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See the License for the specific language governing permissions and limitations under the License. ################################################################################ ========================== 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.get_state_backend StreamExecutionEnvironment.set_state_backend StreamExecutionEnvironment.enable_changelog_state_backend StreamExecutionEnvironment.is_changelog_state_backend_enabled StreamExecutionEnvironment.set_default_savepoint_directory StreamExecutionEnvironment.get_default_savepoint_directory StreamExecutionEnvironment.set_restart_strategy StreamExecutionEnvironment.get_restart_strategy StreamExecutionEnvironment.add_default_kryo_serializer StreamExecutionEnvironment.register_type_with_kryo_serializer StreamExecutionEnvironment.register_type StreamExecutionEnvironment.set_stream_time_characteristic StreamExecutionEnvironment.get_stream_time_characteristic 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.read_text_file 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