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Airflow task to refresh PostgreSQL Materialized Views

In this post, we'll see how to refresh a PostgreSQL materialized view using an Airflow task.

In case you're not familiarized with materialized views, they're a special kind of view in which the data has been materialized. This means the result of the query has been saved somewhere, making the retrieval much faster than a regular view.

Materialized views are static. You need to run a command to update the data to the latest version. As you may imagine, that makes them not very suitable for transactional querying, but very useful to cache OLAP queries that don't need to be updated that often.

Since the update command, it's just a SQL command, you could write a simple bash script to run it and then create a crontab entry to schedule the job.

The advantage of using something like Airflow here is that you can easily monitor every run of the task. You can also stop it or starting it with just one click. It might seem that using Airflow just to run a task like this one is overkilling, and I would agree if that were the case. But assuming you are already using Airflow to orchestrate your task workflow, it would make sense to add every automated task to the same pool.

Now, in Airflow we have several operators which we can use for this task. Since we have a PostgreSQL database and we need to run a SQL command, let's just stick to the PostgresOperator. The advantage of this operator is that once you have configured a database connection in Airflow, you only need its connection_id instead of authenticating against the database in your code every time.

The DAG can be something as simple as this:

from airflow import DAG
from airflow.operators.postgres_operator import PostgresOperator
from datetime import datetime, timedelta

default_args = {
    "owner": "airflow",
    "depends_on_past": False,
    "start_date": datetime(2018, 7, 3),
    "email": ["[email protected]"],
    "email_on_failure": False,
    "email_on_retry": False,
    "retries": 1,
    "retry_delay": timedelta(minutes=5),
    # 'queue': 'bash_queue',
    # 'pool': 'backfill',
    # 'priority_weight': 10,
    # 'end_date': datetime(2016, 1, 1),

dag = DAG("refresh_sales_matviews", default_args=default_args, schedule_interval='@daily', catchup=False)

# We want to refresh the sales view
# Replace with your connection id
t1 = PostgresOperator(
    sql="refresh materialized view sales",

This will run a refresh command on a daily basis. Remember that in order to use the PostgresOperator, you first need to create a connection using the Airflow connections tab:

And then use that connection_id for the postgres_conn_id parameter. You can execute any SQL command by using this operator.

One thing to notice is that just as the previous Airflow post I wrote, we would never need to run backfills for this DAG, so the catchup parameter is set to False. We wouldn't also need to run concurrent tasks, since we only need the latest refresh. In this case, the DAG is idempotent though. Concurrent runs wouldn't affect the data consistency, but it also wouldn't make sense to do it. So be careful and always check the parameters that control the concurrency of the DAG and its tasks.

Thanks for reading!

#airflow #postgresql

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