### What changes were proposed in this pull request?
When we do self join with transform in a CTE, spark will throw AnalysisException.
A simple way to reproduce is
```
create temporary view t as select * from values 0, 1, 2 as t(a);
WITH temp AS (
SELECT TRANSFORM(a) USING 'cat' AS (b string) FROM t
)
SELECT t1.b FROM temp t1 JOIN temp t2 ON t1.b = t2.b
```
before this patch, it throws
```
org.apache.spark.sql.AnalysisException: cannot resolve '`t1.b`' given input columns: [t1.b]; line 6 pos 41;
'Project ['t1.b]
+- 'Join Inner, ('t1.b = 't2.b)
:- SubqueryAlias t1
: +- SubqueryAlias temp
: +- ScriptTransformation [a#1], cat, [b#2], ScriptInputOutputSchema(List(),List(),Some(org.apache.hadoop.hive.serde2.DelimitedJSONSerDe),Some(org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe),List((field.delim, )),List((field.delim, )),Some(org.apache.hadoop.hive.ql.exec.TextRecordReader),Some(org.apache.hadoop.hive.ql.exec.TextRecordWriter),false)
: +- SubqueryAlias t
: +- Project [a#1]
: +- SubqueryAlias t
: +- LocalRelation [a#1]
+- SubqueryAlias t2
+- SubqueryAlias temp
+- ScriptTransformation [a#1], cat, [b#2], ScriptInputOutputSchema(List(),List(),Some(org.apache.hadoop.hive.serde2.DelimitedJSONSerDe),Some(org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe),List((field.delim, )),List((field.delim, )),Some(org.apache.hadoop.hive.ql.exec.TextRecordReader),Some(org.apache.hadoop.hive.ql.exec.TextRecordWriter),false)
+- SubqueryAlias t
+- Project [a#1]
+- SubqueryAlias t
+- LocalRelation [a#1]
```
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
Add a UT
Closes#31752 from WangGuangxin/selfjoin-with-transform.
Authored-by: wangguangxin.cn <wangguangxin.cn@bytedance.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This pr make DPP support RLIKE expression:
```sql
SELECT date_id, product_id FROM fact_sk f
JOIN dim_store s
ON f.store_id = s.store_id WHERE s.country RLIKE '[DE|US]'
```
### 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#31722 from chaojun-zhang/SPARK-34595.
Authored-by: helloman <zcj23085@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Unify output of ShowCreateTableAsSerdeCommand and ShowCreateTableCommand
### Why are the changes needed?
Unify output of ShowCreateTableAsSerdeCommand and ShowCreateTableCommand
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Closes#31737 from AngersZhuuuu/SPARK-34621.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Remove unused output of AddJarCommand, keep consistence and clean
### Why are the changes needed?
Keep consistence and clean
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Not need
Closes#31725 from AngersZhuuuu/SPARK-34608.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add allow list to capture sql config for view.
### Why are the changes needed?
Spark use origin text sql to store view then capture and store sql config into view metadata.
Capture config will skip some config with some prefix, e.g. `spark.sql.optimizer.` but unfortunately `spark.sql.optimizer.disableHints` is start with `spark.sql.optimizer.`.
We need a allow list to help capture the config.
### Does this PR introduce _any_ user-facing change?
Yes bug fix.
### How was this patch tested?
Add test.
Closes#31732 from ulysses-you/SPARK-34613.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
We have equality in `SqlBase.g4` for `RLIKE: 'RLIKE' | 'REGEXP';`
We seemed to miss adding` REGEXP` as a SQL function just like` RLIKE`
### Why are the changes needed?
symmetry and beauty
This is also a builtin function in Hive, we can reduce the migration pain for those users
### Does this PR introduce _any_ user-facing change?
yes new regexp function as an alias as rlike
### How was this patch tested?
new tests
Closes#31488 from yaooqinn/SPARK-34376.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In the PR, I propose to extend Spark SQL API to accept [`java.time.Duration`](https://docs.oracle.com/javase/8/docs/api/java/time/Duration.html) as an external type of recently added new Catalyst type - `DayTimeIntervalType` (see #31614). The Java class `java.time.Duration` has similar semantic to ANSI SQL day-time interval type, and it is the most suitable to be an external type for `DayTimeIntervalType`. In more details:
1. Added `DurationConverter` which converts `java.time.Duration` instances to/from internal representation of the Catalyst type `DayTimeIntervalType` (to `Long` type). The `DurationConverter` object uses new methods of `IntervalUtils`:
- `durationToMicros()` converts the input duration to the total length in microseconds. If this duration is too large to fit `Long`, the method throws the exception `ArithmeticException`. **Note:** _the input duration has nanosecond precision, the method casts the nanos part to microseconds by dividing by 1000._
- `microsToDuration()` obtains a `java.time.Duration` representing a number of microseconds.
2. Support new type `DayTimeIntervalType` in `RowEncoder` via the methods `createDeserializerForDuration()` and `createSerializerForJavaDuration()`.
3. Extended the Literal API to construct literals from `java.time.Duration` instances.
### Why are the changes needed?
1. To allow users parallelization of `java.time.Duration` collections, and construct day-time interval columns. Also to collect such columns back to the driver side.
2. This will allow to write tests in other sub-tasks of SPARK-27790.
### Does this PR introduce _any_ user-facing change?
The PR extends existing functionality. So, users can parallelize instances of the `java.time.Duration` class and collect them back:
```Scala
scala> val ds = Seq(java.time.Duration.ofDays(10)).toDS
ds: org.apache.spark.sql.Dataset[java.time.Duration] = [value: daytimeinterval]
scala> ds.collect
res0: Array[java.time.Duration] = Array(PT240H)
```
### How was this patch tested?
- Added a few tests to `CatalystTypeConvertersSuite` to check conversion from/to `java.time.Duration`.
- Checking row encoding by new tests in `RowEncoderSuite`.
- Making literals of `DayTimeIntervalType` are tested in `LiteralExpressionSuite`
- Check collecting by `DatasetSuite` and `JavaDatasetSuite`.
Closes#31729 from MaxGekk/java-time-duration.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Set the active SparkSession to `sparkSessionForStream` and diable AQE & CBO before initializing the `StreamExecution.logicalPlan`.
### Why are the changes needed?
The active session should be `sparkSessionForStream`. Otherwise, settings like
6b34745cb9/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamExecution.scala (L332-L335)
wouldn't take effect if callers access them from the active SQLConf, e.g., the rule of `InsertAdaptiveSparkPlan`. Besides, unlike `InsertAdaptiveSparkPlan` (which skips streaming plan), `CostBasedJoinReorder` seems to have the chance to take effect theoretically.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Tested manually. Before the fix, `InsertAdaptiveSparkPlan` would try to apply AQE on the plan(wouldn't take effect though). After this fix, the rule returns directly.
Closes#31600 from Ngone51/active-session-for-stream.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In ANSI mode, casting String to Boolean should throw an exception on parse error, instead of returning null
### Why are the changes needed?
For better ANSI compliance
### Does this PR introduce _any_ user-facing change?
Yes, in ANSI mode there will be an exception on parse failure of casting String value to Boolean type.
### How was this patch tested?
Unit tests.
Closes#31734 from gengliangwang/ansiCastToBoolean.
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?
`ResolveInsertInto.staticDeleteExpression` should use `UnresolvedAttribute.quoted` to create the delete expression so that we will treat the entire `attr.name` as a column name.
### Why are the changes needed?
When users use `dot` in a partition column name, queries like ```INSERT OVERWRITE $t1 PARTITION (`a.b` = 'a') (`c.d`) VALUES('b')``` is not working.
### Does this PR introduce _any_ user-facing change?
Without this test, the above query will throw
```
[info] org.apache.spark.sql.AnalysisException: cannot resolve '`a.b`' given input columns: [a.b, c.d];
[info] 'OverwriteByExpression RelationV2[a.b#17, c.d#18] default.tbl, ('a.b <=> cast(a as string)), false
[info] +- Project [a.b#19, ansi_cast(col1#16 as string) AS c.d#20]
[info] +- Project [cast(a as string) AS a.b#19, col1#16]
[info] +- LocalRelation [col1#16]
```
With the fix, the query will run correctly.
### How was this patch tested?
The new added test.
Closes#31713 from zsxwing/SPARK-34599.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to generate "stable" output attributes per the logical node of the DESCRIBE NAMESPACE command.
### Why are the changes needed?
This fixes the issue demonstrated by the example:
```
sql(s"CREATE NAMESPACE ns")
val description = sql(s"DESCRIBE NAMESPACE ns")
description.drop("name")
```
```
[info] org.apache.spark.sql.AnalysisException: Resolved attribute(s) name#74 missing from name#25,value#26 in operator !Project [name#74]. Attribute(s) with the same name appear in the operation: name. Please check if the right attribute(s) are used.;
[info] !Project [name#74]
[info] +- LocalRelation [name#25, value#26]
```
### Does this PR introduce _any_ user-facing change?
After this change user `drop()/add()` works well.
### How was this patch tested?
Added UT
Closes#31705 from AngersZhuuuu/SPARK-34577.
Authored-by: Angerszhuuuu <angers.zhu@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/27597 and simply apply the fix in the v2 table insertion code path.
### Why are the changes needed?
bug fix
### Does this PR introduce _any_ user-facing change?
yes, now v2 table insertion with static partitions also follow StoreAssignmentPolicy.
### How was this patch tested?
moved the test from https://github.com/apache/spark/pull/27597 to the general test suite `SQLInsertTestSuite`, which covers DS v2, file source, and hive tables.
Closes#31726 from cloud-fan/insert.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add the `ResetSystemProperties` trait to `SQLQuerySuite` so that system property changes made by any of the tests will not affect other suites/tests. Specifically, the system property changes made by `SPARK-33084: Add jar support Ivy URI in SQL -- jar contains udf class` are targeted here (which sets and then clears `ivy.home`).
### Why are the changes needed?
PR #29966 added a new test case that adjusts the `ivy.home` system property to force Ivy to resolve an artifact from a custom location. At the end of the test, the value is cleared. Clearing the value meant that, if a custom value of `ivy.home` was configured externally, it would not apply for tests run after this test case.
### Does this PR introduce _any_ user-facing change?
No, this is only in tests.
### How was this patch tested?
Existing unit tests continue to pass, whether or not `spark.jars.ivySettings` is configured (which adjusts the behavior of Ivy w.r.t. handling of `ivy.home` and `ivy.default.ivy.user.dir` properties).
Closes#31694 from xkrogen/xkrogen-SPARK-33084-ivyhome-sysprop-followon.
Authored-by: Erik Krogen <xkrogen@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add metadataOutput as a fallback to resolution.
Builds off https://github.com/apache/spark/pull/31654.
### Why are the changes needed?
The metadata columns could not be resolved via `df.col("metadataColName")` from the DataFrame API.
### Does this PR introduce _any_ user-facing change?
Yes, the metadata columns can now be resolved as described above.
### How was this patch tested?
Scala unit test.
Closes#31668 from karenfeng/spark-34555.
Authored-by: Karen Feng <karen.feng@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Hive support type constructed value as partition spec value, spark should support too.
### Why are the changes needed?
Support TypeConstructed partition spec value keep same with hive
### Does this PR introduce _any_ user-facing change?
Yes, user can use TypeConstruct value as partition spec value such as
```
CREATE TABLE t1(name STRING) PARTITIONED BY (part DATE)
INSERT INTO t1 PARTITION(part = date'2019-01-02') VALUES('a')
CREATE TABLE t2(name STRING) PARTITIONED BY (part TIMESTAMP)
INSERT INTO t2 PARTITION(part = timestamp'2019-01-02 11:11:11') VALUES('a')
CREATE TABLE t4(name STRING) PARTITIONED BY (part BINARY)
INSERT INTO t4 PARTITION(part = X'537061726B2053514C') VALUES('a')
```
### How was this patch tested?
Added UT
Closes#30421 from AngersZhuuuu/SPARK-33474.
Lead-authored-by: angerszhu <angers.zhu@gmail.com>
Co-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
spark.sql.adaptive.coalescePartitions.initialPartitionNum 200 -> (none)
spark.sql.adaptive.skewJoin.skewedPartitionFactor is 10 -> 5
### Why are the changes needed?
the wrong doc misguide people
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
passing doc
Closes#31717 from yaooqinn/minordoc0.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In the PR, I propose to generate "stable" output attributes per the logical node of the DESCRIBE COLUMN command.
### Why are the changes needed?
This fixes the issue demonstrated by the example:
```
val tbl = "testcat.ns1.ns2.tbl"
sql(s"CREATE TABLE $tbl (c0 INT) USING _")
val description = sql(s"DESCRIBE TABLE $tbl c0")
description.drop("info_name")
```
```
[info] org.apache.spark.sql.AnalysisException: Resolved attribute(s) info_name#74 missing from info_name#25,info_value#26 in operator !Project [info_name#74]. Attribute(s) with the same name appear in the operation: info_name. Please check if the right attribute(s) are used.;
[info] !Project [info_name#74]
[info] +- LocalRelation [info_name#25, info_value#26]
```
### Does this PR introduce _any_ user-facing change?
After this change user `drop()/add()` works well.
### How was this patch tested?
Added UT
Closes#31696 from AngersZhuuuu/SPARK-34576.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`BroadcastNestedLoopJoinExec` does not preserve `outputPartitioning` and `outputOrdering` right now. But it can preserve the streamed side partitioning and ordering when possible. This can help avoid shuffle and sort in later stage, if there's join and aggregation in the query. See example queries in added unit test in `JoinSuite.scala`.
In addition, fix a bunch of minor places in `BroadcastNestedLoopJoinExec.scala` for better style and readability.
### Why are the changes needed?
Avoid shuffle and sort for certain complicated query shape. Better query performance can be achieved.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added unit test in `JoinSuite.scala`.
Closes#31708 from c21/nested-join.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch proposes to remove unnecessary children from Union under Distince and Deduplicate
### Why are the changes needed?
If there are any duplicate child of `Union` under `Distinct` and `Deduplicate`, it can be removed to simplify query plan.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test
Closes#31656 from viirya/SPARK-34548.
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?
Currently, the warehouse path gets fully qualified in the caller side for creating a database, table, partition, etc. An unqualified path is populated into Spark and Hadoop confs, which leads to inconsistent API behaviors. We should make it qualified ahead.
When the value is a relative path `spark.sql.warehouse.dir=lakehouse`, some behaviors become inconsistent, for example.
If the default database is absent at runtime, the app fails with
```java
Caused by: java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: file:./lakehouse
at org.apache.hadoop.fs.Path.initialize(Path.java:263)
at org.apache.hadoop.fs.Path.<init>(Path.java:254)
at org.apache.hadoop.hive.metastore.Warehouse.getDnsPath(Warehouse.java:133)
at org.apache.hadoop.hive.metastore.Warehouse.getDnsPath(Warehouse.java:137)
at org.apache.hadoop.hive.metastore.Warehouse.getWhRoot(Warehouse.java:150)
at org.apache.hadoop.hive.metastore.Warehouse.getDefaultDatabasePath(Warehouse.java:163)
at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB_core(HiveMetaStore.java:636)
at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:655)
at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.init(HiveMetaStore.java:431)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.hadoop.hive.metastore.RetryingHMSHandler.invokeInternal(RetryingHMSHandler.java:148)
at org.apache.hadoop.hive.metastore.RetryingHMSHandler.invoke(RetryingHMSHandler.java:107)
at org.apache.hadoop.hive.metastore.RetryingHMSHandler.<init>(RetryingHMSHandler.java:79)
... 73 more
```
If the default database is present at runtime, the app can work with it, and if we create a database, it gets fully qualified, for example
```sql
spark-sql> create database test;
Time taken: 0.052 seconds
spark-sql> desc database test;
Database Name test
Comment
Location file:/Users/kentyao/Downloads/spark/spark-3.2.0-SNAPSHOT-bin-20210226/lakehouse/test.db
Owner kentyao
Time taken: 0.023 seconds, Fetched 4 row(s)
```
Another thing is that the log becomes nubilous, for example.
```logtalk
21/02/27 13:54:17 INFO SharedState: Setting hive.metastore.warehouse.dir ('null') to the value of spark.sql.warehouse.dir ('datalake').
21/02/27 13:54:17 INFO SharedState: Warehouse path is 'lakehouse'.
```
### Why are the changes needed?
fix bug and ambiguity
### Does this PR introduce _any_ user-facing change?
yes, the path now resolved with proper order - `warehouse->database->table->partition`
### How was this patch tested?
w/ ut added
Closes#31671 from yaooqinn/SPARK-34558.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a followup of #31560,
In #31560, we added `JavaSimpleWritableDataSource ` and left some little problems like unused interface `SessionConfigSupport` 、 inconsistent schema between `JavaSimpleWritableDataSource ` and `SimpleWritableDataSource`.
This PR fixes the remaining problems in #31560.
### Why are the changes needed?
1. `SessionConfigSupport` in `JavaSimpleWritableDataSource ` and `SimpleWritableDataSource` is never used, so we don't need to implement it.
2. change the schema of `SimpleWritableDataSource`, to match `TestingV2Source`
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
existing testsuites
Closes#31621 from kevincmchen/SPARK-34498.
Authored-by: kevincmchen <kevincmchen@tencent.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Today, child expressions may be resolved based on "real" or metadata output attributes. We should prefer the real attribute during resolution if one exists.
### Why are the changes needed?
Today, attempting to resolve an expression when there is a "real" output attribute and a metadata attribute with the same name results in resolution failure. This is likely unexpected, as the user may not know about the metadata attribute.
### Does this PR introduce _any_ user-facing change?
Yes. Previously, the user would see an error message when resolving a column with the same name as a "real" output attribute and a metadata attribute as below:
```
org.apache.spark.sql.AnalysisException: Reference 'index' is ambiguous, could be: testcat.ns1.ns2.tableTwo.index, testcat.ns1.ns2.tableOne.index.; line 1 pos 71
at org.apache.spark.sql.catalyst.expressions.package$AttributeSeq.resolve(package.scala:363)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveChildren(LogicalPlan.scala:107)
```
Now, resolution succeeds and provides the "real" output attribute.
### How was this patch tested?
Added a unit test.
Closes#31654 from karenfeng/fallback-resolve-metadata.
Authored-by: Karen Feng <karen.feng@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
**What changes were proposed in this pull request?**
This PR fixes dataframe.na.fillMap() for column having a dot in the name as mentioned in [SPARK-34417](https://issues.apache.org/jira/browse/SPARK-34417).
Use resolved attributes of a column for replacing null values.
**Why are the changes needed?**
dataframe.na.fillMap() does not work for column having a dot in the name
**Does this PR introduce any user-facing change?**
None
**How was this patch tested?**
Added unit test for the same
Closes#31545 from amandeep-sharma/master.
Lead-authored-by: Amandeep Sharma <happyaman91@gmail.com>
Co-authored-by: Amandeep Sharma <amandeep.sharma@oracle.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
As a follow-up to SPARK-34456, this PR removes `BatchWriteHelper` completely.
### Why are the changes needed?
These changes remove no longer used code.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#31699 from aokolnychyi/spark-34585.
Authored-by: Anton Okolnychyi <aokolnychyi@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1. This PR aims to ignore ORC encryption tests when ORC shim is loaded by old Hadoop library by some other tests. The test coverage is preserved by Jenkins SBT runs and GitHub Action jobs. This PR only aims to recover Maven Jenkins jobs.
2. In addition, this PR simplifies SBT testing by refactor the test config to `SparkBuild.scala/pom.xml` and remove `DedicatedJVMTest`. This will remove one GitHub Action job which was recently added for `DedicatedJVMTest` tag.
### Why are the changes needed?
Currently, Maven test fails when it runs in a batch mode because `HadoopShimsPre2_3$NullKeyProvider` is loaded.
**MVN COMMAND**
```
$ mvn test -pl sql/core --am -Dtest=none -DwildcardSuites=org.apache.spark.sql.execution.datasources.orc.OrcV1QuerySuite,org.apache.spark.sql.execution.datasources.orc.OrcEncryptionSuite
```
**BEFORE**
```
- Write and read an encrypted table *** FAILED ***
...
Cause: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 1) (localhost executor driver): java.lang.IllegalArgumentException: Unknown key pii
at org.apache.orc.impl.HadoopShimsPre2_3$NullKeyProvider.getCurrentKeyVersion(HadoopShimsPre2_3.java:71)
at org.apache.orc.impl.WriterImpl.getKey(WriterImpl.java:871)
```
**AFTER**
```
OrcV1QuerySuite
...
OrcEncryptionSuite:
- Write and read an encrypted file !!! CANCELED !!!
[] was empty org.apache.orc.impl.HadoopShimsPre2_3$NullKeyProvider1b705f65 doesn't has the test keys. ORC shim is created with old Hadoop libraries (OrcEncryptionSuite.scala:39)
- Write and read an encrypted table !!! CANCELED !!!
[] was empty org.apache.orc.impl.HadoopShimsPre2_3$NullKeyProvider22adeee1 doesn't has the test keys. ORC shim is created with old Hadoop libraries (OrcEncryptionSuite.scala:67)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the Jenkins Maven tests.
For SBT command,
- the test suite required a dedicated JVM (Before)
- the test suite doesn't require a dedicated JVM (After)
```
$ build/sbt "sql/testOnly *.OrcV1QuerySuite *.OrcEncryptionSuite"
...
[info] OrcV1QuerySuite
...
[info] - SPARK-20728 Make ORCFileFormat configurable between sql/hive and sql/core (26 milliseconds)
[info] OrcEncryptionSuite:
[info] - Write and read an encrypted file (431 milliseconds)
[info] - Write and read an encrypted table (359 milliseconds)
[info] All tests passed.
[info] Passed: Total 35, Failed 0, Errors 0, Passed 35
```
Closes#31697 from dongjoon-hyun/SPARK-34578-TEST.
Lead-authored-by: Dongjoon Hyun <dhyun@apple.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Currently in `SpecificParquetRecordReaderBase` we use deprecated APIs in a few places from Parquet, such as `readFooter`, `ParquetInputSplit`, `new ParquetFileReader`, `filterRowGroups`, etc. This replaces these with the newer APIs. In specific this:
- Replaces `ParquetInputSplit` with `FileSplit`. We never use specific things in the former such as `rowGroupOffsets` so the swap is pretty simple.
- Removes `readFooter` calls by using `ParquetFileReader.open`
- Replace deprecated `ParquetFileReader` ctor with the newer API which takes `ParquetReadOptions`.
- Removes the unnecessary handling of case when `rowGroupOffsets` is not null. It seems this never happens.
### Why are the changes needed?
The aforementioned APIs were deprecated and is going to be removed at some point in future. This is to ensure better supportability.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
This is a cleanup and relies on existing tests on the relevant code paths.
Closes#31667 from sunchao/SPARK-32703.
Lead-authored-by: Chao Sun <sunchao@apache.org>
Co-authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### Why is this change being proposed?
This patch adds support for a new "product" aggregation function in `sql.functions` which multiplies-together all values in an aggregation group.
This is likely to be useful in statistical applications which involve combining probabilities, or financial applications that involve combining cumulative interest rates, but is also a versatile mathematical operation of similar status to `sum` or `stddev`. Other users [have noted](https://stackoverflow.com/questions/52991640/cumulative-product-in-spark) the absence of such a function in current releases of Spark.
This function is both much more concise than an expression of the form `exp(sum(log(...)))`, and avoids awkward edge-cases associated with some values being zero or negative, as well as being less computationally costly.
### Does this PR introduce _any_ user-facing change?
No - only adds new function.
### How was this patch tested?
Built-in tests have been added for the new `catalyst.expressions.aggregate.Product` class and its invocation via the (scala) `sql.functions.product` function. The latter, and the PySpark wrapper have also been manually tested in spark-shell and pyspark sessions. The SparkR wrapper is currently untested, and may need separate validation (I'm not an "R" user myself).
An illustration of the new functionality, within PySpark is as follows:
```
import pyspark.sql.functions as pf, pyspark.sql.window as pw
df = sqlContext.range(1, 17).toDF("x")
win = pw.Window.partitionBy(pf.lit(1)).orderBy(pf.col("x"))
df.withColumn("factorial", pf.product("x").over(win)).show(20, False)
+---+---------------+
|x |factorial |
+---+---------------+
|1 |1.0 |
|2 |2.0 |
|3 |6.0 |
|4 |24.0 |
|5 |120.0 |
|6 |720.0 |
|7 |5040.0 |
|8 |40320.0 |
|9 |362880.0 |
|10 |3628800.0 |
|11 |3.99168E7 |
|12 |4.790016E8 |
|13 |6.2270208E9 |
|14 |8.71782912E10 |
|15 |1.307674368E12 |
|16 |2.0922789888E13|
+---+---------------+
```
Closes#30745 from rwpenney/feature/agg-product.
Lead-authored-by: Richard Penney <rwp@rwpenney.uk>
Co-authored-by: Richard Penney <rwpenney@users.noreply.github.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In the PR, I propose to generate unique attributes in the logical nodes of the `SHOW TABLES` command.
Also, this PR fixes similar issues in other logical nodes:
- ShowTableExtended
- ShowViews
- ShowTableProperties
- ShowFunctions
- ShowColumns
- ShowPartitions
- ShowNamespaces
### Why are the changes needed?
This fixes the issue which is demonstrated by the example below:
```scala
scala> val show1 = sql("SHOW TABLES IN ns1")
show1: org.apache.spark.sql.DataFrame = [namespace: string, tableName: string ... 1 more field]
scala> val show2 = sql("SHOW TABLES IN ns2")
show2: org.apache.spark.sql.DataFrame = [namespace: string, tableName: string ... 1 more field]
scala> show1.show
+---------+---------+-----------+
|namespace|tableName|isTemporary|
+---------+---------+-----------+
| ns1| tbl1| false|
+---------+---------+-----------+
scala> show2.show
+---------+---------+-----------+
|namespace|tableName|isTemporary|
+---------+---------+-----------+
| ns2| tbl2| false|
+---------+---------+-----------+
scala> show1.join(show2).where(show1("tableName") =!= show2("tableName")).show
org.apache.spark.sql.AnalysisException: Column tableName#17 are ambiguous. It's probably because you joined several Datasets together, and some of these Datasets are the same. This column points to one of the Datasets but Spark is unable to figure out which one. Please alias the Datasets with different names via `Dataset.as` before joining them, and specify the column using qualified name, e.g. `df.as("a").join(df.as("b"), $"a.id" > $"b.id")`. You can also set spark.sql.analyzer.failAmbiguousSelfJoin to false to disable this check.
at org.apache.spark.sql.execution.analysis.DetectAmbiguousSelfJoin$.apply(DetectAmbiguousSelfJoin.scala:157)
```
### Does this PR introduce _any_ user-facing change?
Yes. After the changes, the example above works as expected:
```scala
scala> show1.join(show2).where(show1("tableName") =!= show2("tableName")).show
+---------+---------+-----------+---------+---------+-----------+
|namespace|tableName|isTemporary|namespace|tableName|isTemporary|
+---------+---------+-----------+---------+---------+-----------+
| ns1| tbl1| false| ns2| tbl2| false|
+---------+---------+-----------+---------+---------+-----------+
```
### How was this patch tested?
By running the new test:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *ShowTablesSuite"
```
Closes#31675 from MaxGekk/fix-output-attrs.
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 generate "stable" output attributes per the logical node of the `DESCRIBE TABLE` command.
### Why are the changes needed?
This fixes the issue demonstrated by the example:
```scala
val tbl = "testcat.ns1.ns2.tbl"
sql(s"CREATE TABLE $tbl (c0 INT) USING _")
val description = sql(s"DESCRIBE TABLE $tbl")
description.drop("comment")
```
The `drop()` method fails with the error:
```
org.apache.spark.sql.AnalysisException: Resolved attribute(s) col_name#102,data_type#103 missing from col_name#29,data_type#30,comment#31 in operator !Project [col_name#102, data_type#103]. Attribute(s) with the same name appear in the operation: col_name,data_type. Please check if the right attribute(s) are used.;
!Project [col_name#102, data_type#103]
+- LocalRelation [col_name#29, data_type#30, comment#31]
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis.failAnalysis(CheckAnalysis.scala:51)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis.failAnalysis$(CheckAnalysis.scala:50)
```
### Does this PR introduce _any_ user-facing change?
Yes. After the changes, `drop()`/`add()` works as expected:
```scala
description.drop("comment").show()
+---------------+---------+
| col_name|data_type|
+---------------+---------+
| c0| int|
| | |
| # Partitioning| |
|Not partitioned| |
+---------------+---------+
```
### How was this patch tested?
1. Run new test:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *DataSourceV2SQLSuite"
```
2. Run existing test suite:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *CatalogedDDLSuite"
```
Closes#31676 from MaxGekk/describe-table-drop-column.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR makes partition spec parsing respect case sensitive conf.
### Why are the changes needed?
When parsing the partition spec, Spark will call `org.apache.spark.sql.catalyst.parser.ParserUtils.checkDuplicateKeys` to check if there are duplicate partition column names in the list. But this method is always case sensitive and doesn't detect duplicate partition column names when using different cases.
### Does this PR introduce _any_ user-facing change?
Yep. This prevents users from writing incorrect queries such as `INSERT OVERWRITE t PARTITION (c='2', C='3') VALUES (1)` when they don't enable case sensitive conf.
### How was this patch tested?
The new added test will fail without this change.
Closes#31669 from zsxwing/SPARK-34556.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
the parameter - `options` is never used. The changes here was part of https://github.com/apache/spark/pull/30642, It got reverted for easier backporting #30642 as a hotfix by dad24543aa, this PR brings it back to master.
### Why are the changes needed?
remove unless dead code
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
Passing CI is enough.
Closes#31683 from yaooqinn/SPARK-34570.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
SPARK-33084 added the ability to use ivy coordinates with `SparkContext.addJar`. PR #29966 claims to mimic Hive behavior although I found a few cases where it doesn't
1) The default value of the transitive parameter is false, both in case of parameter not being specified in coordinate or parameter value being invalid. The Hive behavior is that transitive is [true if not specified](cb2ac3dcc6/ql/src/java/org/apache/hadoop/hive/ql/util/DependencyResolver.java (L169)) in the coordinate and [false for invalid values](cb2ac3dcc6/ql/src/java/org/apache/hadoop/hive/ql/util/DependencyResolver.java (L124)). Also, regardless of Hive, I think a default of true for the transitive parameter also matches [ivy's own defaults](https://ant.apache.org/ivy/history/2.5.0/ivyfile/dependency.html#_attributes).
2) The parameter value for transitive parameter is regarded as case-sensitive [based on the understanding](https://github.com/apache/spark/pull/29966#discussion_r547752259) that Hive behavior is case-sensitive. However, this is not correct, Hive [treats the parameter value case-insensitively](cb2ac3dcc6/ql/src/java/org/apache/hadoop/hive/ql/util/DependencyResolver.java (L122)).
I propose that we be compatible with Hive for these behaviors
### Why are the changes needed?
To make `ADD JAR` with ivy coordinates compatible with Hive's transitive behavior
### Does this PR introduce _any_ user-facing change?
The user-facing changes here are within master as the feature introduced in SPARK-33084 has not been released yet
1. Previously an ivy coordinate without `transitive` parameter specified did not resolve transitive dependency, now it does.
2. Previously an `transitive` parameter value was treated case-sensitively. e.g. `transitive=TRUE` would be treated as false as it did not match exactly `true`. Now it will be treated case-insensitively.
### How was this patch tested?
Modified existing unit tests to test new behavior
Add new unit test to cover usage of `exclude` with unspecified `transitive`
Closes#31623 from shardulm94/spark-34506.
Authored-by: Shardul Mahadik <smahadik@linkedin.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
if child rdd has only one partition or zero partition, skip the shuffle
### Why are the changes needed?
skip shuffle if possible
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing testsuites
Closes#31468 from zhengruifeng/collect_limit_single_partition.
Authored-by: Ruifeng Zheng <ruifengz@foxmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@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?
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?
I discovered from review discussion - https://github.com/apache/spark/pull/31630#discussion_r581774000 , that we can eliminate LEFT ANTI join (with no join condition) to empty relation, if the right side is known to be non-empty. So with AQE, this is doable similar to https://github.com/apache/spark/pull/29484 .
### Why are the changes needed?
This can help eliminate the join operator during logical plan optimization.
Before this PR, [left side physical plan `execute()` will be called](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoinExec.scala#L192), so if left side is complicated (e.g. contain broadcast exchange operator), then some computation would happen. However after this PR, the join operator will be removed during logical plan, and nothing will be computed from left side. Potentially it can save resource for these kinds of query.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added unit tests for positive and negative queries in `AdaptiveQueryExecSuite.scala`.
Closes#31641 from c21/left-anti-aqe.
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 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?
Preprocess the partition spec passed to the V1 `ALTER TABLE .. SET LOCATION` implementation `AlterTableSetLocationCommand`, and normalize the passed spec according to the partition columns w.r.t the case sensitivity flag **spark.sql.caseSensitive**.
### Why are the changes needed?
V1 `ALTER TABLE .. SET LOCATION` is case sensitive in fact, and doesn't respect the SQL config **spark.sql.caseSensitive** which is false by default, for instance:
```sql
spark-sql> CREATE TABLE tbl (id INT, part INT) PARTITIONED BY (part);
spark-sql> INSERT INTO tbl PARTITION (part=0) SELECT 0;
spark-sql> SHOW TABLE EXTENDED LIKE 'tbl' PARTITION (part=0);
Location: file:/Users/maximgekk/proj/set-location-case-sense/spark-warehouse/tbl/part=0
spark-sql> ALTER TABLE tbl ADD PARTITION (part=1);
spark-sql> SELECT * FROM tbl;
0 0
```
Create new partition folder in the file system:
```
$ cp -r /Users/maximgekk/proj/set-location-case-sense/spark-warehouse/tbl/part=0 /Users/maximgekk/proj/set-location-case-sense/spark-warehouse/tbl/aaa
```
Set new location for the partition part=1:
```sql
spark-sql> ALTER TABLE tbl PARTITION (part=1) SET LOCATION '/Users/maximgekk/proj/set-location-case-sense/spark-warehouse/tbl/aaa';
spark-sql> SELECT * FROM tbl;
0 0
0 1
spark-sql> ALTER TABLE tbl ADD PARTITION (PART=2);
spark-sql> SELECT * FROM tbl;
0 0
0 1
```
Set location for a partition in the upper case:
```
$ cp -r /Users/maximgekk/proj/set-location-case-sense/spark-warehouse/tbl/part=0 /Users/maximgekk/proj/set-location-case-sense/spark-warehouse/tbl/bbb
```
```sql
spark-sql> ALTER TABLE tbl PARTITION (PART=2) SET LOCATION '/Users/maximgekk/proj/set-location-case-sense/spark-warehouse/tbl/bbb';
Error in query: Partition spec is invalid. The spec (PART) must match the partition spec (part) defined in table '`default`.`tbl`'
```
### Does this PR introduce _any_ user-facing change?
Yes. After the changes, the command above works as expected:
```sql
spark-sql> ALTER TABLE tbl PARTITION (PART=2) SET LOCATION '/Users/maximgekk/proj/set-location-case-sense/spark-warehouse/tbl/bbb';
spark-sql> SELECT * FROM tbl;
0 0
0 1
0 2
```
### How was this patch tested?
By running the modified test suite:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *CatalogedDDLSuite"
```
Closes#31651 from MaxGekk/set-location-case-sense.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Cleanup unused symbol in Orc related code as follows:
- `OrcDeserializer` : parameter `dataSchema` in constructor
- `OrcFilters` : parameter `schema ` in method `convertibleFilters`.
- `OrcPartitionReaderFactory`: ignore return value of `OrcUtils.orcResultSchemaString` in method `buildReader(file: PartitionedFile)`
### Why are the changes needed?
Cleanup code.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or GitHub Action
Closes#31644 from LuciferYang/cleanup-orc-unused-symbol.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR fixes a mistake in https://github.com/apache/spark/pull/31273. When CREATE OR REPLACE a temp view, we need to uncache the to-be-replaced existing temp view. However, we shouldn't uncache if there is no existing temp view.
This doesn't cause real issues because the uncache action is failure-safe. But it produces a lot of warning messages.
### Why are the changes needed?
Avoid unnecessary warning logs.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
manually run tests and check the warning messages.
Closes#31650 from cloud-fan/warnning.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.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?
This pr make DPP support LIKE ANY/ALL expression:
```sql
SELECT date_id, product_id FROM fact_sk f
JOIN dim_store s
ON f.store_id = s.store_id WHERE s.country LIKE ANY ('%D%E%', '%A%B%')
```
### 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#31563 from wangyum/SPARK-34436.
Lead-authored-by: Yuming Wang <yumwang@apache.org>
Co-authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Rename the execution node `AlterTableRecoverPartitionsCommand` for the commands:
- `MSCK REPAIR TABLE table [{ADD|DROP|SYNC} PARTITIONS]`
- `ALTER TABLE table RECOVER PARTITIONS`
to `RepairTableCommand`.
### Why are the changes needed?
1. After the PR https://github.com/apache/spark/pull/31499, `ALTER TABLE table RECOVER PARTITIONS` is equal to `MSCK REPAIR TABLE table ADD PARTITIONS`. And mapping of the generic command `MSCK REPAIR TABLE` to the more specific execution node `AlterTableRecoverPartitionsCommand` can confuse devs in the future.
2. `ALTER TABLE table RECOVER PARTITIONS` does not support any options/extensions. So, additional parameters `enableAddPartitions` and `enableDropPartitions` in `AlterTableRecoverPartitionsCommand` confuse as well.
### Does this PR introduce _any_ user-facing change?
No because this is internal API.
### How was this patch tested?
By running the existing test suites:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableRecoverPartitionsSuite"
$ build/sbt "test:testOnly *AlterTableRecoverPartitionsParserSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *MsckRepairTableSuite"
$ build/sbt "test:testOnly *MsckRepairTableParserSuite"
```
Closes#31635 from MaxGekk/rename-recover-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?
Some of the built-in JDBC connection providers are changing the JVM security context to do the authentication which is fine. The problematic part is that executors can be reused by another query. The following situation leads to incorrect behaviour:
* Query1 opens JDBC connection and changes JVM security context in Executor1
* Query2 tries to open JDBC connection but it realizes there is already an entry for that DB type in Executor1
* Query2 is not changing JVM security context and uses Query1 keytab and principal
* Query2 fails with authentication error
In this PR I've changed to code such a way that JVM security context is changed all the time but only temporarily until the connection built-up and then rolled back. Since `getConnection` is synchronised with `SecurityConfigurationLock` it ends-up in correct behaviour without any race.
### Why are the changes needed?
Incorrect JVM security context handling.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit + integration tests.
Closes#31622 from gaborgsomogyi/SPARK-34497.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### 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>
