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

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from time import sleep
from typing import Any, Dict, Optional

from botocore.exceptions import ClientError

from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.base_aws import AwsBaseHook


[docs]class EMRContainerHook(AwsBaseHook): """ Interact with AWS EMR Virtual Cluster to run, poll jobs and return job status Additional arguments (such as ``aws_conn_id``) may be specified and are passed down to the underlying AwsBaseHook. .. seealso:: :class:`~airflow.providers.amazon.aws.hooks.base_aws.AwsBaseHook` :param virtual_cluster_id: Cluster ID of the EMR on EKS virtual cluster :type virtual_cluster_id: str """
[docs] INTERMEDIATE_STATES = ( "PENDING", "SUBMITTED", "RUNNING",
)
[docs] FAILURE_STATES = ( "FAILED", "CANCELLED", "CANCEL_PENDING",
)
[docs] SUCCESS_STATES = ("COMPLETED",)
def __init__(self, *args: Any, virtual_cluster_id: str = None, **kwargs: Any) -> None: super().__init__(client_type="emr-containers", *args, **kwargs) # type: ignore self.virtual_cluster_id = virtual_cluster_id
[docs] def submit_job( self, name: str, execution_role_arn: str, release_label: str, job_driver: dict, configuration_overrides: Optional[dict] = None, client_request_token: Optional[str] = None, ) -> str: """ Submit a job to the EMR Containers API and and return the job ID. A job run is a unit of work, such as a Spark jar, PySpark script, or SparkSQL query, that you submit to Amazon EMR on EKS. See: https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/emr-containers.html#EMRContainers.Client.start_job_run # noqa: E501 :param name: The name of the job run. :type name: 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. :type client_request_token: str :return: Job ID """ params = { "name": name, "virtualClusterId": self.virtual_cluster_id, "executionRoleArn": execution_role_arn, "releaseLabel": release_label, "jobDriver": job_driver, "configurationOverrides": configuration_overrides or {}, } if client_request_token: params["clientToken"] = client_request_token response = self.conn.start_job_run(**params) if response['ResponseMetadata']['HTTPStatusCode'] != 200: raise AirflowException(f'Start Job Run failed: {response}') else: self.log.info( "Start Job Run success - Job Id %s and virtual cluster id %s", response['id'], response['virtualClusterId'], ) return response['id']
[docs] def get_job_failure_reason(self, job_id: str) -> Optional[str]: """ Fetch the reason for a job failure (e.g. error message). Returns None or reason string. :param job_id: Id of submitted job run :type job_id: str :return: str """ # We absorb any errors if we can't retrieve the job status reason = None try: response = self.conn.describe_job_run( virtualClusterId=self.virtual_cluster_id, id=job_id, ) reason = response['jobRun']['failureReason'] except KeyError: self.log.error('Could not get status of the EMR on EKS job') except ClientError as ex: self.log.error('AWS request failed, check logs for more info: %s', ex) return reason
[docs] def check_query_status(self, job_id: str) -> Optional[str]: """ Fetch the status of submitted job run. Returns None or one of valid query states. See: https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/emr-containers.html#EMRContainers.Client.describe_job_run # noqa: E501 :param job_id: Id of submitted job run :type job_id: str :return: str """ try: response = self.conn.describe_job_run( virtualClusterId=self.virtual_cluster_id, id=job_id, ) return response["jobRun"]["state"] except self.conn.exceptions.ResourceNotFoundException: # If the job is not found, we raise an exception as something fatal has happened. raise AirflowException(f'Job ID {job_id} not found on Virtual Cluster {self.virtual_cluster_id}') except ClientError as ex: # If we receive a generic ClientError, we swallow the exception so that the self.log.error('AWS request failed, check logs for more info: %s', ex) return None
[docs] def poll_query_status( self, job_id: str, max_tries: Optional[int] = None, poll_interval: int = 30 ) -> Optional[str]: """ Poll the status of submitted job run until query state reaches final state. Returns one of the final states. :param job_id: Id of submitted job run :type job_id: str :param max_tries: Number of times to poll for query state before function exits :type max_tries: int :param poll_interval: Time (in seconds) to wait between calls to check query status on EMR :type poll_interval: int :return: str """ try_number = 1 final_query_state = None # Query state when query reaches final state or max_tries reached # TODO: Make this logic a little bit more robust. # Currently this polls until the state is *not* one of the INTERMEDIATE_STATES # While that should work in most cases...it might not. :) while True: query_state = self.check_query_status(job_id) if query_state is None: self.log.info("Try %s: Invalid query state. Retrying again", try_number) elif query_state in self.INTERMEDIATE_STATES: self.log.info("Try %s: Query is still in an intermediate state - %s", try_number, query_state) else: self.log.info("Try %s: Query execution completed. Final state is %s", try_number, query_state) final_query_state = query_state break if max_tries and try_number >= max_tries: # Break loop if max_tries reached final_query_state = query_state break try_number += 1 sleep(poll_interval) return final_query_state
[docs] def stop_query(self, job_id: str) -> Dict: """ Cancel the submitted job_run :param job_id: Id of submitted job_run :type job_id: str :return: dict """ return self.conn.cancel_job_run( virtualClusterId=self.virtual_cluster_id, id=job_id,
)

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