def get_default() -> Optional[MLEnvironment]:
"""
Get the MLEnvironment use the default MLEnvironmentId.
:return: the default MLEnvironment.
"""
with MLEnvironmentFactory._lock:
if MLEnvironmentFactory._map[
MLEnvironmentFactory._default_ml_environment_id] is None:
j_ml_env = get_gateway().\
jvm.org.apache.flink.ml.common.MLEnvironmentFactory.getDefault()
ml_env = MLEnvironment(
ExecutionEnvironment(j_ml_env.getExecutionEnvironment()),
StreamExecutionEnvironment(
j_ml_env.getStreamExecutionEnvironment()),
BatchTableEnvironment(j_ml_env.getBatchTableEnvironment()),
StreamTableEnvironment(
j_ml_env.getStreamTableEnvironment()))
MLEnvironmentFactory._map[
MLEnvironmentFactory._default_ml_environment_id] = ml_env
return MLEnvironmentFactory._map[
MLEnvironmentFactory._default_ml_environment_id]
def setup_pyflink_env(gateway: JavaGateway, j_benv: JavaObject, j_senv: JavaObject) \
-> Tuple[ExecutionEnvironment, BatchTableEnvironment, StreamExecutionEnvironment, StreamTableEnvironment]:
"""
Setup Py4J for PyFlink, and create Python instances environments.
"""
from pyflink.dataset import ExecutionEnvironment
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import BatchTableEnvironment, StreamTableEnvironment
import pyflink
# noinspection PyUnresolvedReferences
pyflink.java_gateway._gateway = gateway
# noinspection PyUnresolvedReferences
pyflink.java_gateway.import_flink_view(gateway)
benv = ExecutionEnvironment(j_benv)
senv = StreamExecutionEnvironment(j_senv)
# noinspection PyDeprecation
btenv = BatchTableEnvironment.create(benv)
# noinspection PyDeprecation
stenv_settings = EnvironmentSettings.new_instance().use_old_planner().in_streaming_mode().build()
stenv = StreamTableEnvironment.create(senv, environment_settings=stenv_settings)
return benv, btenv, senv, stenv
def exec_env(self):
"""
:return: The stream execution environment of this table environment.
"""
return ExecutionEnvironment(self._j_tenv.execEnv())