#
# 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_minimum_pandas_version():
""" Raise ImportError if minimum version of Pandas is not installed
"""
# TODO(HyukjinKwon): Relocate and deduplicate the version specification.
minimum_pandas_version = "0.23.2"
from distutils.version import LooseVersion
try:
import pandas
have_pandas = True
except ImportError:
have_pandas = False
if not have_pandas:
raise ImportError("Pandas >= %s must be installed; however, "
"it was not found." % minimum_pandas_version)
if LooseVersion(pandas.__version__) < LooseVersion(minimum_pandas_version):
raise ImportError("Pandas >= %s must be installed; however, "
"your version was %s." % (minimum_pandas_version, pandas.__version__))
def require_minimum_pyarrow_version():
""" Raise ImportError if minimum version of pyarrow is not installed
"""
# TODO(HyukjinKwon): Relocate and deduplicate the version specification.
minimum_pyarrow_version = "0.15.1"
from distutils.version import LooseVersion
import os
try:
import pyarrow
have_arrow = True
except ImportError:
have_arrow = False
if not have_arrow:
raise ImportError("PyArrow >= %s must be installed; however, "
"it was not found." % minimum_pyarrow_version)
if LooseVersion(pyarrow.__version__) < LooseVersion(minimum_pyarrow_version):
raise ImportError("PyArrow >= %s must be installed; however, "
"your version was %s." % (minimum_pyarrow_version, pyarrow.__version__))
if os.environ.get("ARROW_PRE_0_15_IPC_FORMAT", "0") == "1":
raise RuntimeError("Arrow legacy IPC format is not supported in PySpark, "
"please unset ARROW_PRE_0_15_IPC_FORMAT")
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']
[docs]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)
