How to Deal With Apache Airflow Solutions Performance Issues?

This guide shows how to deal with Apache Airflow performance issues. Apache Airflow is a top choice to orchestrate data pipelines and manage workflows. Developers use it widely for data engineering and data analytics. Also, it defines workflows as code. As a result, workflows become easier to manage and maintain, and they run faster.

However, trouble starts when you run tens of workflows with hundreds of tasks in the Airflow Scheduler. The tasks get messy, and Apache Airflow performance issues appear. This makes it hard to use Airflow for bigger and more complex cases.

Clearly, there is room for improvement. To get the best from Airflow, you need to tackle these performance issues. But how do you deal with them?

First, find out where the issues come from. For this, you need to understand your DAG schedule.

DAG Schedule and Apache Airflow Performance Issues

A DAG, or Directed Acyclic Graph, is a collection of tasks that you want to run on your Airflow Scheduler. You organize the tasks to create relationships and dependencies between them. Then they run smoothly and support automated, fast-paced workflows.

However, the way a DAG works is tricky. When you create a DAG schedule in Airflow, it runs periodically. It uses the start_date and schedule_interval that you set in the DAG file. But the Airflow Scheduler does not run a triggered DAG at the start of the schedule period. Instead, it runs the DAG at the end of the scheduled period. This confuses users and causes Apache Airflow performance issues.

So, it is important to understand the scheduling mechanism of a DAG. Your schedule runs on the basis of start_date and schedule_time. Different tasks, jobs and workflows have different schedules. Each DAG run starts when it meets a time dependency. That dependency is based on the end of the schedule period, not the start. So, your tasks start to run at the end of the period you scheduled.

Basically, the problem is with the execution time of the tasks in the Airflow DAG schedule. It does not depend on the run time that you specify. Instead, it works on the timestamp set within the schedule period.

Because the scheduling mechanism is complicated, users need to set a static start_date. That is not when the DAG run actually starts. With a static start_date, the DAG runs start just when you want them to. Your tasks then run as you expect, which removes these performance issues.

Another part of DAG work is Catchup and the idempotent DAG.

Catchup and Idempotent DAG: Fix Apache Airflow Performance Issues

Catchup is an important function in Airflow. It backfills the DAG schedules that ran earlier. If you turn it off, you have no record of earlier DAG entries. The Airflow Scheduler then shows only the current and running DAGs. So, it is important to configure this setting in your Airflow solution.

There are two ways to configure it.

1. Airflow Cluster Level

This is a default setting. It applies to all the DAGs unless you configure a DAG level catchup.

2. DAG level Catchup

To configure this, run the command below in your DAG file.
dag = DAG(‘sample_dag’, catchup=False, default_args=default_args)

This is how you configure the DAG catchup. It makes sure you keep a record of all your schedules. Then you can go back and check the tasks and their performance at any time. However, catchup can backfill every DAG. So, you also need to make sure that different schedules do not mix up. This is why you keep all DAG schedules independent from each other. That is where the idempotent DAG comes in.

An idempotent DAG gives the same results no matter how often it runs. So, even when catchup backfills your DAGs, they give the same performance. They do not create performance issues.

Finally, keep up with the metadata of your Airflow solution. It helps you tackle the performance issues.

Airflow Metadata and Apache Airflow Performance Issues

Airflow metadata has two parts.

1. Metadata Database

This database holds and manages all the information about your DAGs, their execution and task status.

2. Scheduler

This is where the main work happens. The scheduler processes and manages the DAG files. It reads the scheduled tasks from the metadata database and decides when they must run.

The scheduler keeps processing DAG files while tasks run. If these files are too big, performance issues occur. The best way to tackle this is to keep your DAG files light. Then the scheduler works on them quickly and runs the DAG schedules at their best.

Basically, the scheduler should not need to process the files. It should simply run them quickly, in a heartbeat. That gives your Airflow solutions good and efficient performance.

Besides DAG scheduling and metadata, never rename the DAG files unless you really have to. Renaming a DAG file creates a new DAG. As a result, the previous DAG history is deleted. Then catchup tries to backfill the same DAG file all over again. The same tasks run again, which is bad for performance.

Conclusion

So, these are some ways to tackle Apache Airflow performance issues in your Airflow Scheduler and keep your workflows seamless. The bottom line is that Apache Airflow manages your workflows and data pipelines. However, it is tricky to use. So, learn the basics and work through them to get the best from this robust workflow automation solution.

If you need help with data engineering work, see our custom software services.