def _get_java_python_function_operator(self, func: Union[Function,
FunctionWrapper],
type_info: TypeInformation,
func_name: str, func_type: int):
"""
Create a flink operator according to user provided function object, data types,
function name and function type.
:param func: a function object that implements the Function interface.
:param type_info: the data type of the function output data.
:param func_name: function name.
:param func_type: function type, supports MAP, FLAT_MAP, etc.
:return: A flink java operator which is responsible for execution user defined python
function.
"""
gateway = get_gateway()
import cloudpickle
serialized_func = cloudpickle.dumps(func)
j_input_types = self._j_data_stream.getTransformation().getOutputType()
if type_info is None:
output_type_info = PickledBytesTypeInfo.PICKLED_BYTE_ARRAY_TYPE_INFO(
)
else:
if isinstance(type_info, list):
output_type_info = RowTypeInfo(type_info)
else:
output_type_info = type_info
DataStreamPythonFunction = gateway.jvm.org.apache.flink.datastream.runtime.functions \
.python.DataStreamPythonFunction
j_python_data_stream_scalar_function = DataStreamPythonFunction(
func_name, bytearray(serialized_func), _get_python_env())
DataStreamPythonFunctionInfo = gateway.jvm. \
org.apache.flink.datastream.runtime.functions.python \
.DataStreamPythonFunctionInfo
j_python_data_stream_function_info = DataStreamPythonFunctionInfo(
j_python_data_stream_scalar_function, func_type)
j_env = self._j_data_stream.getExecutionEnvironment()
PythonConfigUtil = gateway.jvm.org.apache.flink.python.util.PythonConfigUtil
j_conf = PythonConfigUtil.getMergedConfig(j_env)
DataStreamPythonFunctionOperator = gateway.jvm.org.apache.flink.datastream.runtime \
.operators.python.DataStreamPythonStatelessFunctionOperator
j_python_data_stream_scalar_function_operator = DataStreamPythonFunctionOperator(
j_conf, j_input_types, output_type_info.get_java_type_info(),
j_python_data_stream_function_info)
return j_python_data_stream_scalar_function_operator, output_type_info
def test_row_type(self):
self.assertEqual(RowTypeInfo([Types.STRING(), Types.STRING()])
.get_field_names(), ['f0', 'f1'])
self.assertEqual(RowTypeInfo([Types.STRING(), Types.STRING()],
['a', 'b']).get_field_names(), ['a', 'b'])
self.assertEqual(RowTypeInfo([Types.STRING(), Types.STRING()],
['a', 'b']) == RowTypeInfo([Types.STRING(),
Types.STRING()], ['a', 'b']), True)
self.assertEqual(RowTypeInfo([Types.STRING(),
Types.STRING()],
['a', 'b']) == RowTypeInfo([Types.STRING(),
Types.INT()],
['a', 'b']), False)
self.assertEqual(RowTypeInfo([Types.STRING(),
Types.STRING()],
['a', 'b']).__str__(), "RowTypeInfo(a: String, b: String)")
self.assertEqual(Types.ROW([Types.STRING(), Types.STRING()]),
RowTypeInfo([Types.STRING(), Types.STRING()]), True)
self.assertEqual(Types.ROW_NAMED(['a', 'b'], [Types.STRING(), Types.STRING()])
.get_field_names(), ['a', 'b'], True)
self.assertEqual(Types.ROW_NAMED(['a', 'b'], [Types.STRING(), Types.STRING()])
.get_field_types(), [Types.STRING(), Types.STRING()], True)
def __init__(self,
tag_id: str,
type_info: Optional[Union[TypeInformation, list]] = None):
if not tag_id:
raise ValueError("OutputTag tag_id cannot be None or empty string")
self.tag_id = tag_id
if type_info is None:
self.type_info = Types.PICKLED_BYTE_ARRAY()
elif isinstance(type_info, list):
self.type_info = RowTypeInfo(type_info)
elif not isinstance(type_info, TypeInformation):
raise TypeError(
"OutputTag type_info must be None, list or TypeInformation")
else:
self.type_info = type_info
def _get_connected_stream_operator(self, func: Union[Function, FunctionWrapper],
type_info: TypeInformation, func_name: str,
func_type: int):
gateway = get_gateway()
import cloudpickle
serialized_func = cloudpickle.dumps(func)
j_input_types1 = self.stream1._j_data_stream.getTransformation().getOutputType()
j_input_types2 = self.stream2._j_data_stream.getTransformation().getOutputType()
if type_info is None:
output_type_info = PickledBytesTypeInfo.PICKLED_BYTE_ARRAY_TYPE_INFO()
else:
if isinstance(type_info, list):
output_type_info = RowTypeInfo(type_info)
else:
output_type_info = type_info
DataStreamPythonFunction = gateway.jvm.org.apache.flink.datastream.runtime.functions \
.python.DataStreamPythonFunction
j_python_data_stream_scalar_function = DataStreamPythonFunction(
func_name,
bytearray(serialized_func),
_get_python_env())
DataStreamPythonFunctionInfo = gateway.jvm. \
org.apache.flink.datastream.runtime.functions.python \
.DataStreamPythonFunctionInfo
j_python_data_stream_function_info = DataStreamPythonFunctionInfo(
j_python_data_stream_scalar_function,
func_type)
j_conf = gateway.jvm.org.apache.flink.configuration.Configuration()
DataStreamPythonFunctionOperator = gateway.jvm.org.apache.flink.datastream.runtime \
.operators.python.DataStreamTwoInputPythonStatelessFunctionOperator
j_python_data_stream_function_operator = DataStreamPythonFunctionOperator(
j_conf,
j_input_types1,
j_input_types2,
output_type_info.get_java_type_info(),
j_python_data_stream_function_info,
self._is_keyed_stream())
return j_python_data_stream_function_operator, output_type_info.get_java_type_info()
def _build_orc_job(self, row_type: RowType, row_type_info: RowTypeInfo, data: List[Row]):
jvm = get_gateway().jvm
sink = FileSink.for_bulk_format(
self.orc_dir_name, OrcBulkWriters.for_row_type(row_type)
).build()
j_list = jvm.java.util.ArrayList()
for d in data:
j_list.add(to_java_data_structure(d))
ds = DataStream(self.env._j_stream_execution_environment.fromCollection(
j_list,
row_type_info.get_java_type_info()
))
ds.sink_to(sink)
class OutputTag(object):
"""
An :class:`OutputTag` is a typed and named tag to use for tagging side outputs of an operator.
Example:
::
# Explicitly specify output type
>>> info = OutputTag("late-data", Types.TUPLE([Types.STRING(), Types.LONG()]))
# Implicitly wrap list to Types.ROW
>>> info_row = OutputTag("row", [Types.STRING(), Types.LONG()])
# Implicitly use pickle serialization
>>> info_side = OutputTag("side")
# ERROR: tag id cannot be empty string (extra requirement for Python API)
>>> info_error = OutputTag("")
"""
def __init__(self,
tag_id: str,
type_info: Optional[Union[TypeInformation, list]] = None):
if not tag_id:
raise ValueError("OutputTag tag_id cannot be None or empty string")
self.tag_id = tag_id
if type_info is None:
self.type_info = Types.PICKLED_BYTE_ARRAY()
elif isinstance(type_info, list):
self.type_info = RowTypeInfo(type_info)
elif not isinstance(type_info, TypeInformation):
raise TypeError(
"OutputTag type_info must be None, list or TypeInformation")
else:
self.type_info = type_info
def get_java_output_tag(self):
gateway = get_gateway()
j_obj = gateway.jvm.org.apache.flink.util.OutputTag(
self.tag_id, self.type_info.get_java_type_info())
# deal with serializability
self.type_info._j_typeinfo = None
return j_obj
def sink(sql: str,
type_info: RowTypeInfo,
jdbc_connection_options: 'JdbcConnectionOptions',
jdbc_execution_options: 'JdbcExecutionOptions' = None):
"""
Create a JDBC sink.
:param sql: arbitrary DML query (e.g. insert, update, upsert)
:param type_info: A RowTypeInfo for query field types.
:param jdbc_execution_options: parameters of execution, such as batch size and maximum
retries.
:param jdbc_connection_options: parameters of connection, such as JDBC URL.
:return: A JdbcSink.
"""
sql_types = []
gateway = get_gateway()
JJdbcTypeUtil = gateway.jvm.org.apache.flink.connector.jdbc.utils.JdbcTypeUtil
for field_type in type_info.get_field_types():
sql_types.append(
JJdbcTypeUtil.typeInformationToSqlType(
field_type.get_java_type_info()))
j_sql_type = to_jarray(gateway.jvm.int, sql_types)
output_format_clz = gateway.jvm.Class\
.forName('org.apache.flink.connector.jdbc.internal.JdbcBatchingOutputFormat', False,
get_gateway().jvm.Thread.currentThread().getContextClassLoader())
j_int_array_type = to_jarray(gateway.jvm.int, []).getClass()
j_builder_method = output_format_clz.getDeclaredMethod(
'createRowJdbcStatementBuilder',
to_jarray(gateway.jvm.Class, [j_int_array_type]))
j_builder_method.setAccessible(True)
j_statement_builder = j_builder_method.invoke(
None, to_jarray(gateway.jvm.Object, [j_sql_type]))
jdbc_execution_options = jdbc_execution_options if jdbc_execution_options is not None \
else JdbcExecutionOptions.defaults()
j_jdbc_sink = gateway.jvm.org.apache.flink.connector.jdbc.JdbcSink\
.sink(sql, j_statement_builder, jdbc_execution_options._j_jdbc_execution_options,
jdbc_connection_options._j_jdbc_connection_options)
return JdbcSink(j_jdbc_sink=j_jdbc_sink)
def _get_java_python_function_operator(self, func: Union[Function,
FunctionWrapper],
type_info: TypeInformation,
func_name: str, func_type: int):
"""
Create a flink operator according to user provided function object, data types,
function name and function type.
:param func: a function object that implements the Function interface.
:param type_info: the data type of the function output data.
:param func_name: function name.
:param func_type: function type, supports MAP, FLAT_MAP, etc.
:return: A flink java operator which is responsible for execution user defined python
function.
"""
gateway = get_gateway()
import cloudpickle
serialized_func = cloudpickle.dumps(func)
j_input_types = self._j_data_stream.getTransformation().getOutputType()
if type_info is None:
output_type_info = PickledBytesTypeInfo.PICKLED_BYTE_ARRAY_TYPE_INFO(
)
else:
if isinstance(type_info, list):
output_type_info = RowTypeInfo(type_info)
else:
output_type_info = type_info
DataStreamPythonFunction = gateway.jvm.org.apache.flink.datastream.runtime.functions \
.python.DataStreamPythonFunction
j_python_data_stream_scalar_function = DataStreamPythonFunction(
func_name, bytearray(serialized_func), _get_python_env())
DataStreamPythonFunctionInfo = gateway.jvm. \
org.apache.flink.datastream.runtime.functions.python \
.DataStreamPythonFunctionInfo
j_python_data_stream_function_info = DataStreamPythonFunctionInfo(
j_python_data_stream_scalar_function, func_type)
j_conf = gateway.jvm.org.apache.flink.configuration.Configuration()
# set max bundle size to 1 to force synchronize process for reduce function.
from pyflink.fn_execution.flink_fn_execution_pb2 import UserDefinedDataStreamFunction
if func_type == UserDefinedDataStreamFunction.REDUCE:
j_conf.setInteger(
gateway.jvm.org.apache.flink.python.PythonOptions.
MAX_BUNDLE_SIZE, 1)
DataStreamPythonReduceFunctionOperator = gateway.jvm.org.apache.flink.datastream \
.runtime.operators.python.DataStreamPythonReduceFunctionOperator
j_output_type_info = j_input_types.getTypeAt(1)
j_python_data_stream_function_operator = DataStreamPythonReduceFunctionOperator(
j_conf, j_input_types, j_output_type_info,
j_python_data_stream_function_info)
return j_python_data_stream_function_operator, j_output_type_info
else:
if str(func) == '_Flink_PartitionCustomMapFunction':
DataStreamPythonFunctionOperator = gateway.jvm.org.apache.flink.datastream.runtime \
.operators.python.DataStreamPythonPartitionCustomFunctionOperator
else:
DataStreamPythonFunctionOperator = gateway.jvm.org.apache.flink.datastream.runtime \
.operators.python.DataStreamPythonStatelessFunctionOperator
j_python_data_stream_function_operator = DataStreamPythonFunctionOperator(
j_conf, j_input_types, output_type_info.get_java_type_info(),
j_python_data_stream_function_info)
return j_python_data_stream_function_operator, output_type_info.get_java_type_info(
)
