spark-instrumented-optimizer/python/pyspark/sql/utils.py
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

192 lines
6.1 KiB
Python

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import py4j
import sys
if sys.version_info.major >= 3:
unicode = str
class CapturedException(Exception):
def __init__(self, desc, stackTrace, cause=None):
self.desc = desc
self.stackTrace = stackTrace
self.cause = convert_exception(cause) if cause is not None else None
def __str__(self):
desc = self.desc
# encode unicode instance for python2 for human readable description
if sys.version_info.major < 3 and isinstance(desc, unicode):
return str(desc.encode('utf-8'))
else:
return str(desc)
class AnalysisException(CapturedException):
"""
Failed to analyze a SQL query plan.
"""
class ParseException(CapturedException):
"""
Failed to parse a SQL command.
"""
class IllegalArgumentException(CapturedException):
"""
Passed an illegal or inappropriate argument.
"""
class StreamingQueryException(CapturedException):
"""
Exception that stopped a :class:`StreamingQuery`.
"""
class QueryExecutionException(CapturedException):
"""
Failed to execute a query.
"""
class UnknownException(CapturedException):
"""
None of the above exceptions.
"""
def convert_exception(e):
s = e.toString()
stackTrace = '\n\t at '.join(map(lambda x: x.toString(), e.getStackTrace()))
c = e.getCause()
if s.startswith('org.apache.spark.sql.AnalysisException: '):
return AnalysisException(s.split(': ', 1)[1], stackTrace, c)
if s.startswith('org.apache.spark.sql.catalyst.analysis'):
return AnalysisException(s.split(': ', 1)[1], stackTrace, c)
if s.startswith('org.apache.spark.sql.catalyst.parser.ParseException: '):
return ParseException(s.split(': ', 1)[1], stackTrace, c)
if s.startswith('org.apache.spark.sql.streaming.StreamingQueryException: '):
return StreamingQueryException(s.split(': ', 1)[1], stackTrace, c)
if s.startswith('org.apache.spark.sql.execution.QueryExecutionException: '):
return QueryExecutionException(s.split(': ', 1)[1], stackTrace, c)
if s.startswith('java.lang.IllegalArgumentException: '):
return IllegalArgumentException(s.split(': ', 1)[1], stackTrace, c)
return UnknownException(s, stackTrace, c)
def capture_sql_exception(f):
def deco(*a, **kw):
try:
return f(*a, **kw)
except py4j.protocol.Py4JJavaError as e:
converted = convert_exception(e.java_exception)
if not isinstance(converted, UnknownException):
raise converted
else:
raise
return deco
def install_exception_handler():
"""
Hook an exception handler into Py4j, which could capture some SQL exceptions in Java.
When calling Java API, it will call `get_return_value` to parse the returned object.
If any exception happened in JVM, the result will be Java exception object, it raise
py4j.protocol.Py4JJavaError. We replace the original `get_return_value` with one that
could capture the Java exception and throw a Python one (with the same error message).
It's idempotent, could be called multiple times.
"""
original = py4j.protocol.get_return_value
# The original `get_return_value` is not patched, it's idempotent.
patched = capture_sql_exception(original)
# only patch the one used in py4j.java_gateway (call Java API)
py4j.java_gateway.get_return_value = patched
def toJArray(gateway, jtype, arr):
"""
Convert python list to java type array
:param gateway: Py4j Gateway
:param jtype: java type of element in array
:param arr: python type list
"""
jarr = gateway.new_array(jtype, len(arr))
for i in range(0, len(arr)):
jarr[i] = arr[i]
return jarr
def require_test_compiled():
""" Raise Exception if test classes are not compiled
"""
import os
import glob
try:
spark_home = os.environ['SPARK_HOME']
except KeyError:
raise RuntimeError('SPARK_HOME is not defined in environment')
test_class_path = os.path.join(
spark_home, 'sql', 'core', 'target', '*', 'test-classes')
paths = glob.glob(test_class_path)
if len(paths) == 0:
raise RuntimeError(
"%s doesn't exist. Spark sql test classes are not compiled." % test_class_path)
class ForeachBatchFunction(object):
"""
This is the Python implementation of Java interface 'ForeachBatchFunction'. This wraps
the user-defined 'foreachBatch' function such that it can be called from the JVM when
the query is active.
"""
def __init__(self, sql_ctx, func):
self.sql_ctx = sql_ctx
self.func = func
def call(self, jdf, batch_id):
from pyspark.sql.dataframe import DataFrame
try:
self.func(DataFrame(jdf, self.sql_ctx), batch_id)
except Exception as e:
self.error = e
raise e
class Java:
implements = ['org.apache.spark.sql.execution.streaming.sources.PythonForeachBatchFunction']
def to_str(value):
"""
A wrapper over str(), but converts bool values to lower case strings.
If None is given, just returns None, instead of converting it to string "None".
"""
if isinstance(value, bool):
return str(value).lower()
elif value is None:
return value
else:
return str(value)