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========================== StreamExecutionEnvironment

StreamExecutionEnvironment

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).

.. currentmodule:: pyflink.datastream.stream_execution_environment

.. autosummary:: :toctree: api/

StreamExecutionEnvironment.get_config
StreamExecutionEnvironment.set_parallelism
StreamExecutionEnvironment.set_max_parallelism
StreamExecutionEnvironment.register_slot_sharing_group
StreamExecutionEnvironment.get_parallelism
StreamExecutionEnvironment.get_max_parallelism
StreamExecutionEnvironment.set_runtime_mode
StreamExecutionEnvironment.set_buffer_timeout
StreamExecutionEnvironment.get_buffer_timeout
StreamExecutionEnvironment.disable_operator_chaining
StreamExecutionEnvironment.is_chaining_enabled
StreamExecutionEnvironment.get_checkpoint_config
StreamExecutionEnvironment.enable_checkpointing
StreamExecutionEnvironment.get_checkpoint_interval
StreamExecutionEnvironment.get_checkpointing_mode
StreamExecutionEnvironment.enable_changelog_state_backend
StreamExecutionEnvironment.is_changelog_state_backend_enabled
StreamExecutionEnvironment.set_default_savepoint_directory
StreamExecutionEnvironment.get_default_savepoint_directory
StreamExecutionEnvironment.configure
StreamExecutionEnvironment.add_python_file
StreamExecutionEnvironment.set_python_requirements
StreamExecutionEnvironment.add_python_archive
StreamExecutionEnvironment.set_python_executable
StreamExecutionEnvironment.add_jars
StreamExecutionEnvironment.add_classpaths
StreamExecutionEnvironment.get_default_local_parallelism
StreamExecutionEnvironment.set_default_local_parallelism
StreamExecutionEnvironment.execute
StreamExecutionEnvironment.execute_async
StreamExecutionEnvironment.get_execution_plan
StreamExecutionEnvironment.register_cached_file
StreamExecutionEnvironment.get_execution_environment
StreamExecutionEnvironment.create_input
StreamExecutionEnvironment.add_source
StreamExecutionEnvironment.from_source
StreamExecutionEnvironment.from_collection
StreamExecutionEnvironment.is_unaligned_checkpoints_enabled
StreamExecutionEnvironment.is_force_unaligned_checkpoints
StreamExecutionEnvironment.close

RuntimeExecutionMode

Runtime execution mode of DataStream programs. Among other things, this controls task scheduling, network shuffle behavior, and time semantics. Some operations will also change their record emission behaviour based on the configured execution mode.

:data:STREAMING:

The Pipeline will be executed with Streaming Semantics. All tasks will be deployed before execution starts, checkpoints will be enabled, and both processing and event time will be fully supported.

:data:BATCH:

The Pipeline will be executed with Batch Semantics. Tasks will be scheduled gradually based on the scheduling region they belong, shuffles between regions will be blocking, watermarks are assumed to be "perfect" i.e. no late data, and processing time is assumed to not advance during execution.

:data:AUTOMATIC:

Flink will set the execution mode to BATCH if all sources are bounded, or STREAMING if there is at least one source which is unbounded.

.. currentmodule:: pyflink.datastream.execution_mode

.. autosummary:: :toctree: api/

RuntimeExecutionMode.STREAMING
RuntimeExecutionMode.BATCH
RuntimeExecutionMode.AUTOMATIC

SlotSharingGroup

.. currentmodule:: pyflink.datastream.slot_sharing_group

.. autosummary:: :toctree: api/

SlotSharingGroup
MemorySize