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Author SHA1 Message Date
Josh Soref 13fd272cd3 Spelling r common dev mlib external project streaming resource managers python 
### What changes were proposed in this pull request?

This PR intends to fix typos in the sub-modules:
* `R`
* `common`
* `dev`
* `mlib`
* `external`
* `project`
* `streaming`
* `resource-managers`
* `python`

Split per srowen https://github.com/apache/spark/pull/30323#issuecomment-728981618

NOTE: The misspellings have been reported at 706a726f87 (commitcomment-44064356)

### Why are the changes needed?

Misspelled words make it harder to read / understand content.

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

There are various fixes to documentation, etc...

### How was this patch tested?

No testing was performed

Closes #30402 from jsoref/spelling-R_common_dev_mlib_external_project_streaming_resource-managers_python.

Authored-by: Josh Soref <jsoref@users.noreply.github.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
 2020-11-27 10:22:45 -06:00
Bryan Cutler 8e2a0bdce7 [SPARK-24554][PYTHON][SQL] Add MapType support for PySpark with Arrow 
### What changes were proposed in this pull request?

This change adds MapType support for PySpark with Arrow, if using pyarrow >= 2.0.0.

### Why are the changes needed?

MapType was previous unsupported with Arrow.

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

User can now enable MapType for `createDataFrame()`, `toPandas()` with Arrow optimization, and with Pandas UDFs.

### How was this patch tested?

Added new PySpark tests for createDataFrame(), toPandas() and Scalar Pandas UDFs.

Closes #30393 from BryanCutler/arrow-add-MapType-SPARK-24554.

Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-11-18 21:18:19 +09:00
HyukjinKwon 3959f0d987 [SPARK-33250][PYTHON][DOCS] Migration to NumPy documentation style in SQL (pyspark.sql.*) 
### What changes were proposed in this pull request?

This PR proposes to migrate to [NumPy documentation style](https://numpydoc.readthedocs.io/en/latest/format.html), see also SPARK-33243.
While I am migrating, I also fixed some Python type hints accordingly.

### Why are the changes needed?

For better documentation as text itself, and generated HTMLs

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

Yes, they will see a better format of HTMLs, and better text format. See SPARK-33243.

### How was this patch tested?

Manually tested via running `./dev/lint-python`.

Closes #30181 from HyukjinKwon/SPARK-33250.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-11-03 10:00:49 +09:00
HyukjinKwon 5775073a01 [SPARK-32722][PYTHON][DOCS] Update document type conversion for Pandas UDFs (pyarrow 1.0.1, pandas 1.1.1, Python 3.7) 
### What changes were proposed in this pull request?

This PR updates the chart generated at SPARK-25666. We bumped up the minimal PyArrow version. It's better to use PyArrow 0.15.1+

### Why are the changes needed?

To track the changes in type coercion of PySpark <> PyArrow <> pandas.

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

No.

### How was this patch tested?

Use this code to generate the chart:

```python
from pyspark.sql.types import *
from pyspark.sql.functions import pandas_udf

columns = [
    ('none', 'object(NoneType)'),
    ('bool', 'bool'),
    ('int8', 'int8'),
    ('int16', 'int16'),
    ('int32', 'int32'),
    ('int64', 'int64'),
    ('uint8', 'uint8'),
    ('uint16', 'uint16'),
    ('uint32', 'uint32'),
    ('uint64', 'uint64'),
    ('float64', 'float16'),
    ('float64', 'float32'),
    ('float64', 'float64'),
    ('date', 'datetime64[ns]'),
    ('tz_aware_dates', 'datetime64[ns, US/Eastern]'),
    ('string', 'object(string)'),
    ('decimal', 'object(Decimal)'),
    ('array', 'object(array[int32])'),
    ('float128', 'float128'),
    ('complex64', 'complex64'),
    ('complex128', 'complex128'),
    ('category', 'category'),
    ('tdeltas', 'timedelta64[ns]'),
]

def create_dataframe():
    import pandas as pd
    import numpy as np
    import decimal
    pdf = pd.DataFrame({
        'none': [None, None],
        'bool': [True, False],
        'int8': np.arange(1, 3).astype('int8'),
        'int16': np.arange(1, 3).astype('int16'),
        'int32': np.arange(1, 3).astype('int32'),
        'int64': np.arange(1, 3).astype('int64'),
        'uint8': np.arange(1, 3).astype('uint8'),
        'uint16': np.arange(1, 3).astype('uint16'),
        'uint32': np.arange(1, 3).astype('uint32'),
        'uint64': np.arange(1, 3).astype('uint64'),
        'float16': np.arange(1, 3).astype('float16'),
        'float32': np.arange(1, 3).astype('float32'),
        'float64': np.arange(1, 3).astype('float64'),
        'float128': np.arange(1, 3).astype('float128'),
        'complex64': np.arange(1, 3).astype('complex64'),
        'complex128': np.arange(1, 3).astype('complex128'),
        'string': list('ab'),
        'array': pd.Series([np.array([1, 2, 3], dtype=np.int32), np.array([1, 2, 3], dtype=np.int32)]),
        'decimal': pd.Series([decimal.Decimal('1'), decimal.Decimal('2')]),
        'date': pd.date_range('19700101', periods=2).values,
        'category': pd.Series(list("AB")).astype('category')})
    pdf['tdeltas'] = [pdf.date.diff()[1], pdf.date.diff()[0]]
    pdf['tz_aware_dates'] = pd.date_range('19700101', periods=2, tz='US/Eastern')
    return pdf

types =  [
    BooleanType(),
    ByteType(),
    ShortType(),
    IntegerType(),
    LongType(),
    FloatType(),
    DoubleType(),
    DateType(),
    TimestampType(),
    StringType(),
    DecimalType(10, 0),
    ArrayType(IntegerType()),
    MapType(StringType(), IntegerType()),
    StructType([StructField("_1", IntegerType())]),
    BinaryType(),
]

df = spark.range(2).repartition(1)
results = []
count = 0
total = len(types) * len(columns)
values = []
spark.sparkContext.setLogLevel("FATAL")
for t in types:
    result = []
    for column, pandas_t in columns:
        v = create_dataframe()[column][0]
        values.append(v)
        try:
            row = df.select(pandas_udf(lambda _: create_dataframe()[column], t)(df.id)).first()
            ret_str = repr(row[0])
        except Exception:
            ret_str = "X"
        result.append(ret_str)
        progress = "SQL Type: [%s]\n  Pandas Value(Type): %s(%s)]\n  Result Python Value: [%s]" % (
            t.simpleString(), v, pandas_t, ret_str)
        count += 1
        print("%s/%s:\n  %s" % (count, total, progress))
    results.append([t.simpleString()] + list(map(str, result)))

schema = ["SQL Type \\ Pandas Value(Type)"] + list(map(lambda values_column: "%s(%s)" % (values_column[0], values_column[1][1]), zip(values, columns)))
strings = spark.createDataFrame(results, schema=schema)._jdf.showString(20, 20, False)
print("\n".join(map(lambda line: "    # %s  # noqa" % line, strings.strip().split("\n"))))
```

Closes #29569 from HyukjinKwon/SPARK-32722.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-08-28 15:38:39 +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
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 c279e6b091 [SPARK-30722][DOCS][FOLLOW-UP] Explicitly mention the same entire input/output length restriction of Series Iterator UDF 
### What changes were proposed in this pull request?

This PR explicitly mention that the requirement of Iterator of Series to Iterator of Series and Iterator of Multiple Series to Iterator of Series (previously Scalar Iterator pandas UDF).

The actual limitation of this UDF is the same length of the _entire input and output_, instead of each series's length. Namely you can do something as below:

```python
from typing import Iterator, Tuple
import pandas as pd
from pyspark.sql.functions import pandas_udf

pandas_udf("long")
def func(
        iterator: Iterator[pd.Series]) -> Iterator[pd.Series]:
    return iter([pd.concat(iterator)])

spark.range(100).select(func("id")).show()
```

This characteristic allows you to prefetch the data from the iterator to speed up, compared to the regular Scalar to Scalar (previously Scalar pandas UDF).

### Why are the changes needed?

To document the correct restriction and characteristics of a feature.

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

Yes in the documentation but only in unreleased branches.

### How was this patch tested?

Github Actions should test the documentation build

Closes #28160 from HyukjinKwon/SPARK-30722-followup.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-04-09 16:46:27 +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
HyukjinKwon aa6a60530e [SPARK-30722][PYTHON][DOCS] Update documentation for Pandas UDF with Python type hints 
### What changes were proposed in this pull request?

This PR targets to document the Pandas UDF redesign with type hints introduced at SPARK-28264.
Mostly self-describing; however, there are few things to note for reviewers.

1. This PR replace the existing documentation of pandas UDFs to the newer redesign to promote the Python type hints. I added some words that Spark 3.0 still keeps the compatibility though.

2. This PR proposes to name non-pandas UDFs as "Pandas Function API"

3. SCALAR_ITER become two separate sections to reduce confusion:
  - `Iterator[pd.Series]` -> `Iterator[pd.Series]`
  - `Iterator[Tuple[pd.Series, ...]]` -> `Iterator[pd.Series]`

4. I removed some examples that look overkill to me.

5. I also removed some information in the doc, that seems duplicating or too much.

### Why are the changes needed?

To document new redesign in pandas UDF.

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

No.

### How was this patch tested?

Existing tests should cover.

Closes #27466 from HyukjinKwon/SPARK-30722.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-02-12 10:49:46 +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
HyukjinKwon ee8d661058 [SPARK-30434][PYTHON][SQL] Move pandas related functionalities into 'pandas' sub-package 
### What changes were proposed in this pull request?

This PR proposes to move pandas related functionalities into pandas package. Namely:

```bash
pyspark/sql/pandas
├── __init__.py
├── conversion.py  # Conversion between pandas <> PySpark DataFrames
├── functions.py   # pandas_udf
├── group_ops.py   # Grouped UDF / Cogrouped UDF + groupby.apply, groupby.cogroup.apply
├── map_ops.py     # Map Iter UDF + mapInPandas
├── serializers.py # pandas <> PyArrow serializers
├── types.py       # Type utils between pandas <> PyArrow
└── utils.py       # Version requirement checks
```

In order to separately locate `groupby.apply`, `groupby.cogroup.apply`, `mapInPandas`, `toPandas`, and `createDataFrame(pdf)` under `pandas` sub-package, I had to use a mix-in approach which Scala side uses often by `trait`, and also pandas itself uses this approach (see `IndexOpsMixin` as an example) to group related functionalities. Currently, you can think it's like Scala's self typed trait. See the structure below:

```python
class PandasMapOpsMixin(object):
    def mapInPandas(self, ...):
        ...
        return ...

    # other Pandas <> PySpark APIs
```

```python
class DataFrame(PandasMapOpsMixin):

    # other DataFrame APIs equivalent to Scala side.

```

Yes, This is a big PR but they are mostly just moving around except one case `createDataFrame` which I had to split the methods.

### Why are the changes needed?

There are pandas functionalities here and there and I myself gets lost where it was. Also, when you have to make a change commonly for all of pandas related features, it's almost impossible now.

Also, after this change, `DataFrame` and `SparkSession` become more consistent with Scala side since pandas is specific to Python, and this change separates pandas-specific APIs away from `DataFrame` or `SparkSession`.

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

No.

### How was this patch tested?

Existing tests should cover. Also, I manually built the PySpark API documentation and checked.

Closes #27109 from HyukjinKwon/pandas-refactoring.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
 2020-01-09 10:22:50 +09:00