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import os
import tempfile
from typing import List, Any, Optional, cast
from py4j.java_gateway import JavaObject
from pyflink.common import Configuration, WatermarkStrategy
from pyflink.common.execution_config import ExecutionConfig
from pyflink.common.io import InputFormat
from pyflink.common.job_client import JobClient
from pyflink.common.job_execution_result import JobExecutionResult
from pyflink.common.typeinfo import TypeInformation, Types
from pyflink.datastream import SlotSharingGroup
from pyflink.datastream.checkpoint_config import CheckpointConfig
from pyflink.datastream.checkpointing_mode import CheckpointingMode
from pyflink.datastream.connectors import Source
from pyflink.datastream.data_stream import DataStream
from pyflink.datastream.execution_mode import RuntimeExecutionMode
from pyflink.datastream.functions import SourceFunction
from pyflink.datastream.utils import ResultTypeQueryable
from pyflink.java_gateway import get_gateway
from pyflink.serializers import PickleSerializer
from pyflink.util.java_utils import add_jars_to_context_class_loader, \
invoke_method, get_field_value, is_local_deployment, get_j_env_configuration
__all__ = ['StreamExecutionEnvironment']
class StreamExecutionEnvironment(object):
"""
The StreamExecutionEnvironment is the context in which a streaming program is executed. A
*LocalStreamEnvironment* will cause execution in the attached JVM, a
*RemoteStreamEnvironment* will cause execution on a remote setup.
The environment provides methods to control the job execution (such as setting the parallelism
or the fault tolerance/checkpointing parameters) and to interact with the outside world (data
access).
"""
def __init__(self, j_stream_execution_environment, serializer=PickleSerializer()):
self._j_stream_execution_environment = j_stream_execution_environment
self.serializer = serializer
self._open()
[docs] def get_config(self) -> ExecutionConfig:
"""
Gets the config object.
:return: The :class:`~pyflink.common.ExecutionConfig` object.
"""
return ExecutionConfig(self._j_stream_execution_environment.getConfig())
[docs] def set_parallelism(self, parallelism: int) -> 'StreamExecutionEnvironment':
"""
Sets the parallelism for operations executed through this environment.
Setting a parallelism of x here will cause all operators (such as map,
batchReduce) to run with x parallel instances. This method overrides the
default parallelism for this environment. The
*LocalStreamEnvironment* uses by default a value equal to the
number of hardware contexts (CPU cores / threads). When executing the
program via the command line client from a JAR file, the default degree
of parallelism is the one configured for that setup.
:param parallelism: The parallelism.
:return: This object.
"""
self._j_stream_execution_environment = \
self._j_stream_execution_environment.setParallelism(parallelism)
return self
[docs] def set_max_parallelism(self, max_parallelism: int) -> 'StreamExecutionEnvironment':
"""
Sets the maximum degree of parallelism defined for the program. The upper limit (inclusive)
is 32768.
The maximum degree of parallelism specifies the upper limit for dynamic scaling. It also
defines the number of key groups used for partitioned state.
:param max_parallelism: Maximum degree of parallelism to be used for the program,
with 0 < maxParallelism <= 2^15.
:return: This object.
"""
self._j_stream_execution_environment = \
self._j_stream_execution_environment.setMaxParallelism(max_parallelism)
return self
[docs] def register_slot_sharing_group(self, slot_sharing_group: SlotSharingGroup) \
-> 'StreamExecutionEnvironment':
"""
Register a slot sharing group with its resource spec.
Note that a slot sharing group hints the scheduler that the grouped operators CAN be
deployed into a shared slot. There's no guarantee that the scheduler always deploy the
grouped operators together. In cases grouped operators are deployed into separate slots, the
slot resources will be derived from the specified group requirements.
:param slot_sharing_group: Which contains name and its resource spec.
:return: This object.
"""
self._j_stream_execution_environment = \
self._j_stream_execution_environment.registerSlotSharingGroup(
slot_sharing_group.get_java_slot_sharing_group())
return self
[docs] def get_parallelism(self) -> int:
"""
Gets the parallelism with which operation are executed by default.
Operations can individually override this value to use a specific
parallelism.
:return: The parallelism used by operations, unless they override that value.
"""
return self._j_stream_execution_environment.getParallelism()
[docs] def get_max_parallelism(self) -> int:
"""
Gets the maximum degree of parallelism defined for the program.
The maximum degree of parallelism specifies the upper limit for dynamic scaling. It also
defines the number of key groups used for partitioned state.
:return: Maximum degree of parallelism.
"""
return self._j_stream_execution_environment.getMaxParallelism()
[docs] def set_runtime_mode(self, execution_mode: RuntimeExecutionMode):
"""
Sets the runtime execution mode for the application
:class:`~pyflink.datastream.execution_mode.RuntimeExecutionMode`. This
is equivalent to setting the `execution.runtime-mode` in your application's
configuration file.
We recommend users to NOT use this method but set the `execution.runtime-mode` using
the command-line when submitting the application. Keeping the application code
configuration-free allows for more flexibility as the same application will be able to be
executed in any execution mode.
:param execution_mode: The desired execution mode.
:return: The execution environment of your application.
.. versionadded:: 1.13.0
"""
return self._j_stream_execution_environment.setRuntimeMode(
execution_mode._to_j_execution_mode())
[docs] def set_buffer_timeout(self, timeout_millis: int) -> 'StreamExecutionEnvironment':
"""
Sets the maximum time frequency (milliseconds) for the flushing of the
output buffers. By default the output buffers flush frequently to provide
low latency and to aid smooth developer experience. Setting the parameter
can result in three logical modes:
- A positive integer triggers flushing periodically by that integer
- 0 triggers flushing after every record thus minimizing latency
- -1 triggers flushing only when the output buffer is full thus maximizing throughput
:param timeout_millis: The maximum time between two output flushes.
:return: This object.
"""
self._j_stream_execution_environment = \
self._j_stream_execution_environment.setBufferTimeout(timeout_millis)
return self
[docs] def get_buffer_timeout(self) -> int:
"""
Gets the maximum time frequency (milliseconds) for the flushing of the
output buffers. For clarification on the extremal values see
:func:`set_buffer_timeout`.
:return: The timeout of the buffer.
"""
return self._j_stream_execution_environment.getBufferTimeout()
[docs] def disable_operator_chaining(self) -> 'StreamExecutionEnvironment':
"""
Disables operator chaining for streaming operators. Operator chaining
allows non-shuffle operations to be co-located in the same thread fully
avoiding serialization and de-serialization.
:return: This object.
"""
self._j_stream_execution_environment = \
self._j_stream_execution_environment.disableOperatorChaining()
return self
[docs] def is_chaining_enabled(self) -> bool:
"""
Returns whether operator chaining is enabled.
:return: True if chaining is enabled, false otherwise.
"""
return self._j_stream_execution_environment.isChainingEnabled()
def is_chaining_of_operators_with_different_max_parallelism_enabled(self) -> bool:
"""
Returns whether operators that have a different max parallelism can be chained.
:return: True if chaining is enabled, false otherwise
"""
return self._j_stream_execution_environment\
.isChainingOfOperatorsWithDifferentMaxParallelismEnabled()
[docs] def get_checkpoint_config(self) -> CheckpointConfig:
"""
Gets the checkpoint config, which defines values like checkpoint interval, delay between
checkpoints, etc.
:return: The :class:`~pyflink.datastream.CheckpointConfig`.
"""
j_checkpoint_config = self._j_stream_execution_environment.getCheckpointConfig()
return CheckpointConfig(j_checkpoint_config)
[docs] def enable_checkpointing(self, interval: int, mode: CheckpointingMode = None) \
-> 'StreamExecutionEnvironment':
"""
Enables checkpointing for the streaming job. The distributed state of the streaming
dataflow will be periodically snapshotted. In case of a failure, the streaming
dataflow will be restarted from the latest completed checkpoint.
The job draws checkpoints periodically, in the given interval. The system uses the
given :class:`~pyflink.datastream.CheckpointingMode` for the checkpointing ("exactly once"
vs "at least once"). The state will be stored in the configured state backend.
.. note::
Checkpointing iterative streaming dataflows in not properly supported at
the moment. For that reason, iterative jobs will not be started if used
with enabled checkpointing.
Example:
::
>>> env.enable_checkpointing(300000, CheckpointingMode.AT_LEAST_ONCE)
:param interval: Time interval between state checkpoints in milliseconds.
:param mode: The checkpointing mode, selecting between "exactly once" and "at least once"
guaranteed.
:return: This object.
"""
if mode is None:
self._j_stream_execution_environment = \
self._j_stream_execution_environment.enableCheckpointing(interval)
else:
j_checkpointing_mode = CheckpointingMode._to_j_checkpointing_mode(mode)
self._j_stream_execution_environment.enableCheckpointing(
interval,
j_checkpointing_mode)
return self
[docs] def get_checkpoint_interval(self) -> int:
"""
Returns the checkpointing interval or -1 if checkpointing is disabled.
Shorthand for get_checkpoint_config().get_checkpoint_interval().
:return: The checkpointing interval or -1.
"""
return self._j_stream_execution_environment.getCheckpointInterval()
[docs] def get_checkpointing_mode(self) -> CheckpointingMode:
"""
Returns the checkpointing mode (exactly-once vs. at-least-once).
Shorthand for get_checkpoint_config().get_checkpointing_mode().
:return: The :class:`~pyflink.datastream.CheckpointingMode`.
"""
return self.get_checkpointing_consistency_mode()
def get_checkpointing_consistency_mode(self) -> CheckpointingMode:
"""
Returns the checkpointing mode (exactly-once vs. at-least-once).
Shorthand for get_checkpoint_config().get_checkpointing_mode().
:return: The :class:`~pyflink.datastream.CheckpointingMode`.
"""
j_checkpointing_mode = \
self._j_stream_execution_environment.getCheckpointingConsistencyMode()
return CheckpointingMode._from_j_checkpointing_mode(j_checkpointing_mode)
[docs] def enable_changelog_state_backend(self, enabled: bool) -> 'StreamExecutionEnvironment':
"""
Enable the change log for current state backend. This change log allows operators to persist
state changes in a very fine-grained manner. Currently, the change log only applies to keyed
state, so non-keyed operator state and channel state are persisted as usual. The 'state'
here refers to 'keyed state'. Details are as follows:
* Stateful operators write the state changes to that log (logging the state), in addition \
to applying them to the state tables in RocksDB or the in-mem Hashtable.
* An operator can acknowledge a checkpoint as soon as the changes in the log have reached \
the durable checkpoint storage.
* The state tables are persisted periodically, independent of the checkpoints. We call \
this the materialization of the state on the checkpoint storage.
* Once the state is materialized on checkpoint storage, the state changelog can be \
truncated to the corresponding point.
It establish a way to drastically reduce the checkpoint interval for streaming
applications across state backends. For more details please check the FLIP-158.
If this method is not called explicitly, it means no preference for enabling the change
log. Configs for change log enabling will override in different config levels
(job/local/cluster).
.. seealso:: :func:`is_changelog_state_backend_enabled`
:param enabled: True if enable the change log for state backend explicitly, otherwise
disable the change log.
:return: This object.
.. versionadded:: 1.14.0
"""
self._j_stream_execution_environment = \
self._j_stream_execution_environment.enableChangelogStateBackend(enabled)
return self
[docs] def is_changelog_state_backend_enabled(self) -> Optional[bool]:
"""
Gets the enable status of change log for state backend.
.. seealso:: :func:`enable_changelog_state_backend`
:return: An :class:`Optional[bool]` for the enable status of change log for state backend.
Could be None if user never specify this by calling
:func:`enable_changelog_state_backend`.
.. versionadded:: 1.14.0
"""
j_ternary_boolean = self._j_stream_execution_environment.isChangelogStateBackendEnabled()
return j_ternary_boolean.getAsBoolean()
[docs] def set_default_savepoint_directory(self, directory: str) -> 'StreamExecutionEnvironment':
"""
Sets the default savepoint directory, where savepoints will be written to if none
is explicitly provided when triggered.
Example:
::
>>> env.set_default_savepoint_directory("hdfs://savepoints")
:param directory The savepoint directory
:return: This object.
"""
self._j_stream_execution_environment.setDefaultSavepointDirectory(directory)
return self
[docs] def get_default_savepoint_directory(self) -> Optional[str]:
"""
Gets the default savepoint directory for this Job.
"""
j_path = self._j_stream_execution_environment.getDefaultSavepointDirectory()
if j_path is None:
return None
else:
return j_path.toString()
[docs] def add_python_file(self, file_path: str):
"""
Adds a python dependency which could be python files, python packages or
local directories. They will be added to the PYTHONPATH of the python UDF worker.
Please make sure that these dependencies can be imported.
:param file_path: The path of the python dependency.
"""
jvm = get_gateway().jvm
env_config = jvm.org.apache.flink.python.util.PythonConfigUtil\
.getEnvironmentConfig(self._j_stream_execution_environment)
python_files = env_config.getString(jvm.PythonOptions.PYTHON_FILES.key(), None)
if python_files is not None:
python_files = jvm.PythonDependencyUtils.FILE_DELIMITER.join([file_path, python_files])
else:
python_files = file_path
env_config.setString(jvm.PythonOptions.PYTHON_FILES.key(), python_files)
[docs] def set_python_requirements(self, requirements_file_path: str,
requirements_cache_dir: str = None):
"""
Specifies a requirements.txt file which defines the third-party dependencies.
These dependencies will be installed to a temporary directory and added to the
PYTHONPATH of the python UDF worker.
For the dependencies which could not be accessed in the cluster, a directory which contains
the installation packages of these dependencies could be specified using the parameter
"requirements_cached_dir". It will be uploaded to the cluster to support offline
installation.
Example:
::
# commands executed in shell
$ echo numpy==1.16.5 > requirements.txt
$ pip download -d cached_dir -r requirements.txt --no-binary :all:
# python code
>>> stream_env.set_python_requirements("requirements.txt", "cached_dir")
.. note::
Please make sure the installation packages matches the platform of the cluster
and the python version used. These packages will be installed using pip,
so also make sure the version of Pip (version >= 20.3) and the version of
SetupTools (version >= 37.0.0).
:param requirements_file_path: The path of "requirements.txt" file.
:param requirements_cache_dir: The path of the local directory which contains the
installation packages.
"""
jvm = get_gateway().jvm
python_requirements = requirements_file_path
if requirements_cache_dir is not None:
python_requirements = jvm.PythonDependencyUtils.PARAM_DELIMITER.join(
[python_requirements, requirements_cache_dir])
env_config = jvm.org.apache.flink.python.util.PythonConfigUtil \
.getEnvironmentConfig(self._j_stream_execution_environment)
env_config.setString(jvm.PythonOptions.PYTHON_REQUIREMENTS.key(), python_requirements)
[docs] def add_python_archive(self, archive_path: str, target_dir: str = None):
"""
Adds a python archive file. The file will be extracted to the working directory of
python UDF worker.
If the parameter "target_dir" is specified, the archive file will be extracted to a
directory named ${target_dir}. Otherwise, the archive file will be extracted to a
directory with the same name of the archive file.
If python UDF depends on a specific python version which does not exist in the cluster,
this method can be used to upload the virtual environment.
Note that the path of the python interpreter contained in the uploaded environment
should be specified via the method :func:`pyflink.table.TableConfig.set_python_executable`.
The files uploaded via this method are also accessible in UDFs via relative path.
Example:
::
# command executed in shell
# assert the relative path of python interpreter is py_env/bin/python
$ zip -r py_env.zip py_env
# python code
>>> stream_env.add_python_archive("py_env.zip")
>>> stream_env.set_python_executable("py_env.zip/py_env/bin/python")
# or
>>> stream_env.add_python_archive("py_env.zip", "myenv")
>>> stream_env.set_python_executable("myenv/py_env/bin/python")
# the files contained in the archive file can be accessed in UDF
>>> def my_udf():
... with open("myenv/py_env/data/data.txt") as f:
... ...
.. note::
Please make sure the uploaded python environment matches the platform that the cluster
is running on and that the python version must be 3.6 or higher.
.. note::
Currently only zip-format is supported. i.e. zip, jar, whl, egg, etc.
The other archive formats such as tar, tar.gz, 7z, rar, etc are not supported.
:param archive_path: The archive file path.
:param target_dir: Optional, the target dir name that the archive file extracted to.
"""
jvm = get_gateway().jvm
if target_dir is not None:
archive_path = jvm.PythonDependencyUtils.PARAM_DELIMITER.join(
[archive_path, target_dir])
env_config = jvm.org.apache.flink.python.util.PythonConfigUtil \
.getEnvironmentConfig(self._j_stream_execution_environment)
python_archives = env_config.getString(jvm.PythonOptions.PYTHON_ARCHIVES.key(), None)
if python_archives is not None:
python_files = jvm.PythonDependencyUtils.FILE_DELIMITER.join(
[python_archives, archive_path])
else:
python_files = archive_path
env_config.setString(jvm.PythonOptions.PYTHON_ARCHIVES.key(), python_files)
[docs] def set_python_executable(self, python_exec: str):
"""
Sets the path of the python interpreter which is used to execute the python udf workers.
e.g. "/usr/local/bin/python3".
If python UDF depends on a specific python version which does not exist in the cluster,
the method :func:`pyflink.datastream.StreamExecutionEnvironment.add_python_archive` can be
used to upload a virtual environment. The path of the python interpreter contained in the
uploaded environment can be specified via this method.
Example:
::
# command executed in shell
# assume that the relative path of python interpreter is py_env/bin/python
$ zip -r py_env.zip py_env
# python code
>>> stream_env.add_python_archive("py_env.zip")
>>> stream_env.set_python_executable("py_env.zip/py_env/bin/python")
.. note::
Please make sure the uploaded python environment matches the platform that the cluster
is running on and that the python version must be 3.7 or higher.
.. note::
The python udf worker depends on Apache Beam (version == 2.43.0).
Please ensure that the specified environment meets the above requirements.
:param python_exec: The path of python interpreter.
"""
jvm = get_gateway().jvm
env_config = jvm.org.apache.flink.python.util.PythonConfigUtil \
.getEnvironmentConfig(self._j_stream_execution_environment)
env_config.setString(jvm.PythonOptions.PYTHON_EXECUTABLE.key(), python_exec)
[docs] def add_jars(self, *jars_path: str):
"""
Adds a list of jar files that will be uploaded to the cluster and referenced by the job.
:param jars_path: Path of jars.
"""
add_jars_to_context_class_loader(jars_path)
jvm = get_gateway().jvm
jars_key = jvm.org.apache.flink.configuration.PipelineOptions.JARS.key()
env_config = jvm.org.apache.flink.python.util.PythonConfigUtil \
.getEnvironmentConfig(self._j_stream_execution_environment)
old_jar_paths = env_config.getString(jars_key, None)
joined_jars_path = ';'.join(jars_path)
if old_jar_paths and old_jar_paths.strip():
joined_jars_path = ';'.join([old_jar_paths, joined_jars_path])
env_config.setString(jars_key, joined_jars_path)
[docs] def add_classpaths(self, *classpaths: str):
"""
Adds a list of URLs that are added to the classpath of each user code classloader of the
program. Paths must specify a protocol (e.g. file://) and be accessible on all nodes
:param classpaths: Classpaths that will be added.
"""
add_jars_to_context_class_loader(classpaths)
jvm = get_gateway().jvm
classpaths_key = jvm.org.apache.flink.configuration.PipelineOptions.CLASSPATHS.key()
env_config = jvm.org.apache.flink.python.util.PythonConfigUtil \
.getEnvironmentConfig(self._j_stream_execution_environment)
old_classpaths = env_config.getString(classpaths_key, None)
joined_classpaths = ';'.join(list(classpaths))
if old_classpaths and old_classpaths.strip():
joined_classpaths = ';'.join([old_classpaths, joined_classpaths])
env_config.setString(classpaths_key, joined_classpaths)
[docs] def get_default_local_parallelism(self) -> int:
"""
Gets the default parallelism that will be used for the local execution environment.
:return: The default local parallelism.
"""
return self._j_stream_execution_environment.getDefaultLocalParallelism()
[docs] def set_default_local_parallelism(self, parallelism: int):
"""
Sets the default parallelism that will be used for the local execution environment.
:param parallelism: The parallelism to use as the default local parallelism.
"""
self._j_stream_execution_environment.setDefaultLocalParallelism(parallelism)
[docs] def execute(self, job_name: str = None) -> JobExecutionResult:
"""
Triggers the program execution. The environment will execute all parts of
the program that have resulted in a "sink" operation. Sink operations are
for example printing results or forwarding them to a message queue.
The program execution will be logged and displayed with the provided name
:param job_name: Desired name of the job, optional.
:return: The result of the job execution, containing elapsed time and accumulators.
"""
j_stream_graph = self._generate_stream_graph(clear_transformations=True, job_name=job_name)
return JobExecutionResult(self._j_stream_execution_environment.execute(j_stream_graph))
[docs] def execute_async(self, job_name: str = 'Flink Streaming Job') -> JobClient:
"""
Triggers the program asynchronously. The environment will execute all parts of the program
that have resulted in a "sink" operation. Sink operations are for example printing results
or forwarding them to a message queue.
The program execution will be logged and displayed with a generated default name.
:param job_name: Desired name of the job.
:return: A JobClient that can be used to communicate with the submitted job, completed on
submission succeeded.
"""
j_stream_graph = self._generate_stream_graph(clear_transformations=True, job_name=job_name)
j_job_client = self._j_stream_execution_environment.executeAsync(j_stream_graph)
return JobClient(j_job_client=j_job_client)
[docs] def get_execution_plan(self) -> str:
"""
Creates the plan with which the system will execute the program, and returns it as
a String using a JSON representation of the execution data flow graph.
Note that this needs to be called, before the plan is executed.
If the compiler could not be instantiated, or the master could not
be contacted to retrieve information relevant to the execution planning,
an exception will be thrown.
:return: The execution plan of the program, as a JSON String.
"""
j_stream_graph = self._generate_stream_graph(False)
return j_stream_graph.getStreamingPlanAsJSON()
[docs] def register_cached_file(self, file_path: str, name: str, executable: bool = False):
"""
Registers a file at the distributed cache under the given name. The file will be accessible
from any user-defined function in the (distributed) runtime under a local path. Files may be
local files (which will be distributed via BlobServer), or files in a distributed file
system. The runtime will copy the files temporarily to a local cache, if needed.
:param file_path: The path of the file, as a URI (e.g. "file:///some/path" or
hdfs://host:port/and/path").
:param name: The name under which the file is registered.
:param executable: Flag indicating whether the file should be executable.
.. versionadded:: 1.16.0
"""
self._j_stream_execution_environment.registerCachedFile(file_path, name, executable)
[docs] @staticmethod
def get_execution_environment(configuration: Configuration = None) \
-> 'StreamExecutionEnvironment':
"""
Creates an execution environment that represents the context in which the
program is currently executed. If the program is invoked standalone, this
method returns a local execution environment.
When executed from the command line the given configuration is stacked on top of the
global configuration which comes from the config.yaml, potentially overriding
duplicated options.
:param configuration: The configuration to instantiate the environment with.
:return: The execution environment of the context in which the program is executed.
"""
gateway = get_gateway()
JStreamExecutionEnvironment = gateway.jvm.org.apache.flink.streaming.api.environment \
.StreamExecutionEnvironment
if configuration:
j_stream_exection_environment = JStreamExecutionEnvironment.getExecutionEnvironment(
configuration._j_configuration)
else:
j_stream_exection_environment = JStreamExecutionEnvironment.getExecutionEnvironment()
return StreamExecutionEnvironment(j_stream_exection_environment)
[docs] def add_source(self, source_func: SourceFunction, source_name: str = 'Custom Source',
type_info: TypeInformation = None) -> 'DataStream':
"""
Adds a data source to the streaming topology.
:param source_func: the user defined function.
:param source_name: name of the data source. Optional.
:param type_info: type of the returned stream. Optional.
:return: the data stream constructed.
"""
if type_info:
j_type_info = type_info.get_java_type_info()
else:
j_type_info = None
j_data_stream = self._j_stream_execution_environment.addSource(source_func
.get_java_function(),
source_name,
j_type_info)
return DataStream(j_data_stream=j_data_stream)
[docs] def from_source(self,
source: Source,
watermark_strategy: WatermarkStrategy,
source_name: str,
type_info: TypeInformation = None) -> 'DataStream':
"""
Adds a data :class:`~pyflink.datastream.connectors.Source` to the environment to get a
:class:`~pyflink.datastream.DataStream`.
The result will be either a bounded data stream (that can be processed in a batch way) or
an unbounded data stream (that must be processed in a streaming way), based on the
boundedness property of the source.
This method takes an explicit type information for the produced data stream, so that
callers can define directly what type/serializer will be used for the produced stream. For
sources that describe their produced type, the parameter type_info should not be specified
to avoid specifying the produced type redundantly.
.. versionadded:: 1.13.0
"""
if type_info:
j_type_info = type_info.get_java_type_info()
else:
j_type_info = None
j_data_stream = self._j_stream_execution_environment.fromSource(
source.get_java_function(),
watermark_strategy._j_watermark_strategy,
source_name,
j_type_info)
return DataStream(j_data_stream=j_data_stream)
[docs] def from_collection(self, collection: List[Any],
type_info: TypeInformation = None) -> DataStream:
"""
Creates a data stream from the given non-empty collection. The type of the data stream is
that of the elements in the collection.
Note that this operation will result in a non-parallel data stream source, i.e. a data
stream source with parallelism one.
:param collection: The collection of elements to create the data stream from.
:param type_info: The TypeInformation for the produced data stream
:return: the data stream representing the given collection.
"""
if type_info is not None:
collection = [type_info.to_internal_type(element) for element in collection]
return self._from_collection(collection, type_info)
def _from_collection(self, elements: List[Any],
type_info: TypeInformation = None) -> DataStream:
temp_file = tempfile.NamedTemporaryFile(delete=False, dir=tempfile.mkdtemp())
serializer = self.serializer
try:
with temp_file:
# dumps elements to a temporary file by pickle serializer.
serializer.serialize(elements, temp_file)
gateway = get_gateway()
# if user does not defined the element data types, read the pickled data as a byte array
# list.
if type_info is None:
j_objs = gateway.jvm.PythonBridgeUtils.readPickledBytes(temp_file.name)
out_put_type_info = Types.PICKLED_BYTE_ARRAY() # type: TypeInformation
else:
j_objs = gateway.jvm.PythonBridgeUtils.readPythonObjects(temp_file.name)
out_put_type_info = type_info
PythonTypeUtils = gateway.jvm\
.org.apache.flink.streaming.api.utils.PythonTypeUtils
execution_config = self._j_stream_execution_environment.getConfig()
j_input_format = PythonTypeUtils.getCollectionInputFormat(
j_objs,
out_put_type_info.get_java_type_info(),
execution_config
)
JInputFormatSourceFunction = gateway.jvm.org.apache.flink.streaming.api.functions.\
source.legacy.InputFormatSourceFunction
JBoundedness = gateway.jvm.org.apache.flink.api.connector.source.Boundedness
j_data_stream_source = invoke_method(
self._j_stream_execution_environment,
"org.apache.flink.streaming.api.environment.StreamExecutionEnvironment",
"addSource",
[JInputFormatSourceFunction(j_input_format, out_put_type_info.get_java_type_info()),
"Collection Source",
out_put_type_info.get_java_type_info(),
JBoundedness.BOUNDED],
["org.apache.flink.streaming.api.functions.source.legacy.SourceFunction",
"java.lang.String",
"org.apache.flink.api.common.typeinfo.TypeInformation",
"org.apache.flink.api.connector.source.Boundedness"])
j_data_stream_source.forceNonParallel()
return DataStream(j_data_stream=j_data_stream_source)
finally:
os.unlink(temp_file.name)
def _generate_stream_graph(self, clear_transformations: bool = False, job_name: str = None) \
-> JavaObject:
gateway = get_gateway()
JPythonConfigUtil = gateway.jvm.org.apache.flink.python.util.PythonConfigUtil
JPythonConfigUtil.configPythonOperator(self._j_stream_execution_environment)
gateway.jvm.org.apache.flink.python.chain.PythonOperatorChainingOptimizer.apply(
self._j_stream_execution_environment)
JPythonConfigUtil.setPartitionCustomOperatorNumPartitions(
get_field_value(self._j_stream_execution_environment, "transformations"))
j_stream_graph = self._j_stream_execution_environment.getStreamGraph(clear_transformations)
if job_name is not None:
j_stream_graph.setJobName(job_name)
return j_stream_graph
def _open(self):
# start BeamFnLoopbackWorkerPoolServicer when executed in MiniCluster
j_configuration = get_j_env_configuration(self._j_stream_execution_environment)
def startup_loopback_server():
from pyflink.common import Configuration
from pyflink.fn_execution.beam.beam_worker_pool_service import \
BeamFnLoopbackWorkerPoolServicer
config = Configuration(j_configuration=j_configuration)
config.set_string(
"python.loopback-server.address", BeamFnLoopbackWorkerPoolServicer().start())
python_worker_execution_mode = os.environ.get('_python_worker_execution_mode')
if python_worker_execution_mode is None:
if is_local_deployment(j_configuration):
startup_loopback_server()
elif python_worker_execution_mode == 'loopback':
if is_local_deployment(j_configuration):
startup_loopback_server()
else:
raise ValueError("Loopback mode is enabled, however the job wasn't configured to "
"run in local deployment mode")
elif python_worker_execution_mode != 'process':
raise ValueError(
"It only supports to execute the Python worker in 'loopback' mode and 'process' "
"mode, unknown mode '%s' is configured" % python_worker_execution_mode)
[docs] def is_unaligned_checkpoints_enabled(self):
"""
Returns whether Unaligned Checkpoints are enabled.
"""
return self._j_stream_execution_environment.isUnalignedCheckpointsEnabled()
[docs] def is_force_unaligned_checkpoints(self):
"""
Returns whether Unaligned Checkpoints are force-enabled.
"""
return self._j_stream_execution_environment.isForceUnalignedCheckpoints()
[docs] def close(self):
"""
Close and clean up the execution environment. All the cached intermediate results will be
released physically.
.. versionadded:: 1.16.0
"""
self._j_stream_execution_environment.close()