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* Require secure transport on buckets * EMR on EKS support
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,122 @@ | ||
| import re | ||
| import sys | ||
| from os.path import join | ||
| from time import sleep | ||
| from typing import List, Optional | ||
|
|
||
| import boto3 | ||
| from emr_cli.deployments.emr_serverless import DeploymentPackage | ||
| from emr_cli.utils import console_log, print_s3_gz | ||
|
|
||
|
|
||
| class EMREKS: | ||
| def __init__( | ||
| self, virtual_cluster_id: str, job_role: str, deployment_package: DeploymentPackage, region: str = "" | ||
| ) -> None: | ||
| self.virtual_cluster_id = virtual_cluster_id | ||
| self.job_role = job_role | ||
| self.dp = deployment_package | ||
| self.s3_client = boto3.client("s3") | ||
| if region: | ||
| self.client = boto3.client("emr-containers", region_name=region) | ||
| else: | ||
| # Note that boto3 uses AWS_DEFAULT_REGION, not AWS_REGION | ||
| # We may want to add an extra check here for the latter. | ||
| self.client = boto3.client("emr-containers") | ||
|
|
||
| def run_job( | ||
| self, | ||
| job_name: str, | ||
| job_args: Optional[List[str]] = None, | ||
| spark_submit_opts: Optional[str] = None, | ||
| wait: bool = True, | ||
| show_logs: bool = False, | ||
| s3_logs_uri: Optional[str] = None, | ||
| ): | ||
| if show_logs and not s3_logs_uri: | ||
| raise RuntimeError("--show-stdout requires --s3-logs-uri to be set.") | ||
|
|
||
| # If job_name is the default, just replace the space. | ||
| # Otherwise throw an error | ||
| if job_name == "emr-cli job": | ||
| job_name = "emr-cli_job" | ||
| elif not re.fullmatch("[\.\-_/#A-Za-z0-9]+", job_name): | ||
| console_log(f"Invalid characters in job name {job_name} - EMR on EKS must match [\.\-_/#A-Za-z0-9]+") | ||
| sys.exit(1) | ||
|
|
||
| jobDriver = { | ||
| "sparkSubmitJobDriver": { | ||
| "entryPoint": self.dp.entrypoint_uri(), | ||
| } | ||
| } | ||
| spark_submit_parameters = self.dp.spark_submit_parameters().params_for("emr_eks") | ||
|
|
||
| if spark_submit_opts: | ||
| spark_submit_parameters = f"{spark_submit_parameters} {spark_submit_opts}".strip() | ||
|
|
||
| if spark_submit_parameters: | ||
| jobDriver["sparkSubmitJobDriver"]["sparkSubmitParameters"] = spark_submit_parameters | ||
|
|
||
| if job_args: | ||
| jobDriver["sparkSubmitJobDriver"]["entryPointArguments"] = job_args # type: ignore | ||
|
|
||
| config_overrides = {} | ||
| if s3_logs_uri: | ||
| config_overrides = {"monitoringConfiguration": {"s3MonitoringConfiguration": {"logUri": s3_logs_uri}}} | ||
|
|
||
| response = self.client.start_job_run( | ||
| virtualClusterId=self.virtual_cluster_id, | ||
| executionRoleArn=self.job_role, | ||
| name=job_name, | ||
| jobDriver=jobDriver, | ||
| configurationOverrides=config_overrides, | ||
| releaseLabel="emr-6.15.0-latest", | ||
| ) | ||
| job_run_id = response.get("id") | ||
|
|
||
| console_log(f"Job submitted to EMR Virtual Cluster (Job Run ID: {job_run_id})") | ||
| if not wait and not show_logs: | ||
| return job_run_id | ||
|
|
||
| console_log("Waiting for job to complete...") | ||
| job_done = False | ||
| job_state = "SUBMITTED" | ||
| jr_response = {} | ||
| while not job_done: | ||
| jr_response = self.get_job_run(job_run_id) | ||
| new_state = jr_response.get("state") | ||
| if new_state != job_state: | ||
| console_log(f"Job state is now: {new_state}") | ||
| job_state = new_state | ||
| job_done = new_state in [ | ||
| "COMPLETED", | ||
| "FAILED", | ||
| "CANCEL_PENDING", | ||
| "CANCELLED", | ||
| ] | ||
| sleep(2) | ||
|
|
||
| if show_logs: | ||
| console_log(f"stdout for {job_run_id}\n{'-'*38}") | ||
| log_location = join( | ||
| f"{s3_logs_uri}", | ||
| self.virtual_cluster_id, | ||
| "jobs", | ||
| job_run_id, | ||
| "containers", | ||
| f"spark-{job_run_id}", | ||
| f"spark-{job_run_id}-driver", | ||
| "stdout.gz", | ||
| ) | ||
| print_s3_gz(self.s3_client, log_location) | ||
|
|
||
| if jr_response.get("state") != "COMPLETED": | ||
| console_log(f"EMR Containers job failed: {jr_response.get('stateDetails')}") | ||
| sys.exit(1) | ||
| console_log("Job completed successfully!") | ||
|
|
||
| return job_run_id | ||
|
|
||
| def get_job_run(self, job_run_id: str) -> dict: | ||
| response = self.client.describe_job_run(virtualClusterId=self.virtual_cluster_id, id=job_run_id) | ||
| return response.get("jobRun") |
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