Where at all possible, use Connections to store data securely in Airflow backend and retrieve them using a unique connection id. The tasks should also not store any authentication parameters such as passwords or token inside them. If possible, use XCom to communicate small messages between tasks and a good way of passing larger data between tasks is to use a remote storage such as S3/HDFS.įor example, if we have a task that stores processed data in S3 that task can push the S3 path for the output data in Xcom,Īnd the downstream tasks can pull the path from XCom and use it to read the data. Storing a file on disk can make retries harder e.g., your task requires a config file that is deleted by another task in DAG. Therefore, you should not store any file or config in the local filesystem as the next task is likely to run on a different server without access to it - for example, a task that downloads the data file that the next task processes. It, for example, to generate a temporary log.Īirflow executes tasks of a DAG on different servers in case you are using Kubernetes executor or Celery executor. Thisįunction should never be used inside a task, especially to do the criticalĬomputation, as it leads to different outcomes on each run. The Python datetime now() function gives the current datetime object. You shouldįollow this partitioning method while writing data in S3/HDFS as well. You can use data_interval_start as a partition. A better way is to read the input data from a specific Someone may update the input data between re-runs, which results inĭifferent outputs. Some of the ways you can avoid producing a differentĭo not use INSERT during a task re-run, an INSERT statement might lead toĭuplicate rows in your database. AnĮxample is not to produce incomplete data in HDFS or S3 at the end of aĪirflow can retry a task if it fails. Implies that you should never produce incomplete results from your tasks. You should treat tasks in Airflow equivalent to transactions in a database. Using multiple Docker Images and Celery Queues.Using DockerOperator or Kubernetes Pod Operator.Handling conflicting/complex Python dependencies.Using AirflowClusterPolicySkipDag exception in cluster policies to skip specific DAGs.Example of watcher pattern with trigger rules.How to check if my code is “top-level” code.Our pipeline is complete and scheduled to automatically update on a daily basis!Ĭheck out the full repository on my GitHub. Head over to the Postgres database and perform a SELECT on the covid_data table to verify that our DAG has successfully executed. Make sure you toggle the covid_nyc_data DAG on, and click the play button under the links column to immediately trigger the DAG. You should be able to access Airflow’s UI by going to your localhost:8080 in your browser. To test our project, navigate to your terminal and run the following commands airflow initdb "start_date": datetime.today() - timedelta(days=1)Īppend this piece of code to the main covid_dag.py script and voila! our ETL/DAG is complete. from airflow import DAGįrom _operator import PythonOperator Note the value of “0 1 ” in our schedule_interval argument which is just CRON language for “run daily at 1am”. To get started, we set the owner and start date (there are many more arguments that can be set) in our default arguments, establish our scheduling interval, and finally define the dependency between tasks using the bit shift operator. In our case, we will be using two PythonOperator classes, one for each ETL function that we previously defined. Here is a complete look after wrapping our ETL tasks in functions and importing the necessary libraries Setting Up Our Airflow DAGĪirflow DAGs are composed of tasks created once an operator class is instantiated. Taking a peek at an example response from the NYC OpenData API, you can see that it shouldn’t be too difficult coming up with a schema for our database.csv".format(date.today().strftime("%Y%m%d"))) as f: Project Structure airflowĮvery pipeline should start with a careful look at the data that will be ingested. For the sake of keeping this article short and focused on Airflow’s scheduling capabilities, please check out this link to setup Postgres and Airflow. Setting up Airflow and an Airflow database is fairly simple but can involve a few steps. Finally, we’ll be using Airflow to orchestrate and automate this pipeline by scheduling the aforementioned steps on a daily basis. The dataset will be pulled as a JSON file and converted to CSV format before being loaded into a PostgreSQL database. We will be using a public open dataset on the counts of COVID-19 related hospitalization, cases, and deaths in New York City as our external data source.
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