flytekitplugins.airflow.connector

Directory

Classes

Methods

Methods

get_log_links()

def get_log_links(
    airflow_operator: airflow.models.baseoperator.BaseOperator,
    airflow_trigger: typing.Optional[airflow.triggers.base.BaseTrigger],
) -> typing.Optional[typing.List[flyteidl.core.execution_pb2.TaskLog]]

flytekitplugins.airflow.connector.AirflowConnector

It is used to run Airflow tasks. It is registered as an connector in the Connector Registry. There are three kinds of Airflow tasks: AirflowOperator, AirflowSensor, and AirflowHook.

Sensor is always invoked in get method. Calling get method to check if the certain condition is met. For example, FileSensor is used to check if the file exists. If file doesn’t exist, connector returns RUNNING status, otherwise, it returns SUCCEEDED status.

Hook is a high-level interface to an external platform that lets you quickly and easily talk to them without having to write low-level code that hits their API or uses special libraries. For example, SlackHook is used to send messages to Slack. Therefore, Hooks are also invoked in get method. Note: There is no running state for Hook. It is either successful or failed.

Operator is invoked in create method. Flytekit will always set deferrable to True for Operator. Therefore, operator.execute will always raise TaskDeferred exception after job is submitted. In the get method, we create a trigger to check if the job is finished. Note: some of the operators are not deferrable. For example, BeamRunJavaPipelineOperator, BeamRunPythonPipelineOperator. In this case, those operators will be converted to AirflowContainerTask and executed in the pod.

Parameters

def AirflowConnector()

Properties

Methods

create()

def create(
    task_template: flytekit.models.task.TaskTemplate,
    inputs: typing.Optional[flytekit.models.literals.LiteralMap],
    kwargs,
) -> flytekitplugins.airflow.connector.AirflowMetadata

Return a resource meta that can be used to get the status of the task.

delete()

def delete(
    resource_meta: flytekitplugins.airflow.connector.AirflowMetadata,
    kwargs,
)

Delete the task. This call should be idempotent. It should raise an error if fails to delete the task.

get()

def get(
    resource_meta: flytekitplugins.airflow.connector.AirflowMetadata,
    kwargs,
) -> flytekit.extend.backend.base_connector.Resource

Return the status of the task, and return the outputs in some cases. For example, bigquery job can’t write the structured dataset to the output location, so it returns the output literals to the propeller, and the propeller will write the structured dataset to the blob store.

get_logs()

def get_logs(
    resource_meta: flytekit.extend.backend.base_connector.ResourceMeta,
    kwargs,
) -> flyteidl.admin.agent_pb2.GetTaskLogsResponse

Return the metrics for the task.

get_metrics()

def get_metrics(
    resource_meta: flytekit.extend.backend.base_connector.ResourceMeta,
    kwargs,
) -> flyteidl.admin.agent_pb2.GetTaskMetricsResponse

Return the metrics for the task.

flytekitplugins.airflow.connector.AirflowMetadata

This class is used to store the Airflow task configuration. It is serialized and returned to FlytePropeller.

Parameters

class AirflowMetadata(
    airflow_operator: flytekitplugins.airflow.task.AirflowObj,
    airflow_trigger: flytekitplugins.airflow.task.AirflowObj,
    airflow_trigger_callback: str,
    job_id: typing.Optional[str],
)

Methods

decode()

def decode(
    data: bytes,
) -> AirflowMetadata

Decode the resource meta from bytes.

encode()

def encode()

Encode the resource meta to bytes.