Airflow DAG definition file to dynamically generate DAGs based on a variable (pull economic data when it is released)

economic_events_update_dag.py

#/usr/bin/python3
# -*- coding: utf-8 -*-
 
import logging
import airflow
from airflow import DAG
from datetime import timedelta, datetime
from airflow.operators.dummy_operator import DummyOperator
from airflow.operators.python_operator import PythonOperator
from airflow.operators.http_operator import SimpleHttpOperator
from airflow.models import Variable

from airflow.contrib.hooks.mongo_hook import MongoHook

import datetime
import dateutil.parser
import json
import requests
import logging
import pytz

logging.basicConfig(level=logging.INFO)
log = logging.getLogger('events_extraction_agent')

ECONOMIC_EVENTS_API_HEADERS = Variable.get("economic_events_api_headers", deserialize_json = True)

DAG_DEFAULT_ARGS = {
  'owner': 'Tom',
  'depends_on_past': False,
  'email': ['mail@example.com'],
  'email_on_failure': True,
  'email_on_retry': False,
  'retries': 3,
  'retry_delay': timedelta(minutes=5),
  'provide_context': True
}

EXTRACT_TASK_ID = 'economic_event_extract_task'
TRANSFORM_TASK_ID = 'economic_event_transform_task'
LOAD_TASK_ID = 'economic_event_load_task'


def check_event_updated(response):
  if not response.ok:
    return False
  event = response.json()[0]
  # Fail task if data not yet available
  if not event['actual']:
    log.warning("Event data not updated. Failing task.")
    return False
  return True

def event_transform_task(**context):
  event_response = context['ti'].xcom_pull(task_ids=EXTRACT_TASK_ID)
  log.info("Event from API: {0}".format(event_response))
  e = json.loads(event_response)[0]
  e['_id'] = e['id']
  del e['id']
  return e

def event_load_task(**context):
  event = context['ti'].xcom_pull(task_ids=TRANSFORM_TASK_ID)

  # Parse string datetimes
  event['dateUtc'] = dateutil.parser.parse(event['dateUtc'])
  if event['periodDateUtc']:
    event['periodDateUtc'] = dateutil.parser.parse(event['periodDateUtc'])

  # Load to Mongo
  mongo = MongoHook(conn_id="findata_mongo")
  collection = mongo.get_collection('CalendarEventData')
  res = collection.update_one({ '_id': event['_id'] }, { '$set': event })
  log.info("Got update result: {0}".format(res.raw_result))

def add_event_update_dag(event):
  event_date = dateutil.parser.parse(event['dateUtc'])
  dag_id = "economic_event_update_{0}".format(event['eventId'])
  desc = "Scheduling DAG at {2} for event {0} with ID {1}".format(event['name'], event['eventId'], event_date)
  globals()[dag_id] = DAG(
        dag_id,
        default_args=DAG_DEFAULT_ARGS,
        description=desc,
        schedule_interval='@once',
        start_date=event_date,
        is_paused_upon_creation=False
      )

  log.info(desc)

  endpoint = "/events/{0}/historical?take=1".format(event['eventId'])
  globals()[EXTRACT_TASK_ID] = SimpleHttpOperator(task_id=EXTRACT_TASK_ID,
    endpoint=endpoint,
    method='GET',
    headers=ECONOMIC_EVENTS_API_HEADERS,
    response_check=check_event_updated,
    xcom_push=True,
    http_conn_id="economic_events_api",
    dag=globals()[dag_id])

  globals()[TRANSFORM_TASK_ID] = PythonOperator(task_id=TRANSFORM_TASK_ID,
    python_callable=event_transform_task,
    provide_context=True,
    dag=globals()[dag_id])

  globals()[LOAD_TASK_ID] = PythonOperator(task_id=LOAD_TASK_ID,
    python_callable=event_load_task,
    provide_context=True,
    dag=globals()[dag_id])
  
  globals()[EXTRACT_TASK_ID] >> globals()[TRANSFORM_TASK_ID] >> globals()[LOAD_TASK_ID]

# Dynamically generate DAGs
events = Variable.get("economic_events", deserialize_json = True, default_var = [])
for e in events:
  add_event_update_dag(e)