### What changes were proposed in this pull request?
Support extracting hour/minute/second fields from timestamp without time zone values. In details, the following syntaxes are supported:
- extract [hour | minute | second] from timestampWithoutTZ
- date_part('[hour | minute | second]', timestampWithoutTZ)
- hour(timestampWithoutTZ)
- minute(timestampWithoutTZ)
- second(timestampWithoutTZ)
### Why are the changes needed?
Support basic operations for the new timestamp type.
### Does this PR introduce _any_ user-facing change?
No, the timestamp without time zone type is not release yet.
### How was this patch tested?
Unit test
Closes#33136 from gengliangwang/field.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
There are two new Avro options `datetimeRebaseMode` and `positionalFieldMatching` after Spark 3.2.
We should document the since version so that users can know whether the option works in their Spark version.
### Why are the changes needed?
Better documentation.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Manual preview on local setup.
<img width="828" alt="Screen Shot 2021-06-30 at 5 05 54 PM" src="https://user-images.githubusercontent.com/1097932/123934000-ba833b00-d947-11eb-9ca5-ce8ff8add74b.png">
<img width="711" alt="Screen Shot 2021-06-30 at 5 06 34 PM" src="https://user-images.githubusercontent.com/1097932/123934126-d4bd1900-d947-11eb-8d80-69df8f3d9900.png">
Closes#33153 from gengliangwang/version.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Unifies exceptions thrown from Spark under a single base trait `SparkError`, which unifies:
- Error classes
- Parametrized error messages
- SQLSTATE, as discussed in http://apache-spark-developers-list.1001551.n3.nabble.com/DISCUSS-Add-error-IDs-td31126.html.
### Why are the changes needed?
- Adding error classes creates a consistent label for exceptions, even as error messages change
- Creating a single, centralized source-of-truth for parametrized error messages improves auditing for error message quality
- Adding SQLSTATE helps ODBC/JDBC users receive standardized error codes
### Does this PR introduce _any_ user-facing change?
Yes, changes ODBC experience by:
- Adding error classes to error messages
- Adding SQLSTATE to TStatus
### How was this patch tested?
Unit tests, as well as local tests with PyODBC.
Closes#32850 from karenfeng/SPARK-34920.
Authored-by: Karen Feng <karen.feng@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Provide the (configurable) ability to perform Avro-to-Catalyst schema field matching using the position of the fields instead of their names. A new `option` is added for the Avro datasource, `positionalFieldMatching`, which instructs `AvroSerializer`/`AvroDeserializer` to perform positional field matching instead of matching by name.
### Why are the changes needed?
This by-name matching is somewhat recent; prior to PR #24635, at least on the write path, schemas were matched by positionally ("structural" comparison). While by-name is better behavior as a default, it will be better to make this configurable by a user. Even at the time that PR #24635 was handled, there was [interest in making this behavior configurable](https://github.com/apache/spark/pull/24635#issuecomment-494205251), but it appears it went unaddressed.
There is precedence for configurability of this behavior as seen in PR #29737, which added this support for ORC. Besides this precedence, the behavior of Hive is to perform matching positionally ([ref](https://cwiki.apache.org/confluence/display/Hive/AvroSerDe#AvroSerDe-WritingtablestoAvrofiles)), so this is behavior that Hadoop/Hive ecosystem users are familiar with.
### Does this PR introduce _any_ user-facing change?
Yes, a new option is provided for the Avro datasource, `positionalFieldMatching`, which provides compatibility with Hive and pre-3.0.0 Spark behavior.
### How was this patch tested?
New unit tests are added within `AvroSuite`, `AvroSchemaHelperSuite`, and `AvroSerdeSuite`; and most of the existing tests within `AvroSerdeSuite` are adapted to perform the same test using by-name and positional matching to ensure feature parity.
Closes#31490 from xkrogen/xkrogen-SPARK-34365-avro-positional-field-matching.
Authored-by: Erik Krogen <xkrogen@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This is the followup from https://github.com/apache/spark/pull/33012#discussion_r659440833, where we want to add `Unstable` to `FileCommitProtocol`, to give people a better idea of API.
### Why are the changes needed?
Make it easier for people to follow and understand code. Clean up code.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests, as no real logic change.
Closes#33148 from c21/bucket-followup.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to clean up Spark 2.4 and Java7 code path from the release scripts.
### Why are the changes needed?
To simplify the logic.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
N/A
Closes#33150 from dongjoon-hyun/SPARK-35948.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Like SPARK-35825, this PR aims to increase JVM stack size via `MAVEN_OPTS` in release-build.sh.
### Why are the changes needed?
This will mitigate the failure in publishing snapshot GitHub Action job and during the release.
- https://github.com/apache/spark/actions/workflows/publish_snapshot.yml (3-day consecutive failures)
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
N/A
Closes#33149 from dongjoon-hyun/SPARK-35947.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In the PR, I propose to catch all non-fatal exceptions coming `refreshTable()` at the final stage of table repairing, and output an error message instead of failing with an exception.
### Why are the changes needed?
1. The uncaught exceptions from table refreshing might be considered as regression comparing to previous Spark versions. Table refreshing was introduced by https://github.com/apache/spark/pull/31066.
2. This should improve user experience with Spark SQL. For instance, when the `MSCK REPAIR TABLE` is performed in a chain of command in SQL where catching exception is difficult or even impossible.
### Does this PR introduce _any_ user-facing change?
Yes. Before the changes the `MSCK REPAIR TABLE` command can fail with the exception portrayed in SPARK-35935. After the changes, the same command outputs error message, and completes successfully.
### How was this patch tested?
By existing test suites.
Closes#33137 from MaxGekk/msck-repair-catch-except.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This reverts commit e6753c9402.
### Why are the changes needed?
The `make_interval` function aims to construct values of the legacy interval type `CalendarIntervalType` which will be substituted by ANSI interval types (see SPARK-27790). Since the function has not been released yet, it would be better to don't expose it via public API at all.
### Does this PR introduce _any_ user-facing change?
Should not since the `make_interval` function has not been released yet.
### How was this patch tested?
By existing test suites, and GA/jenkins builds.
Closes#33143 from MaxGekk/revert-make_interval.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
Add a new ANSI type coercion rule: when getting a date field from a Timestamp column, cast the column as Date type.
This is Spark's current hack to make the implementation simple. In the default type coercion rules, the implicit cast rule does the work. However, The ANSI implicit cast rule doesn't allow converting Timestamp type as Date type, so we need to have this additional rule to make sure the date field extraction from Timestamp columns works.
### Why are the changes needed?
Fix a bug.
### Does this PR introduce _any_ user-facing change?
No, the new type coercion rules are not released yet.
### How was this patch tested?
Unit test
Closes#33138 from gengliangwang/fixGetDateField.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Currently ,we sets the environment variable `PYSPARK_PIN_THREAD` at the client side of `InhertiableThread` API for Py4J (`python/pyspark/util.py`). If the Py4J gateway is created somewhere else (e.g., Zeppelin, etc), it could introduce a breakage at:
```python
from pyspark import SparkContext
jvm = SparkContext._jvm
thread_connection = jvm._gateway_client.get_thread_connection()
# `AttributeError: 'GatewayClient' object has no attribute 'get_thread_connection'` (non-pinned thread mode)
# `get_thread_connection` is only in 'ClientServer' (pinned thread mode)
```
This PR proposes to check the given gateway created, and do the pinned thread mode behaviour accordingly so we can avoid any breakage when Py4J server/gateway is created separately from somewhere else without a pinned thread mode.
### Why are the changes needed?
To avoid any potential breakage.
### Does this PR introduce _any_ user-facing change?
No, the change happened only in the master (fdd7ca5f4e).
### How was this patch tested?
This is actually a partial revert of fdd7ca5f4e. As long as the existing tests pass, I guess we're all good.
I also manually tested to make doubly sure:
**Before**:
```python
>>> from pyspark import InheritableThread, inheritable_thread_target
>>> InheritableThread(lambda: 1).start()
>>> inheritable_thread_target(lambda: 1)()
Traceback (most recent call last):
File "/.../python3.8/lib/python3.8/threading.py", line 932, in _bootstrap_inner
self.run()
File "/.../python3.8/lib/python3.8/threading.py", line 870, in run
self._target(*self._args, **self._kwargs)
File "/.../spark/python/pyspark/util.py", line 361, in copy_local_properties
InheritableThread._clean_py4j_conn_for_current_thread()
File "/.../spark/python/pyspark/util.py", line 381, in _clean_py4j_conn_for_current_thread
thread_connection = jvm._gateway_client.get_thread_connection()
AttributeError: 'GatewayClient' object has no attribute 'get_thread_connection'
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/util.py", line 324, in wrapped
InheritableThread._clean_py4j_conn_for_current_thread()
File "/.../spark/python/pyspark/util.py", line 381, in _clean_py4j_conn_for_current_thread
thread_connection = jvm._gateway_client.get_thread_connection()
AttributeError: 'GatewayClient' object has no attribute 'get_thread_connection'
```
**After**:
```python
>>> from pyspark import InheritableThread, inheritable_thread_target
>>> InheritableThread(lambda: 1).start()
>>> inheritable_thread_target(lambda: 1)()
1
```
Closes#33147 from HyukjinKwon/SPARK-35946.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This patch refactors the evaluation of subexpressions.
There are two changes:
1. Clean up subexpression code after evaluation to avoid duplicate evaluation.
2. Evaluate all children subexpressions when evaluating a subexpression.
### Why are the changes needed?
Currently `subexpressionEliminationForWholeStageCodegen` return the gen-ed code of subexpressions. The caller simply puts the code into its code block. We need more flexible evaluation here. For example, for Filter operator's subexpression evaluation, we may need to evaluate particular subexpression for one predicate. Current approach cannot satisfy the requirement.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests.
Closes#32980 from viirya/subexpr-eval.
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?
Introduces `Axis` type alias for `axis` argument to be consistent.
### Why are the changes needed?
There are many places to use `axis` argument. We should define `Axis` type alias and reuse it to be consistent.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33144 from ueshin/issues/SPARK-35943/axis.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR reverts the change of SPARK-34549 ( #31658).
### Why are the changes needed?
See #33133.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Closes#33145 from sarutak/revert-SPARK-34549.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes removing the legacy Koalas version from pandas API on Spark package.
And also remove the Python version check logic since now pandas-on-Spark should follow the PySpark's Python version.
### Why are the changes needed?
Since Koalas is ported into PySpark, we don't need to keep the version logic for Koalas.
### Does this PR introduce _any_ user-facing change?
Now the legacy Koalas user should follow the version from PySpark.
### How was this patch tested?
Manually built the package and see it's successfully done.
Closes#33128 from itholic/SPARK-35873.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The implementation for the RocksDB instance, which is used in the RocksDB state store. It plays a role as a handler for the RocksDB instance and RocksDBFileManager.
### Why are the changes needed?
Part of the RocksDB state store implementation.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New UT added.
Closes#32928 from xuanyuanking/SPARK-35784.
Authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
This is the shuffle fetch side change where executors can fetch local/remote push-merged shuffle data from shuffle services. This is needed for push-based shuffle - SPIP [SPARK-30602](https://issues.apache.org/jira/browse/SPARK-30602).
The change adds support to the `ShuffleBlockFetchIterator` to fetch push-merged block meta and shuffle chunks from local and remote ESS. If the fetch of any of these fails, then the iterator fallsback to fetch the original shuffle blocks that belonged to the push-merged block.
### Why are the changes needed?
These changes are needed for push-based shuffle. Refer to the SPIP in [SPARK-30602](https://issues.apache.org/jira/browse/SPARK-30602).
### Does this PR introduce _any_ user-facing change?
When push-based shuffle is turned on then that will fetch push-merged blocks from the remote shuffle service. The client logs will indicate this.
### How was this patch tested?
Added unit tests.
The reference PR with the consolidated changes covering the complete implementation is also provided in [SPARK-30602](https://issues.apache.org/jira/browse/SPARK-30602).
We have already verified the functionality and the improved performance as documented in the SPIP doc.
Lead-authored-by: Chandni Singh chsinghlinkedin.com
Co-authored-by: Min Shen mshenlinkedin.com
Co-authored-by: Ye Zhou yezhoulinkedin.com
Closes#32140 from otterc/SPARK-32922.
Lead-authored-by: Chandni Singh <singh.chandni@gmail.com>
Co-authored-by: Chandni Singh <chsingh@linkedin.com>
Co-authored-by: Min Shen <mshen@linkedin.com>
Co-authored-by: otterc <singh.chandni@gmail.com>
Signed-off-by: Mridul Muralidharan <mridul<at>gmail.com>
### What changes were proposed in this pull request?
This PR changes `SparkBuild.scala` to edit `config.properties` in `yarn` sub-module in build with SBT like as build with Maven does.
### Why are the changes needed?
yarn sub-module contains config.properties.
```
spark.yarn.isHadoopProvided = ${spark.yarn.isHadoopProvided}
```
The `${spark.yarn.isHadoopProvided}` part is replaced with `true` or `false` in build depending on whether Hadoop is provided or not (specified by -Phadoop-provided).
The edited config.properties will be loaded at runtime to control how to populate Hadoop-related classpath.
If we build with Maven, these process works but doesn't with SBT.
If we build with SBT and deploy apps on YARN, the following warning appears and classpath is not populated correctly.
```
21/06/29 10:51:20 WARN config.package: Can not load the default value of `spark.yarn.isHadoopProvided` from `org/apache/spark/deploy/yarn/config.properties` with error, java.lang.IllegalArgumentException: For input string: "${spark.yarn.isHadoopProvided}". Using `false` as a default value.
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Built with SBT and extracted `config.properties` from the build artifact and confirmed `${spark.yarn.isHadoopProvided} was correctly edited with `true` or `false`.
```
cat org/apache/spark/deploy/yarn/config.properties
spark.yarn.isHadoopProvided = false # In case build with -Pyarn and without -Phadoop-provided
spark.yarn.isHadoopProvided = true # In case build with -Pyarn and -Phadoop-provided
```
I also confirmed the warning message shown above no longer appears.
Closes#33121 from sarutak/sbt-yarn-config-properties.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
This PR aims to add Java 17-ea build test to GitHub action.
### Why are the changes needed?
To improve test coverage.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass newly added Java 17-ea GitHub action job.
Closes#33126 from williamhyun/SPARK-35924.
Authored-by: William Hyun <william@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR removes order by if the maximum number of rows less than or equal to 1. For example:
```scala
spark.sql("select count(*) from range(1, 10, 2, 2) order by 1 limit 10").explain("cost")
```
Before this pr:
```
== Optimized Logical Plan ==
Sort [count(1)#2L ASC NULLS FIRST], true, Statistics(sizeInBytes=16.0 B)
+- Aggregate [count(1) AS count(1)#2L], Statistics(sizeInBytes=16.0 B, rowCount=1)
+- Project, Statistics(sizeInBytes=20.0 B)
+- Range (1, 10, step=2, splits=Some(2)), Statistics(sizeInBytes=40.0 B, rowCount=5)
```
After this pr:
```
== Optimized Logical Plan ==
Aggregate [count(1) AS count(1)#2L], Statistics(sizeInBytes=16.0 B, rowCount=1)
+- Project, Statistics(sizeInBytes=20.0 B)
+- Range (1, 10, step=2, splits=Some(2)), Statistics(sizeInBytes=40.0 B, rowCount=5)
```
### 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#33100 from wangyum/SPARK-35906.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Cleaning up the type hints in pandas-on-Spark.
- Use a single file `_typing.py` for type variables or aliases
- Rename `IndexOpsLike` to `SeriesOrIndex`.
- Rename `T_Frame` and `T_IndexOps` to `FrameLike` and `IndexOpsLike` respectively
- Introduce `DataFrameOrSeries` for `Union[DataFrame, Series]`
### Why are the changes needed?
This is a cleanup for the mypy check stuff series.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33117 from ueshin/issues/SPARK-35859/cleanup.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
This PR aims to upgrade ASM to 9.1
### Why are the changes needed?
The latest `xbean-asm9-shaded` is built with ASM 9.1.
- https://mvnrepository.com/artifact/org.apache.xbean/xbean-asm9-shaded/4.20
- 5e0e3c0c64/pom.xml (L67)
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CIs.
Closes#33130 from dongjoon-hyun/SPARK-35928.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Skip empty partitions in `ShufflePartitionsUtil.coalescePartitionsWithSkew`.
### Why are the changes needed?
Since [SPARK-35447](https://issues.apache.org/jira/browse/SPARK-35447), we apply `OptimizeSkewedJoin` before `CoalesceShufflePartitions`. However, There are something different with the order of these two rules.
Let's say if we have a skewed partitions: [0, 128MB, 0, 128MB, 0]:
* coalesce partitions first then optimize skewed partitions:
[64MB, 64MB, 64MB, 64MB]
* optimize skewed partition first then coalesce partitions:
[0, 64MB, 64MB, 0, 64MB, 64MB, 0]
So we can do coalesce in `ShufflePartitionsUtil.coalescePartitionsWithSkew` with mixed `CoalescedPartitionSpec` and `PartialReducerPartitionSpec` if `CoalescedPartitionSpec` is empty.
### Does this PR introduce _any_ user-facing change?
No, not release yet.
### How was this patch tested?
Add test.
Closes#33123 from ulysses-you/SPARK-35923.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Replace the type collection `AllTimestampTypes` with the new data type `AnyTimestampType`
### Why are the changes needed?
As discussed in https://github.com/apache/spark/pull/33115#discussion_r659866760, it is more convenient to have a new data type "AnyTimestampType" instead of using type collection `AllTimestampTypes`:
1. simplify the pattern match
2. In the default type coercion rules, when implicit casting a type to a TypeCollection type, Spark chooses the first convertible data type as the result. If we are going to make the default timestamp type configurable, having AnyTimestampType is better
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing UT
Closes#33129 from gengliangwang/allTimestampTypes.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
Add path level discover for python unittests.
### Why are the changes needed?
Now we need to specify the python test cases by manually when we add a new testcase. Sometime, we forgot to add the testcase to module list, the testcase would not be executed.
Such as:
- pyspark-core pyspark.tests.test_pin_thread
Thus we need some auto-discover way to find all testcase rather than specified every case by manually.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Add below code in end of `dev/sparktestsupport/modules.py`
```python
for m in sorted(all_modules):
for g in sorted(m.python_test_goals):
print(m.name, g)
```
Compare the result before and after:
https://www.diffchecker.com/iO3FvhKLCloses#32867 from Yikun/SPARK_DISCOVER_TEST.
Authored-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Support the following operations:
- TimestampWithoutTZ - Date
- Date - TimestampWithoutTZ
- TimestampWithoutTZ - Timestamp
- Timestamp - TimestampWithoutTZ
- TimestampWithoutTZ - TimestampWithoutTZ
For subtraction between `TimestampWithoutTZ` and `Timestamp`, the `Timestamp` column is cast as TimestampWithoutTZType.
### Why are the changes needed?
Support basic subtraction among Date/Timestamp/TimestampWithoutTZ.
### Does this PR introduce _any_ user-facing change?
No, the timestamp without time zone type is not release yet.
### How was this patch tested?
Unit tests
Closes#33115 from gengliangwang/subtractTimestampWithoutTz.
Authored-by: Gengliang Wang <gengliang@apache.org>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
### What changes were proposed in this pull request?
This PR updates the doctests in `run-tests.py`.
### Why are the changes needed?
This should be consists with `modules.py` behavior.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the GitHub Action.
I checked manually.
```
$ python dev/run-tests.py
Cannot install SparkR as R was not found in PATH
[info] Using build tool sbt with Hadoop profile hadoop3.2 and Hive profile hive2.3 under environment local
[info] Found the following changed modules: root
[info] Setup the following environment variables for tests:
========================================================================
Running Apache RAT checks
========================================================================
RAT checks passed.
```
Closes#33127 from dongjoon-hyun/SPARK-35483-2.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR aims to update JDBC v2 integration suite by adding `catalogName`.
### Why are the changes needed?
To recover the integration test suite.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the GitHub Action.
Closes#33124 from dongjoon-hyun/SPARK-34302.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR aims to enable `docker_integration_tests` when `catalyst` and `sql` module changes additionally.
### Why are the changes needed?
Currently, `catalyst` and `sql` module changes do not trigger the JDBC integration test.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
N/A
Closes#33125 from dongjoon-hyun/SPARK-35483.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR addresses post-review comments on PR #33096:
- removes `private[sql]` modifier
- removes the option to pass a resolver to simplify the API
### Why are the changes needed?
These changes are needed to simply the utility API.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33120 from aokolnychyi/spark-35899-follow-up.
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 aims to upgrade `maven-shade-plugin` to 3.2.4.
### Why are the changes needed?
This is required to build with Java 17-ea.
Since `maven-shade-plugin` 3.2.3, `asm` 8.0 is used now. We should remove our custom dependency of `7.3.1`.
- https://mvnrepository.com/artifact/org.apache.maven.plugins/maven-shade-plugin/3.2.4
- https://mvnrepository.com/artifact/org.apache.maven.plugins/maven-shade-plugin/3.2.3
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CIs.
Closes#33122 from dongjoon-hyun/SPARK-35922.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR aims to upgrade Chill to 0.10.0.
### Why are the changes needed?
This is a maintenance release having cross-compilation to 2.12.14 and 2.13.6 .
- https://github.com/twitter/chill/releases/tag/v0.10.0
### Does this PR introduce _any_ user-facing change?
No, this is a dependency change.
### How was this patch tested?
Pass the CIs.
Closes#33119 from dongjoon-hyun/SPARK-35920.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR fixes an issue that field names of structs generated by `arrays_zip` function could be unexpectedly re-written by analyzer/optimizer.
Here is an example.
```
val df = sc.parallelize(Seq((Array(1, 2), Array(3, 4)))).toDF("a1", "b1").selectExpr("arrays_zip(a1, b1) as zipped")
df.printSchema
root
|-- zipped: array (nullable = true)
| |-- element: struct (containsNull = false)
| | |-- a1: integer (nullable = true) // OK. a1 is expected name
| | |-- b1: integer (nullable = true) // OK. b1 is expected name
df.explain
== Physical Plan ==
*(1) Project [arrays_zip(_1#3, _2#4) AS zipped#12] // Not OK. field names are re-written as _1 and _2 respectively
df.write.parquet("/tmp/test.parquet")
val df2 = spark.read.parquet("/tmp/test.parquet")
df2.printSchema
root
|-- zipped: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- _1: integer (nullable = true) // Not OK. a1 is expected but got _1
| | |-- _2: integer (nullable = true) // Not OK. b1 is expected but got _2
```
This issue happens when aliases are eliminated by `AliasHelper.replaceAliasButKeepName` or `AliasHelper.trimNonTopLevelAliases` called via analyzer/optimizer
b89cd8d75a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala (L883)b89cd8d75a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala (L3759)
I investigated functions which can be affected this issue but I found only `arrays_zip` so far.
To fix this issue, this PR changes the definition of `ArraysZip` to retain field names to avoid being re-written by analyzer/optimizer.
### Why are the changes needed?
This is apparently a bug.
### Does this PR introduce _any_ user-facing change?
No. After this change, the field names are no longer re-written but it should be expected behavior for users.
### How was this patch tested?
New tests.
Closes#33106 from sarutak/arrays-zip-retain-names.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to migrate the following `ALTER TABLE ... CHANGE COLUMN` command to use `UnresolvedTable` as a `child` to resolve the table identifier. This allows consistent resolution rules (temp view first, etc.) to be applied for both v1/v2 commands. More info about the consistent resolution rule proposal can be found in [JIRA](https://issues.apache.org/jira/browse/SPARK-29900) or [proposal doc](https://docs.google.com/document/d/1hvLjGA8y_W_hhilpngXVub1Ebv8RsMap986nENCFnrg/edit?usp=sharing).
### Why are the changes needed?
This is a part of effort to make the relation lookup behavior consistent: [SPARK-29900](https://issues.apache.org/jira/browse/SPARK-29900).
### Does this PR introduce _any_ user-facing change?
After this PR, the above `ALTER TABLE ... CHANGE COLUMN` commands will have a consistent resolution behavior.
### How was this patch tested?
Updated existing tests.
Closes#33113 from imback82/alter_change_column.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
* add `dataSize` field in `CoalescedPartitionSpec`
* add data size test suite in `ShufflePartitionsUtilSuite`
### Why are the changes needed?
Currently, all test suite about `CoalescedPartitionSpec` do not check the data size due to it doesn't contains data size field.
We can add data size in `CoalescedPartitionSpec` and then add test case for better coverage.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass CI
Closes#33079 from ulysses-you/SPARK-35888.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Implement V2 execution node `ShowCreateTableExec` similar to V1 `ShowCreateTableCommand`
2. No support `SHOW CREATE TABLE XXX AS SERDE`
### Why are the changes needed?
[SPARK-33898](https://issues.apache.org/jira/browse/SPARK-33898)
### Does this PR introduce _any_ user-facing change?
Yes. Support the user to execute `SHOW CREATE TABLE` command in V2 table
### How was this patch tested?
Add two UT tests
1. ./dev/scalastyle
2. run test DataSourceV2SQLSuite
Closes#32931 from Peng-Lei/SPARK-33898.
Lead-authored-by: PengLei <18066542445@189.cn>
Co-authored-by: PengLei <peng.8lei@gmail.com>
Signed-off-by: Kent Yao <yao@apache.org>
### What changes were proposed in this pull request?
The PR is proposed to support creating a Column of numpy literal value in pandas-on-Spark. It consists of three changes mainly:
- Enable the `lit` function defined in `pyspark.pandas.spark.functions` to support numpy literals input.
```py
>>> from pyspark.pandas.spark import functions as SF
>>> SF.lit(np.int64(1))
Column<'CAST(1 AS BIGINT)'>
>>> SF.lit(np.int32(1))
Column<'CAST(1 AS INT)'>
>>> SF.lit(np.int8(1))
Column<'CAST(1 AS TINYINT)'>
>>> SF.lit(np.byte(1))
Column<'CAST(1 AS TINYINT)'>
>>> SF.lit(np.float32(1))
Column<'CAST(1.0 AS FLOAT)'>
```
- Substitute `F.lit` by `SF.lit`, that is, use `lit` function defined in `pyspark.pandas.spark.functions` rather than `lit` function defined in `pyspark.sql.functions` to allow creating columns out of numpy literals.
- Enable numpy literals input in `isin` method
Non-goal:
- Some pandas-on-Spark APIs use PySpark column-related APIs internally, and these column-related APIs don't support numpy literals, thus numpy literals are disallowed as input (e.g. `to_replace` parameter in `replace` API). This PR doesn't aim to adjust all of them. This PR adjusts `isin` only, because the PR is inspired by that (as https://github.com/databricks/koalas/issues/2161).
- To complete mappings between all kinds of numpy literals and Spark data types should be a followup task.
### Why are the changes needed?
Spark (`lit` function defined in `pyspark.sql.functions`) doesn't support creating a Column out of numpy literal value.
So `lit` function defined in `pyspark.pandas.spark.functions` is adjusted in order to support that in pandas-on-Spark.
### Does this PR introduce _any_ user-facing change?
Yes.
Before:
```py
>>> a = ps.DataFrame({'source': [1,2,3,4,5]})
>>> a.source.isin([np.int64(1), np.int64(2)])
Traceback (most recent call last):
...
AttributeError: 'numpy.int64' object has no attribute '_get_object_id'
```
After:
```py
>>> a = ps.DataFrame({'source': [1,2,3,4,5]})
>>> a.source.isin([np.int64(1), np.int64(2)])
0 True
1 True
2 False
3 False
4 False
Name: source, dtype: bool
```
### How was this patch tested?
Unit tests.
Closes#32955 from xinrong-databricks/datatypeops_literal.
Authored-by: Xinrong Meng <xinrong.meng@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
### What changes were proposed in this pull request?
Currently, we calculate the `remoteBlockBytes` based on the original block info list. It's not efficient. Usually, it costs more ~25% time to be spent here.
If the original reducer size is big but the actual reducer size is small due to automatically partition coalescing of AQE, the reducer will take more time to calculate `remoteBlockBytes`.
We can reduce this cost via remote requests which contain merged block info lists.
### Why are the changes needed?
improve task performance
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
new unit tests and verified manually.
Closes#33109 from yaooqinn/SPARK-35910.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR fixes support for arrays and maps in `RowToColumnConverter`. In particular this PR fixes two bugs:
1. `appendArray` in `WritableColumnVector` does not reserve any elements in its child arrays, which causes the assertion in `OffHeapColumnVector.putArray` to fail.
2. The nullability of the child columns is propagated incorrectly when creating the child converters of `ArrayConverter` and `MapConverter` in `RowToColumnConverter`.
This PR fixes these issues.
### Why are the changes needed?
Both bugs cause an exception to be thrown.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
I added additional test cases to `ColumnVectorSuite` to catch the first bug, and I added `RowToColumnConverterSuite` to catch the both bugs (but specifically the second).
Closes#33108 from tomvanbussel/SPARK-35898.
Authored-by: Tom van Bussel <tom.vanbussel@databricks.com>
Signed-off-by: herman <herman@databricks.com>
### What changes were proposed in this pull request?
[SPARK-35728](https://issues.apache.org/jira/browse/SPARK-35728): Add test case to check multiply/divide of day-time
intervals of any fields by numeric
[SPARK-35778](https://issues.apache.org/jira/browse/SPARK-35778): Add test case to check multiply/divide of year-month intervals of any fields by numeric
### Why are the changes needed?
Improve test coverage
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Add ut tests
Lead-authored-by: Lei Peng <peng.8leigmail.com>
Co-authored-by: AngersZhuuuu <angers.zhugmail.com>
Closes#33080 from Peng-Lei/SPARK-35728-35778.
Lead-authored-by: PengLei <peng.8lei@gmail.com>
Co-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: PengLei <18066542445@189.cn>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
### What changes were proposed in this pull request?
This adds two new additional metrics to `ExternalBlockHandler`:
- `blockTransferRate` -- for indicating the rate of transferring blocks, vs. the data within them
- `blockTransferAvgSize_1min` -- a 1-minute trailing average of block sizes transferred by the ESS
Additionally, this enhances `YarnShuffleServiceMetrics` to expose the histogram/`Snapshot` information from `Timer` metrics within `ExternalBlockHandler`.
### Why are the changes needed?
Currently `ExternalBlockHandler` exposes some useful metrics, but is lacking around metrics for the rate of block transfers. We have `blockTransferRateBytes` to tell us the rate of _bytes_, but no metric to tell us the rate of _blocks_, which is especially relevant when running the ESS on HDDs that are sensitive to random reads. Many small block transfers can have a negative impact on performance, but won't show up as a spike in `blockTransferRateBytes` since the sizes are small. Thus the new metrics to show information around average block size and block transfer rate are very useful to monitor the health/performance of the ESS, especially when running on HDDs.
For the `YarnShuffleServiceMetrics`, currently the three `Timer` metrics exposed by `ExternalBlockHandler` are being underutilized in a YARN-based environment -- they are basically treated as a `Meter`, only exposing rate-based information, when the metrics themselves are collected detailed histograms of timing information. We should expose this information for better observability.
### Does this PR introduce _any_ user-facing change?
Yes, there are two entirely new metrics for the ESS, as documented in `monitoring.md`. Additionally in a YARN environment, `Timer` metrics exposed by the ESS will include more rich timing information.
### How was this patch tested?
New unit tests are added to verify that new metrics are showing up as expected.
We have been running this patch internally for approx. 1 year and have found it to be useful for monitoring the health of ESS and diagnosing performance issues.
Closes#32388 from xkrogen/xkrogen-SPARK-35258-ess-new-metrics.
Authored-by: Erik Krogen <xkrogen@apache.org>
Signed-off-by: Mridul Muralidharan <mridul<at>gmail.com>
### What changes were proposed in this pull request?
This PR group exception messages in sql/catalyst/src/main/scala/org/apache/spark/sql (except catalyst)
### 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#32916 from dgd-contributor/SPARK-35064_catalyst_group_error.
Authored-by: dgd-contributor <dgd_contributor@viettel.com.vn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
PR #32441 hid the internal view properties for describe table command, But the `show tblproperties view` case is not covered.
### Why are the changes needed?
Avoid internal properties confusing the users.
### Does this PR introduce _any_ user-facing change?
Yes
Before this change, the user will see below output for `show tblproperties test_view`
```
....
p1 v1
p2 v2
view.catalogAndNamespace.numParts 2
view.catalogAndNamespace.part.0 spark_catalog
view.catalogAndNamespace.part.1 default
view.query.out.col.0 c1
view.query.out.numCols 1
view.referredTempFunctionsNames []
view.referredTempViewNames []
...
```
After this change, the internal properties will be hidden.
```
....
p1 v1
p2 v2
...
```
### How was this patch tested?
existing UT
Closes#33016 from jerqi/hide_show_tblproperties.
Authored-by: RoryQi <1242949407@qq.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add a metric to track the number of duplicates dropped in input in streaming deduplication operator. Also introduce a `StatefulOperatorCustomMetric` to allow stateful operators to output their own unique metrics in `StateOperatorProgress.customMetrics` in `StreamingQueryProgress`.
### Why are the changes needed?
1. Having the duplicates dropped count help monitor and debug any incorrect results issue or find reasons for state size increases in dedupe operator.
2. New API `StatefulOperatorCustomMetric` allows stateful operators to expose their own unique metrics in `StateOperatorProgress.customMetrics` in `StreamingQueryProgress`
### Does this PR introduce _any_ user-facing change?
Yes. For deduplication stateful operator a new metric `numDuplicatesDropped` is shown in `StateOperatorProgress` within `StreamingQueryProgress`. Example `StreamingQueryProgress` output in JSON form.
```
{
"id" : "510be3cd-a955-4faf-8456-d97c78d39af5",
"runId" : "c170c4cd-04cb-4a28-b054-74020e3998e1",
...
,
"stateOperators" : [ {
"numRowsTotal" : 1,
"numRowsUpdated" : 1,
"numRowsDroppedByWatermark" : 0,
"customMetrics" : {
"loadedMapCacheHitCount" : 0,
"loadedMapCacheMissCount" : 0,
"numDuplicatesDropped" : 0,
"stateOnCurrentVersionSizeBytes" : 392
}
}],
...
}
```
### How was this patch tested?
Existing UTs for regression and added a UT.
Closes#33065 from vkorukanti/SPARK-35880.
Authored-by: Venki Korukanti <venki.korukanti@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Refines type hints in `pyspark.pandas.window`.
Also, some refactoring is included to clean up the type hierarchy of `Rolling` and `Expanding`.
### Why are the changes needed?
We can use more strict type hints for functions in pyspark.pandas.window using the generic way.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#33097 from ueshin/issues/SPARK-35901/window.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes move `to_pandas_on_spark` function from `pyspark.pandas.frame` to `pyspark.sql.dataframe`, and added the related tests to the PySpark DataFrame tests.
### Why are the changes needed?
Because now the Koalas is ported into PySpark, so we don't need to Spark auto-patch anymore.
And also `to_pandas_on_spark` is belongs to the pandas-on-Spark DataFrame doesn't look make sense.
### Does this PR introduce _any_ user-facing change?
No, it's kinda internal refactoring stuff.
### How was this patch tested?
Added the related tests and manually check they're passed.
Closes#33054 from itholic/SPARK-35605.
Authored-by: itholic <haejoon.lee@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The hyperlinks in Python code blocks in [Spark SQL Guide - Getting Started](https://spark.apache.org/docs/latest/sql-getting-started.html) currently point to invalid addresses and return 404. This pull request fixes that issue by pointing them to correct links in Python API docs.
### Why are the changes needed?
Error in documentation classifies as a bug and hence needs to be fixed.
### Does this PR introduce _any_ user-facing change?
Yes. This PR fixes documentation error in https://spark.apache.org/docs/latest/sql-getting-started.html
### How was this patch tested?
This patch was locally built after cloning the repo from scratch and then doing a clean build after fixing the required problems.
Closes#33107 from dhruvildave/sql-doc.
Authored-by: Dhruvil Dave <dhruvil.dave@outlook.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
