Source code for airflow.providers.amazon.aws.operators.emr_containers

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from typing import Any, Optional
from uuid import uuid4

from airflow.exceptions import AirflowException

try:
    from functools import cached_property
except ImportError:
    from cached_property import cached_property

from airflow.models import BaseOperator
from airflow.providers.amazon.aws.hooks.emr_containers import EMRContainerHook


[docs]class EMRContainerOperator(BaseOperator):
    """
    An operator that submits jobs to EMR on EKS virtual clusters.

    :param name: The name of the job run.
    :type name: str
    :param virtual_cluster_id: The EMR on EKS virtual cluster ID
    :type virtual_cluster_id: str
    :param execution_role_arn: The IAM role ARN associated with the job run.
    :type execution_role_arn: str
    :param release_label: The Amazon EMR release version to use for the job run.
    :type release_label: str
    :param job_driver: Job configuration details, e.g. the Spark job parameters.
    :type job_driver: dict
    :param configuration_overrides: The configuration overrides for the job run,
        specifically either application configuration or monitoring configuration.
    :type configuration_overrides: dict
    :param client_request_token: The client idempotency token of the job run request.
        Use this if you want to specify a unique ID to prevent two jobs from getting started.
        If no token is provided, a UUIDv4 token will be generated for you.
    :type client_request_token: str
    :param aws_conn_id: The Airflow connection used for AWS credentials.
    :type aws_conn_id: str
    :param poll_interval: Time (in seconds) to wait between two consecutive calls to check query status on EMR
    :type poll_interval: int
    :param max_tries: Maximum number of times to wait for the job run to finish.
        Defaults to None, which will poll until the job is *not* in a pending, submitted, or running state.
    :type max_tries: int
    """

[docs]    template_fields = ["name", "virtual_cluster_id", "execution_role_arn", "release_label", "job_driver"]
[docs]    ui_color = "#f9c915"

    def __init__(
        self,
        *,
        name: str,
        virtual_cluster_id: str,
        execution_role_arn: str,
        release_label: str,
        job_driver: dict,
        configuration_overrides: Optional[dict] = None,
        client_request_token: Optional[str] = None,
        aws_conn_id: str = "aws_default",
        poll_interval: int = 30,
        max_tries: Optional[int] = None,
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
        self.name = name
        self.virtual_cluster_id = virtual_cluster_id
        self.execution_role_arn = execution_role_arn
        self.release_label = release_label
        self.job_driver = job_driver
        self.configuration_overrides = configuration_overrides or {}
        self.aws_conn_id = aws_conn_id
        self.client_request_token = client_request_token or str(uuid4())
        self.poll_interval = poll_interval
        self.max_tries = max_tries
        self.job_id = None

    @cached_property
[docs]    def hook(self) -> EMRContainerHook:
        """Create and return an EMRContainerHook."""
        return EMRContainerHook(
            self.aws_conn_id,
            virtual_cluster_id=self.virtual_cluster_id,
        )

[docs]    def execute(self, context: dict) -> Optional[str]:
        """Run job on EMR Containers"""
        self.job_id = self.hook.submit_job(
            self.name,
            self.execution_role_arn,
            self.release_label,
            self.job_driver,
            self.configuration_overrides,
            self.client_request_token,
        )
        query_status = self.hook.poll_query_status(self.job_id, self.max_tries, self.poll_interval)

        if query_status in EMRContainerHook.FAILURE_STATES:
            error_message = self.hook.get_job_failure_reason(self.job_id)
            raise AirflowException(
                f"EMR Containers job failed. Final state is {query_status}. "
                f"query_execution_id is {self.job_id}. Error: {error_message}"
            )
        elif not query_status or query_status in EMRContainerHook.INTERMEDIATE_STATES:
            raise AirflowException(
                f"Final state of EMR Containers job is {query_status}. "
                f"Max tries of poll status exceeded, query_execution_id is {self.job_id}."
            )

        return self.job_id

[docs]    def on_kill(self) -> None:
        """Cancel the submitted job run"""
        if self.job_id:
            self.log.info("Stopping job run with jobId - %s", self.job_id)
            response = self.hook.stop_query(self.job_id)
            http_status_code = None
            try:
                http_status_code = response["ResponseMetadata"]["HTTPStatusCode"]
            except Exception as ex:
                self.log.error("Exception while cancelling query: %s", ex)
            finally:
                if http_status_code is None or http_status_code != 200:
                    self.log.error("Unable to request query cancel on EMR. Exiting")
                else:
                    self.log.info(
                        "Polling EMR for query with id %s to reach final state",
                        self.job_id,
                    )
                    self.hook.poll_query_status(self.job_id)