### What changes were proposed in this pull request?
This PR aims to update `master` branch version to 3.2.0-SNAPSHOT.
### Why are the changes needed?
Start to prepare Apache Spark 3.2.0.
### Does this PR introduce _any_ user-facing change?
N/A.
### How was this patch tested?
Pass the CIs.
Closes#30606 from dongjoon-hyun/SPARK-3.2.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Update kafka headers documentation, type is not longer a map but an array
[jira](https://issues.apache.org/jira/browse/SPARK-33660)
### Why are the changes needed?
To help users
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
It is only documentation
Closes#30605 from Gschiavon/SPARK-33660-fix-kafka-headers-documentation.
Authored-by: german <germanschiavon@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/30289. It removes the hack in `View.effectiveSQLConf`, by putting the max nested view depth in `AnalysisContext`. Then we don't get the max nested view depth from the active SQLConf, which keeps changing during nested view resolution.
### Why are the changes needed?
remove hacks.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
If I just remove the hack, `SimpleSQLViewSuite.restrict the nested level of a view` fails. With this fix, it passes again.
Closes#30575 from cloud-fan/view.
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 proposes to make `spark.archives` configuration working in Kubernates.
It works without a problem in standalone cluster but there seems a bug in Kubernates.
It fails to fetch the file on the driver side as below:
```
20/12/03 13:33:53 INFO SparkContext: Added JAR file:/tmp/spark-75004286-c83a-4369-b624-14c5d2d2a748/spark-examples_2.12-3.1.0-SNAPSHOT.jar at spark://spark-test-app-48ae737628cee6f8-driver-svc.spark-integration-test.svc:7078/jars/spark-examples_2.12-3.1.0-SNAPSHOT.jar with timestamp 1607002432558
20/12/03 13:33:53 INFO SparkContext: Added archive file:///tmp/tmp4542734800151332666.txt.tar.gz#test_tar_gz at spark://spark-test-app-48ae737628cee6f8-driver-svc.spark-integration-test.svc:7078/files/tmp4542734800151332666.txt.tar.gz with timestamp 1607002432558
20/12/03 13:33:53 INFO TransportClientFactory: Successfully created connection to spark-test-app-48ae737628cee6f8-driver-svc.spark-integration-test.svc/172.17.0.4:7078 after 83 ms (47 ms spent in bootstraps)
20/12/03 13:33:53 INFO Utils: Fetching spark://spark-test-app-48ae737628cee6f8-driver-svc.spark-integration-test.svc:7078/files/tmp4542734800151332666.txt.tar.gz to /tmp/spark-66573e24-27a3-427c-99f4-36f06d9e9cd5/fetchFileTemp2665785666227461849.tmp
20/12/03 13:33:53 ERROR SparkContext: Error initializing SparkContext.
java.lang.RuntimeException: Stream '/files/tmp4542734800151332666.txt.tar.gz' was not found.
at org.apache.spark.network.client.TransportResponseHandler.handle(TransportResponseHandler.java:242)
at org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:142)
at org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:53)
```
This is because `spark.archives` was not actually added on the driver side correctly. The changes here fix it by adding and resolving URIs correctly.
### Why are the changes needed?
`spark.archives` feature can be leveraged for many things such as Conda support. We should make it working in Kubernates as well.
This is a bug fix too.
### Does this PR introduce _any_ user-facing change?
No, this feature is not out yet.
### How was this patch tested?
I manually tested with Minikube 1.15.1. For an environment issue (?), I had to use a custom namespace, service account and roles. `default` service account does not work for me and complains it doesn't have permissions to get/list pods, etc.
```bash
minikube delete
minikube start --cpus 12 --memory 16384
kubectl create namespace spark-integration-test
cat <<EOF | kubectl apply -f -
apiVersion: v1
kind: ServiceAccount
metadata:
name: spark
namespace: spark-integration-test
EOF
kubectl create clusterrolebinding spark-role --clusterrole=edit --serviceaccount=spark-integration-test:spark --namespace=spark-integration-test
dev/make-distribution.sh --pip --tgz -Pkubernetes
resource-managers/kubernetes/integration-tests/dev/dev-run-integration-tests.sh --spark-tgz `pwd`/spark-3.1.0-SNAPSHOT-bin-3.2.0.tgz --service-account spark --namespace spark-integration-test
```
Closes#30581 from HyukjinKwon/SPARK-33615.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Please refer the description of [SPARK-27237](https://issues.apache.org/jira/browse/SPARK-27237) 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?
This PR extends the connection timeout to the DB server for DB2IntegrationSuite and its variants.
The container image ibmcom/db2 creates a database when it starts up.
The database creation can take over 2 minutes.
DB2IntegrationSuite and its variants use the container image but the connection timeout is set to 2 minutes so these suites almost always fail.
### Why are the changes needed?
To pass those suites.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I confirmed the suites pass with the following commands.
```
$ build/sbt -Pdocker-integration-tests -Phive -Phive-thriftserver package "testOnly org.apache.spark.sql.jdbc.DB2IntegrationSuite"
$ build/sbt -Pdocker-integration-tests -Phive -Phive-thriftserver package "testOnly org.apache.spark.sql.jdbc.v2.DB2IntegrationSuite"
$ build/sbt -Pdocker-integration-tests -Phive -Phive-thriftserver package "testOnly org.apache.spark.sql.jdbc.DB2KrbIntegrationSuite"
Closes#30583 from sarutak/extend-timeout-for-db2.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR add an option to keep container after DockerJDBCIntegrationSuites (e.g. DB2IntegrationSuite, PostgresIntegrationSuite) finish.
By setting a system property `spark.test.docker.keepContainer` to `true`, we can use this option.
### Why are the changes needed?
If some error occur during the tests, it would be useful to keep the container for debug.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I confirmed that the container is kept after the test by the following commands.
```
# With sbt
$ build/sbt -Dspark.test.docker.keepContainer=true -Pdocker-integration-tests -Phive -Phive-thriftserver package "testOnly org.apache.spark.sql.jdbc.MariaDBKrbIntegrationSuite"
# With Maven
$ build/mvn -Dspark.test.docker.keepContainer=true -Pdocker-integration-tests -Phive -Phive-thriftserver -Dtest=none -DwildcardSuites=org.apache.spark.sql.jdbc.MariaDBKrbIntegrationSuite test
$ docker container ls
```
I also confirmed that there are no regression for all the subclasses of `DockerJDBCIntegrationSuite` with sbt/Maven.
* MariaDBKrbIntegrationSuite
* DB2KrbIntegrationSuite
* PostgresKrbIntegrationSuite
* MySQLIntegrationSuite
* PostgresIntegrationSuite
* DB2IntegrationSuite
* MsSqlServerintegrationsuite
* OracleIntegrationSuite
* v2.MySQLIntegrationSuite
* v2.PostgresIntegrationSuite
* v2.DB2IntegrationSuite
* v2.MsSqlServerIntegrationSuite
* v2.OracleIntegrationSuite
NOTE: `DB2IntegrationSuite`, `v2.DB2IntegrationSuite` and `DB2KrbIntegrationSuite` can fail due to the too much short connection timeout. It's a separate issue and I'll fix it in #30583Closes#30601 from sarutak/keepContainer.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
After SPARK-32896, we have table API for stream writer but only support DataSource v2 tables. Here we add the following enhancements:
- Create non-existing tables by default
- Support both managed and external V1Tables
### Why are the changes needed?
Make the API covers more use cases. Especially for the file provider based tables.
### Does this PR introduce _any_ user-facing change?
Yes, new features added.
### How was this patch tested?
Add new UTs.
Closes#30521 from xuanyuanking/SPARK-33577.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### 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?
Add namespaces support in JDBC v2 Table Catalog by making ```JDBCTableCatalog``` extends```SupportsNamespaces```
### Why are the changes needed?
make v2 JDBC implementation complete
### Does this PR introduce _any_ user-facing change?
Yes. Add the following to ```JDBCTableCatalog```
- listNamespaces
- listNamespaces(String[] namespace)
- namespaceExists(String[] namespace)
- loadNamespaceMetadata(String[] namespace)
- createNamespace
- alterNamespace
- dropNamespace
### How was this patch tested?
Add new docker tests
Closes#30473 from huaxingao/name_space.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### 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?
remove USING _ in CREATE TABLE in JDBCTableCatalog docker tests
### Why are the changes needed?
Previously CREATE TABLE syntax forces users to specify a provider so we have to add a USING _ . Now the problem was fix and we need to remove it.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests
Closes#30599 from huaxingao/remove_USING.
Authored-by: Huaxin Gao <huaxing@us.ibm.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?
make CrossValidator/TrainValidateSplit/OneVsRest Reader/Writer support Python backend estimator/model
### Why are the changes needed?
Currently, pyspark support third-party library to define python backend estimator/evaluator, i.e., estimator that inherit `Estimator` instead of `JavaEstimator`, and only can be used in pyspark.
CrossValidator and TrainValidateSplit support tuning these python backend estimator,
but cannot support saving/load, becase CrossValidator and TrainValidateSplit writer implementation is use JavaMLWriter, which require to convert nested estimator and evaluator into java instance.
OneVsRest saving/load now only support java backend classifier due to similar issue.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Closes#30471 from WeichenXu123/support_pyio_tuning.
Authored-by: Weichen Xu <weichen.xu@databricks.com>
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
### What changes were proposed in this pull request?
Instead of using several analyzer rules, this PR uses the actual analyzer to run tests in `PlanResolutionSuite`.
### Why are the changes needed?
Make the test suite to match reality.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
test-only
Closes#30574 from cloud-fan/test.
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 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?
`spark.buffer.size` not applied in driver from pyspark. In this PR I've fixed this issue.
### Why are the changes needed?
Apply the mentioned config on driver side.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests + manually.
Added the following code temporarily:
```
def local_connect_and_auth(port, auth_secret):
...
sock.connect(sa)
print("SPARK_BUFFER_SIZE: %d" % int(os.environ.get("SPARK_BUFFER_SIZE", 65536))) <- This is the addition
sockfile = sock.makefile("rwb", int(os.environ.get("SPARK_BUFFER_SIZE", 65536)))
...
```
Test:
```
#Compile Spark
echo "spark.buffer.size 10000" >> conf/spark-defaults.conf
$ ./bin/pyspark
Python 3.8.5 (default, Jul 21 2020, 10:48:26)
[Clang 11.0.3 (clang-1103.0.32.62)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
20/12/03 13:38:13 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
20/12/03 13:38:14 WARN SparkEnv: I/O encryption enabled without RPC encryption: keys will be visible on the wire.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Python version 3.8.5 (default, Jul 21 2020 10:48:26)
Spark context Web UI available at http://192.168.0.189:4040
Spark context available as 'sc' (master = local[*], app id = local-1606999094506).
SparkSession available as 'spark'.
>>> sc.setLogLevel("TRACE")
>>> sc.parallelize([0, 2, 3, 4, 6], 5).glom().collect()
...
SPARK_BUFFER_SIZE: 10000
...
[[0], [2], [3], [4], [6]]
>>>
```
Closes#30592 from gaborgsomogyi/SPARK-33629.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@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?
This is to update `labelsArray`'s since tag.
### Why are the changes needed?
The original change was backported to branch-3.0 for 3.0.2 version. So it is better to update the since tag to reflect the fact.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N/A. Just tag change.
Closes#30582 from viirya/SPARK-33636-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a followup to add missing `labelsArray` to PySpark `StringIndexer`.
### Why are the changes needed?
`labelsArray` is for multi-column case for `StringIndexer`. We should provide this accessor at PySpark side too.
### Does this PR introduce _any_ user-facing change?
Yes, `labelsArray` was missing in PySpark `StringIndexer` in Spark 3.0.
### How was this patch tested?
Unit test.
Closes#30579 from viirya/SPARK-22798-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
As the discussion in https://github.com/apache/spark/pull/30521#discussion_r531463427, rename the API to `toTable`.
### Why are the changes needed?
Rename the API for further extension and accuracy.
### Does this PR introduce _any_ user-facing change?
Yes, it's an API change but the new API is not released yet.
### How was this patch tested?
Existing UT.
Closes#30571 from xuanyuanking/SPARK-32896-follow.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
### What changes were proposed in this pull request?
on each call of `transform`, a head() job will be triggered, which can be skipped by using a lazy var.
### Why are the changes needed?
avoiding duplicate head() jobs
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing tests
Closes#30550 from zhengruifeng/imputer_transform.
Authored-by: Ruifeng Zheng <ruifengz@foxmail.com>
Signed-off-by: Ruifeng Zheng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Currently, GitHub Action is using two docker images.
```
$ git grep dongjoon/apache-spark-github-action-image
.github/workflows/build_and_test.yml: image: dongjoon/apache-spark-github-action-image:20201015
.github/workflows/build_and_test.yml: image: dongjoon/apache-spark-github-action-image:20201025
```
This PR aims to make it consistent by using the latest one.
```
- image: dongjoon/apache-spark-github-action-image:20201015
+ image: dongjoon/apache-spark-github-action-image:20201025
```
### Why are the changes needed?
This is for better maintainability. The image size is almost the same.
```
$ docker images | grep 202010
dongjoon/apache-spark-github-action-image 20201025 37adfa3d226a 5 weeks ago 2.18GB
dongjoon/apache-spark-github-action-image 20201015 ff6fee8dc36d 6 weeks ago 2.16GB
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the GitHub Action.
Closes#30578 from dongjoon-hyun/SPARK-MINOR.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
SPARK-9767 remove `ConnectionManager` and related files, the configuration `spark.core.connection.ack.wait.timeout` previously used by `ConnectionManager` is no longer used by other Spark code, but it still exists in the `configuration.md`.
So this pr cleans up the useless configuration item spark.core.connection.ack.wait.timeout` from `configuration.md`.
### Why are the changes needed?
Clean up useless configuration from `configuration.md`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or GitHub Action
Closes#30569 from LuciferYang/SPARK-33631.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### 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?
Allow k8s integration tests to assert both driver and executor logs for expected log(s)
### Why are the changes needed?
Some of the tests will be able to provide full coverage of the use case, by asserting both driver and executor logs.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
TBD
Closes#30568 from ScrapCodes/expectedDriverLogChanges.
Authored-by: Prashant Sharma <prashsh1@in.ibm.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### 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?
To make sure the sensitive attributes to be redacted in the history server log.
### Why are the changes needed?
We found the secure attributes like password in SparkListenerJobStart and SparkListenerStageSubmitted events would not been redated, resulting in sensitive attributes can be viewd directly.
The screenshot can be viewed in the attachment of JIRA spark-33504
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
muntual test works well, I have also added unit testcase.
Closes#30446 from akiyamaneko/eventlog_unredact.
Authored-by: neko <echohlne@gmail.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
As described in SPARK-33557, `HeartbeatReceiver` and `MesosCoarseGrainedSchedulerBackend` will always use `Network.NETWORK_TIMEOUT.defaultValueString` as value of `STORAGE_BLOCKMANAGER_HEARTBEAT_TIMEOUT` when we configure `NETWORK_TIMEOUT` without configure `STORAGE_BLOCKMANAGER_HEARTBEAT_TIMEOUT`, this is different from the relationship described in `configuration.md`.
To fix this problem，the main change of this pr as follow:
- Remove the explicitly default value of `STORAGE_BLOCKMANAGER_HEARTBEAT_TIMEOUT`
- Use actual value of `NETWORK_TIMEOUT` as `STORAGE_BLOCKMANAGER_HEARTBEAT_TIMEOUT` when `STORAGE_BLOCKMANAGER_HEARTBEAT_TIMEOUT` not configured in `HeartbeatReceiver` and `MesosCoarseGrainedSchedulerBackend`
### Why are the changes needed?
To ensure the relationship between `NETWORK_TIMEOUT` and `STORAGE_BLOCKMANAGER_HEARTBEAT_TIMEOUT` as we described in `configuration.md`
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Pass the Jenkins or GitHub Action
- Manual test configure `NETWORK_TIMEOUT` and `STORAGE_BLOCKMANAGER_HEARTBEAT_TIMEOUT` locally
Closes#30547 from LuciferYang/SPARK-33557.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### 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?
Per request from https://github.com/apache/spark/pull/30395#issuecomment-735028698, here we remove `Windowed` from methods names `setupWindowedJoinWithRangeCondition` and `setupWindowedSelfJoin` as they don't join on time window.
### Why are the changes needed?
There's no such official name for `windowed join`, so this is to help avoid confusion for future developers.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests.
Closes#30563 from c21/stream-minor.
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?
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 `array_to_vector` to R API.
### Why are the changes needed?
Feature parity.
### Does this PR introduce _any_ user-facing change?
New function exposed in the public API.
### How was this patch tested?
New unit test.
Manual verification of the documentation examples.
Closes#30561 from zero323/SPARK-33622.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
When running Spark behind a reverse proxy(e.g. Nginx, Apache HTTP server), the request URL can be encoded twice if we pass the query string directly to the constructor of `java.net.URI`:
```
> val uri = "http://localhost:8081/test"
> val query = "order%5B0%5D%5Bcolumn%5D=0" // query string of URL from the reverse proxy
> val rewrittenURI = URI.create(uri.toString())
> new URI(rewrittenURI.getScheme(),
rewrittenURI.getAuthority(),
rewrittenURI.getPath(),
query,
rewrittenURI.getFragment()).toString
result: http://localhost:8081/test?order%255B0%255D%255Bcolumn%255D=0
```
In Spark's stage page, the URL of "/taskTable" contains query parameter order[0][dir]. After encoding twice, the query parameter becomes `order%255B0%255D%255Bdir%255D` and it will be decoded as `order%5B0%5D%5Bdir%5D` instead of `order[0][dir]`. As a result, there will be NullPointerException from https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/status/api/v1/StagesResource.scala#L176
Other than that, the other parameter may not work as expected after encoded twice.
This PR is to fix the bug by calling the method `URI.create(String URL)` directly. This convenience method can avoid encoding twice on the query parameter.
```
> val uri = "http://localhost:8081/test"
> val query = "order%5B0%5D%5Bcolumn%5D=0"
> URI.create(s"$uri?$query").toString
result: http://localhost:8081/test?order%5B0%5D%5Bcolumn%5D=0
> URI.create(s"$uri?$query").getQuery
result: order[0][column]=0
```
### Why are the changes needed?
Fix a potential bug when Spark's reverse proxy is enabled.
The bug itself is similar to https://github.com/apache/spark/pull/29271.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Add a new unit test.
Also, Manual UI testing for master, worker and app UI with an nginx proxy
Spark config:
```
spark.ui.port 8080
spark.ui.reverseProxy=true
spark.ui.reverseProxyUrl=/path/to/spark/
```
nginx config:
```
server {
listen 9000;
set $SPARK_MASTER http://127.0.0.1:8080;
# split spark UI path into prefix and local path within master UI
location ~ ^(/path/to/spark/) {
# strip prefix when forwarding request
rewrite /path/to/spark(/.*) $1 break;
#rewrite /path/to/spark/ "/" ;
# forward to spark master UI
proxy_pass $SPARK_MASTER;
proxy_intercept_errors on;
error_page 301 302 307 = handle_redirects;
}
location handle_redirects {
set $saved_redirect_location '$upstream_http_location';
proxy_pass $saved_redirect_location;
}
}
```
Closes#30552 from gengliangwang/decodeProxyRedirect.
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?
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?
Throw Exception if JDBC Table Catalog has provider in create table.
### Why are the changes needed?
JDBC Table Catalog doesn't support provider and we should throw Exception. Previously CREATE TABLE syntax forces people to specify a provider so we have to add a `USING_`. Now the problem was fix and we will throw Exception for provider.
### Does this PR introduce _any_ user-facing change?
Yes. We throw Exception if a provider is specified in CREATE TABLE for JDBC Table catalog.
### How was this patch tested?
Existing tests (remove `USING _`)
Closes#30544 from huaxingao/followup.
Authored-by: Huaxin Gao <huaxing@us.ibm.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?
This patch proposes to provide a new option to specify time-to-live (TTL) for output file entries in FileStreamSink. TTL is defined via current timestamp - the last modified time for the file.
This patch will filter out outdated output files in metadata while compacting batches (other batches don't have functionality to clean entries), which helps metadata to not grow linearly, as well as filtered out files will be "eventually" no longer seen in reader queries which leverage File(Stream)Source.
### Why are the changes needed?
The metadata log greatly helps to easily achieve exactly-once but given the output path is open to arbitrary readers, there's no way to compact the metadata log, which ends up growing the metadata file as query runs for long time, especially for compacted batch.
Lots of end users have been reporting the issue: see comments in [SPARK-24295](https://issues.apache.org/jira/browse/SPARK-24295) and [SPARK-29995](https://issues.apache.org/jira/browse/SPARK-29995), and [SPARK-30462](https://issues.apache.org/jira/browse/SPARK-30462).
(There're some reports from end users which include their workarounds: SPARK-24295)
### Does this PR introduce any user-facing change?
No, as the configuration is new and by default it is not applied.
### How was this patch tested?
New UT.
Closes#28363 from HeartSaVioR/SPARK-27188-v2.
Lead-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Co-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
