## What changes were proposed in this pull request?
This increases the minimum support version of pyarrow to 0.12.1 and removes workarounds in pyspark to remain compatible with prior versions. This means that users will need to have at least pyarrow 0.12.1 installed and available in the cluster or an `ImportError` will be raised to indicate an upgrade is needed.
## How was this patch tested?
Existing tests using:
Python 2.7.15, pyarrow 0.12.1, pandas 0.24.2
Python 3.6.7, pyarrow 0.12.1, pandas 0.24.0
Closes#24298 from BryanCutler/arrow-bump-min-pyarrow-SPARK-27276.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Running PySpark tests with Pandas 0.24.x causes a failure in `test_pandas_udf_grouped_map` test_supported_types:
`ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()`
This is because a column is an ArrayType and the method `sqlutils ReusedSQLTestCase.assertPandasEqual ` does not properly check this.
This PR removes `assertPandasEqual` and replaces it with the built-in `pandas.util.testing.assert_frame_equal` which can properly handle columns of ArrayType and also prints out better diff between the DataFrames when an error occurs.
Additionally, imports of pandas and pyarrow were moved to the top of related test files to avoid duplicating the same import many times.
## How was this patch tested?
Existing tests
Closes#24306 from BryanCutler/python-pandas-assert_frame_equal-SPARK-27387.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Now that we support returning pandas DataFrame for struct type in Scalar Pandas UDF.
If we chain another Pandas UDF after the Scalar Pandas UDF returning pandas DataFrame, the argument of the chained UDF will be pandas DataFrame, but currently we don't support pandas DataFrame as an argument of Scalar Pandas UDF. That means there is an inconsistency between the chained UDF and the single UDF.
We should support taking pandas DataFrame for struct type argument in Scalar Pandas UDF to be consistent.
Currently pyarrow >=0.11 is supported.
## How was this patch tested?
Modified and added some tests.
Closes#24177 from ueshin/issues/SPARK-27240/structtype_argument.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
This change adds support for returning StructType from a scalar Pandas UDF, where the return value of the function is a pandas.DataFrame. Nested structs are not supported and an error will be raised, child types can be any other type currently supported.
## How was this patch tested?
Added additional unit tests to `test_pandas_udf_scalar`
Closes#23900 from BryanCutler/pyspark-support-scalar_udf-StructType-SPARK-23836.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Currently `DataFrame.toPandas()` with arrow enabled or `ArrowStreamPandasSerializer` for pandas UDF with pyarrow<0.12 creates `datetime64[ns]` type series as intermediate data and then convert to `datetime.date` series, but the intermediate `datetime64[ns]` might cause an overflow even if the date is valid.
```
>>> import datetime
>>>
>>> t = [datetime.date(2262, 4, 12), datetime.date(2263, 4, 12)]
>>>
>>> df = spark.createDataFrame(t, 'date')
>>> df.show()
+----------+
| value|
+----------+
|2262-04-12|
|2263-04-12|
+----------+
>>>
>>> spark.conf.set("spark.sql.execution.arrow.enabled", "true")
>>>
>>> df.toPandas()
value
0 1677-09-21
1 1678-09-21
```
We should avoid creating such intermediate data and create `datetime.date` series directly instead.
## How was this patch tested?
Modified some tests to include the date which overflow caused by the intermediate conversion.
Run tests with pyarrow 0.8, 0.10, 0.11, 0.12 in my local environment.
Closes#23795 from ueshin/issues/SPARK-26887/date_as_object.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Clean up unconditional import statements and move them to the top.
Conditional imports (pandas, numpy, pyarrow) are left as-is.
## How was this patch tested?
Exising tests.
Closes#23314 from icexelloss/clean-up-test-imports.
Authored-by: Li Jin <ice.xelloss@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR continues to break down a big large file into smaller files. See https://github.com/apache/spark/pull/23021. It targets to follow https://github.com/numpy/numpy/tree/master/numpy.
Basically this PR proposes to break down `pyspark/tests.py` into ...:
```
pyspark
...
├── testing
...
│ └── utils.py
├── tests
│ ├── __init__.py
│ ├── test_appsubmit.py
│ ├── test_broadcast.py
│ ├── test_conf.py
│ ├── test_context.py
│ ├── test_daemon.py
│ ├── test_join.py
│ ├── test_profiler.py
│ ├── test_rdd.py
│ ├── test_readwrite.py
│ ├── test_serializers.py
│ ├── test_shuffle.py
│ ├── test_taskcontext.py
│ ├── test_util.py
│ └── test_worker.py
...
```
## How was this patch tested?
Existing tests should cover.
`cd python` and .`/run-tests-with-coverage`. Manually checked they are actually being ran.
Each test (not officially) can be ran via:
```bash
SPARK_TESTING=1 ./bin/pyspark pyspark.tests.test_context
```
Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`.
Closes#23033 from HyukjinKwon/SPARK-26036.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This is the official first attempt to break huge single `tests.py` file - I did it locally before few times and gave up for some reasons. Now, currently it really makes the unittests super hard to read and difficult to check. To me, it even bothers me to to scroll down the big file. It's one single 7000 lines file!
This is not only readability issue. Since one big test takes most of tests time, the tests don't run in parallel fully - although it will costs to start and stop the context.
We could pick up one example and follow. Given my investigation, the current style looks closer to NumPy structure and looks easier to follow. Please see https://github.com/numpy/numpy/tree/master/numpy.
Basically this PR proposes to break down `pyspark/sql/tests.py` into ...:
```bash
pyspark
...
├── sql
...
│ ├── tests # Includes all tests broken down from 'pyspark/sql/tests.py'
│ │ │ # Each matchs to module in 'pyspark/sql'. Additionally, some logical group can
│ │ │ # be added. For instance, 'test_arrow.py', 'test_datasources.py' ...
│ │ ├── __init__.py
│ │ ├── test_appsubmit.py
│ │ ├── test_arrow.py
│ │ ├── test_catalog.py
│ │ ├── test_column.py
│ │ ├── test_conf.py
│ │ ├── test_context.py
│ │ ├── test_dataframe.py
│ │ ├── test_datasources.py
│ │ ├── test_functions.py
│ │ ├── test_group.py
│ │ ├── test_pandas_udf.py
│ │ ├── test_pandas_udf_grouped_agg.py
│ │ ├── test_pandas_udf_grouped_map.py
│ │ ├── test_pandas_udf_scalar.py
│ │ ├── test_pandas_udf_window.py
│ │ ├── test_readwriter.py
│ │ ├── test_serde.py
│ │ ├── test_session.py
│ │ ├── test_streaming.py
│ │ ├── test_types.py
│ │ ├── test_udf.py
│ │ └── test_utils.py
...
├── testing # Includes testing utils that can be used in unittests.
│ ├── __init__.py
│ └── sqlutils.py
...
```
## How was this patch tested?
Existing tests should cover.
`cd python` and `./run-tests-with-coverage`. Manually checked they are actually being ran.
Each test (not officially) can be ran via:
```
SPARK_TESTING=1 ./bin/pyspark pyspark.sql.tests.test_pandas_udf_scalar
```
Note that if you're using Mac and Python 3, you might have to `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`.
Closes#23021 from HyukjinKwon/SPARK-25344.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
