def __init__(self,
executor: PythonObjectExecutor) -> None:
super().__init__()
self.executor: Executor = deserialize(executor.python_object)
if not isinstance(self.executor, Executor):
raise Exception("python ai flow only support Executor class but config executor is {}"
.format(type(self.executor)))
def stream_run_func(context: JobContext, graph: RunGraph):
executors: List[Executor] = []
contexts: List[FunctionContext] = []
for index in range(len(graph.nodes)):
caller: Executor = deserialize(graph.executor_bytes[index])
executors.append(caller)
node: AINode = graph.nodes[index]
function_context = FunctionContext(node_spec=node, job_context=context)
contexts.append(function_context)
def setup():
for ii in range(len(executors)):
cc = executors[ii]
cc.setup(contexts[ii])
def close():
for ii in range(len(executors)):
cc = executors[ii]
cc.close(contexts[ii])
setup()
value_map = {}
for i in range(len(graph.nodes)):
node = graph.nodes[i]
c = executors[i]
if node.instance_id in graph.dependencies:
ds = graph.dependencies[node.instance_id]
params = []
for d in ds:
params.append(value_map[d.target_node_id][d.port])
value_map[node.instance_id] = c.execute(contexts[i], params)
else:
value_map[node.instance_id] = c.execute(contexts[i], [])
close()
def __init__(self, executor: FlinkPythonExecutor) -> None:
super().__init__()
self.executor: Union[Executor, SinkExecutor, SourceExecutor] \
= serialization_utils.deserialize(executor.python_object)
if not isinstance(self.executor, Executor) \
and not isinstance(self.executor, SourceExecutor) \
and not isinstance(self.executor, SinkExecutor):
raise Exception(
"python ai flow only support Executor, SourceExecutor, SinkExecutor class "
"but config executor is {}".format(type(self.executor)))
def batch_run_func(context: JobContext, graph: RunGraph):
value_map = {}
for i in range(len(graph.nodes)):
node = graph.nodes[i]
function_context: FunctionContext = FunctionContext(node_spec=node, job_context=context)
c: Executor = deserialize(graph.executor_bytes[i])
c.setup(function_context)
if node.instance_id in graph.dependencies:
ds = graph.dependencies[node.instance_id]
params = []
for d in ds:
params.append(value_map[d.target_node_id][d.port])
value_map[node.instance_id] = c.execute(function_context, params)
else:
value_map[node.instance_id] = c.execute(function_context, [])
c.close(function_context)
def get_table_env_create_func(self):
return serialization_utils.deserialize(self.table_env_create_func)
def read_config_file(file_path):
with open(file_path, 'rb') as f:
config_bytes = f.read()
return serialization_utils.deserialize(config_bytes)
def stream_executor(self) -> Executor:
executor: ExampleExecutor = deserialize(self.example_node.executor.python_object)
executor.example_meta = self.example_meta
return executor
def read_from_serialized_file(file_path):
with open(file_path, 'rb') as f:
serialized_bytes = f.read()
return deserialize(serialized_bytes)