airflow glue 源码
airflow glue 代码
文件路径：/airflow/providers/amazon/aws/hooks/glue.py
#
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# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations
import time
import boto3
from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.base_aws import AwsBaseHook
DEFAULT_LOG_SUFFIX = 'output'
FAILURE_LOG_SUFFIX = 'error'
# A filter value of ' ' translates to "match all".
# see: https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/FilterAndPatternSyntax.html
DEFAULT_LOG_FILTER = ' '
FAILURE_LOG_FILTER = '?ERROR ?Exception'
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
"""
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)
def list_jobs(self) -> list:
""":return: Lists of Jobs"""
conn = self.get_conn()
return conn.get_jobs()
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
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:
"""
glue_client = self.get_conn()
script_arguments = script_arguments or {}
run_kwargs = run_kwargs or {}
try:
job_name = self.get_or_create_glue_job()
return glue_client.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
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
"""
glue_client = self.get_conn()
job_run = glue_client.get_job_run(JobName=job_name, RunId=run_id, PredecessorsIncluded=True)
return job_run['JobRun']['JobRunState']
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
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,
)
def get_or_create_glue_job(self) -> str:
"""
Creates(or just returns) and returns the Job name
:return:Name of the Job
"""
glue_client = self.get_conn()
try:
get_job_response = glue_client.get_job(JobName=self.job_name)
self.log.info("Job Already exist. Returning Name of the job")
return get_job_response['Job']['Name']
except glue_client.exceptions.EntityNotFoundException:
self.log.info("Job doesn't exist. Now creating and running AWS Glue Job")
if self.s3_bucket is None:
raise AirflowException('Could not initialize glue job, error: Specify Parameter `s3_bucket`')
s3_log_path = f's3://{self.s3_bucket}/{self.s3_glue_logs}{self.job_name}'
execution_role = self.get_iam_execution_role()
try:
default_command = {
"Name": "glueetl",
"ScriptLocation": self.script_location,
}
command = self.create_job_kwargs.pop("Command", default_command)
if "WorkerType" in self.create_job_kwargs and "NumberOfWorkers" in self.create_job_kwargs:
create_job_response = glue_client.create_job(
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,
)
else:
create_job_response = glue_client.create_job(
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,
MaxCapacity=self.num_of_dpus,
**self.create_job_kwargs,
)
return create_job_response['Name']
except Exception as general_error:
self.log.error("Failed to create aws glue job, error: %s", general_error)
raise
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