers before comparing them in these places:
1. GROUP BY
2. join keys
3. window partition keys
This PR fixes one more place that compares values without using comparison expressions: HyperLogLog++
### Why are the changes needed?
Fix the query result
### Does this PR introduce _any_ user-facing change?
Yes, the result of HyperLogLog++ becomes correct now.
### How was this patch tested?
a new test case, and a few more test cases that pass before this PR to improve test coverage.
Closes#30673 from cloud-fan/bug.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR introduces `UnresolvedView` in the resolution framework to resolve the identifier.
This PR then migrates `DROP VIEW` to use `UnresolvedView` 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).
### Why are the changes needed?
To use `UnresolvedView` for view resolution. Note that 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#30636 from imback82/drop_view_v2.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Remove old statement `ShowTableStatement`
2. Introduce new command `ShowTableExtended` for `SHOW TABLE EXTENDED`.
This PR is the first step of new V2 implementation of `SHOW TABLE EXTENDED`, see SPARK-33393.
### Why are the changes needed?
This is a part of effort to make the relation lookup behavior consistent: SPARK-29900.
### Does this PR introduce _any_ user-facing change?
The changes should not affect V1 tables. For V2, Spark outputs the error:
```
SHOW TABLE EXTENDED is not supported for v2 tables.
```
### How was this patch tested?
By running `SHOW TABLE EXTENDED` tests:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *ShowTablesSuite"
```
Closes#30645 from MaxGekk/show-table-extended-statement.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`LikeSimplification` rule does not work correctly for many cases that have patterns containing escape characters, for example:
`SELECT s LIKE 'm%aca' ESCAPE '%' FROM t`
`SELECT s LIKE 'maacaa' ESCAPE 'a' FROM t`
For simpilicy, this PR makes this rule just be skipped if `pattern` contains any `escapeChar`.
### Why are the changes needed?
Result corrupt.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added Unit test.
Closes#30625 from luluorta/SPARK-33677.
Authored-by: luluorta <luluorta@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR aims to enable `spark.sql.adaptive.enabled` by default for Apache Spark **3.2.0**.
### Why are the changes needed?
By switching the default for Apache Spark 3.2, the whole community can focus more on the stabilizing this feature in the various situation more seriously.
### Does this PR introduce _any_ user-facing change?
Yes, but this is an improvement and it's supposed to have no bugs.
### How was this patch tested?
Pass the CIs.
Closes#30628 from dongjoon-hyun/SPARK-33679.
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 proposes to migrate `ALTER [TABLE|ViEW] ... RENAME TO` 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).
### Why are the changes needed?
To use `UnresolvedTableOrView` for table/view resolution. Note that `AlterTableRenameCommand` 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#30610 from imback82/rename_v2.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/30412. This PR updates the error message of char/varchar table insertion length check, to not expose user data.
### Why are the changes needed?
This is risky to expose user data in the error message, especially the string data, as it may contain sensitive data.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
updated tests
Closes#30653 from cloud-fan/minor2.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/30554 . Now we have a new config for converting CREATE TABLE, we don't need the old config that only works for CTAS.
### Why are the changes needed?
It's confusing for having two config while one can cover another completely.
### Does this PR introduce _any_ user-facing change?
no, it's deprecating not removing.
### How was this patch tested?
N/A
Closes#30651 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR intends to fix typos in the sub-modules:
* `sql/catalyst`
* `sql/hive-thriftserver`
* `sql/hive`
Split per srowen https://github.com/apache/spark/pull/30323#issuecomment-728981618
NOTE: The misspellings have been reported at 706a726f87 (commitcomment-44064356)
### Why are the changes needed?
Misspelled words make it harder to read / understand content.
### Does this PR introduce _any_ user-facing change?
There are various fixes to documentation, etc...
### How was this patch tested?
No testing was performed
Closes#30532 from jsoref/spelling-sql-not-core.
Authored-by: Josh Soref <jsoref@users.noreply.github.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
In this PR, we suppose to narrow the use cases of the char/varchar data types, of which are invalid now or later
### Why are the changes needed?
1. udf
```scala
scala> spark.udf.register("abcd", () => "12345", org.apache.spark.sql.types.VarcharType(2))
scala> spark.sql("select abcd()").show
scala.MatchError: CharType(2) (of class org.apache.spark.sql.types.VarcharType)
at org.apache.spark.sql.catalyst.encoders.RowEncoder$.externalDataTypeFor(RowEncoder.scala:215)
at org.apache.spark.sql.catalyst.encoders.RowEncoder$.externalDataTypeForInput(RowEncoder.scala:212)
at org.apache.spark.sql.catalyst.expressions.objects.ValidateExternalType.<init>(objects.scala:1741)
at org.apache.spark.sql.catalyst.encoders.RowEncoder$.$anonfun$serializerFor$3(RowEncoder.scala:175)
at scala.collection.TraversableLike.$anonfun$flatMap$1(TraversableLike.scala:245)
at scala.collection.IndexedSeqOptimized.foreach(IndexedSeqOptimized.scala:36)
at scala.collection.IndexedSeqOptimized.foreach$(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:198)
at scala.collection.TraversableLike.flatMap(TraversableLike.scala:245)
at scala.collection.TraversableLike.flatMap$(TraversableLike.scala:242)
at scala.collection.mutable.ArrayOps$ofRef.flatMap(ArrayOps.scala:198)
at org.apache.spark.sql.catalyst.encoders.RowEncoder$.serializerFor(RowEncoder.scala:171)
at org.apache.spark.sql.catalyst.encoders.RowEncoder$.apply(RowEncoder.scala:66)
at org.apache.spark.sql.Dataset$.$anonfun$ofRows$2(Dataset.scala:99)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:768)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:96)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:611)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:768)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:606)
... 47 elided
```
2. spark.createDataframe
```
scala> spark.createDataFrame(spark.read.text("README.md").rdd, new org.apache.spark.sql.types.StructType().add("c", "char(1)")).show
+--------------------+
| c|
+--------------------+
| # Apache Spark|
| |
|Spark is a unifie...|
|high-level APIs i...|
|supports general ...|
|rich set of highe...|
|MLlib for machine...|
|and Structured St...|
| |
|<https://spark.ap...|
| |
|[![Jenkins Build]...|
|[ to see rationalization of this patch.
This patch proposes to introduce state schema validation, via storing key schema and value schema to `schema` file (for the first time) and verify new key schema and value schema for state are compatible with existing one. To be clear for definition of "compatible", state schema is "compatible" when number of fields are same and data type for each field is same - Spark has been allowing rename of field.
This patch will prevent query run which has incompatible state schema, which would reduce the chance to get indeterministic behavior (actually renaming of field is also the smell of semantically incompatible, but end users could just modify its name so we can't say) as well as providing more informative error message.
## How was this patch tested?
Added UTs.
Closes#24173 from HeartSaVioR/SPARK-27237.
Lead-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Co-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
For the SQL configs `spark.sql.legacy.parquet.datetimeRebaseModeInWrite` and `spark.sql.legacy.parquet.datetimeRebaseModeInRead`, improve their descriptions by:
1. Explicitly document on which parquet types, those configs influence on
2. Refer to corresponding configs for `INT96`
### Why are the changes needed?
To avoid user confusions like reposted in SPARK-33571, and make the config description more precise.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running `./dev/scalastyle`.
Closes#30596 from MaxGekk/clarify-rebase-docs.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Suggest users using Datetime conversion functions in the error message of invalid ANSI explicit casting.
### Why are the changes needed?
In ANSI mode, explicit cast between DateTime types and Numeric types is not allowed.
As of now, we have introduced new functions `UNIX_SECONDS`/`UNIX_MILLIS`/`UNIX_MICROS`/`UNIX_DATE`/`DATE_FROM_UNIX_DATE`, we can show suggestions to users so that they can complete these type conversions precisely and easily in ANSI mode.
### Does this PR introduce _any_ user-facing change?
Yes, better error messages
### How was this patch tested?
Unit test
Closes#30603 from gengliangwang/improveErrorMsgOfExplicitCast.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Currently, in spark, the temp view is saved as its analyzed logical plan, while the permanent view
is kept in HMS with its origin SQL text. As a result, permanent and temporary views have
different behaviors in some cases. In this PR we store the SQL text for temporary view in order
to unify the behavior between permanent and temporary views.
### Why are the changes needed?
to unify the behavior between permanent and temporary views
### Does this PR introduce _any_ user-facing change?
Yes, with this PR, the temporary view will be re-analyzed when it's referred. So if the
underlying datasource changed, the view will also be updated.
### How was this patch tested?
existing and newly added test cases
Closes#30567 from linhongliu-db/SPARK-33142.
Authored-by: Linhong Liu <linhong.liu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Improve the documentation of SQL configuration `spark.sql.ansi.enabled`
### Why are the changes needed?
As there are more and more new features under the SQL configuration `spark.sql.ansi.enabled`, we should make it more clear about:
1. what exactly it is
2. where can users find all the features of the ANSI mode
3. whether all the features are exactly from the SQL standard
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
It's just doc change.
Closes#30593 from gengliangwang/reviseAnsiDoc.
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?
In the PR, I propose to change the order of post-analysis checks for the `ALTER TABLE .. ADD/DROP PARTITION` command, and perform the general check (does the table support partition management at all) before specific checks.
### Why are the changes needed?
The error message for the table which doesn't support partition management can mislead users:
```java
PartitionSpecs are not resolved;;
'AlterTableAddPartition [UnresolvedPartitionSpec(Map(id -> 1),None)], false
+- ResolvedTable org.apache.spark.sql.connector.InMemoryTableCatalog2fd64b11, ns1.ns2.tbl, org.apache.spark.sql.connector.InMemoryTable5d3ff859
```
because it says nothing about the root cause of the issue.
### Does this PR introduce _any_ user-facing change?
Yes. After the change, the error message will be:
```
Table ns1.ns2.tbl can not alter partitions
```
### How was this patch tested?
By running the affected test suite `AlterTablePartitionV2SQLSuite`.
Closes#30594 from MaxGekk/check-order-AlterTablePartition.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR provides us with a way to check if a data source is going to reject the delete via `deleteWhere` at planning time.
### Why are the changes needed?
The only way to support delete statements right now is to implement ``SupportsDelete``. According to its Javadoc, that interface is meant for cases when we can delete data without much effort (e.g. like deleting a complete partition in a Hive table).
This PR actually provides us with a way to check if a data source is going to reject the delete via `deleteWhere` at planning time instead of just getting an exception during execution. In the future, we can use this functionality to decide whether Spark should rewrite this delete and execute a distributed query or it can just pass a set of filters.
Consider an example of a partitioned Hive table. If we have a delete predicate like `part_col = '2020'`, we can just drop the matching partition to satisfy this delete. In this case, the data source should return `true` from `canDeleteWhere` and use the filters it accepts in `deleteWhere` to drop the partition. I consider this as a delete without significant effort. At the same time, if we have a delete predicate like `id = 10`, Hive tables would not be able to execute this delete using a metadata only operation without rewriting files. In that case, the data source should return `false` from `canDeleteWhere` and we should use a more sophisticated row-level API to find out which records should be removed (the API is yet to be discussed, but we need this PR as a basis).
If we decide to support subqueries and all delete use cases by simply extending the existing API, this will mean all data sources will have to implement a lot of Spark logic to determine which records changed. I don't think we want to go that way as the Spark logic to determine which records should be deleted is independent of the underlying data source. So the assumption is that Spark will execute a plan to find which records must be deleted for data sources that return `false` from `canDeleteWhere`.
### Does this PR introduce _any_ user-facing change?
Yes but it is backward compatible.
### How was this patch tested?
This PR comes with a new test.
Closes#30562 from aokolnychyi/spark-33623.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
For CRETE TABLE [AS SELECT] command, creates native Parquet table if neither USING nor STORE AS is specified and `spark.sql.legacy.createHiveTableByDefault` is false.
This is a retry after we unify the CREATE TABLE syntax. It partially reverts d2bec5e265
This PR allows `CREATE EXTERNAL TABLE` when `LOCATION` is present. This was not allowed for data source tables before, which is an unnecessary behavior different with hive tables.
### Why are the changes needed?
Changing from Hive text table to native Parquet table has many benefits:
1. be consistent with `DataFrameWriter.saveAsTable`.
2. better performance
3. better support for nested types (Hive text table doesn't work well with nested types, e.g. `insert into t values struct(null)` actually inserts a null value not `struct(null)` if `t` is a Hive text table, which leads to wrong result)
4. better interoperability as Parquet is a more popular open file format.
### Does this PR introduce _any_ user-facing change?
No by default. If the config is set, the behavior change is described below:
Behavior-wise, the change is very small as the native Parquet table is also Hive-compatible. All the Spark DDL commands that works for hive tables also works for native Parquet tables, with two exceptions: `ALTER TABLE SET [SERDE | SERDEPROPERTIES]` and `LOAD DATA`.
char/varchar behavior has been taken care by https://github.com/apache/spark/pull/30412, and there is no behavior difference between data source and hive tables.
One potential issue is `CREATE TABLE ... LOCATION ...` while users want to directly access the files later. It's more like a corner case and the legacy config should be good enough.
Another potential issue is users may use Spark to create the table and then use Hive to add partitions with different serde. This is not allowed for Spark native tables.
### How was this patch tested?
Re-enable the tests
Closes#30554 from cloud-fan/create-table.
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 followup of [#27151](https://github.com/apache/spark/pull/27151). It fixes the same issue for the codegen path.
### Why are the changes needed?
Result corrupt.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added Unit test.
Closes#30585 from luluorta/SPARK-26218.
Authored-by: luluorta <luluorta@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add new functions DATE_FROM_UNIX_DATE and UNIX_DATE for conversion between Date type and Numeric types.
### Why are the changes needed?
1. Explicit conversion between Date type and Numeric types is disallowed in ANSI mode. We need to provide new functions for users to complete the conversion.
2. We have introduced new functions from Bigquery for conversion between Timestamp type and Numeric types: TIMESTAMP_SECONDS, TIMESTAMP_MILLIS, TIMESTAMP_MICROS , UNIX_SECONDS, UNIX_MILLIS, and UNIX_MICROS. It makes sense to add functions for conversion between Date type and Numeric types as well.
### Does this PR introduce _any_ user-facing change?
Yes, two new datetime functions are added.
### How was this patch tested?
Unit tests
Closes#30588 from gengliangwang/dateToNumber.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
As https://github.com/apache/spark/pull/28534 adds functions from [BigQuery](https://cloud.google.com/bigquery/docs/reference/standard-sql/timestamp_functions) for converting numbers to timestamp, this PR is to add functions UNIX_SECONDS, UNIX_MILLIS and UNIX_MICROS for converting timestamp to numbers.
### Why are the changes needed?
1. Symmetry of the conversion functions
2. Casting timestamp type to numeric types is disallowed in ANSI mode, we should provide functions for users to complete the conversion.
### Does this PR introduce _any_ user-facing change?
3 new functions UNIX_SECONDS, UNIX_MILLIS and UNIX_MICROS for converting timestamp to long type.
### How was this patch tested?
Unit tests.
Closes#30566 from gengliangwang/timestampLong.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Currently, `join()` uses `withPlan(logicalPlan)` for convenient to call some Dataset functions. But it leads to the `dataset_id` inconsistent between the `logicalPlan` and the original `Dataset`(because `withPlan(logicalPlan)` will create a new Dataset with the new id and reset the `dataset_id` with the new id of the `logicalPlan`). As a result, it breaks the rule `DetectAmbiguousSelfJoin`.
In this PR, we propose to drop the usage of `withPlan` but use the `logicalPlan` directly so its `dataset_id` doesn't change.
Besides, this PR also removes related metadata (`DATASET_ID_KEY`, `COL_POS_KEY`) when an `Alias` tries to construct its own metadata. Because the `Alias` is no longer a reference column after converting to an `Attribute`. To achieve that, we add a new field, `deniedMetadataKeys`, to indicate the metadata that needs to be removed.
### Why are the changes needed?
For the query below, it returns the wrong result while it should throws ambiguous self join exception instead:
```scala
val emp1 = Seq[TestData](
TestData(1, "sales"),
TestData(2, "personnel"),
TestData(3, "develop"),
TestData(4, "IT")).toDS()
val emp2 = Seq[TestData](
TestData(1, "sales"),
TestData(2, "personnel"),
TestData(3, "develop")).toDS()
val emp3 = emp1.join(emp2, emp1("key") === emp2("key")).select(emp1("*"))
emp1.join(emp3, emp1.col("key") === emp3.col("key"), "left_outer")
.select(emp1.col("*"), emp3.col("key").as("e2")).show()
// wrong result
+---+---------+---+
|key| value| e2|
+---+---------+---+
| 1| sales| 1|
| 2|personnel| 2|
| 3| develop| 3|
| 4| IT| 4|
+---+---------+---+
```
This PR fixes the wrong behaviour.
### Does this PR introduce _any_ user-facing change?
Yes, users hit the exception instead of the wrong result after this PR.
### How was this patch tested?
Added a new unit test.
Closes#30488 from Ngone51/fix-self-join.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Code Gen bug fix that introduced by SPARK-33460
```
GetMapValueUtil
s"""throw new NoSuchElementException("Key " + $eval2 + " does not exist.");"""
SHOULD BE
s"""throw new java.util.NoSuchElementException("Key " + $eval2 + " does not exist.");"""
```
And the reason why SPARK-33460 failed to detect this bug via UT, it was because that `checkExceptionInExpression ` did not work as expect like `checkEvaluation` which will try eval expression with BOTH `CODEGEN_ONLY` and `NO_CODEGEN` mode, and in this PR, will also fix this Test bug, too.
### Why are the changes needed?
Bug Fix.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Add UT and Existing UT.
Closes#30560 from leanken/leanken-SPARK-33619.
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?
This PR is a followup of https://github.com/apache/spark/pull/30504. It proposes:
- Rename `NoSideEffect` to `NoThrow`, and use `Expression.deterministic` together where it is used.
- Clarify, in the docs in the expressions, that it means they don't throw exceptions
### Why are the changes needed?
`NoSideEffect` virtually means that `Expression.eval` does not throw an exception, and the expressions are deterministic.
It's best to be explicit so `NoThrow` was proposed - I looked if there's a similar name to represent this concept and borrowed the name of [nothrow](https://clang.llvm.org/docs/AttributeReference.html#nothrow).
For determinism, we already have a way to note it under `Expression.deterministic`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Manually ran the existing unittests written.
Closes#30570 from HyukjinKwon/SPARK-33544.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c936) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes#30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR is to add full outer stream-stream join, and the implementation of full outer join is:
* For left side input row, check if there's a match on right side state store.
* if there's a match, output the joined row, o.w. output nothing. Put the row in left side state store.
* For right side input row, check if there's a match on left side state store.
* if there's a match, output the joined row, o.w. output nothing. Put the row in right side state store.
* State store eviction: evict rows from left/right side state store below watermark, and output rows never matched before (a combination of left outer and right outer join).
### Why are the changes needed?
Enable more use cases for spark stream-stream join.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added unit tests in `UnsupportedOperationChecker.scala` and `StreamingJoinSuite.scala`.
Closes#30395 from c21/stream-foj.
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?
https://issues.apache.org/jira/browse/SPARK-32295 added in an optimization to insert a filter for not null and size > 0 when using inner explode/inline. This is fine in most cases but the extra filter is not needed if the explode is with a create array and not using Literals (it already handles LIterals). When this happens you know that the values aren't null and it has a size. It already handles the empty array.
The not null check is already optimized out because Createarray and createMap are not nullable, that leaves the size > 0 check. To handle that this PR makes it so that the size > 0 check gets optimized in ConstantFolding to be the size of the children in the array or map. That makes it a literal and then makes it ultimately be optimized out.
### Why are the changes needed?
remove unneeded filter
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
Unit tests added and manually tested various cases
Closes#30504 from tgravescs/SPARK-33544.
Lead-authored-by: Thomas Graves <tgraves@nvidia.com>
Co-authored-by: Thomas Graves <tgraves@apache.org>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adds a new batch to the optimizer for executing rules that rewrite plans for data sources.
### Why are the changes needed?
Right now, we have a special place in the optimizer where we construct v2 scans. As time shows, we need more rewrite rules that would be executed after the operator optimization and before any stats-related rules for v2 tables. Not all rules will be specific to reads. One option is to rename the current batch into something more generic but it would require changing quite some places. That's why it seems better to introduce a new batch and use it for all rewrites. The name is generic so that we don't limit ourselves to v2 data sources only.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
The change is trivial and SPARK-23889 will depend on it.
Closes#30558 from aokolnychyi/spark-33612.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR adds logic to handle DELETE/UPDATE/MERGE plans in `PullupCorrelatedPredicates`.
### Why are the changes needed?
Right now, `PullupCorrelatedPredicates` applies only to filters and unary nodes. As a result, correlated predicates in DELETE/UPDATE/MERGE are not rewritten.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
The PR adds 3 new test cases.
Closes#30555 from aokolnychyi/spark-33608.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a followup of #30302 . As part of this PR, sameOrderExpressions set is made part of children of SortOrder node - so that they don't need any special handling as done in #30302 .
### Why are the changes needed?
sameOrderExpressions should get same treatment as child. So making them part of children helps in transforming them easily.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing UTs
Closes#30430 from prakharjain09/SPARK-33400-sortorder-refactor.
Authored-by: Prakhar Jain <prakharjain09@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Spark already support `LIKE ANY` syntax, but it will throw `StackOverflowError` if there are many elements(more than 14378 elements). We should implement built-in function for LIKE ANY 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 ANY` and avoid this issue.
### Why are the changes needed?
1.Fix the `StackOverflowError` issue.
2.Support built-in function `like_any`.
### Does this PR introduce _any_ user-facing change?
'No'.
### How was this patch tested?
Jenkins test.
Closes#30465 from beliefer/SPARK-33045-like_any-bak.
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?
Deprecated `KafkaConsumer.poll(long)` API calls may cause infinite wait in the driver. In this PR I've added a new `AdminClient` based offset fetching which is turned off by default. There is a new flag named `spark.sql.streaming.kafka.useDeprecatedOffsetFetching` (default: `true`) which can be set to `false` to reach the newly added functionality. The Structured Streaming migration guide contains more information what migration consideration must be done. Please see the following [doc](https://docs.google.com/document/d/1gAh0pKgZUgyqO2Re3sAy-fdYpe_SxpJ6DkeXE8R1P7E/edit?usp=sharing) for further details.
The PR contains the following changes:
* Added `AdminClient` based offset fetching
* GroupId prefix feature removed from driver but only in `AdminClient` based approach (`AdminClient` doesn't need any GroupId)
* GroupId override feature removed from driver but only in `AdminClient` based approach (`AdminClient` doesn't need any GroupId)
* Additional unit tests
* Code comment changes
* Minor bugfixes here and there
* Removed Kafka auto topic creation feature but only in `AdminClient` based approach (please see doc for rationale). In short, it's super hidden, not sure anybody ever used in production + error prone.
* Added documentation to `ss-migration-guide` and `structured-streaming-kafka-integration`
### Why are the changes needed?
Driver may hang forever.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing + additional unit tests.
Cluster test with simple Kafka topic to another topic query.
Documentation:
```
cd docs/
SKIP_API=1 jekyll build
```
Manual webpage check.
Closes#29729 from gaborgsomogyi/SPARK-32032.
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?
Datetime building should fail if the year, month, ..., second combination is invalid, when ANSI mode is enabled. This patch should update MakeDate, MakeTimestamp and MakeInterval.
### 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#30516 from waitinfuture/SPARK-33498.
Lead-authored-by: zky.zhoukeyong <zky.zhoukeyong@alibaba-inc.com>
Co-authored-by: waitinfuture <waitinfuture@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Remove the method `listPartitionIdentifiers()` from the `SupportsPartitionManagement` interface. The method lists partitions by ident prefix.
2. Rename `listPartitionByNames()` to `listPartitionIdentifiers()`.
3. Re-implement the default method `partitionExists()` using new method.
### Why are the changes needed?
Getting partitions by ident prefix only is not used, and it can be removed to improve code maintenance. Also this makes the `SupportsPartitionManagement` interface cleaner.
### Does this PR introduce _any_ user-facing change?
Should not.
### How was this patch tested?
By running the affected test suites:
```
$ build/sbt "test:testOnly org.apache.spark.sql.connector.catalog.*"
```
Closes#30514 from MaxGekk/remove-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?
1. Remove V2 logical node `ShowPartitionsStatement `, and replace it by V2 `ShowPartitions`.
2. Implement V2 execution node `ShowPartitionsExec` similar to V1 `ShowPartitionsCommand`.
### Why are the changes needed?
To have feature parity with Datasource V1.
### Does this PR introduce _any_ user-facing change?
Yes.
Before the change, `SHOW PARTITIONS` fails in V2 table catalogs with the exception:
```
org.apache.spark.sql.AnalysisException: SHOW PARTITIONS is only supported with v1 tables.
at org.apache.spark.sql.catalyst.analysis.ResolveSessionCatalog.org$apache$spark$sql$catalyst$analysis$ResolveSessionCatalog$$parseV1Table(ResolveSessionCatalog.scala:628)
at org.apache.spark.sql.catalyst.analysis.ResolveSessionCatalog$$anonfun$apply$1.applyOrElse(ResolveSessionCatalog.scala:466)
```
### How was this patch tested?
By running the following test suites:
1. Modified `ShowPartitionsParserSuite` where `ShowPartitionsStatement` is replaced by V2 `ShowPartitions`.
2. `v2.ShowPartitionsSuite`
Closes#30398 from MaxGekk/show-partitions-exec-v2.
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 adds the char/varchar type which is kind of a variant of string type:
1. Char type is fixed-length string. When comparing char type values, we need to pad the shorter one to the longer length.
2. Varchar type is string with a length limitation.
To implement the char/varchar semantic, this PR:
1. Do string length check when writing to char/varchar type columns.
2. Do string padding when reading char type columns. We don't do it at the writing side to save storage space.
3. Do string padding when comparing char type column with string literal or another char type column. (string literal is fixed length so should be treated as char type as well)
To simplify the implementation, this PR doesn't propagate char/varchar type info through functions/operators(e.g. `substring`). That said, a column can only be char/varchar type if it's a table column, not a derived column like `SELECT substring(col)`.
To be safe, this PR doesn't add char/varchar type to the query engine(expression input check, internal row framework, codegen framework, etc.). We will replace char/varchar type by string type with metadata (`Attribute.metadata` or `StructField.metadata`) that includes the original type string before it goes into the query engine. That said, the existing code will not see char/varchar type but only string type.
char/varchar type may come from several places:
1. v1 table from hive catalog.
2. v2 table from v2 catalog.
3. user-specified schema in `spark.read.schema` and `spark.readStream.schema`
4. `Column.cast`
5. schema string in places like `from_json`, pandas UDF, etc. These places use SQL parser which replaces char/varchar with string already, even before this PR.
This PR covers all the above cases, implements the length check and padding feature by looking at string type with special metadata.
### Why are the changes needed?
char and varchar are standard SQL types. varchar is widely used in other databases instead of string type.
### Does this PR introduce _any_ user-facing change?
For hive tables: now the table insertion fails if the value exceeds char/varchar length. Previously we truncate the value silently.
For other tables:
1. now char type is allowed.
2. now we have length check when inserting to varchar columns. Previously we write the value as it is.
### How was this patch tested?
new tests
Closes#30412 from cloud-fan/char.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, Spark allows calls to `count` even for non parameterless aggregate function. For example, the following query actually works:
`SELECT count() FROM tenk1;`
On the other hand, mainstream databases will throw an error.
**Oracle**
`> ORA-00909: invalid number of arguments`
**PgSQL**
`ERROR: count(*) must be used to call a parameterless aggregate function`
**MySQL**
`> 1064 - You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')`
### Why are the changes needed?
Fix a bug so that consistent with mainstream databases.
There is an example query output with/without this fix.
`SELECT count() FROM testData;`
The output before this fix:
`0`
The output after this fix:
```
org.apache.spark.sql.AnalysisException
cannot resolve 'count()' due to data type mismatch: count requires at least one argument.; line 1 pos 7
```
### Does this PR introduce _any_ user-facing change?
Yes.
If not specify parameter for `count`, will throw an error.
### How was this patch tested?
Jenkins test.
Closes#30541 from beliefer/SPARK-28646.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Dup code removed in SPARK-33498 as follow-up.
### Why are the changes needed?
Nit.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT.
Closes#30540 from leanken/leanken-SPARK-33498.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to support `CHACHE/UNCACHE TABLE` commands for v2 tables.
In addtion, this PR proposes to migrate `CACHE/UNCACHE TABLE` to use `UnresolvedTableOrView` 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).
### Why are the changes needed?
To support `CACHE/UNCACHE TABLE` commands for v2 tables.
Note that `CACHE/UNCACHE TABLE` for v1 tables/views go through `SparkSession.table` to resolve identifier, which resolves temp views first, so there is no change in the behavior by moving to the new framework.
### Does this PR introduce _any_ user-facing change?
Yes. Now the user can run `CACHE/UNCACHE TABLE` commands on v2 tables.
### How was this patch tested?
Added/updated existing tests.
Closes#30403 from imback82/cache_table.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
#### JIRA expectations
```
INSERT currently does not support named column lists.
INSERT INTO <table> (col1, col2,…) VALUES( 'val1', 'val2', … )
Note, we assume the column list contains all the column names. Issue an exception if the list is not complete. The column order could be different from the column order defined in the table definition.
```
#### implemetations
In this PR, we add a column list as an optional part to the `INSERT OVERWRITE/INTO` statements:
```
/**
* {{{
* INSERT OVERWRITE TABLE tableIdentifier [partitionSpec [IF NOT EXISTS]]? [identifierList] ...
* INSERT INTO [TABLE] tableIdentifier [partitionSpec] [identifierList] ...
* }}}
*/
```
The column list represents all expected columns with an explicit order that you want to insert to the target table. **Particularly**, we assume the column list contains all the column names in the current implementation, it will fail when the list is incomplete.
In **Analyzer**, we add a code path to resolve the column list in the `ResolveOutputRelation` rule before it is transformed to v1 or v2 command. It will fail here if the list has any field that not belongs to the target table.
Then, for v2 command, e.g. `AppendData`, we use the resolved column list and output of the target table to resolve the output of the source query `ResolveOutputRelation` rule. If the list has duplicated columns, we fail. If the list is not empty but the list size does not match the target table, we fail. If no other exceptions occur, we use the column list to map the output of the source query to the output of the target table. The column list will be set to Nil and it will not hit the rule again after it is resolved.
for v1 command, those all happen in the `PreprocessTableInsertion` rule
### Why are the changes needed?
new feature support
### Does this PR introduce _any_ user-facing change?
yes, insert into/overwrite table support specify column list
### How was this patch tested?
new tests
Closes#29893 from yaooqinn/SPARK-32976.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR makes CreateViewCommand/AlterViewAsCommand capturing runtime SQL configs and store them as view properties. These configs will be applied during the parsing and analysis phases of the view resolution. Users can set `spark.sql.legacy.useCurrentConfigsForView` to `true` to restore the behavior before.
### Why are the changes needed?
This PR is a sub-task of [SPARK-33138](https://issues.apache.org/jira/browse/SPARK-33138) that proposes to unify temp view and permanent view behaviors. This PR makes permanent views mimicking the temp view behavior that "fixes" view semantic by directly storing resolved LogicalPlan. For example, if a user uses spark 2.4 to create a view that contains null values from division-by-zero expressions, she may not want that other users' queries which reference her view throw exceptions when running on spark 3.x with ansi mode on.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
added UT + existing UTs (improved)
Closes#30289 from luluorta/SPARK-33141.
Authored-by: luluorta <luluorta@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Datetime parsing should fail if the input string can't be parsed, or the pattern string is invalid, when ANSI mode is enable. This patch should update GetTimeStamp, UnixTimeStamp, ToUnixTimeStamp and Cast.
### 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#30442 from leanken/leanken-SPARK-33498.
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?
Currently in Spark one could redefine a window. For instance:
`select count(*) OVER w FROM tenk1 WINDOW w AS (ORDER BY unique1), w AS (ORDER BY unique1);`
The window `w` is defined two times. In PgSQL, on the other hand, a thrown will happen:
`ERROR: window "w" is already defined`
### Why are the changes needed?
The current implement gives the following window definitions a higher priority. But it wasn't Spark's intention and users can't know from any document of Spark.
This PR fixes the bug.
### Does this PR introduce _any_ user-facing change?
Yes.
There is an example query output with/without this fix.
```
SELECT
employee_name,
salary,
first_value(employee_name) OVER w highest_salary,
nth_value(employee_name, 2) OVER w second_highest_salary
FROM
basic_pays
WINDOW
w AS (ORDER BY salary DESC ROWS BETWEEN UNBOUNDED PRECEDING AND 1 FOLLOWING),
w AS (ORDER BY salary DESC ROWS BETWEEN UNBOUNDED PRECEDING AND 2 FOLLOWING)
ORDER BY salary DESC
```
The output before this fix:
```
Larry Bott 11798 Larry Bott Gerard Bondur
Gerard Bondur 11472 Larry Bott Gerard Bondur
Pamela Castillo 11303 Larry Bott Gerard Bondur
Barry Jones 10586 Larry Bott Gerard Bondur
George Vanauf 10563 Larry Bott Gerard Bondur
Loui Bondur 10449 Larry Bott Gerard Bondur
Mary Patterson 9998 Larry Bott Gerard Bondur
Steve Patterson 9441 Larry Bott Gerard Bondur
Julie Firrelli 9181 Larry Bott Gerard Bondur
Jeff Firrelli 8992 Larry Bott Gerard Bondur
William Patterson 8870 Larry Bott Gerard Bondur
Diane Murphy 8435 Larry Bott Gerard Bondur
Leslie Jennings 8113 Larry Bott Gerard Bondur
Gerard Hernandez 6949 Larry Bott Gerard Bondur
Foon Yue Tseng 6660 Larry Bott Gerard Bondur
Anthony Bow 6627 Larry Bott Gerard Bondur
Leslie Thompson 5186 Larry Bott Gerard Bondur
```
The output after this fix:
```
struct<>
-- !query output
org.apache.spark.sql.catalyst.parser.ParseException
The definition of window 'w' is repetitive(line 8, pos 0)
```
### How was this patch tested?
Jenkins test.
Closes#30512 from beliefer/SPARK-28645.
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 PR proposes to improve the exception messages while `UnresolvedTableOrView` is handled based on this suggestion: https://github.com/apache/spark/pull/30321#discussion_r521127001.
Currently, when an identifier is resolved to a temp view when a table/permanent view is expected, the following exception message is displayed (e.g., for `SHOW CREATE TABLE`):
```
t is a temp view not table or permanent view.
```
After this PR, the message will be:
```
t is a temp view. 'SHOW CREATE TABLE' expects a table or permanent view.
```
Also, if an identifier is not resolved, the following exception message is currently used:
```
Table or view not found: t
```
After this PR, the message will be:
```
Table or permanent view not found for 'SHOW CREATE TABLE': t
```
or
```
Table or view not found for 'ANALYZE TABLE ... FOR COLUMNS ...': 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#30475 from imback82/unresolved_table_or_view.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
There are some differences between Spark CSV, opencsv and commons-csv, the typical case are described in SPARK-33566, When there are both unescaped quotes and unescaped qualifier in value, the results of parsing are different.
The reason for the difference is Spark use `STOP_AT_DELIMITER` as default `UnescapedQuoteHandling` to build `CsvParser` and it not configurable.
On the other hand, opencsv and commons-csv use the parsing mechanism similar to `STOP_AT_CLOSING_QUOTE ` by default.
So this pr make `unescapedQuoteHandling` option configurable to get the same parsing result as opencsv and commons-csv.
### Why are the changes needed?
Make unescapedQuoteHandling option configurable when read CSV to make parsing more flexible。
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Pass the Jenkins or GitHub Action
- Add a new case similar to that described in SPARK-33566
Closes#30518 from LuciferYang/SPARK-33566.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR fixes an AQE issue where local shuffle reader, partition coalescing, or skew join optimization can be mistakenly applied to a shuffle introduced by repartition or a regular shuffle that logically replaces a repartition shuffle.
The proposed solution checks for the presence of any repartition shuffle and filters out not applicable optimization rules for the final stage in an AQE plan.
### Why are the changes needed?
Without the change, the output of a repartition query may not be correct.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added UT.
Closes#30494 from maryannxue/csr-repartition.
Authored-by: Maryann Xue <maryann.xue@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### 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>
