Create a Dynamically Created DAG and TroubleshootAirflow’s Webserver in Google Cloud Composer

Irvi Aini
Irvi Aini
Dec 24, 2019 · 4 min read

There was a condition where I need to fetch my configuration from Google Cloud SQL and create a dynamically created DAG. The first step that I do is trying to create a clousql proxy in my Composer cluster. This can be done, because in general all of your Airflow metadata also stored inside of Cloud SQL, so you can create your own cloudsql proxy based on available cloudsql proxy Dockerimage in your Composer cluster. Depends on your airflow version, you can get the following Pod and Service for the proxy.

kubectl get svc airflow-sqlproxy-service -o yamlkubectl get deploy airflow-sqlproxy -o yaml > airflow-sql-proxy-2.yaml # change the -instance in the deployment, don't forget to add your composer service account to IAM of the project where your Cloud SQL instance belongs to

For a temporary storage you also can see the default Redis service inside of your Composer cluster.

airflow-redis-service.<NAMESPACE> # Depends on your Composer version

Create your dynamically generated DAG file:

import json
import re
from datetime import datetime, timedelta
import logging
from airflow import DAG
from airflow.models import Variable
from airflow.hooks.postgres_hook import PostgresHook
from airflow.operators.python_operator import PythonOperator
from airflow.hooks.base_hook import BaseHook
from airflow.contrib.operators.slack_webhook_operator import SlackWebhookOperator
from psycopg2.extras import RealDictCursor
import redis
logger = logging.getLogger(__name__)class Variables(object):
DATABASE_CONNECTION_ID = Variable.get('DB_CONNECTION_ID')
ENVIRONMENT = Variable.get('ENVIRONMENT')
SLACK_CONN_ID = Variable.get("SLACK_WEBHOOK_ID")
MYAIRFLOW_DASHBOARD_URL = Variable.get("EXPAIRFLOW_DASHBOARD_URL")
AIRFLOW_DASHBOARD_URL = Variable.get("AIRFLOW_DASHBOARD_URL")
def standardize_naming(input):
return '{}_{}'.format(input, Variables.ENVIRONMENT)
class Constants(object):
REDIS_KEY = standardize_naming("my-redis")
REDIS_CONNECION_SERVICE = #your redis service
ALPHANUM_UNDERSCORE_ONLY_REGEX = '[^0-9a-zA-Z_]+'
STRPTIME_FORMAT = '%Y-%m-%d %X'
MAX_ETL_DURATION_DAYS = 35
PG_QUERY = # your config query
def remove_unsafe_character(input):
return re.sub(Constants.ALPHANUM_UNDERSCORE_ONLY_REGEX, '_', input)
def failed_task_slack_alert(context):
slack_webhook_token = BaseHook.get_connection(Variables.SLACK_CONN_ID).password
base_log_url = context.get('task_instance').log_url
updated_log_url = base_log_url.replace(Variables.AIRFLOW_DASHBOARD_URL,
Variables.MYAIRFLOW_DASHBOARD_URL)
slack_msg = """
:red_circle: Task Failed.
*Task*: {task}
*Dag*: {dag}
*Execution Time*: {exec_date}
*Log Url*: {log_url}
""".format(
task=context.get('task_instance').task_id,
dag=context.get('task_instance').dag_id,
ti=context.get('task_instance'),
exec_date=context.get('execution_date'),
log_url=updated_log_url,
)
failed_alert = SlackWebhookOperator(
task_id='slack_test',
http_conn_id=Variables.SLACK_CONN_ID,
webhook_token=slack_webhook_token,
message=slack_msg,
username='airflow')
return failed_alert.execute(context=context)
default_args = {
"owner": "someone",
"depends_on_past": False,
"start_date": datetime(2019, 1, 1),
"email": [],
"email_on_failure": True,
"email_on_retry": False,
"retries": 1,
"retry_delay": timedelta(minutes=5),
"on_failure_callback": failed_task_slack_alert,
# 'queue': 'bash_queue',
# 'pool': 'backfill',
# 'priority_weight': 10,
# 'end_date': datetime(2016, 1, 1),
}
def deduplicate_email(emails):
tmp = set(emails)
return list(tmp)
def convertCamelToSnake(camel):
s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', camel)
s2 = re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower()
s3 = s2.replace("-", "_").replace(".", "_")
while "__" in s3:
s3 = s3.replace("__", "_")
return s3
"""
https://stackoverflow.com/questions/47705060/running-bigquery-query-uncached-using-python-api
https://github.com/tylertreat/BigQuery-Python/blob/master/bigquery/client.py
https://stackoverflow.com/questions/47172150/google-cloud-bigquery-python-how-to-get-the-bytes-processed-by-query
"""
def create_dag(dag_id, config, default_args):
def dummy(task_id):
print(task_id)
start_date = datetime.strptime(config['startdatestr'], '%Y-%m-%d')
if config['enddatestr'] is not None:
end_date = datetime.strptime(config['enddatestr'], '%Y-%m-%d')
else:
end_date = start_date + timedelta(days=Constants.MAX_ETL_DURATION_DAYS)
if len(config['notificationemails']) > 0:
default_args['email'].extend([str(email) for email in config['notificationemails']])
default_args['email'] = deduplicate_email(default_args['email'])execution_period = 24dag = DAG(dag_id,
default_args=default_args,
schedule_interval=timedelta(hours=execution_period),
start_date=start_date,
end_date=end_date,
catchup=False)
logger.info("Exp Processing: Create DAG with ID {}".format(dag_id))with dag:
"""
common var
"""
start_date_safe = remove_unsafe_character(config['startdatestr'])
cur_date = '{{ ds }}'
cur_time = '{{ ts }}'
cur_date_safe = '{{ execution_date.strftime("%Y_%m_%d") }}'
version = str(config['version'])
task_id = '{prefix}_{name}_{version}'.format(
prefix=standardize_naming('my-dag'),
version=version)
task = PyhtonOperator(
task_id=task_id,
provide_context=True,
python_callable=dummy,
op_kwargs={'task_id': task_id}
)
return dag"""
handle datetime format when fetching from pg
so that the fetch result can be stored as json instead of plain string
"""
def converter(o):
if isinstance(o, datetime):
return o.__str__()
"""
Bahavior:
Create an exact DAG which in turn will create it's own file
https://www.astronomer.io/guides/dynamically-generating-dags/
"""
redis_conn = redis.StrictRedis(host=Constants.REDIS_CONNECTION_SERVICE, port=6379,
db=0)
serialized_configs = redis_conn.get(Constants.REDIS_KEY)
configs = []
if serialized_configs == None:
pg_hook = PostgresHook(postgres_conn_id=Variables.DATABASE_CONNECTION_ID)
pg_conn = pg_hook.get_conn()
cursor = pg_conn.cursor(cursor_factory=RealDictCursor)
cursor.execute(Constannts.PG_QUERY)
res = cursor.fetchall()logger.info(
"{res} fetched on {time}".format(res=json.dumps(res, default=converter),
time=datetime.now()))
# Set expiration to 1 minutes, to prevent to much query on the DB.
redis_conn.setex(Constants.REDIS_KEY, timedelta(minutes=1),
json.dumps(res, default=converter))
configs = res
else:
configs = json.loads(serialized_configs)
for config in configs:
if config['hasetlstarted'] == True:
id = re.sub(Constants.ALPHANUM_UNDERSCORE_ONLY_REGEX, '_',
config['id'])
version = str(config['version'])
dag_id = "{prefix}_{id}_{version}".format(
prefix=standardize_naming('my_dag'),
id=id,
version=version)
logger.info("Dag with ID : {} is created.".format(dag_id))
globals()[dag_id] = create_dag(dag_id, config, default_args)

You also can create a Cloudbuild file to enable sync between your Github and your Composer Airflow directory:

steps:- name: 'google/cloud-sdk'
entrypoint: 'bash'
args:
- '-c'
- |
gsutil rsync -R dags ${_BUCKET}
id: 'deploy'
substitutions:
_BUCKET: gs://<your-composer-dags-gcs-id>/dags

I found a problem where my DAG can’t be clicked in Composer Webserver. The DAGs are not clickable and the columns “Recent Tasks” and “DAG Runs” are loading forever. The “info” mark next to each DAG name says :

This DAG isn't available in the webserver DagBag object. It shows up in this list because the scheduler marked it as active in the metadata database.
Dynamically Created DAG can’t be clicked in Composer Webserver.

This is because of the fact that Webserver using App Engine, and the metadata itself is stored in the Cloud SQL and GCS, not directly communicated to the local Redis inside of the Composer cluster. The fact that autogenerated DAG is generated by a file with all the information regarding the DAG existed only inside of the Redis within the Composer cluster might caused such problem. You can handle this by deploying your self managed Airflow Webserver can be found in there. However, if you wants to be able to access your Webserver using public API you’ll need to create an ingress.

apiVersion: v1
items:
- apiVersion: extensions/v1beta1
kind: Ingress
metadata:
annotations:
kubernetes.io/ingress.global-static-ip-name: <YOUR-STATIC-IP-THAT_ALREADY-BIND-WITH-AN-IP>
name: airflow-webserver
namespace: <YOUR-NAMESPACE>
spec:
backend:
serviceName: airflow-webserver-service
servicePort: 80
tls:
- secretName: <YOUr-SSL-SECRET>

And secure that using anything that you want, as for me I’m using Google IAP.

C’est tout ce que je peux dire. Au revoir! 👋

Irvi Aini

Written by

Irvi Aini

On ne voit bien qu’avec le cœur. Ingénieur. J’adore Traitement Automatique des Langues (NLP) et Source Ouverte (OSS). Co-leads @CloudNativeID .

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A collection of technical articles published or curated by Google Cloud Developer Advocates. The views expressed are those of the authors and don't necessarily reflect those of Google.

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