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from __future__ import annotations

import json
import time
import warnings
from typing import Any

from botocore.exceptions import ClientError

from airflow.exceptions import AirflowException, AirflowNotFoundException
from import AwsBaseHook
from import wait

[docs]class EmrHook(AwsBaseHook): """ Interact with Amazon Elastic MapReduce Service (EMR). Provide thick wrapper around :external+boto3:py:class:`boto3.client("emr") <EMR.Client>`. :param emr_conn_id: :ref:`Amazon Elastic MapReduce Connection <howto/connection:emr>`. This attribute is only necessary when using the :meth:``. Additional arguments (such as ``aws_conn_id``) may be specified and are passed down to the underlying AwsBaseHook. .. seealso:: :class:`` """
[docs] conn_name_attr = "emr_conn_id"
[docs] default_conn_name = "emr_default"
[docs] conn_type = "emr"
[docs] hook_name = "Amazon Elastic MapReduce"
def __init__(self, emr_conn_id: str | None = default_conn_name, *args, **kwargs) -> None: self.emr_conn_id = emr_conn_id kwargs["client_type"] = "emr" super().__init__(*args, **kwargs)
[docs] def get_cluster_id_by_name(self, emr_cluster_name: str, cluster_states: list[str]) -> str | None: """ Fetch id of EMR cluster with given name and (optional) states; returns only if single id is found. .. seealso:: - :external+boto3:py:meth:`EMR.Client.list_clusters` :param emr_cluster_name: Name of a cluster to find :param cluster_states: State(s) of cluster to find :return: id of the EMR cluster """ response_iterator = ( self.get_conn().get_paginator("list_clusters").paginate(ClusterStates=cluster_states) ) matching_clusters = [ cluster for page in response_iterator for cluster in page["Clusters"] if cluster["Name"] == emr_cluster_name ] if len(matching_clusters) == 1: cluster_id = matching_clusters[0]["Id"]"Found cluster name = %s id = %s", emr_cluster_name, cluster_id) return cluster_id elif len(matching_clusters) > 1: raise AirflowException(f"More than one cluster found for name {emr_cluster_name}") else:"No cluster found for name %s", emr_cluster_name) return None
[docs] def create_job_flow(self, job_flow_overrides: dict[str, Any]) -> dict[str, Any]: """ Create and start running a new cluster (job flow). .. seealso:: - :external+boto3:py:meth:`EMR.Client.run_job_flow` This method uses ``EmrHook.emr_conn_id`` to receive the initial Amazon EMR cluster configuration. If ``EmrHook.emr_conn_id`` is empty or the connection does not exist, then an empty initial configuration is used. :param job_flow_overrides: Is used to overwrite the parameters in the initial Amazon EMR configuration cluster. The resulting configuration will be used in the :external+boto3:py:meth:`EMR.Client.run_job_flow`. .. seealso:: - :ref:`Amazon Elastic MapReduce Connection <howto/connection:emr>` - :external+boto3:py:meth:`EMR.Client.run_job_flow` - `API RunJobFlow <>`_ """ config = {} if self.emr_conn_id: try: emr_conn = self.get_connection(self.emr_conn_id) except AirflowNotFoundException: warnings.warn( f"Unable to find {self.hook_name} Connection ID {self.emr_conn_id!r}, " "using an empty initial configuration. If you want to get rid of this warning " "message please provide a valid `emr_conn_id` or set it to None.", UserWarning, stacklevel=2, ) else: if emr_conn.conn_type and emr_conn.conn_type != self.conn_type: warnings.warn( f"{self.hook_name} Connection expected connection type {self.conn_type!r}, " f"Connection {self.emr_conn_id!r} has conn_type={emr_conn.conn_type!r}. " f"This connection might not work correctly.", UserWarning, stacklevel=2, ) config = emr_conn.extra_dejson.copy() config.update(job_flow_overrides) response = self.get_conn().run_job_flow(**config) return response
[docs] def add_job_flow_steps( self, job_flow_id: str, steps: list[dict] | str | None = None, wait_for_completion: bool = False, waiter_delay: int | None = None, waiter_max_attempts: int | None = None, execution_role_arn: str | None = None, ) -> list[str]: """ Add new steps to a running cluster. .. seealso:: - :external+boto3:py:meth:`EMR.Client.add_job_flow_steps` :param job_flow_id: The id of the job flow to which the steps are being added :param steps: A list of the steps to be executed by the job flow :param wait_for_completion: If True, wait for the steps to be completed. Default is False :param waiter_delay: The amount of time in seconds to wait between attempts. Default is 5 :param waiter_max_attempts: The maximum number of attempts to be made. Default is 100 :param execution_role_arn: The ARN of the runtime role for a step on the cluster. """ config = {} waiter_delay = waiter_delay or 30 waiter_max_attempts = waiter_max_attempts or 60 if execution_role_arn: config["ExecutionRoleArn"] = execution_role_arn response = self.get_conn().add_job_flow_steps(JobFlowId=job_flow_id, Steps=steps, **config) if response["ResponseMetadata"]["HTTPStatusCode"] != 200: raise AirflowException(f"Adding steps failed: {response}")"Steps %s added to JobFlow", response["StepIds"]) if wait_for_completion: waiter = self.get_conn().get_waiter("step_complete") for step_id in response["StepIds"]: try: wait( waiter=waiter, waiter_max_attempts=waiter_max_attempts, waiter_delay=waiter_delay, args={"ClusterId": job_flow_id, "StepId": step_id}, failure_message=f"EMR Steps failed: {step_id}", status_message="EMR Step status is", status_args=["Step.Status.State", "Step.Status.StateChangeReason"], ) except AirflowException as ex: if "EMR Steps failed" in str(ex): resp = self.get_conn().describe_step(ClusterId=job_flow_id, StepId=step_id) failure_details = resp["Step"]["Status"].get("FailureDetails", None) if failure_details: self.log.error("EMR Steps failed: %s", failure_details) raise return response["StepIds"]
[docs] def test_connection(self): """ Return failed state for test Amazon Elastic MapReduce Connection (untestable). We need to overwrite this method because this hook is based on :class:``, otherwise it will try to test connection to AWS STS by using the default boto3 credential strategy. """ msg = ( f"{self.hook_name!r} Airflow Connection cannot be tested, by design it stores " f"only key/value pairs and does not make a connection to an external resource." ) return False, msg
[docs] def get_ui_field_behaviour() -> dict[str, Any]: """Return custom UI field behaviour for Amazon Elastic MapReduce Connection.""" return { "hidden_fields": ["host", "schema", "port", "login", "password"], "relabeling": { "extra": "Run Job Flow Configuration", }, "placeholders": { "extra": json.dumps( { "Name": "MyClusterName", "ReleaseLabel": "emr-5.36.0", "Applications": [{"Name": "Spark"}], "Instances": { "InstanceGroups": [ { "Name": "Primary node", "Market": "SPOT", "InstanceRole": "MASTER", "InstanceType": "m5.large", "InstanceCount": 1, }, ], "KeepJobFlowAliveWhenNoSteps": False, "TerminationProtected": False, }, "StepConcurrencyLevel": 2, }, indent=2, ), }, }
[docs]class EmrServerlessHook(AwsBaseHook): """ Interact with Amazon EMR Serverless. Provide thin wrapper around :py:class:`boto3.client("emr-serverless") <EMRServerless.Client>`. Additional arguments (such as ``aws_conn_id``) may be specified and are passed down to the underlying AwsBaseHook. .. seealso:: - :class:`` """
def __init__(self, *args: Any, **kwargs: Any) -> None: kwargs["client_type"] = "emr-serverless" super().__init__(*args, **kwargs)
[docs] def cancel_running_jobs( self, application_id: str, waiter_config: dict | None = None, wait_for_completion: bool = True ) -> int: """ Cancel jobs in an intermediate state, and return the number of cancelled jobs. If wait_for_completion is True, then the method will wait until all jobs are cancelled before returning. Note: if new jobs are triggered while this operation is ongoing, it's going to time out and return an error. """ paginator = self.conn.get_paginator("list_job_runs") results_per_response = 50 iterator = paginator.paginate( applicationId=application_id, states=list(self.JOB_INTERMEDIATE_STATES), PaginationConfig={ "PageSize": results_per_response, }, ) count = 0 for r in iterator: job_ids = [jr["id"] for jr in r["jobRuns"]] count += len(job_ids) if job_ids: "Cancelling %s pending job(s) for the application %s so that it can be stopped", len(job_ids), application_id, ) for job_id in job_ids: self.conn.cancel_job_run(applicationId=application_id, jobRunId=job_id) if wait_for_completion: if count > 0:"now waiting for the %s cancelled job(s) to terminate", count) self.get_waiter("no_job_running").wait( applicationId=application_id, states=list(self.JOB_INTERMEDIATE_STATES.union({"CANCELLING"})), WaiterConfig=waiter_config or {}, ) return count
[docs]class EmrContainerHook(AwsBaseHook): """ Interact with Amazon EMR Containers (Amazon EMR on EKS). Provide thick wrapper around :py:class:`boto3.client("emr-containers") <EMRContainers.Client>`. :param virtual_cluster_id: Cluster ID of the EMR on EKS virtual cluster Additional arguments (such as ``aws_conn_id``) may be specified and are passed down to the underlying AwsBaseHook. .. seealso:: - :class:`` """
def __init__(self, *args: Any, virtual_cluster_id: str | None = None, **kwargs: Any) -> None: super().__init__(client_type="emr-containers", *args, **kwargs) # type: ignore self.virtual_cluster_id = virtual_cluster_id
[docs] def create_emr_on_eks_cluster( self, virtual_cluster_name: str, eks_cluster_name: str, eks_namespace: str, tags: dict | None = None, ) -> str: response = self.conn.create_virtual_cluster( name=virtual_cluster_name, containerProvider={ "id": eks_cluster_name, "type": "EKS", "info": {"eksInfo": {"namespace": eks_namespace}}, }, tags=tags or {}, ) if response["ResponseMetadata"]["HTTPStatusCode"] != 200: raise AirflowException(f"Create EMR EKS Cluster failed: {response}") else: "Create EMR EKS Cluster success - virtual cluster id %s", response["id"], ) return response["id"]
[docs] def submit_job( self, name: str, execution_role_arn: str, release_label: str, job_driver: dict, configuration_overrides: dict | None = None, client_request_token: str | None = None, tags: dict | None = None, ) -> str: """ Submit a job to the EMR Containers API 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. .. seealso:: - :external+boto3:py:meth:`EMRContainers.Client.start_job_run` :param name: The name of the job run. :param execution_role_arn: The IAM role ARN associated with the job run. :param release_label: The Amazon EMR release version to use for the job run. :param job_driver: Job configuration details, e.g. the Spark job parameters. :param configuration_overrides: The configuration overrides for the job run, specifically either application configuration or monitoring configuration. :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. :param tags: The tags assigned to job runs. :return: The ID of the job run request. """ params = { "name": name, "virtualClusterId": self.virtual_cluster_id, "executionRoleArn": execution_role_arn, "releaseLabel": release_label, "jobDriver": job_driver, "configurationOverrides": configuration_overrides or {}, "tags": tags 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: "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) -> str | None: """ Fetch the reason for a job failure (e.g. error message). Returns None or reason string. .. seealso:: - :external+boto3:py:meth:`EMRContainers.Client.describe_job_run` :param job_id: The ID of the job run request. """ reason = None # We absorb any errors if we can't retrieve the job status try: response = self.conn.describe_job_run( virtualClusterId=self.virtual_cluster_id, id=job_id, ) failure_reason = response["jobRun"]["failureReason"] state_details = response["jobRun"]["stateDetails"] reason = f"{failure_reason} - {state_details}" 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) -> str | None: """ Fetch the status of submitted job run. Returns None or one of valid query states. .. seealso:: - :external+boto3:py:meth:`EMRContainers.Client.describe_job_run` :param job_id: The ID of the job run request. """ 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, poll_interval: int = 30, max_polling_attempts: int | None = None, ) -> str | None: """ Poll the status of submitted job run until query state reaches final state; returns the final state. :param job_id: The ID of the job run request. :param poll_interval: Time (in seconds) to wait between calls to check query status on EMR :param max_polling_attempts: Number of times to poll for query state before function exits """ try_number = 1 final_query_state = None # Query state when query reaches final state or max_polling_attempts reached while True: query_state = self.check_query_status(job_id) if query_state is None:"Try %s: Invalid query state. Retrying again", try_number) elif query_state in self.TERMINAL_STATES:"Try %s: Query execution completed. Final state is %s", try_number, query_state) final_query_state = query_state break else:"Try %s: Query is still in non-terminal state - %s", try_number, query_state) if ( max_polling_attempts and try_number >= max_polling_attempts ): # Break loop if max_polling_attempts reached final_query_state = query_state break try_number += 1 time.sleep(poll_interval) return final_query_state
[docs] def stop_query(self, job_id: str) -> dict: """ Cancel the submitted job_run. .. seealso:: - :external+boto3:py:meth:`EMRContainers.Client.cancel_job_run` :param job_id: The ID of the job run to cancel. """ return self.conn.cancel_job_run( virtualClusterId=self.virtual_cluster_id, id=job_id, )

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