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Author SHA1 Message Date
Bryan Cutler 0812d6c17c [SPARK-33073][PYTHON] Improve error handling on Pandas to Arrow conversion failures 
### What changes were proposed in this pull request?

This improves error handling when a failure in conversion from Pandas to Arrow occurs. And fixes tests to be compatible with upcoming Arrow 2.0.0 release.

### Why are the changes needed?

Current tests will fail with Arrow 2.0.0 because of a change in error message when the schema is invalid. For these cases, the current error message also includes information on disabling safe conversion config, which is mainly meant for floating point truncation and overflow. The tests have been updated to use a message that is show for past Arrow versions, and upcoming.

If the user enters an invalid schema, the error produced by pyarrow is not consistent and either `TypeError` or `ArrowInvalid`, with the latter being caught, and raised as a `RuntimeError` with the extra info.

The error handling is improved by:

- narrowing the exception type to `TypeError`s, which `ArrowInvalid` is a subclass and what is raised on safe conversion failures.
- The exception is only raised with additional information on disabling "spark.sql.execution.pandas.convertToArrowArraySafely" if it is enabled in the first place.
- The original exception is chained to better show it to the user.

### Does this PR introduce _any_ user-facing change?

Yes, the error re-raised changes from a RuntimeError to a ValueError, which better categorizes this type of error and in-line with the original Arrow error.

### How was this patch tested?

Existing tests, using pyarrow 1.0.1 and 2.0.0-snapshot

Closes #29951 from BryanCutler/arrow-better-handle-pandas-errors-SPARK-33073.

Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-10-06 18:11:24 +09:00
HyukjinKwon 6868b40517 [SPARK-33020][PYTHON] Add nth_value as a PySpark function 
### What changes were proposed in this pull request?

`nth_value` was added at SPARK-27951. This PR adds the corresponding PySpark API.

### Why are the changes needed?

To support the consistent APIs

### Does this PR introduce _any_ user-facing change?

Yes, it introduces a new PySpark function API.

### How was this patch tested?

Unittest was added.

Closes #29899 from HyukjinKwon/SPARK-33020.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
 2020-09-28 22:14:28 -07:00
HyukjinKwon 376ede1301 [SPARK-33021][PYTHON][TESTS] Move functions related test cases into test_functions.py 
### What changes were proposed in this pull request?

Move functions related test cases from `test_context.py` to `test_functions.py`.

### Why are the changes needed?

To group the similar test cases.

### Does this PR introduce _any_ user-facing change?

Nope, test-only.

### How was this patch tested?

Jenkins and GitHub Actions should test.

Closes #29898 from HyukjinKwon/SPARK-33021.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
 2020-09-28 21:54:00 -07:00
zero323 31a16fbb40 [SPARK-32714][PYTHON] Initial pyspark-stubs port 
### What changes were proposed in this pull request?

This PR proposes migration of [`pyspark-stubs`](https://github.com/zero323/pyspark-stubs) into Spark codebase.

### Why are the changes needed?

### Does this PR introduce _any_ user-facing change?

Yes. This PR adds type annotations directly to Spark source.

This can impact interaction with development tools for users, which haven't used `pyspark-stubs`.

### How was this patch tested?

- [x] MyPy tests of the PySpark source
    ```
    mypy --no-incremental --config python/mypy.ini python/pyspark
    ```
- [x] MyPy tests of Spark examples
    ```
   MYPYPATH=python/ mypy --no-incremental --config python/mypy.ini examples/src/main/python/ml examples/src/main/python/sql examples/src/main/python/sql/streaming
    ```
- [x] Existing Flake8 linter

- [x] Existing unit tests

Tested against:

- `mypy==0.790+dev.e959952d9001e9713d329a2f9b196705b028f894`
- `mypy==0.782`

Closes #29591 from zero323/SPARK-32681.

Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-09-24 14:15:36 +09:00
Adam Binford e884290587 [SPARK-32835][PYTHON] Add withField method to the pyspark Column class 
### What changes were proposed in this pull request?

This PR adds a `withField` method on the pyspark Column class to call the Scala API method added in https://github.com/apache/spark/pull/27066.

### Why are the changes needed?

To update the Python API to match a new feature in the Scala API.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

New unit test

Closes #29699 from Kimahriman/feature/pyspark-with-field.

Authored-by: Adam Binford <adam.binford@radiantsolutions.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-09-16 20:18:36 +09:00
Wenchen Fan f7995c576a Revert "[SPARK-32677][SQL] Load function resource before create" 
This reverts commit 05fcf26b79.
 2020-09-09 18:15:22 +00:00
ulysses 05fcf26b79 [SPARK-32677][SQL] Load function resource before create 
### What changes were proposed in this pull request?

Change `CreateFunctionCommand` code that add class check before create function.

### Why are the changes needed?

We have different behavior between create permanent function and temporary function when function class is invaild. e.g.,
```
create function f as 'test.non.exists.udf';
-- Time taken: 0.104 seconds

create temporary function f as 'test.non.exists.udf'
-- Error in query: Can not load class 'test.non.exists.udf' when registering the function 'f', please make sure it is on the classpath;
```

And Hive also fails both of them.

### Does this PR introduce _any_ user-facing change?

Yes, user will get exception when create a invalid udf.

### How was this patch tested?

New test.

Closes #29502 from ulysses-you/function.

Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
 2020-09-07 06:00:23 +00:00
Fokko Driesprong a1e459ed9f [SPARK-32719][PYTHON] Add Flake8 check missing imports 
https://issues.apache.org/jira/browse/SPARK-32719

### What changes were proposed in this pull request?

Add a check to detect missing imports. This makes sure that if we use a specific class, it should be explicitly imported (not using a wildcard).

### Why are the changes needed?

To make sure that the quality of the Python code is up to standard.

### Does this PR introduce _any_ user-facing change?

No

### How was this patch tested?

Existing unit-tests and Flake8 static analysis

Closes #29563 from Fokko/fd-add-check-missing-imports.

Authored-by: Fokko Driesprong <fokko@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-08-31 11:23:31 +09:00
Terry Kim baaa756dee [SPARK-32516][SQL][FOLLOWUP] 'path' option cannot coexist with path parameter for DataFrameWriter.save(), DataStreamReader.load() and DataStreamWriter.start() 
### What changes were proposed in this pull request?

This is a follow up PR to #29328 to apply the same constraint where `path` option cannot coexist with path parameter to `DataFrameWriter.save()`, `DataStreamReader.load()` and `DataStreamWriter.start()`.

### Why are the changes needed?

The current behavior silently overwrites the `path` option if path parameter is passed to `DataFrameWriter.save()`, `DataStreamReader.load()` and `DataStreamWriter.start()`.

For example,
```
Seq(1).toDF.write.option("path", "/tmp/path1").parquet("/tmp/path2")
```
will write the result to `/tmp/path2`.

### Does this PR introduce _any_ user-facing change?

Yes, if `path` option coexists with path parameter to any of the above methods, it will throw `AnalysisException`:
```
scala> Seq(1).toDF.write.option("path", "/tmp/path1").parquet("/tmp/path2")
org.apache.spark.sql.AnalysisException: There is a 'path' option set and save() is called with a  path parameter. Either remove the path option, or call save() without the parameter. To ignore this check, set 'spark.sql.legacy.pathOptionBehavior.enabled' to 'true'.;
```

The user can restore the previous behavior by setting `spark.sql.legacy.pathOptionBehavior.enabled` to `true`.

### How was this patch tested?

Added new tests.

Closes #29543 from imback82/path_option.

Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
 2020-08-27 06:21:04 +00:00
Nicholas Chammas f540031419 [SPARK-31000][PYTHON][SQL] Add ability to set table description via Catalog.createTable() 
### What changes were proposed in this pull request?

This PR enhances `Catalog.createTable()` to allow users to set the table's description. This corresponds to the following SQL syntax:

```sql
CREATE TABLE ...
COMMENT 'this is a fancy table';
```

### Why are the changes needed?

This brings the Scala/Python catalog APIs a bit closer to what's already possible via SQL.

### Does this PR introduce any user-facing change?

Yes, it adds a new parameter to `Catalog.createTable()`.

### How was this patch tested?

Existing unit tests:

```sh
./python/run-tests \
  --python-executables python3.7 \
  --testnames 'pyspark.sql.tests.test_catalog,pyspark.sql.tests.test_context'
```

```
$ ./build/sbt
testOnly org.apache.spark.sql.internal.CatalogSuite org.apache.spark.sql.CachedTableSuite org.apache.spark.sql.hive.MetastoreDataSourcesSuite org.apache.spark.sql.hive.execution.HiveDDLSuite
```

Closes #27908 from nchammas/SPARK-31000-table-description.

Authored-by: Nicholas Chammas <nicholas.chammas@liveramp.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-08-25 13:42:31 +09:00
Weichen Xu fc62d72076 [MINOR] add test_createDataFrame_empty_partition in pyspark arrow tests 
### What changes were proposed in this pull request?
add test_createDataFrame_empty_partition in pyspark arrow tests

### Why are the changes needed?
test edge cases.

### Does this PR introduce _any_ user-facing change?
no.

### How was this patch tested?
N/A

Closes #29398 from WeichenXu123/add_one_pyspark_arrow_test.

Authored-by: Weichen Xu <weichen.xu@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-08-10 18:43:41 +09:00
Fokko Driesprong 9fcf0ea718 [SPARK-32319][PYSPARK] Disallow the use of unused imports 
Disallow the use of unused imports:

- Unnecessary increases the memory footprint of the application
- Removes the imports that are required for the examples in the docstring from the file-scope to the example itself. This keeps the files itself clean, and gives a more complete example as it also includes the imports :)

```
fokkodriesprongFan spark % flake8 python | grep -i "imported but unused"
python/pyspark/cloudpickle.py:46:1: F401 'functools.partial' imported but unused
python/pyspark/cloudpickle.py:55:1: F401 'traceback' imported but unused
python/pyspark/heapq3.py:868:5: F401 '_heapq.*' imported but unused
python/pyspark/__init__.py:61:1: F401 'pyspark.version.__version__' imported but unused
python/pyspark/__init__.py:62:1: F401 'pyspark._globals._NoValue' imported but unused
python/pyspark/__init__.py:115:1: F401 'pyspark.sql.SQLContext' imported but unused
python/pyspark/__init__.py:115:1: F401 'pyspark.sql.HiveContext' imported but unused
python/pyspark/__init__.py:115:1: F401 'pyspark.sql.Row' imported but unused
python/pyspark/rdd.py:21:1: F401 're' imported but unused
python/pyspark/rdd.py:29:1: F401 'tempfile.NamedTemporaryFile' imported but unused
python/pyspark/mllib/regression.py:26:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused
python/pyspark/mllib/clustering.py:28:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused
python/pyspark/mllib/clustering.py:28:1: F401 'pyspark.mllib.linalg.DenseVector' imported but unused
python/pyspark/mllib/classification.py:26:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused
python/pyspark/mllib/feature.py:28:1: F401 'pyspark.mllib.linalg.DenseVector' imported but unused
python/pyspark/mllib/feature.py:28:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused
python/pyspark/mllib/feature.py:30:1: F401 'pyspark.mllib.regression.LabeledPoint' imported but unused
python/pyspark/mllib/tests/test_linalg.py:18:1: F401 'sys' imported but unused
python/pyspark/mllib/tests/test_linalg.py:642:5: F401 'pyspark.mllib.tests.test_linalg.*' imported but unused
python/pyspark/mllib/tests/test_feature.py:21:1: F401 'numpy.random' imported but unused
python/pyspark/mllib/tests/test_feature.py:21:1: F401 'numpy.exp' imported but unused
python/pyspark/mllib/tests/test_feature.py:23:1: F401 'pyspark.mllib.linalg.Vector' imported but unused
python/pyspark/mllib/tests/test_feature.py:23:1: F401 'pyspark.mllib.linalg.VectorUDT' imported but unused
python/pyspark/mllib/tests/test_feature.py:185:5: F401 'pyspark.mllib.tests.test_feature.*' imported but unused
python/pyspark/mllib/tests/test_util.py:97:5: F401 'pyspark.mllib.tests.test_util.*' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.Vector' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.SparseVector' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.DenseVector' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.VectorUDT' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg._convert_to_vector' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.DenseMatrix' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.SparseMatrix' imported but unused
python/pyspark/mllib/tests/test_stat.py:23:1: F401 'pyspark.mllib.linalg.MatrixUDT' imported but unused
python/pyspark/mllib/tests/test_stat.py:181:5: F401 'pyspark.mllib.tests.test_stat.*' imported but unused
python/pyspark/mllib/tests/test_streaming_algorithms.py:18:1: F401 'time.time' imported but unused
python/pyspark/mllib/tests/test_streaming_algorithms.py:18:1: F401 'time.sleep' imported but unused
python/pyspark/mllib/tests/test_streaming_algorithms.py:470:5: F401 'pyspark.mllib.tests.test_streaming_algorithms.*' imported but unused
python/pyspark/mllib/tests/test_algorithms.py:295:5: F401 'pyspark.mllib.tests.test_algorithms.*' imported but unused
python/pyspark/tests/test_serializers.py:90:13: F401 'xmlrunner' imported but unused
python/pyspark/tests/test_rdd.py:21:1: F401 'sys' imported but unused
python/pyspark/tests/test_rdd.py:29:1: F401 'pyspark.resource.ResourceProfile' imported but unused
python/pyspark/tests/test_rdd.py:885:5: F401 'pyspark.tests.test_rdd.*' imported but unused
python/pyspark/tests/test_readwrite.py:19:1: F401 'sys' imported but unused
python/pyspark/tests/test_readwrite.py:22:1: F401 'array.array' imported but unused
python/pyspark/tests/test_readwrite.py:309:5: F401 'pyspark.tests.test_readwrite.*' imported but unused
python/pyspark/tests/test_join.py:62:5: F401 'pyspark.tests.test_join.*' imported but unused
python/pyspark/tests/test_taskcontext.py:19:1: F401 'shutil' imported but unused
python/pyspark/tests/test_taskcontext.py:325:5: F401 'pyspark.tests.test_taskcontext.*' imported but unused
python/pyspark/tests/test_conf.py:36:5: F401 'pyspark.tests.test_conf.*' imported but unused
python/pyspark/tests/test_broadcast.py:148:5: F401 'pyspark.tests.test_broadcast.*' imported but unused
python/pyspark/tests/test_daemon.py:76:5: F401 'pyspark.tests.test_daemon.*' imported but unused
python/pyspark/tests/test_util.py:77:5: F401 'pyspark.tests.test_util.*' imported but unused
python/pyspark/tests/test_pin_thread.py:19:1: F401 'random' imported but unused
python/pyspark/tests/test_pin_thread.py:149:5: F401 'pyspark.tests.test_pin_thread.*' imported but unused
python/pyspark/tests/test_worker.py:19:1: F401 'sys' imported but unused
python/pyspark/tests/test_worker.py:26:5: F401 'resource' imported but unused
python/pyspark/tests/test_worker.py:203:5: F401 'pyspark.tests.test_worker.*' imported but unused
python/pyspark/tests/test_profiler.py:101:5: F401 'pyspark.tests.test_profiler.*' imported but unused
python/pyspark/tests/test_shuffle.py:18:1: F401 'sys' imported but unused
python/pyspark/tests/test_shuffle.py:171:5: F401 'pyspark.tests.test_shuffle.*' imported but unused
python/pyspark/tests/test_rddbarrier.py:43:5: F401 'pyspark.tests.test_rddbarrier.*' imported but unused
python/pyspark/tests/test_context.py:129:13: F401 'userlibrary.UserClass' imported but unused
python/pyspark/tests/test_context.py:140:13: F401 'userlib.UserClass' imported but unused
python/pyspark/tests/test_context.py:310:5: F401 'pyspark.tests.test_context.*' imported but unused
python/pyspark/tests/test_appsubmit.py:241:5: F401 'pyspark.tests.test_appsubmit.*' imported but unused
python/pyspark/streaming/dstream.py:18:1: F401 'sys' imported but unused
python/pyspark/streaming/tests/test_dstream.py:27:1: F401 'pyspark.RDD' imported but unused
python/pyspark/streaming/tests/test_dstream.py:647:5: F401 'pyspark.streaming.tests.test_dstream.*' imported but unused
python/pyspark/streaming/tests/test_kinesis.py:83:5: F401 'pyspark.streaming.tests.test_kinesis.*' imported but unused
python/pyspark/streaming/tests/test_listener.py:152:5: F401 'pyspark.streaming.tests.test_listener.*' imported but unused
python/pyspark/streaming/tests/test_context.py:178:5: F401 'pyspark.streaming.tests.test_context.*' imported but unused
python/pyspark/testing/utils.py:30:5: F401 'scipy.sparse' imported but unused
python/pyspark/testing/utils.py:36:5: F401 'numpy as np' imported but unused
python/pyspark/ml/regression.py:25:1: F401 'pyspark.ml.tree._TreeEnsembleParams' imported but unused
python/pyspark/ml/regression.py:25:1: F401 'pyspark.ml.tree._HasVarianceImpurity' imported but unused
python/pyspark/ml/regression.py:29:1: F401 'pyspark.ml.wrapper.JavaParams' imported but unused
python/pyspark/ml/util.py:19:1: F401 'sys' imported but unused
python/pyspark/ml/__init__.py:25:1: F401 'pyspark.ml.pipeline' imported but unused
python/pyspark/ml/pipeline.py:18:1: F401 'sys' imported but unused
python/pyspark/ml/stat.py:22:1: F401 'pyspark.ml.linalg.DenseMatrix' imported but unused
python/pyspark/ml/stat.py:22:1: F401 'pyspark.ml.linalg.Vectors' imported but unused
python/pyspark/ml/tests/test_training_summary.py:18:1: F401 'sys' imported but unused
python/pyspark/ml/tests/test_training_summary.py:364:5: F401 'pyspark.ml.tests.test_training_summary.*' imported but unused
python/pyspark/ml/tests/test_linalg.py:381:5: F401 'pyspark.ml.tests.test_linalg.*' imported but unused
python/pyspark/ml/tests/test_tuning.py:427:9: F401 'pyspark.sql.functions as F' imported but unused
python/pyspark/ml/tests/test_tuning.py:757:5: F401 'pyspark.ml.tests.test_tuning.*' imported but unused
python/pyspark/ml/tests/test_wrapper.py:120:5: F401 'pyspark.ml.tests.test_wrapper.*' imported but unused
python/pyspark/ml/tests/test_feature.py:19:1: F401 'sys' imported but unused
python/pyspark/ml/tests/test_feature.py:304:5: F401 'pyspark.ml.tests.test_feature.*' imported but unused
python/pyspark/ml/tests/test_image.py:19:1: F401 'py4j' imported but unused
python/pyspark/ml/tests/test_image.py:22:1: F401 'pyspark.testing.mlutils.PySparkTestCase' imported but unused
python/pyspark/ml/tests/test_image.py:71:5: F401 'pyspark.ml.tests.test_image.*' imported but unused
python/pyspark/ml/tests/test_persistence.py:456:5: F401 'pyspark.ml.tests.test_persistence.*' imported but unused
python/pyspark/ml/tests/test_evaluation.py:56:5: F401 'pyspark.ml.tests.test_evaluation.*' imported but unused
python/pyspark/ml/tests/test_stat.py:43:5: F401 'pyspark.ml.tests.test_stat.*' imported but unused
python/pyspark/ml/tests/test_base.py:70:5: F401 'pyspark.ml.tests.test_base.*' imported but unused
python/pyspark/ml/tests/test_param.py:20:1: F401 'sys' imported but unused
python/pyspark/ml/tests/test_param.py:375:5: F401 'pyspark.ml.tests.test_param.*' imported but unused
python/pyspark/ml/tests/test_pipeline.py:62:5: F401 'pyspark.ml.tests.test_pipeline.*' imported but unused
python/pyspark/ml/tests/test_algorithms.py:333:5: F401 'pyspark.ml.tests.test_algorithms.*' imported but unused
python/pyspark/ml/param/__init__.py:18:1: F401 'sys' imported but unused
python/pyspark/resource/tests/test_resources.py:17:1: F401 'random' imported but unused
python/pyspark/resource/tests/test_resources.py:20:1: F401 'pyspark.resource.ResourceProfile' imported but unused
python/pyspark/resource/tests/test_resources.py:75:5: F401 'pyspark.resource.tests.test_resources.*' imported but unused
python/pyspark/sql/functions.py:32:1: F401 'pyspark.sql.udf.UserDefinedFunction' imported but unused
python/pyspark/sql/functions.py:34:1: F401 'pyspark.sql.pandas.functions.pandas_udf' imported but unused
python/pyspark/sql/session.py:30:1: F401 'pyspark.sql.types.Row' imported but unused
python/pyspark/sql/session.py:30:1: F401 'pyspark.sql.types.StringType' imported but unused
python/pyspark/sql/readwriter.py:1084:5: F401 'pyspark.sql.Row' imported but unused
python/pyspark/sql/context.py:26:1: F401 'pyspark.sql.types.IntegerType' imported but unused
python/pyspark/sql/context.py:26:1: F401 'pyspark.sql.types.Row' imported but unused
python/pyspark/sql/context.py:26:1: F401 'pyspark.sql.types.StringType' imported but unused
python/pyspark/sql/context.py:27:1: F401 'pyspark.sql.udf.UDFRegistration' imported but unused
python/pyspark/sql/streaming.py:1212:5: F401 'pyspark.sql.Row' imported but unused
python/pyspark/sql/tests/test_utils.py:55:5: F401 'pyspark.sql.tests.test_utils.*' imported but unused
python/pyspark/sql/tests/test_pandas_map.py:18:1: F401 'sys' imported but unused
python/pyspark/sql/tests/test_pandas_map.py:22:1: F401 'pyspark.sql.functions.pandas_udf' imported but unused
python/pyspark/sql/tests/test_pandas_map.py:22:1: F401 'pyspark.sql.functions.PandasUDFType' imported but unused
python/pyspark/sql/tests/test_pandas_map.py:119:5: F401 'pyspark.sql.tests.test_pandas_map.*' imported but unused
python/pyspark/sql/tests/test_catalog.py:193:5: F401 'pyspark.sql.tests.test_catalog.*' imported but unused
python/pyspark/sql/tests/test_group.py:39:5: F401 'pyspark.sql.tests.test_group.*' imported but unused
python/pyspark/sql/tests/test_session.py:361:5: F401 'pyspark.sql.tests.test_session.*' imported but unused
python/pyspark/sql/tests/test_conf.py:49:5: F401 'pyspark.sql.tests.test_conf.*' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:19:1: F401 'sys' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:21:1: F401 'pyspark.sql.functions.sum' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:21:1: F401 'pyspark.sql.functions.PandasUDFType' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:29:5: F401 'pandas.util.testing.assert_series_equal' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:32:5: F401 'pyarrow as pa' imported but unused
python/pyspark/sql/tests/test_pandas_cogrouped_map.py:248:5: F401 'pyspark.sql.tests.test_pandas_cogrouped_map.*' imported but unused
python/pyspark/sql/tests/test_udf.py:24:1: F401 'py4j' imported but unused
python/pyspark/sql/tests/test_pandas_udf_typehints.py:246:5: F401 'pyspark.sql.tests.test_pandas_udf_typehints.*' imported but unused
python/pyspark/sql/tests/test_functions.py:19:1: F401 'sys' imported but unused
python/pyspark/sql/tests/test_functions.py:362:9: F401 'pyspark.sql.functions.exists' imported but unused
python/pyspark/sql/tests/test_functions.py:387:5: F401 'pyspark.sql.tests.test_functions.*' imported but unused
python/pyspark/sql/tests/test_pandas_udf_scalar.py:21:1: F401 'sys' imported but unused
python/pyspark/sql/tests/test_pandas_udf_scalar.py:45:5: F401 'pyarrow as pa' imported but unused
python/pyspark/sql/tests/test_pandas_udf_window.py:355:5: F401 'pyspark.sql.tests.test_pandas_udf_window.*' imported but unused
python/pyspark/sql/tests/test_arrow.py:38:5: F401 'pyarrow as pa' imported but unused
python/pyspark/sql/tests/test_pandas_grouped_map.py:20:1: F401 'sys' imported but unused
python/pyspark/sql/tests/test_pandas_grouped_map.py:38:5: F401 'pyarrow as pa' imported but unused
python/pyspark/sql/tests/test_dataframe.py:382:9: F401 'pyspark.sql.DataFrame' imported but unused
python/pyspark/sql/avro/functions.py:125:5: F401 'pyspark.sql.Row' imported but unused
python/pyspark/sql/pandas/functions.py:19:1: F401 'sys' imported but unused
```

After:
```
fokkodriesprongFan spark % flake8 python | grep -i "imported but unused"
fokkodriesprongFan spark %
```

### What changes were proposed in this pull request?

Removing unused imports from the Python files to keep everything nice and tidy.

### Why are the changes needed?

Cleaning up of the imports that aren't used, and suppressing the imports that are used as references to other modules, preserving backward compatibility.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Adding the rule to the existing Flake8 checks.

Closes #29121 from Fokko/SPARK-32319.

Authored-by: Fokko Driesprong <fokko@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
 2020-08-08 08:51:57 -07:00
Liang Zhang 2cb48eabdc [SPARK-32549][PYSPARK] Add column name in _infer_schema error message 
### What changes were proposed in this pull request?

The current error message from `_infer_type` in `_infer_schema` only includes the unsupported column type but not the column name. This PR adds the column name in the error message to make it easier for users to identify which column should they drop or convert.

### Why are the changes needed?

Improve user experience.

### Does this PR introduce _any_ user-facing change?

Yes. The error message from `_infer_schema` is changed.
Before:
"not supported type: foo"
After:
"Column bar contains not supported type: foo"

### How was this patch tested?

Updated the existing unit test.

Closes #29365 from liangz1/types-error-colname.

Authored-by: Liang Zhang <liang.zhang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-08-07 11:50:46 +09:00
Takuya UESHIN 7b66882c9d [SPARK-32338][SQL][PYSPARK][FOLLOW-UP] Update slice to accept Column for start and length 
### What changes were proposed in this pull request?

This is a follow-up of #29138 which added overload `slice` function to accept `Column` for `start` and `length` in Scala.

This PR is updating the equivalent Python function to accept `Column` as well.

### Why are the changes needed?

Now that Scala version accepts `Column`, Python version should also accept it.

### Does this PR introduce _any_ user-facing change?

Yes, PySpark users will also be able to pass Column object to `start` and `length` parameter in `slice` function.

### How was this patch tested?

Added tests.

Closes #29195 from ueshin/issues/SPARK-32338/slice.

Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-07-23 13:53:50 +09:00
zero323 ef3cad17a6 [SPARK-29157][SQL][PYSPARK] Add DataFrameWriterV2 to Python API 
### What changes were proposed in this pull request?

- Adds `DataFramWriterV2` class.
- Adds `writeTo` method to `pyspark.sql.DataFrame`.
- Adds related SQL partitioning functions (`years`, `months`, ..., `bucket`).

### Why are the changes needed?

Feature parity.

### Does this PR introduce any user-facing change?

No.

### How was this patch tested?

Added new unit tests.

TODO: Should we test against `org.apache.spark.sql.connector.InMemoryTableCatalog`? If so, how to expose it in Python tests?

Closes #27331 from zero323/SPARK-29157.

Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-07-20 10:42:33 +09:00
HyukjinKwon 676d92ecce [SPARK-32301][PYTHON][TESTS] Add a test case for toPandas to work with empty partitioned Spark DataFrame 
### What changes were proposed in this pull request?

This PR proposes to port the test case from https://github.com/apache/spark/pull/29098 to branch-3.0 and master.  In the master and branch-3.0, this was fixed together at ecaa495b1f but no partition case is not being tested.

### Why are the changes needed?

To improve test coverage.

### Does this PR introduce _any_ user-facing change?

No, test-only.

### How was this patch tested?

Unit test was forward-ported.

Closes #29099 from HyukjinKwon/SPARK-32300-1.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-07-15 08:44:48 +09:00
Fokko Driesprong c602d79f89 [SPARK-32311][PYSPARK][TESTS] Remove duplicate import 
### What changes were proposed in this pull request?

`datetime` is already imported a few lines below :)

ce27cc54c1/python/pyspark/sql/tests/test_pandas_udf_scalar.py (L24)

### Why are the changes needed?

This is the last instance of the duplicate import.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Manual.

Closes #29109 from Fokko/SPARK-32311.

Authored-by: Fokko Driesprong <fokko@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
 2020-07-14 12:46:11 -07:00
HyukjinKwon 4ad9bfd53b [SPARK-32138] Drop Python 2.7, 3.4 and 3.5 
### What changes were proposed in this pull request?

This PR aims to drop Python 2.7, 3.4 and 3.5.

Roughly speaking, it removes all the widely known Python 2 compatibility workarounds such as `sys.version` comparison, `__future__`. Also, it removes the Python 2 dedicated codes such as `ArrayConstructor` in Spark.

### Why are the changes needed?

 1. Unsupport EOL Python versions
 2. Reduce maintenance overhead and remove a bit of legacy codes and hacks for Python 2.
 3. PyPy2 has a critical bug that causes a flaky test, SPARK-28358 given my testing and investigation.
 4. Users can use Python type hints with Pandas UDFs without thinking about Python version
 5. Users can leverage one latest cloudpickle, https://github.com/apache/spark/pull/28950. With Python 3.8+ it can also leverage C pickle.

### Does this PR introduce _any_ user-facing change?

Yes, users cannot use Python 2.7, 3.4 and 3.5 in the upcoming Spark version.

### How was this patch tested?

Manually tested and also tested in Jenkins.

Closes #28957 from HyukjinKwon/SPARK-32138.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-07-14 11:22:44 +09:00
HyukjinKwon b84ed4146d [SPARK-32245][INFRA] Run Spark tests in Github Actions 
### What changes were proposed in this pull request?

This PR aims to run the Spark tests in Github Actions.

To briefly explain the main idea:

- Reuse `dev/run-tests.py` with SBT build
- Reuse the modules in `dev/sparktestsupport/modules.py` to test each module
- Pass the modules to test into `dev/run-tests.py` directly via `TEST_ONLY_MODULES` environment variable. For example, `pyspark-sql,core,sql,hive`.
- `dev/run-tests.py` _does not_ take the dependent modules into account but solely the specified modules to test.

Another thing to note might be `SlowHiveTest` annotation. Running the tests in Hive modules takes too much so the slow tests are extracted and it runs as a separate job. It was extracted from the actual elapsed time in Jenkins:

![Screen Shot 2020-07-09 at 7 48 13 PM](https://user-images.githubusercontent.com/6477701/87050238-f6098e80-c238-11ea-9c4a-ab505af61381.png)

So, Hive tests are separated into to jobs. One is slow test cases, and the other one is the other test cases.

_Note that_ the current GitHub Actions build virtually copies what the default PR builder on Jenkins does (without other profiles such as JDK 11, Hadoop 2, etc.). The only exception is Kinesis https://github.com/apache/spark/pull/29057/files#diff-04eb107ee163a50b61281ca08f4e4c7bR23

### Why are the changes needed?

Last week and onwards, the Jenkins machines became very unstable for many reasons:
  - Apparently, the machines became extremely slow. Almost all tests can't pass.
  - One machine (worker 4) started to have the corrupt `.m2` which fails the build.
  - Documentation build fails time to time for an unknown reason in Jenkins machine specifically. This is disabled for now at https://github.com/apache/spark/pull/29017.
  - Almost all PRs are basically blocked by this instability currently.

The advantages of using Github Actions:
  - To avoid depending on few persons who can access to the cluster.
  - To reduce the elapsed time in the build - we could split the tests (e.g., SQL, ML, CORE), and run them in parallel so the total build time will significantly reduce.
  - To control the environment more flexibly.
  - Other contributors can test and propose to fix Github Actions configurations so we can distribute this build management cost.

Note that:
- The current build in Jenkins takes _more than 7 hours_. With Github actions it takes _less than 2 hours_
- We can now control the environments especially for Python easily.
- The test and build look more stable than the Jenkins'.

### Does this PR introduce _any_ user-facing change?

No, dev-only change.

### How was this patch tested?

Tested at https://github.com/HyukjinKwon/spark/pull/4

Closes #29057 from HyukjinKwon/migrate-to-github-actions.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
 2020-07-11 13:09:06 -07:00
Bryan Cutler 1d1809636b [SPARK-32162][PYTHON][TESTS] Improve error message of Pandas grouped map test with window 
### What changes were proposed in this pull request?

Improve the error message in test GroupedMapInPandasTests.test_grouped_over_window_with_key to show the incorrect values.

### Why are the changes needed?

This test failure has come up often in Arrow testing because it tests a struct  with timestamp values through a Pandas UDF. The current error message is not helpful as it doesn't show the incorrect values, only that it failed. This change will instead raise an assertion error with the incorrect values on a failure.

Before:

```
======================================================================
FAIL: test_grouped_over_window_with_key (pyspark.sql.tests.test_pandas_grouped_map.GroupedMapInPandasTests)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/spark/python/pyspark/sql/tests/test_pandas_grouped_map.py", line 588, in test_grouped_over_window_with_key
    self.assertTrue(all([r[0] for r in result]))
AssertionError: False is not true
```

After:
```
======================================================================
ERROR: test_grouped_over_window_with_key (pyspark.sql.tests.test_pandas_grouped_map.GroupedMapInPandasTests)
----------------------------------------------------------------------
...
AssertionError: {'start': datetime.datetime(2018, 3, 20, 0, 0), 'end': datetime.datetime(2018, 3, 25, 0, 0)}, != {'start': datetime.datetime(2020, 3, 20, 0, 0), 'end': datetime.datetime(2020, 3, 25, 0, 0)}
```

### Does this PR introduce _any_ user-facing change?

No

### How was this patch tested?

Improved existing test

Closes #28987 from BryanCutler/pandas-grouped-map-test-output-SPARK-32162.

Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-07-06 21:39:41 +09:00
HyukjinKwon 1af19a7b68 [SPARK-32098][PYTHON] Use iloc for positional slicing instead of direct slicing in createDataFrame with Arrow 
### What changes were proposed in this pull request?

When you use floats are index of pandas, it creates a Spark DataFrame with a wrong results as below when Arrow is enabled:

```bash
./bin/pyspark --conf spark.sql.execution.arrow.pyspark.enabled=true
```

```python
>>> import pandas as pd
>>> spark.createDataFrame(pd.DataFrame({'a': [1,2,3]}, index=[2., 3., 4.])).show()
+---+
|  a|
+---+
|  1|
|  1|
|  2|
+---+
```

This is because direct slicing uses the value as index when the index contains floats:

```python
>>> pd.DataFrame({'a': [1,2,3]}, index=[2., 3., 4.])[2:]
     a
2.0  1
3.0  2
4.0  3
>>> pd.DataFrame({'a': [1,2,3]}, index=[2., 3., 4.]).iloc[2:]
     a
4.0  3
>>> pd.DataFrame({'a': [1,2,3]}, index=[2, 3, 4])[2:]
   a
4  3
```

This PR proposes to explicitly use `iloc` to positionally slide when we create a DataFrame from a pandas DataFrame with Arrow enabled.

FWIW, I was trying to investigate why direct slicing refers the index value or the positional index sometimes but I stopped investigating further after reading this https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html#selection

> While standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, `.at`, `.iat`, `.loc` and `.iloc`.

### Why are the changes needed?

To create the correct Spark DataFrame from a pandas DataFrame without a data loss.

### Does this PR introduce _any_ user-facing change?

Yes, it is a bug fix.

```bash
./bin/pyspark --conf spark.sql.execution.arrow.pyspark.enabled=true
```
```python
import pandas as pd
spark.createDataFrame(pd.DataFrame({'a': [1,2,3]}, index=[2., 3., 4.])).show()
```

Before:

```
+---+
|  a|
+---+
|  1|
|  1|
|  2|
+---+
```

After:

```
+---+
|  a|
+---+
|  1|
|  2|
|  3|
+---+
```

### How was this patch tested?

Manually tested and unittest were added.

Closes #28928 from HyukjinKwon/SPARK-32098.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
 2020-06-25 11:04:47 -07:00
GuoPhilipse f0e6d0ec13 [SPARK-31710][SQL] Fail casting numeric to timestamp by default 
## What changes were proposed in this pull request?
we fail casting from numeric to timestamp by default.

## Why are the changes needed?
casting from numeric to timestamp is not a  non-standard,meanwhile it may generate different result between spark and other systems,for example hive

## Does this PR introduce any user-facing change?
Yes,user cannot cast numeric to timestamp directly,user have to use the following function to achieve the same effect:TIMESTAMP_SECONDS/TIMESTAMP_MILLIS/TIMESTAMP_MICROS

## How was this patch tested?
unit test added

Closes #28593 from GuoPhilipse/31710-fix-compatibility.

Lead-authored-by: GuoPhilipse <guofei_ok@126.com>
Co-authored-by: GuoPhilipse <46367746+GuoPhilipse@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
 2020-06-16 08:35:35 +00:00
HyukjinKwon 56264fb5d3 [SPARK-31965][TESTS][PYTHON] Move doctests related to Java function registration to test conditionally 
### What changes were proposed in this pull request?

This PR proposes to move the doctests in `registerJavaUDAF` and `registerJavaFunction` to the proper unittests that run conditionally when the test classes are present.

Both tests are dependent on the test classes in JVM side, `test.org.apache.spark.sql.JavaStringLength` and `test.org.apache.spark.sql.MyDoubleAvg`. So if you run the tests against the plain `sbt package`, it fails as below:

```
**********************************************************************
File "/.../spark/python/pyspark/sql/udf.py", line 366, in pyspark.sql.udf.UDFRegistration.registerJavaFunction
Failed example:
    spark.udf.registerJavaFunction(
        "javaStringLength", "test.org.apache.spark.sql.JavaStringLength", IntegerType())
Exception raised:
    Traceback (most recent call last):
   ...
test.org.apache.spark.sql.JavaStringLength, please make sure it is on the classpath;
...
   6 of   7 in pyspark.sql.udf.UDFRegistration.registerJavaFunction
   2 of   4 in pyspark.sql.udf.UDFRegistration.registerJavaUDAF
***Test Failed*** 8 failures.
```

### Why are the changes needed?

In order to support to run the tests against the plain SBT build. See also https://spark.apache.org/developer-tools.html

### Does this PR introduce _any_ user-facing change?

No, it's test-only.

### How was this patch tested?

Manually tested as below:

```bash
./build/sbt -DskipTests -Phive-thriftserver clean package
cd python
./run-tests --python-executable=python3 --testname="pyspark.sql.udf UserDefinedFunction"
./run-tests --python-executable=python3 --testname="pyspark.sql.tests.test_udf UDFTests"
```

```bash
./build/sbt -DskipTests -Phive-thriftserver clean test:package
cd python
./run-tests --python-executable=python3 --testname="pyspark.sql.udf UserDefinedFunction"
./run-tests --python-executable=python3 --testname="pyspark.sql.tests.test_udf UDFTests"
```

Closes #28795 from HyukjinKwon/SPARK-31965.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
 2020-06-10 21:15:40 -07:00
HyukjinKwon 00d06cad56 [SPARK-31915][SQL][PYTHON] Resolve the grouping column properly per the case sensitivity in grouped and cogrouped pandas UDFs 
### What changes were proposed in this pull request?

This is another approach to fix the issue. See the previous try https://github.com/apache/spark/pull/28745. It was too invasive so I took more conservative approach.

This PR proposes to resolve grouping attributes separately first so it can be properly referred when `FlatMapGroupsInPandas` and `FlatMapCoGroupsInPandas` are resolved without ambiguity.

Previously,

```python
from pyspark.sql.functions import *
df = spark.createDataFrame([[1, 1]], ["column", "Score"])
pandas_udf("column integer, Score float", PandasUDFType.GROUPED_MAP)
def my_pandas_udf(pdf):
    return pdf.assign(Score=0.5)

df.groupby('COLUMN').apply(my_pandas_udf).show()
```

was failed as below:

```
pyspark.sql.utils.AnalysisException: "Reference 'COLUMN' is ambiguous, could be: COLUMN, COLUMN.;"
```
because the unresolved `COLUMN` in `FlatMapGroupsInPandas` doesn't know which reference to take from the child projection.

After this fix, it resolves the child projection first with grouping keys and pass, to `FlatMapGroupsInPandas`, the attribute as a grouping key from the child projection that is positionally selected.

### Why are the changes needed?

To resolve grouping keys correctly.

### Does this PR introduce _any_ user-facing change?

Yes,

```python
from pyspark.sql.functions import *
df = spark.createDataFrame([[1, 1]], ["column", "Score"])
pandas_udf("column integer, Score float", PandasUDFType.GROUPED_MAP)
def my_pandas_udf(pdf):
    return pdf.assign(Score=0.5)

df.groupby('COLUMN').apply(my_pandas_udf).show()
```

```python
df1 = spark.createDataFrame([(1, 1)], ("column", "value"))
df2 = spark.createDataFrame([(1, 1)], ("column", "value"))

df1.groupby("COLUMN").cogroup(
    df2.groupby("COLUMN")
).applyInPandas(lambda r, l: r + l, df1.schema).show()
```

Before:

```
pyspark.sql.utils.AnalysisException: Reference 'COLUMN' is ambiguous, could be: COLUMN, COLUMN.;
```

```
pyspark.sql.utils.AnalysisException: cannot resolve '`COLUMN`' given input columns: [COLUMN, COLUMN, value, value];;
'FlatMapCoGroupsInPandas ['COLUMN], ['COLUMN], <lambda>(column#9L, value#10L, column#13L, value#14L), [column#22L, value#23L]
:- Project [COLUMN#9L, column#9L, value#10L]
:  +- LogicalRDD [column#9L, value#10L], false
+- Project [COLUMN#13L, column#13L, value#14L]
   +- LogicalRDD [column#13L, value#14L], false
```

After:

```
+------+-----+
|column|Score|
+------+-----+
|     1|  0.5|
+------+-----+
```

```
+------+-----+
|column|value|
+------+-----+
|     2|    2|
+------+-----+
```

### How was this patch tested?

Unittests were added and manually tested.

Closes #28777 from HyukjinKwon/SPARK-31915-another.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
 2020-06-10 15:54:07 -07:00
Takuya UESHIN 032d17933b [SPARK-31945][SQL][PYSPARK] Enable cache for the same Python function 
### What changes were proposed in this pull request?

This PR proposes to make `PythonFunction` holds `Seq[Byte]` instead of `Array[Byte]` to be able to compare if the byte array has the same values for the cache manager.

### Why are the changes needed?

Currently the cache manager doesn't use the cache for `udf` if the `udf` is created again even if the functions is the same.

```py
>>> func = lambda x: x

>>> df = spark.range(1)
>>> df.select(udf(func)("id")).cache()
```
```py
>>> df.select(udf(func)("id")).explain()
== Physical Plan ==
*(2) Project [pythonUDF0#14 AS <lambda>(id)#12]
+- BatchEvalPython [<lambda>(id#0L)], [pythonUDF0#14]
 +- *(1) Range (0, 1, step=1, splits=12)
```

This is because `PythonFunction` holds `Array[Byte]`, and `equals` method of array equals only when the both array is the same instance.

### Does this PR introduce _any_ user-facing change?

Yes, if the user reuse the Python function for the UDF, the cache manager will detect the same function and use the cache for it.

### How was this patch tested?

I added a test case and manually.

```py
>>> df.select(udf(func)("id")).explain()
== Physical Plan ==
InMemoryTableScan [<lambda>(id)#12]
   +- InMemoryRelation [<lambda>(id)#12], StorageLevel(disk, memory, deserialized, 1 replicas)
         +- *(2) Project [pythonUDF0#5 AS <lambda>(id)#3]
            +- BatchEvalPython [<lambda>(id#0L)], [pythonUDF0#5]
               +- *(1) Range (0, 1, step=1, splits=12)
```

Closes #28774 from ueshin/issues/SPARK-31945/udf_cache.

Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-06-10 16:38:59 +09:00
HyukjinKwon e69466056f [SPARK-31849][PYTHON][SQL] Make PySpark SQL exceptions more Pythonic 
### What changes were proposed in this pull request?

This PR proposes to make PySpark exception more Pythonic by hiding JVM stacktrace by default. It can be enabled by turning on `spark.sql.pyspark.jvmStacktrace.enabled` configuration.

```
Traceback (most recent call last):
  ...
pyspark.sql.utils.PythonException:
  An exception was thrown from Python worker in the executor. The below is the Python worker stacktrace.
Traceback (most recent call last):
  ...
```

If this `spark.sql.pyspark.jvmStacktrace.enabled` is enabled, it appends:

```
JVM stacktrace:
org.apache.spark.Exception: ...
  ...
```

For example, the codes below:

```python
from pyspark.sql.functions import udf
udf
def divide_by_zero(v):
    raise v / 0

spark.range(1).select(divide_by_zero("id")).show()
```

will show an error messages that looks like Python exception thrown from the local.

<details>
<summary>Python exception message when <code>spark.sql.pyspark.jvmStacktrace.enabled</code> is off (default)</summary>

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/.../spark/python/pyspark/sql/dataframe.py", line 427, in show
    print(self._jdf.showString(n, 20, vertical))
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
  File "/.../spark/python/pyspark/sql/utils.py", line 131, in deco
    raise_from(converted)
  File "<string>", line 3, in raise_from
pyspark.sql.utils.PythonException:
  An exception was thrown from Python worker in the executor. The below is the Python worker stacktrace.
Traceback (most recent call last):
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 605, in main
    process()
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 597, in process
    serializer.dump_stream(out_iter, outfile)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 223, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
    for obj in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 212, in _batched
    for item in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in mapper
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in <genexpr>
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 90, in <lambda>
    return lambda *a: f(*a)
  File "/.../spark/python/lib/pyspark.zip/pyspark/util.py", line 107, in wrapper
    return f(*args, **kwargs)
  File "<stdin>", line 3, in divide_by_zero
ZeroDivisionError: division by zero
```

</details>

<details>
<summary>Python exception message when <code>spark.sql.pyspark.jvmStacktrace.enabled</code> is on</summary>

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/.../spark/python/pyspark/sql/dataframe.py", line 427, in show
    print(self._jdf.showString(n, 20, vertical))
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
  File "/.../spark/python/pyspark/sql/utils.py", line 137, in deco
    raise_from(converted)
  File "<string>", line 3, in raise_from
pyspark.sql.utils.PythonException:
  An exception was thrown from Python worker in the executor. The below is the Python worker stacktrace.
Traceback (most recent call last):
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 605, in main
    process()
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 597, in process
    serializer.dump_stream(out_iter, outfile)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 223, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
    for obj in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 212, in _batched
    for item in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in mapper
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in <genexpr>
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 90, in <lambda>
    return lambda *a: f(*a)
  File "/.../spark/python/lib/pyspark.zip/pyspark/util.py", line 107, in wrapper
    return f(*args, **kwargs)
  File "<stdin>", line 3, in divide_by_zero
ZeroDivisionError: division by zero

JVM stacktrace:
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 4 times, most recent failure: Lost task 0.3 in stage 1.0 (TID 4, 192.168.35.193, executor 0): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 605, in main
    process()
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 597, in process
    serializer.dump_stream(out_iter, outfile)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 223, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
    for obj in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 212, in _batched
    for item in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in mapper
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in <genexpr>
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 90, in <lambda>
    return lambda *a: f(*a)
  File "/.../spark/python/lib/pyspark.zip/pyspark/util.py", line 107, in wrapper
    return f(*args, **kwargs)
  File "<stdin>", line 3, in divide_by_zero
ZeroDivisionError: division by zero

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:516)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:81)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:64)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:469)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:489)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
	at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:753)
	at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:127)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:469)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:472)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
	at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2117)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2066)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2065)
	at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
	at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2065)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1021)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1021)
	at scala.Option.foreach(Option.scala:407)
	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1021)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2297)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2246)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2235)
	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:823)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2108)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2129)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2148)
	at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:467)
	at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:420)
	at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)
	at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3653)
	at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2695)
	at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3644)
	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
	at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
	at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:763)
	at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
	at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3642)
	at org.apache.spark.sql.Dataset.head(Dataset.scala:2695)
	at org.apache.spark.sql.Dataset.take(Dataset.scala:2902)
	at org.apache.spark.sql.Dataset.getRows(Dataset.scala:300)
	at org.apache.spark.sql.Dataset.showString(Dataset.scala:337)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:282)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:238)
	at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 605, in main
    process()
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 597, in process
    serializer.dump_stream(out_iter, outfile)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 223, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
    for obj in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 212, in _batched
    for item in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in mapper
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in <genexpr>
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 90, in <lambda>
    return lambda *a: f(*a)
  File "/.../spark/python/lib/pyspark.zip/pyspark/util.py", line 107, in wrapper
    return f(*args, **kwargs)
  File "<stdin>", line 3, in divide_by_zero
ZeroDivisionError: division by zero

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:516)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:81)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:64)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:469)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:489)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
	at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:753)
	at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:127)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:469)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:472)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	... 1 more
```

</details>

<details>
<summary>Python exception message without this change</summary>

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/.../spark/python/pyspark/sql/dataframe.py", line 427, in show
    print(self._jdf.showString(n, 20, vertical))
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
  File "/.../spark/python/pyspark/sql/utils.py", line 98, in deco
    return f(*a, **kw)
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o160.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 10 in stage 5.0 failed 4 times, most recent failure: Lost task 10.3 in stage 5.0 (TID 37, 192.168.35.193, executor 3): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 605, in main
    process()
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 597, in process
    serializer.dump_stream(out_iter, outfile)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 223, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
    for obj in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 212, in _batched
    for item in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in mapper
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in <genexpr>
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 90, in <lambda>
    return lambda *a: f(*a)
  File "/.../spark/python/lib/pyspark.zip/pyspark/util.py", line 107, in wrapper
    return f(*args, **kwargs)
  File "<stdin>", line 3, in divide_by_zero
ZeroDivisionError: division by zero

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:516)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:81)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:64)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:469)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:489)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
	at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:753)
	at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:127)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:469)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:472)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
	at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2117)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2066)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2065)
	at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
	at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2065)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1021)
	at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1021)
	at scala.Option.foreach(Option.scala:407)
	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1021)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2297)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2246)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2235)
	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:823)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2108)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2129)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2148)
	at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:467)
	at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:420)
	at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)
	at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3653)
	at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2695)
	at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3644)
	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
	at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
	at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:763)
	at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
	at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3642)
	at org.apache.spark.sql.Dataset.head(Dataset.scala:2695)
	at org.apache.spark.sql.Dataset.take(Dataset.scala:2902)
	at org.apache.spark.sql.Dataset.getRows(Dataset.scala:300)
	at org.apache.spark.sql.Dataset.showString(Dataset.scala:337)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:282)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:238)
	at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 605, in main
    process()
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 597, in process
    serializer.dump_stream(out_iter, outfile)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 223, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
    for obj in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/serializers.py", line 212, in _batched
    for item in iterator:
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in mapper
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 450, in <genexpr>
    result = tuple(f(*[a[o] for o in arg_offsets]) for (arg_offsets, f) in udfs)
  File "/.../spark/python/lib/pyspark.zip/pyspark/worker.py", line 90, in <lambda>
    return lambda *a: f(*a)
  File "/.../spark/python/lib/pyspark.zip/pyspark/util.py", line 107, in wrapper
    return f(*args, **kwargs)
  File "<stdin>", line 3, in divide_by_zero
ZeroDivisionError: division by zero

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:516)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:81)
	at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.read(PythonUDFRunner.scala:64)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:469)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:489)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
	at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:753)
	at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:127)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:469)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:472)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	... 1 more
```

</details>

<br/>

Another example with Python 3.7:

```python
sql("a")
```

<details>
<summary>Python exception message when <code>spark.sql.pyspark.jvmStacktrace.enabled</code> is off (default)</summary>

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/.../spark/python/pyspark/sql/session.py", line 646, in sql
    return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped)
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
  File "/.../spark/python/pyspark/sql/utils.py", line 131, in deco
    raise_from(converted)
  File "<string>", line 3, in raise_from
pyspark.sql.utils.ParseException:
mismatched input 'a' expecting {'(', 'ADD', 'ALTER', 'ANALYZE', 'CACHE', 'CLEAR', 'COMMENT', 'COMMIT', 'CREATE', 'DELETE', 'DESC', 'DESCRIBE', 'DFS', 'DROP', 'EXPLAIN', 'EXPORT', 'FROM', 'GRANT', 'IMPORT', 'INSERT', 'LIST', 'LOAD', 'LOCK', 'MAP', 'MERGE', 'MSCK', 'REDUCE', 'REFRESH', 'REPLACE', 'RESET', 'REVOKE', 'ROLLBACK', 'SELECT', 'SET', 'SHOW', 'START', 'TABLE', 'TRUNCATE', 'UNCACHE', 'UNLOCK', 'UPDATE', 'USE', 'VALUES', 'WITH'}(line 1, pos 0)

== SQL ==
a
^^^
```

</details>

<details>
<summary>Python exception message when <code>spark.sql.pyspark.jvmStacktrace.enabled</code> is on</summary>

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/.../spark/python/pyspark/sql/session.py", line 646, in sql
    return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped)
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
  File "/.../spark/python/pyspark/sql/utils.py", line 131, in deco
    raise_from(converted)
  File "<string>", line 3, in raise_from
pyspark.sql.utils.ParseException:
mismatched input 'a' expecting {'(', 'ADD', 'ALTER', 'ANALYZE', 'CACHE', 'CLEAR', 'COMMENT', 'COMMIT', 'CREATE', 'DELETE', 'DESC', 'DESCRIBE', 'DFS', 'DROP', 'EXPLAIN', 'EXPORT', 'FROM', 'GRANT', 'IMPORT', 'INSERT', 'LIST', 'LOAD', 'LOCK', 'MAP', 'MERGE', 'MSCK', 'REDUCE', 'REFRESH', 'REPLACE', 'RESET', 'REVOKE', 'ROLLBACK', 'SELECT', 'SET', 'SHOW', 'START', 'TABLE', 'TRUNCATE', 'UNCACHE', 'UNLOCK', 'UPDATE', 'USE', 'VALUES', 'WITH'}(line 1, pos 0)

== SQL ==
a
^^^

JVM stacktrace:
org.apache.spark.sql.catalyst.parser.ParseException:
mismatched input 'a' expecting {'(', 'ADD', 'ALTER', 'ANALYZE', 'CACHE', 'CLEAR', 'COMMENT', 'COMMIT', 'CREATE', 'DELETE', 'DESC', 'DESCRIBE', 'DFS', 'DROP', 'EXPLAIN', 'EXPORT', 'FROM', 'GRANT', 'IMPORT', 'INSERT', 'LIST', 'LOAD', 'LOCK', 'MAP', 'MERGE', 'MSCK', 'REDUCE', 'REFRESH', 'REPLACE', 'RESET', 'REVOKE', 'ROLLBACK', 'SELECT', 'SET', 'SHOW', 'START', 'TABLE', 'TRUNCATE', 'UNCACHE', 'UNLOCK', 'UPDATE', 'USE', 'VALUES', 'WITH'}(line 1, pos 0)

== SQL ==
a
^^^

	at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:266)
	at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:133)
	at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:49)
	at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:81)
	at org.apache.spark.sql.SparkSession.$anonfun$sql$2(SparkSession.scala:604)
	at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
	at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:604)
	at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:763)
	at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:601)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:282)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:238)
	at java.lang.Thread.run(Thread.java:748)
```

</details>

<details>
<summary>Python exception message without this change</summary>

```
Traceback (most recent call last):
  File "/.../spark/python/pyspark/sql/utils.py", line 98, in deco
    return f(*a, **kw)
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o26.sql.
: org.apache.spark.sql.catalyst.parser.ParseException:
mismatched input 'a' expecting {'(', 'ADD', 'ALTER', 'ANALYZE', 'CACHE', 'CLEAR', 'COMMENT', 'COMMIT', 'CREATE', 'DELETE', 'DESC', 'DESCRIBE', 'DFS', 'DROP', 'EXPLAIN', 'EXPORT', 'FROM', 'GRANT', 'IMPORT', 'INSERT', 'LIST', 'LOAD', 'LOCK', 'MAP', 'MERGE', 'MSCK', 'REDUCE', 'REFRESH', 'REPLACE', 'RESET', 'REVOKE', 'ROLLBACK', 'SELECT', 'SET', 'SHOW', 'START', 'TABLE', 'TRUNCATE', 'UNCACHE', 'UNLOCK', 'UPDATE', 'USE', 'VALUES', 'WITH'}(line 1, pos 0)

== SQL ==
a
^^^

	at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:266)
	at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:133)
	at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:49)
	at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:81)
	at org.apache.spark.sql.SparkSession.$anonfun$sql$2(SparkSession.scala:604)
	at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
	at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:604)
	at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:763)
	at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:601)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:282)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:238)
	at java.lang.Thread.run(Thread.java:748)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/.../spark/python/pyspark/sql/session.py", line 646, in sql
    return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped)
  File "/.../spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
  File "/.../spark/python/pyspark/sql/utils.py", line 102, in deco
    raise converted
pyspark.sql.utils.ParseException:
mismatched input 'a' expecting {'(', 'ADD', 'ALTER', 'ANALYZE', 'CACHE', 'CLEAR', 'COMMENT', 'COMMIT', 'CREATE', 'DELETE', 'DESC', 'DESCRIBE', 'DFS', 'DROP', 'EXPLAIN', 'EXPORT', 'FROM', 'GRANT', 'IMPORT', 'INSERT', 'LIST', 'LOAD', 'LOCK', 'MAP', 'MERGE', 'MSCK', 'REDUCE', 'REFRESH', 'REPLACE', 'RESET', 'REVOKE', 'ROLLBACK', 'SELECT', 'SET', 'SHOW', 'START', 'TABLE', 'TRUNCATE', 'UNCACHE', 'UNLOCK', 'UPDATE', 'USE', 'VALUES', 'WITH'}(line 1, pos 0)

== SQL ==
a
^^^
```

</details>

### Why are the changes needed?

Currently, PySpark exceptions are very unfriendly to Python users with causing a bunch of JVM stacktrace. See "Python exception message without this change" above.

### Does this PR introduce _any_ user-facing change?

Yes, it will change the exception message. See the examples above.

### How was this patch tested?

Manually tested by

```bash
./bin/pyspark --conf spark.sql.pyspark.jvmStacktrace.enabled=true
```

and running the examples above.

Closes #28661 from HyukjinKwon/python-debug.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-06-01 09:45:21 +09:00
Bryan Cutler 8bbb666622 [SPARK-25351][PYTHON][TEST][FOLLOWUP] Fix test assertions to be consistent 
### What changes were proposed in this pull request?
Followup to make assertions from recent test consistent with the rest of the module

### Why are the changes needed?

Better to use assertions from `unittest` and be consistent

### Does this PR introduce _any_ user-facing change?

No

### How was this patch tested?

Existing tests

Closes #28659 from BryanCutler/arrow-category-test-fix-SPARK-25351.

Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-05-28 10:27:15 +09:00
iRakson 2f92ea0df4 [SPARK-31763][PYSPARK] Add inputFiles method in PySpark DataFrame Class 
### What changes were proposed in this pull request?
Adds `inputFiles()` method to PySpark `DataFrame`. Using this, PySpark users can list all files constituting a `DataFrame`.

**Before changes:**

```
>>> spark.read.load("examples/src/main/resources/people.json", format="json").inputFiles()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/***/***/spark/python/pyspark/sql/dataframe.py", line 1388, in __getattr__
    "'%s' object has no attribute '%s'" % (self.__class__.__name__, name))
AttributeError: 'DataFrame' object has no attribute 'inputFiles'
```

**After changes:**

```
>>> spark.read.load("examples/src/main/resources/people.json", format="json").inputFiles()
[u'file:///***/***/spark/examples/src/main/resources/people.json']
```

### Why are the changes needed?
This method is already supported for spark with scala and java.

### Does this PR introduce _any_ user-facing change?
Yes, Now users can list all files of a DataFrame using `inputFiles()`

### How was this patch tested?
UT added.

Closes #28652 from iRakson/SPARK-31763.

Authored-by: iRakson <raksonrakesh@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-05-28 09:52:08 +09:00
Jalpan Randeri 339b0ecadb [SPARK-25351][SQL][PYTHON] Handle Pandas category type when converting from Python with Arrow 
Handle Pandas category type while converting from python with Arrow enabled. The category column will be converted to whatever type the category elements are as is the case with Arrow disabled.

### Does this PR introduce any user-facing change?
No

### How was this patch tested?
New unit tests were added for `createDataFrame` and scalar `pandas_udf`

Closes #26585 from jalpan-randeri/feature-pyarrow-dictionary-type.

Authored-by: Jalpan Randeri <randerij@amazon.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
 2020-05-27 17:27:29 -07:00
Kent Yao b31ae7bb0b [SPARK-31615][SQL] Pretty string output for sql method of RuntimeReplaceable expressions 
### What changes were proposed in this pull request?

The RuntimeReplaceable ones are runtime replaceable, thus, their original parameters are not going to be resolved to PrettyAttribute and remain debug style string if we directly implement their `sql` methods with their parameters' `sql` methods.

This PR is raised with suggestions by maropu and cloud-fan https://github.com/apache/spark/pull/28402/files#r417656589. In this PR, we re-implement the `sql` methods of  the RuntimeReplaceable ones with toPettySQL

### Why are the changes needed?

Consistency of schema output between RuntimeReplaceable expressions and normal ones.

For example, `date_format` vs `to_timestamp`, before this PR, they output differently

#### Before
```sql
select date_format(timestamp '2019-10-06', 'yyyy-MM-dd uuuu')
struct<date_format(TIMESTAMP '2019-10-06 00:00:00', yyyy-MM-dd uuuu):string>

select to_timestamp("2019-10-06S10:11:12.12345", "yyyy-MM-dd'S'HH:mm:ss.SSSSSS")
struct<to_timestamp('2019-10-06S10:11:12.12345', 'yyyy-MM-dd\'S\'HH:mm:ss.SSSSSS'):timestamp>
```
#### After

```sql
select date_format(timestamp '2019-10-06', 'yyyy-MM-dd uuuu')
struct<date_format(TIMESTAMP '2019-10-06 00:00:00', yyyy-MM-dd uuuu):string>

select to_timestamp("2019-10-06T10:11:12'12", "yyyy-MM-dd'T'HH:mm:ss''SSSS")

struct<to_timestamp(2019-10-06T10:11:12'12, yyyy-MM-dd'T'HH:mm:ss''SSSS):timestamp>

````

### Does this PR introduce _any_ user-facing change?

Yes, the schema output style changed for the runtime replaceable expressions as shown in the above example

### How was this patch tested?
regenerate all related tests

Closes #28420 from yaooqinn/SPARK-31615.

Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
 2020-05-07 14:40:26 +09:00
HyukjinKwon 5dd581c88a [SPARK-29664][PYTHON][SQL][FOLLOW-UP] Add deprecation warnings for getItem instead 
### What changes were proposed in this pull request?

This PR proposes to use a different approach instead of breaking it per Micheal's rubric added at https://spark.apache.org/versioning-policy.html. It deprecates the behaviour for now. It will be gradually removed in the future releases.

After this change,

```python
import warnings
warnings.simplefilter("always")
from pyspark.sql.functions import *
df = spark.range(2)
map_col = create_map(lit(0), lit(100), lit(1), lit(200))
df.withColumn("mapped", map_col.getItem(col('id'))).show()
```

```
/.../python/pyspark/sql/column.py:311: DeprecationWarning: A column as 'key' in getItem is
deprecated as of Spark 3.0, and will not be supported in the future release. Use `column[key]`
or `column.key` syntax instead.
  DeprecationWarning)
...
```

```python
import warnings
warnings.simplefilter("always")
from pyspark.sql.functions import *
df = spark.range(2)
struct_col = struct(lit(0), lit(100), lit(1), lit(200))
df.withColumn("struct", struct_col.getField(lit("col1"))).show()
```

```
/.../spark/python/pyspark/sql/column.py:336: DeprecationWarning: A column as 'name'
in getField is deprecated as of Spark 3.0, and will not be supported in the future release. Use
`column[name]` or `column.name` syntax instead.
  DeprecationWarning)
```

### Why are the changes needed?

To prevent the radical behaviour change after the amended versioning policy.

### Does this PR introduce any user-facing change?

Yes, it will show the deprecated warning message.

### How was this patch tested?

Manually tested.

Closes #28327 from HyukjinKwon/SPARK-29664.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-04-27 14:49:22 +09:00
Takuya UESHIN 87be3641eb [SPARK-31441] Support duplicated column names for toPandas with arrow execution 
### What changes were proposed in this pull request?

This PR is adding support duplicated column names for `toPandas` with Arrow execution.

### Why are the changes needed?

When we execute `toPandas()` with Arrow execution, it fails if the column names have duplicates.

```py
>>> spark.sql("select 1 v, 1 v").toPandas()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/path/to/lib/python3.7/site-packages/pyspark/sql/dataframe.py", line 2132, in toPandas
    pdf = table.to_pandas()
  File "pyarrow/array.pxi", line 441, in pyarrow.lib._PandasConvertible.to_pandas
  File "pyarrow/table.pxi", line 1367, in pyarrow.lib.Table._to_pandas
  File "/path/to/lib/python3.7/site-packages/pyarrow/pandas_compat.py", line 653, in table_to_blockmanager
    columns = _deserialize_column_index(table, all_columns, column_indexes)
  File "/path/to/lib/python3.7/site-packages/pyarrow/pandas_compat.py", line 704, in _deserialize_column_index
    columns = _flatten_single_level_multiindex(columns)
  File "/path/to/lib/python3.7/site-packages/pyarrow/pandas_compat.py", line 937, in _flatten_single_level_multiindex
    raise ValueError('Found non-unique column index')
ValueError: Found non-unique column index
```

### Does this PR introduce any user-facing change?

Yes, previously we will face an error above, but after this PR, we will see the result:

```py
>>> spark.sql("select 1 v, 1 v").toPandas()
   v  v
0  1  1
```

### How was this patch tested?

Added and modified related tests.

Closes #28210 from ueshin/issues/SPARK-31441/to_pandas.

Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-04-14 14:08:56 +09:00
HyukjinKwon 4fafdcd63b [SPARK-26412][PYTHON][FOLLOW-UP] Improve error messages in Scala iterator pandas UDF 
### What changes were proposed in this pull request?

This PR proposes to improve the error message from Scalar iterator pandas UDF.

### Why are the changes needed?

To show the correct error messages.

### Does this PR introduce any user-facing change?

Yes, but only in unreleased branches.

```python
import pandas as pd
from pyspark.sql.functions import pandas_udf, PandasUDFType

pandas_udf('long', PandasUDFType.SCALAR_ITER)
def pandas_plus_one(iterator):
      for _ in iterator:
            yield pd.Series(1)

spark.range(10).repartition(1).select(pandas_plus_one("id")).show()
```
```python
import pandas as pd
from pyspark.sql.functions import pandas_udf, PandasUDFType

pandas_udf('long', PandasUDFType.SCALAR_ITER)
def pandas_plus_one(iterator):
      for _ in iterator:
            yield pd.Series(list(range(20)))

spark.range(10).repartition(1).select(pandas_plus_one("id")).show()
```

**Before:**

```
RuntimeError: The number of output rows of pandas iterator UDF should
be the same with input rows. The input rows number is 10 but the output
rows number is 1.
```
```
AssertionError: Pandas MAP_ITER UDF outputted more rows than input rows.
```

**After:**

```
RuntimeError: The length of output in Scalar iterator pandas UDF should be
the same with the input's; however, the length of output was 1 and the length
of input was 10.
```
```
AssertionError: Pandas SCALAR_ITER UDF outputted more rows than input rows.
```

### How was this patch tested?

Unittests were fixed accordingly.

Closes #28135 from HyukjinKwon/SPARK-26412-followup.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-04-09 13:14:41 +09:00
Liang-Chi Hsieh 1f02871489 [SPARK-30921][PYSPARK] Predicates on python udf should not be pushdown through Aggregate 
### What changes were proposed in this pull request?

This patch proposed to skip predicates on PythonUDFs to be pushdown through Aggregate.

### Why are the changes needed?

The predicates on PythonUDFs cannot be pushdown through Aggregate. Pushed down predicates cannot be evaluate because PythonUDFs cannot be evaluated on Filter and cause error like:

```
Caused by: java.lang.UnsupportedOperationException: Cannot generate code for expression: mean(input[1, struct<bar:bigint>, true].bar)
        at org.apache.spark.sql.catalyst.expressions.Unevaluable.doGenCode(Expression.scala:304)
        at org.apache.spark.sql.catalyst.expressions.Unevaluable.doGenCode$(Expression.scala:303)
        at org.apache.spark.sql.catalyst.expressions.PythonUDF.doGenCode(PythonUDF.scala:52)
        at org.apache.spark.sql.catalyst.expressions.Expression.$anonfun$genCode$3(Expression.scala:146)
        at scala.Option.getOrElse(Option.scala:189)
        at org.apache.spark.sql.catalyst.expressions.Expression.genCode(Expression.scala:141)
        at org.apache.spark.sql.catalyst.expressions.CastBase.doGenCode(Cast.scala:821)
        at org.apache.spark.sql.catalyst.expressions.Expression.$anonfun$genCode$3(Expression.scala:146)
        at scala.Option.getOrElse(Option.scala:189)
```

### Does this PR introduce any user-facing change?

Yes. Previously the predicates on PythonUDFs will be pushdown through Aggregate can cause error. After this change, the query can work.

### How was this patch tested?

Unit test.

Closes #28089 from viirya/SPARK-30921.

Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-04-06 09:36:20 +09:00
HyukjinKwon 3165a95a04 [SPARK-31287][PYTHON][SQL] Ignore type hints in groupby.(cogroup.)applyInPandas and mapInPandas 
### What changes were proposed in this pull request?

This PR proposes to make pandas function APIs (`groupby.(cogroup.)applyInPandas` and `mapInPandas`) to ignore Python type hints.

### Why are the changes needed?

Python type hints are optional. It shouldn't affect where pandas UDFs are not used.
This is also a future work for them to support other type hints. We shouldn't at least throw an exception at this moment.

### Does this PR introduce any user-facing change?

No, it's master-only change.

```python
import pandas as pd

def pandas_plus_one(pdf: pd.DataFrame) -> pd.DataFrame:
    return pdf + 1

spark.range(10).groupby('id').applyInPandas(pandas_plus_one, schema="id long").show()
```
```python
import pandas as pd

def pandas_plus_one(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame:
    return left + 1

spark.range(10).groupby('id').cogroup(spark.range(10).groupby("id")).applyInPandas(pandas_plus_one, schema="id long").show()
```

```python
from typing import Iterator
import pandas as pd

def pandas_plus_one(iter: Iterator[pd.DataFrame]) -> Iterator[pd.DataFrame]:
    return map(lambda v: v + 1, iter)

spark.range(10).mapInPandas(pandas_plus_one, schema="id long").show()
```

**Before:**

Exception

**After:**

```
+---+
| id|
+---+
|  1|
|  2|
|  3|
|  4|
|  5|
|  6|
|  7|
|  8|
|  9|
| 10|
+---+
```

### How was this patch tested?

Closes #28052 from HyukjinKwon/SPARK-31287.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-03-29 13:59:18 +09:00
Liang-Chi Hsieh 559d3e4051 [SPARK-31186][PYSPARK][SQL] toPandas should not fail on duplicate column names 
### What changes were proposed in this pull request?

When `toPandas` API works on duplicate column names produced from operators like join, we see the error like:

```
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
```

This patch fixes the error in `toPandas` API.

### Why are the changes needed?

To make `toPandas` work on dataframe with duplicate column names.

### Does this PR introduce any user-facing change?

Yes. Previously calling `toPandas` API on a dataframe with duplicate column names will fail. After this patch, it will produce correct result.

### How was this patch tested?

Unit test.

Closes #28025 from viirya/SPARK-31186.

Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-03-27 12:10:30 +09:00
zero323 01f20394ac [SPARK-30569][SQL][PYSPARK][SPARKR] Add percentile_approx DSL functions 
### What changes were proposed in this pull request?

- Adds following overloaded variants to Scala `o.a.s.sql.functions`:

  - `percentile_approx(e: Column, percentage: Array[Double], accuracy: Long): Column`
  - `percentile_approx(columnName: String, percentage: Array[Double], accuracy: Long): Column`
  - `percentile_approx(e: Column, percentage: Double, accuracy: Long): Column`
  - `percentile_approx(columnName: String, percentage: Double, accuracy: Long): Column`
  - `percentile_approx(e: Column, percentage: Seq[Double], accuracy: Long): Column` (primarily for
Python interop).
  - `percentile_approx(columnName: String, percentage: Seq[Double], accuracy: Long): Column`

- Adds `percentile_approx` to `pyspark.sql.functions`.

- Adds `percentile_approx` function to SparkR.

### Why are the changes needed?

Currently we support `percentile_approx` only in SQL expression. It is inconvenient and makes this function relatively unknown.

### Does this PR introduce any user-facing change?

No.

### How was this patch tested?

New unit tests for SparkR an PySpark.

As for now there are no additional tests in Scala API ‒ `ApproximatePercentile` is well tested and Python (including docstrings) and R tests provide additional tests, so it seems unnecessary.

Closes #27278 from zero323/SPARK-30569.

Lead-authored-by: zero323 <mszymkiewicz@gmail.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-03-17 10:44:21 +09:00
zero323 7de33f56e8 [SPARK-30681][PYSPARK][SQL] Add higher order functions API to PySpark 
### What changes were proposed in this pull request?

This PR add Python API for invoking following higher functions:

- `transform`
- `exists`
- `forall`
- `filter`
- `aggregate`
- `zip_with`
- `transform_keys`
- `transform_values`
- `map_filter`
- `map_zip_with`

to `pyspark.sql`. Each of these accepts plain Python functions of one of the following types

- `(Column) -> Column: ...`
- `(Column, Column) -> Column: ...`
- `(Column, Column, Column) -> Column: ...`

Internally this proposal piggbacks on objects supporting Scala implementation ([SPARK-27297](https://issues.apache.org/jira/browse/SPARK-27297)) by:

1. Creating  required `UnresolvedNamedLambdaVariables`  exposing these as PySpark `Columns`
2. Invoking Python function with these columns as arguments.
3. Using the result, and underlying JVM objects from 1., to create `expressions.LambdaFunction` which is passed to desired expression, and repacked as Python `Column`.

### Why are the changes needed?

Currently higher order functions are available only using SQL and Scala API and can use only SQL expressions

```python
df.selectExpr("transform(values, x -> x + 1)")
```

This works reasonably well for simple functions, but can get really ugly with complex functions (complex functions, casts), resulting objects are somewhat verbose and we don't get any IDE support.  Additionally DSL used, though  very simple, is not documented.

With changes propose here, above query could be rewritten as:

```python
df.select(transform("values", lambda x: x + 1))
```

### Does this PR introduce any user-facing change?

No.

### How was this patch tested?

- For positive cases this PR adds doctest strings covering possible usage patterns.
- For negative cases (unsupported function types) this PR adds unit tests.

### Notes

If approved, the same approach can be used in SparkR.

Closes #27406 from zero323/SPARK-30681.

Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-02-28 12:59:39 +09:00
Alex Favaro 96c1a4401d [SPARK-30856][SQL][PYSPARK] Fix SQLContext.getOrCreate() when SparkContext is restarted 
### What changes were proposed in this pull request?

As discussed on the Jira ticket, this change clears the SQLContext._instantiatedContext class attribute when the SparkSession is stopped. That way, the attribute will be reset with a new, usable SQLContext when a new SparkSession is started.

### Why are the changes needed?

When the underlying SQLContext is instantiated for a SparkSession, the instance is saved as a class attribute and returned from subsequent calls to SQLContext.getOrCreate(). If the SparkContext is stopped and a new one started, the SQLContext class attribute is never cleared so any code which calls SQLContext.getOrCreate() will get a SQLContext with a reference to the old, unusable SparkContext.

A similar issue was identified and fixed for SparkSession in [SPARK-19055](https://issues.apache.org/jira/browse/SPARK-19055), but the fix did not change SQLContext as well. I ran into this because mllib still [uses](https://github.com/apache/spark/blob/master/python/pyspark/mllib/common.py#L105) SQLContext.getOrCreate() under the hood.

### Does this PR introduce any user-facing change?

No

### How was this patch tested?

A new test was added. I verified that the test fails without the included change.

Closes #27610 from afavaro/restart-sqlcontext.

Authored-by: Alex Favaro <alex.favaro@affirm.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-02-20 12:21:24 +09:00
HyukjinKwon e065e22e5e [SPARK-30861][PYTHON][SQL] Deprecate constructor of SQLContext and getOrCreate in SQLContext at PySpark 
### What changes were proposed in this pull request?

This PR proposes to deprecate the APIs at `SQLContext` removed in SPARK-25908. We should remove equivalent APIs; however, seems we missed to deprecate.

While I am here, I fix one more issue. After SPARK-25908, `sc._jvm.SQLContext.getOrCreate` dose not exist anymore. So,

```python
from pyspark.sql import SQLContext
from pyspark import SparkContext
sc = SparkContext.getOrCreate()
SQLContext.getOrCreate(sc).range(10).show()
```

throws an exception as below:

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/.../spark/python/pyspark/sql/context.py", line 110, in getOrCreate
    jsqlContext = sc._jvm.SQLContext.getOrCreate(sc._jsc.sc())
  File "/.../spark/python/lib/py4j-0.10.8.1-src.zip/py4j/java_gateway.py", line 1516, in __getattr__
py4j.protocol.Py4JError: org.apache.spark.sql.SQLContext.getOrCreate does not exist in the JVM
```

After this PR:

```
/.../spark/python/pyspark/sql/context.py:113: DeprecationWarning: Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.
  DeprecationWarning)
+---+
| id|
+---+
|  0|
|  1|
|  2|
|  3|
|  4|
|  5|
|  6|
|  7|
|  8|
|  9|
+---+
```

In case of the constructor of `SQLContext`, after this PR:

```python
from pyspark.sql import SQLContext
sc = SparkContext.getOrCreate()
SQLContext(sc)
```

```
/.../spark/python/pyspark/sql/context.py:77: DeprecationWarning: Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.
  DeprecationWarning)
```

### Why are the changes needed?

To promote to use SparkSession, and keep the API party consistent with Scala side.

### Does this PR introduce any user-facing change?

Yes, it will show deprecation warning to users.

### How was this patch tested?

Manually tested as described above. Unittests were also added.

Closes #27614 from HyukjinKwon/SPARK-30861.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-02-19 11:17:47 +09:00
yi.wu 68d7edf949 [SPARK-30812][SQL][CORE] Revise boolean config name to comply with new config naming policy 
### What changes were proposed in this pull request?

Revise below config names to comply with [new config naming policy](http://apache-spark-developers-list.1001551.n3.nabble.com/DISCUSS-naming-policy-of-Spark-configs-td28875.html):

SQL:
* spark.sql.execution.subquery.reuse.enabled / [SPARK-27083](https://issues.apache.org/jira/browse/SPARK-27083)
* spark.sql.legacy.allowNegativeScaleOfDecimal.enabled / [SPARK-30252](https://issues.apache.org/jira/browse/SPARK-30252)
* spark.sql.adaptive.optimizeSkewedJoin.enabled / [SPARK-29544](https://issues.apache.org/jira/browse/SPARK-29544)
* spark.sql.legacy.property.nonReserved / [SPARK-30183](https://issues.apache.org/jira/browse/SPARK-30183)
* spark.sql.streaming.forceDeleteTempCheckpointLocation.enabled / [SPARK-26389](https://issues.apache.org/jira/browse/SPARK-26389)
* spark.sql.analyzer.failAmbiguousSelfJoin.enabled / [SPARK-28344](https://issues.apache.org/jira/browse/SPARK-28344)
* spark.sql.adaptive.shuffle.reducePostShufflePartitions.enabled / [SPARK-30074](https://issues.apache.org/jira/browse/SPARK-30074)
* spark.sql.execution.pandas.arrowSafeTypeConversion / [SPARK-25811](https://issues.apache.org/jira/browse/SPARK-25811)
* spark.sql.legacy.looseUpcast / [SPARK-24586](https://issues.apache.org/jira/browse/SPARK-24586)
* spark.sql.legacy.arrayExistsFollowsThreeValuedLogic / [SPARK-28052](https://issues.apache.org/jira/browse/SPARK-28052)
* spark.sql.sources.ignoreDataLocality.enabled / [SPARK-29189](https://issues.apache.org/jira/browse/SPARK-29189)
* spark.sql.adaptive.shuffle.fetchShuffleBlocksInBatch.enabled / [SPARK-9853](https://issues.apache.org/jira/browse/SPARK-9853)

CORE:
* spark.eventLog.erasureCoding.enabled / [SPARK-25855](https://issues.apache.org/jira/browse/SPARK-25855)
* spark.shuffle.readHostLocalDisk.enabled / [SPARK-30235](https://issues.apache.org/jira/browse/SPARK-30235)
* spark.scheduler.listenerbus.logSlowEvent.enabled / [SPARK-29001](https://issues.apache.org/jira/browse/SPARK-29001)
* spark.resources.coordinate.enable / [SPARK-27371](https://issues.apache.org/jira/browse/SPARK-27371)
* spark.eventLog.logStageExecutorMetrics.enabled / [SPARK-23429](https://issues.apache.org/jira/browse/SPARK-23429)

### Why are the changes needed?

To comply with the config naming policy.

### Does this PR introduce any user-facing change?

No. Configurations listed above are all newly added in Spark 3.0.

### How was this patch tested?

Pass Jenkins.

Closes #27563 from Ngone51/revise_boolean_conf_name.

Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
 2020-02-18 20:39:50 +08:00
Liang Zhang d8c0599e54 [SPARK-30791][SQL][PYTHON] Add 'sameSemantics' and 'sementicHash' methods in Dataset 
### What changes were proposed in this pull request?
This PR added two DeveloperApis to the Dataset[T] class. Both methods are just exposing lower-level methods to the Dataset[T] class.

### Why are the changes needed?
They are useful for checking whether two dataframes are the same when implementing dataframe caching in python, and also get a unique ID. It's easier to use if we wrap the lower-level APIs.

### Does this PR introduce any user-facing change?
```
scala> val df1 = Seq((1,2),(4,5)).toDF("col1", "col2")
df1: org.apache.spark.sql.DataFrame = [col1: int, col2: int]

scala> val df2 = Seq((1,2),(4,5)).toDF("col1", "col2")
df2: org.apache.spark.sql.DataFrame = [col1: int, col2: int]

scala> val df3 = Seq((0,2),(4,5)).toDF("col1", "col2")
df3: org.apache.spark.sql.DataFrame = [col1: int, col2: int]

scala> val df4 = Seq((0,2),(4,5)).toDF("col0", "col2")
df4: org.apache.spark.sql.DataFrame = [col0: int, col2: int]

scala> df1.semanticHash
res0: Int = 594427822

scala> df2.semanticHash
res1: Int = 594427822

scala> df1.sameSemantics(df2)
res2: Boolean = true

scala> df1.sameSemantics(df3)
res3: Boolean = false

scala> df3.semanticHash
res4: Int = -1592702048

scala> df4.semanticHash
res5: Int = -1592702048

scala> df4.sameSemantics(df3)
res6: Boolean = true
```

### How was this patch tested?
Unit test in scala and doctest in python.

Note: comments are copied from the corresponding lower-level APIs.
Note: There are some issues to be fixed that would improve the hash collision rate: https://github.com/apache/spark/pull/27565#discussion_r379881028

Closes #27565 from liangz1/df-same-result.

Authored-by: Liang Zhang <liang.zhang@databricks.com>
Signed-off-by: WeichenXu <weichen.xu@databricks.com>
 2020-02-18 09:22:26 +08:00
Bryan Cutler 07a9885f27 [SPARK-30777][PYTHON][TESTS] Fix test failures for Pandas >= 1.0.0 
### What changes were proposed in this pull request?

Fix PySpark test failures for using Pandas >= 1.0.0.

### Why are the changes needed?

Pandas 1.0.0 has recently been released and has API changes that result in PySpark test failures, this PR fixes the broken tests.

### Does this PR introduce any user-facing change?

No

### How was this patch tested?

Manually tested with Pandas 1.0.1 and PyArrow 0.16.0

Closes #27529 from BryanCutler/pandas-fix-tests-1.0-SPARK-30777.

Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-02-11 10:03:01 +09:00
HyukjinKwon 692e3ddb4e [SPARK-27870][PYTHON][FOLLOW-UP] Rename spark.sql.pandas.udf.buffer.size to spark.sql.execution.pandas.udf.buffer.size 
### What changes were proposed in this pull request?

This PR renames `spark.sql.pandas.udf.buffer.size` to `spark.sql.execution.pandas.udf.buffer.size` to be more consistent with other pandas configuration prefixes, given:
-  `spark.sql.execution.pandas.arrowSafeTypeConversion`
- `spark.sql.execution.pandas.respectSessionTimeZone`
- `spark.sql.legacy.execution.pandas.groupedMap.assignColumnsByName`
- other configurations like `spark.sql.execution.arrow.*`.

### Why are the changes needed?

To make configuration names consistent.

### Does this PR introduce any user-facing change?

No because this configuration was not released yet.

### How was this patch tested?

Existing tests should cover.

Closes #27450 from HyukjinKwon/SPARK-27870-followup.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-02-05 11:38:33 +09:00
zero323 2330a5682d [SPARK-30607][SQL][PYSPARK][SPARKR] Add overlay wrappers for SparkR and PySpark 
### What changes were proposed in this pull request?

This PR adds:

- `pyspark.sql.functions.overlay` function to PySpark
- `overlay` function to SparkR

### Why are the changes needed?

Feature parity. At the moment R and Python users can access this function only using SQL or `expr` / `selectExpr`.

### Does this PR introduce any user-facing change?

No.

### How was this patch tested?

New unit tests.

Closes #27325 from zero323/SPARK-30607.

Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-01-23 16:16:47 +09:00
HyukjinKwon ab0890bdb1 [SPARK-28264][PYTHON][SQL] Support type hints in pandas UDF and rename/move inconsistent pandas UDF types 
### What changes were proposed in this pull request?

This PR proposes to redesign pandas UDFs as described in [the proposal](https://docs.google.com/document/d/1-kV0FS_LF2zvaRh_GhkV32Uqksm_Sq8SvnBBmRyxm30/edit?usp=sharing).

```python
from pyspark.sql.functions import pandas_udf
import pandas as pd

pandas_udf("long")
def plug_one(s: pd.Series) -> pd.Series:
    return s + 1

spark.range(10).select(plug_one("id")).show()
```

```
+------------+
|plug_one(id)|
+------------+
|           1|
|           2|
|           3|
|           4|
|           5|
|           6|
|           7|
|           8|
|           9|
|          10|
+------------+
```

Note that, this PR address one of the future improvements described [here](https://docs.google.com/document/d/1-kV0FS_LF2zvaRh_GhkV32Uqksm_Sq8SvnBBmRyxm30/edit#heading=h.h3ncjpk6ujqu), "A couple of less-intuitive pandas UDF types" (by zero323) together.

In short,

- Adds new way with type hints as an alternative and experimental way.
    ```python
    pandas_udf(schema='...')
    def func(c1: Series, c2: Series) -> DataFrame:
        pass
    ```

- Replace and/or add an alias for three types below from UDF, and make them as separate standalone APIs. So, `pandas_udf` is now consistent with regular `udf`s and other expressions.

    `df.mapInPandas(udf)`  -replace-> `df.mapInPandas(f, schema)`
    `df.groupby.apply(udf)`  -alias-> `df.groupby.applyInPandas(f, schema)`
    `df.groupby.cogroup.apply(udf)`  -replace-> `df.groupby.cogroup.applyInPandas(f, schema)`

    *`df.groupby.apply` was added from 2.3 while the other were added in the master only.

- No deprecation for the existing ways for now.
    ```python
    pandas_udf(schema='...', functionType=PandasUDFType.SCALAR)
    def func(c1, c2):
        pass
    ```
If users are happy with this, I plan to deprecate the existing way and declare using type hints is not experimental anymore.

One design goal in this PR was that, avoid touching the internal (since we didn't deprecate the old ways for now), but supports type hints with a minimised changes only at the interface.

- Once we deprecate or remove the old ways, I think it requires another refactoring for the internal in the future. At the very least, we should rename internal pandas evaluation types.
- If users find this experimental type hints isn't quite helpful, we should simply revert the changes at the interface level.

### Why are the changes needed?

In order to address old design issues. Please see [the proposal](https://docs.google.com/document/d/1-kV0FS_LF2zvaRh_GhkV32Uqksm_Sq8SvnBBmRyxm30/edit?usp=sharing).

### Does this PR introduce any user-facing change?

For behaviour changes, No.

It adds new ways to use pandas UDFs by using type hints. See below.

**SCALAR**:

```python
pandas_udf(schema='...')
def func(c1: Series, c2: DataFrame) -> Series:
    pass  # DataFrame represents a struct column
```

**SCALAR_ITER**:

```python
pandas_udf(schema='...')
def func(iter: Iterator[Tuple[Series, DataFrame, ...]]) -> Iterator[Series]:
    pass  # Same as SCALAR but wrapped by Iterator
```

**GROUPED_AGG**:

```python
pandas_udf(schema='...')
def func(c1: Series, c2: DataFrame) -> int:
    pass  # DataFrame represents a struct column
```

**GROUPED_MAP**:

This was added in Spark 2.3 as of SPARK-20396. As described above, it keeps the existing behaviour. Additionally, we now have a new alias `groupby.applyInPandas` for `groupby.apply`. See the example below:

```python
def func(pdf):
    return pdf

df.groupby("...").applyInPandas(func, schema=df.schema)
```

**MAP_ITER**: this is not a pandas UDF anymore

This was added in Spark 3.0 as of SPARK-28198; and this PR replaces the usages. See the example below:

```python
def func(iter):
    for df in iter:
        yield df

df.mapInPandas(func, df.schema)
```

**COGROUPED_MAP**: this is not a pandas UDF anymore

This was added in Spark 3.0 as of SPARK-27463; and this PR replaces the usages. See the example below:

```python
def asof_join(left, right):
    return pd.merge_asof(left, right, on="...", by="...")

 df1.groupby("...").cogroup(df2.groupby("...")).applyInPandas(asof_join, schema="...")
```

### How was this patch tested?

Unittests added and tested against Python 2.7, 3.6 and 3.7.

Closes #27165 from HyukjinKwon/revisit-pandas.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-01-22 15:32:58 +09:00
yi.wu ff39c9271c [SPARK-30252][SQL] Disallow negative scale of Decimal 
### What changes were proposed in this pull request?

This PR propose to disallow negative `scale` of `Decimal` in Spark. And this PR brings two behavior changes:

1) for literals like `1.23E4BD` or `1.23E4`(with `spark.sql.legacy.exponentLiteralAsDecimal.enabled`=true, see [SPARK-29956](https://issues.apache.org/jira/browse/SPARK-29956)), we set its `(precision, scale)` to (5, 0) rather than (3, -2);
2) add negative `scale` check inside the decimal method if it exposes to set `scale` explicitly. If check fails, `AnalysisException` throws.

And user could still use `spark.sql.legacy.allowNegativeScaleOfDecimal.enabled` to restore the previous behavior.

### Why are the changes needed?

According to SQL standard,
> 4.4.2 Characteristics of numbers
An exact numeric type has a precision P and a scale S. P is a positive integer that determines the number of significant digits in a particular radix R, where R is either 2 or 10. S is a non-negative integer.

scale of Decimal should always be non-negative. And other mainstream databases, like Presto, PostgreSQL, also don't allow negative scale.

Presto:
```
presto:default> create table t (i decimal(2, -1));
Query 20191213_081238_00017_i448h failed: line 1:30: mismatched input '-'. Expecting: <integer>, <type>
create table t (i decimal(2, -1))
```

PostgrelSQL:
```
postgres=# create table t(i decimal(2, -1));
ERROR:  NUMERIC scale -1 must be between 0 and precision 2
LINE 1: create table t(i decimal(2, -1));
                         ^
```

And, actually, Spark itself already doesn't allow to create table with negative decimal types using SQL:
```
scala> spark.sql("create table t(i decimal(2, -1))");
org.apache.spark.sql.catalyst.parser.ParseException:
no viable alternative at input 'create table t(i decimal(2, -'(line 1, pos 28)

== SQL ==
create table t(i decimal(2, -1))
----------------------------^^^

  at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:263)
  at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:130)
  at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:48)
  at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:76)
  at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:605)
  at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
  at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:605)
  ... 35 elided
```

However, it is still possible to create such table or `DatFrame` using Spark SQL programming API:
```
scala> val tb =
 CatalogTable(
  TableIdentifier("test", None),
  CatalogTableType.MANAGED,
  CatalogStorageFormat.empty,
  StructType(StructField("i", DecimalType(2, -1) ) :: Nil))
```
```
scala> spark.sql("SELECT 1.23E4BD")
res2: org.apache.spark.sql.DataFrame = [1.23E+4: decimal(3,-2)]
```
while, these two different behavior could make user confused.

On the other side, even if user creates such table or `DataFrame` with negative scale decimal type, it can't write data out if using format, like `parquet` or `orc`. Because these formats have their own check for negative scale and fail on it.
```
scala> spark.sql("SELECT 1.23E4BD").write.saveAsTable("parquet")
19/12/13 17:37:04 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0)
java.lang.IllegalArgumentException: Invalid DECIMAL scale: -2
	at org.apache.parquet.Preconditions.checkArgument(Preconditions.java:53)
	at org.apache.parquet.schema.Types$BasePrimitiveBuilder.decimalMetadata(Types.java:495)
	at org.apache.parquet.schema.Types$BasePrimitiveBuilder.build(Types.java:403)
	at org.apache.parquet.schema.Types$BasePrimitiveBuilder.build(Types.java:309)
	at org.apache.parquet.schema.Types$Builder.named(Types.java:290)
	at org.apache.spark.sql.execution.datasources.parquet.SparkToParquetSchemaConverter.convertField(ParquetSchemaConverter.scala:428)
	at org.apache.spark.sql.execution.datasources.parquet.SparkToParquetSchemaConverter.convertField(ParquetSchemaConverter.scala:334)
	at org.apache.spark.sql.execution.datasources.parquet.SparkToParquetSchemaConverter.$anonfun$convert$2(ParquetSchemaConverter.scala:326)
	at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
	at scala.collection.Iterator.foreach(Iterator.scala:941)
	at scala.collection.Iterator.foreach$(Iterator.scala:941)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
	at scala.collection.IterableLike.foreach(IterableLike.scala:74)
	at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
	at org.apache.spark.sql.types.StructType.foreach(StructType.scala:99)
	at scala.collection.TraversableLike.map(TraversableLike.scala:238)
	at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
	at org.apache.spark.sql.types.StructType.map(StructType.scala:99)
	at org.apache.spark.sql.execution.datasources.parquet.SparkToParquetSchemaConverter.convert(ParquetSchemaConverter.scala:326)
	at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.init(ParquetWriteSupport.scala:97)
	at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:388)
	at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:349)
	at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetOutputWriter.scala:37)
	at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anon$1.newInstance(ParquetFileFormat.scala:150)
	at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.newOutputWriter(FileFormatDataWriter.scala:124)
	at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.<init>(FileFormatDataWriter.scala:109)
	at org.apache.spark.sql.execution.datasources.FileFormatWriter$.executeTask(FileFormatWriter.scala:264)
	at org.apache.spark.sql.execution.datasources.FileFormatWriter$.$anonfun$write$15(FileFormatWriter.scala:205)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:127)
	at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:441)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:444)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)
```

So, I think it would be better to disallow negative scale totally and make behaviors above be consistent.

### Does this PR introduce any user-facing change?

Yes, if `spark.sql.legacy.allowNegativeScaleOfDecimal.enabled=false`, user couldn't create Decimal value with negative scale anymore.

### How was this patch tested?

Added new tests in `ExpressionParserSuite` and `DecimalSuite`;
Updated `SQLQueryTestSuite`.

Closes #26881 from Ngone51/nonnegative-scale.

Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
 2020-01-21 21:09:48 +08:00
HyukjinKwon 1881caa95e [SPARK-29188][PYTHON][FOLLOW-UP] Explicitly disable Arrow execution for all test of toPandas empty types 
### What changes were proposed in this pull request?

Another followup of 4398dfa709

I missed two more tests added:

```
======================================================================
ERROR [0.133s]: test_to_pandas_from_mixed_dataframe (pyspark.sql.tests.test_dataframe.DataFrameTests)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/jenkins/python/pyspark/sql/tests/test_dataframe.py", line 617, in test_to_pandas_from_mixed_dataframe
    self.assertTrue(np.all(pdf_with_only_nulls.dtypes == pdf_with_some_nulls.dtypes))
AssertionError: False is not true
======================================================================
ERROR [0.061s]: test_to_pandas_from_null_dataframe (pyspark.sql.tests.test_dataframe.DataFrameTests)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/jenkins/python/pyspark/sql/tests/test_dataframe.py", line 588, in test_to_pandas_from_null_dataframe
    self.assertEqual(types[0], np.float64)
AssertionError: dtype('O') != <class 'numpy.float64'>
----------------------------------------------------------------------
```

### Why are the changes needed?

To make the test independent of default values of configuration.

### Does this PR introduce any user-facing change?

No.

### How was this patch tested?

Manually tested and Jenkins should test.

Closes #27250 from HyukjinKwon/SPARK-29188-followup2.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-01-17 15:00:18 +09:00
HyukjinKwon 4398dfa709 [SPARK-29188][PYTHON][FOLLOW-UP] Explicitly disable Arrow execution for the test of toPandas empty types 
### What changes were proposed in this pull request?

This PR proposes to explicitly disable Arrow execution for the test of toPandas empty types. If `spark.sql.execution.arrow.pyspark.enabled` is enabled by default, this test alone fails as below:

```
======================================================================
ERROR [0.205s]: test_to_pandas_from_empty_dataframe (pyspark.sql.tests.test_dataframe.DataFrameTests)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/.../pyspark/sql/tests/test_dataframe.py", line 568, in test_to_pandas_from_empty_dataframe
    self.assertTrue(np.all(dtypes_when_empty_df == dtypes_when_nonempty_df))
AssertionError: False is not true
----------------------------------------------------------------------
```

it should be best to explicitly disable for the test that only works when it's disabled.

### Why are the changes needed?

To make the test independent of default values of configuration.

### Does this PR introduce any user-facing change?

No.

### How was this patch tested?

Manually tested and Jenkins should test.

Closes #27247 from HyukjinKwon/SPARK-29188-followup.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
 2020-01-16 19:27:30 -08:00
Maxim Gekk 1a9de8c31f [SPARK-30499][SQL] Remove SQL config spark.sql.execution.pandas.respectSessionTimeZone 
### What changes were proposed in this pull request?
In the PR, I propose to remove the SQL config `spark.sql.execution.pandas.respectSessionTimeZone` which has been deprecated since Spark 2.3.

### Why are the changes needed?
To improve code maintainability.

### Does this PR introduce any user-facing change?
Yes.

### How was this patch tested?
by running python tests, https://spark.apache.org/docs/latest/building-spark.html#pyspark-tests-with-maven-or-sbt

Closes #27218 from MaxGekk/remove-respectSessionTimeZone.

Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-01-17 11:44:49 +09:00