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from __future__ import annotations
import time
import boto3
from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.base_aws import AwsBaseHook
[docs]DEFAULT_LOG_SUFFIX = "output"
[docs]FAILURE_LOG_SUFFIX = "error"
# A filter value of ' ' translates to "match all".
# see: https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/FilterAndPatternSyntax.html
[docs]DEFAULT_LOG_FILTER = " "
[docs]FAILURE_LOG_FILTER = "?ERROR ?Exception"
[docs]class GlueJobHook(AwsBaseHook):
"""
Interact with AWS Glue - create job, trigger, crawler
:param s3_bucket: S3 bucket where logs and local etl script will be uploaded
:param job_name: unique job name per AWS account
:param desc: job description
:param concurrent_run_limit: The maximum number of concurrent runs allowed for a job
:param script_location: path to etl script on s3
:param retry_limit: Maximum number of times to retry this job if it fails
:param num_of_dpus: Number of AWS Glue DPUs to allocate to this Job
:param region_name: aws region name (example: us-east-1)
:param iam_role_name: AWS IAM Role for Glue Job Execution
:param create_job_kwargs: Extra arguments for Glue Job Creation
"""
[docs] JOB_POLL_INTERVAL = 6 # polls job status after every JOB_POLL_INTERVAL seconds
def __init__(
self,
s3_bucket: str | None = None,
job_name: str | None = None,
desc: str | None = None,
concurrent_run_limit: int = 1,
script_location: str | None = None,
retry_limit: int = 0,
num_of_dpus: int | None = None,
iam_role_name: str | None = None,
create_job_kwargs: dict | None = None,
*args,
**kwargs,
):
self.job_name = job_name
self.desc = desc
self.concurrent_run_limit = concurrent_run_limit
self.script_location = script_location
self.retry_limit = retry_limit
self.s3_bucket = s3_bucket
self.role_name = iam_role_name
self.s3_glue_logs = "logs/glue-logs/"
self.create_job_kwargs = create_job_kwargs or {}
worker_type_exists = "WorkerType" in self.create_job_kwargs
num_workers_exists = "NumberOfWorkers" in self.create_job_kwargs
if worker_type_exists and num_workers_exists:
if num_of_dpus is not None:
raise ValueError("Cannot specify num_of_dpus with custom WorkerType")
elif not worker_type_exists and num_workers_exists:
raise ValueError("Need to specify custom WorkerType when specifying NumberOfWorkers")
elif worker_type_exists and not num_workers_exists:
raise ValueError("Need to specify NumberOfWorkers when specifying custom WorkerType")
elif num_of_dpus is None:
self.num_of_dpus = 10
else:
self.num_of_dpus = num_of_dpus
kwargs["client_type"] = "glue"
super().__init__(*args, **kwargs)
[docs] def create_glue_job_config(self) -> dict:
if self.s3_bucket is None:
raise ValueError("Could not initialize glue job, error: Specify Parameter `s3_bucket`")
default_command = {
"Name": "glueetl",
"ScriptLocation": self.script_location,
}
command = self.create_job_kwargs.pop("Command", default_command)
s3_log_path = f"s3://{self.s3_bucket}/{self.s3_glue_logs}{self.job_name}"
execution_role = self.get_iam_execution_role()
ret_config = {
"Name": self.job_name,
"Description": self.desc,
"LogUri": s3_log_path,
"Role": execution_role["Role"]["Arn"],
"ExecutionProperty": {"MaxConcurrentRuns": self.concurrent_run_limit},
"Command": command,
"MaxRetries": self.retry_limit,
**self.create_job_kwargs,
}
if hasattr(self, "num_of_dpus"):
ret_config["MaxCapacity"] = self.num_of_dpus
return ret_config
[docs] def list_jobs(self) -> list:
""":return: Lists of Jobs"""
return self.get_conn().get_jobs()
[docs] def get_iam_execution_role(self) -> dict:
""":return: iam role for job execution"""
try:
iam_client = self.get_session(region_name=self.region_name).client(
"iam", endpoint_url=self.conn_config.endpoint_url, config=self.config, verify=self.verify
)
glue_execution_role = iam_client.get_role(RoleName=self.role_name)
self.log.info("Iam Role Name: %s", self.role_name)
return glue_execution_role
except Exception as general_error:
self.log.error("Failed to create aws glue job, error: %s", general_error)
raise
[docs] def initialize_job(
self,
script_arguments: dict | None = None,
run_kwargs: dict | None = None,
) -> dict[str, str]:
"""
Initializes connection with AWS Glue
to run job
:return:
"""
script_arguments = script_arguments or {}
run_kwargs = run_kwargs or {}
try:
job_name = self.create_or_update_glue_job()
return self.get_conn().start_job_run(JobName=job_name, Arguments=script_arguments, **run_kwargs)
except Exception as general_error:
self.log.error("Failed to run aws glue job, error: %s", general_error)
raise
[docs] def get_job_state(self, job_name: str, run_id: str) -> str:
"""
Get state of the Glue job. The job state can be
running, finished, failed, stopped or timeout.
:param job_name: unique job name per AWS account
:param run_id: The job-run ID of the predecessor job run
:return: State of the Glue job
"""
job_run = self.get_conn().get_job_run(JobName=job_name, RunId=run_id, PredecessorsIncluded=True)
return job_run["JobRun"]["JobRunState"]
[docs] def print_job_logs(
self,
job_name: str,
run_id: str,
job_failed: bool = False,
next_token: str | None = None,
) -> str | None:
"""Prints the batch of logs to the Airflow task log and returns nextToken."""
log_client = boto3.client("logs")
response = {}
filter_pattern = FAILURE_LOG_FILTER if job_failed else DEFAULT_LOG_FILTER
log_group_prefix = self.conn.get_job_run(JobName=job_name, RunId=run_id)["JobRun"]["LogGroupName"]
log_group_suffix = FAILURE_LOG_SUFFIX if job_failed else DEFAULT_LOG_SUFFIX
log_group_name = f"{log_group_prefix}/{log_group_suffix}"
try:
if next_token:
response = log_client.filter_log_events(
logGroupName=log_group_name,
logStreamNames=[run_id],
filterPattern=filter_pattern,
nextToken=next_token,
)
else:
response = log_client.filter_log_events(
logGroupName=log_group_name,
logStreamNames=[run_id],
filterPattern=filter_pattern,
)
if len(response["events"]):
messages = "\t".join([event["message"] for event in response["events"]])
self.log.info("Glue Job Run Logs:\n\t%s", messages)
except log_client.exceptions.ResourceNotFoundException:
self.log.warning(
"No new Glue driver logs found. This might be because there are no new logs, "
"or might be an error.\nIf the error persists, check the CloudWatch dashboard "
f"at: https://{self.conn_region_name}.console.aws.amazon.com/cloudwatch/home"
)
# If no new log events are available, filter_log_events will return None.
# In that case, check the same token again next pass.
return response.get("nextToken") or next_token
[docs] def job_completion(self, job_name: str, run_id: str, verbose: bool = False) -> dict[str, str]:
"""
Waits until Glue job with job_name completes or
fails and return final state if finished.
Raises AirflowException when the job failed
:param job_name: unique job name per AWS account
:param run_id: The job-run ID of the predecessor job run
:param verbose: If True, more Glue Job Run logs show in the Airflow Task Logs. (default: False)
:return: Dict of JobRunState and JobRunId
"""
failed_states = ["FAILED", "TIMEOUT"]
finished_states = ["SUCCEEDED", "STOPPED"]
next_log_token = None
job_failed = False
while True:
try:
job_run_state = self.get_job_state(job_name, run_id)
if job_run_state in finished_states:
self.log.info("Exiting Job %s Run State: %s", run_id, job_run_state)
return {"JobRunState": job_run_state, "JobRunId": run_id}
if job_run_state in failed_states:
job_failed = True
job_error_message = f"Exiting Job {run_id} Run State: {job_run_state}"
self.log.info(job_error_message)
raise AirflowException(job_error_message)
else:
self.log.info(
"Polling for AWS Glue Job %s current run state with status %s",
job_name,
job_run_state,
)
time.sleep(self.JOB_POLL_INTERVAL)
finally:
if verbose:
next_log_token = self.print_job_logs(
job_name=job_name,
run_id=run_id,
job_failed=job_failed,
next_token=next_log_token,
)
[docs] def has_job(self, job_name) -> bool:
"""
Checks if the job already exists
:param job_name: unique job name per AWS account
:return: Returns True if the job already exists and False if not.
"""
self.log.info("Checking if job already exists: %s", job_name)
try:
self.get_conn().get_job(JobName=job_name)
return True
except self.get_conn().exceptions.EntityNotFoundException:
return False
[docs] def update_job(self, **job_kwargs) -> bool:
"""
Updates job configurations
:param job_kwargs: Keyword args that define the configurations used for the job
:return: True if job was updated and false otherwise
"""
job_name = job_kwargs.pop("Name")
current_job = self.get_conn().get_job(JobName=job_name)["Job"]
update_config = {
key: value for key, value in job_kwargs.items() if current_job.get(key) != job_kwargs[key]
}
if update_config != {}:
self.log.info("Updating job: %s", job_name)
self.get_conn().update_job(JobName=job_name, JobUpdate=job_kwargs)
self.log.info("Updated configurations: %s", update_config)
return True
else:
return False
[docs] def create_or_update_glue_job(self) -> str | None:
"""
Creates(or updates) and returns the Job name
:return:Name of the Job
"""
config = self.create_glue_job_config()
if self.has_job(self.job_name):
self.update_job(**config)
else:
self.log.info("Creating job: %s", self.job_name)
self.get_conn().create_job(**config)
return self.job_name
