ETL in Airflow: A Comprehensive Guide to Efficient Data Pipelines

  • George Yates

When designing ETL workflows with Airflow, there are a myriad of small optimizations that can be made to massively increase the speed and reliability of your data pipelines. In this blog post, we’ll break down some of the best ways to optimize your data pipelines to ensure you consistently meet your service-level agreements (SLAs), while keeping compute costs as low as possible!

Designing the DAGs for ETL Workflows

Idempotency: Make sure your tasks are idempotent (you can read more about how to do that in this article). This means that even if a task is executed multiple times, the result should be the same. This is crucial for data consistency and for the ability to retry failed tasks. If tasks aren’t idempotent, you might not be able to replicate an error to fix it, making troubleshooting a major headache. Data quality is also critical in ETL workflows, so ensuring consistent results is of the highest priority.

Atomicity: Each task in your pipeline should be atomic. This means it should either fully succeed or fail completely, without leaving partial data or state. This ensures that you don’t end up with hung tasks that can silently block a data pipeline without triggering any failure warning messages. If your tasks are atomic, your log files will be concise and focused solely on the problem area, rather than a wall of text you need to crawl through just to find a Python spacing issue.

Atomic tasks in ETL pipelines are especially helpful because of the large processing times that can occur. By making your ETL pipelines atomic, when a task does fail, its blast radius is reduced, as only that small portion needs to be retried, instead of rerunning an hour-long data ETL task each time any part fails.

DAG Structure: Keep your DAGs small and focused on a single logical workflow. If a DAG becomes too large, consider breaking it into smaller DAGs and use Datasets to define inter-DAG dependencies. As you scale your ETL workloads, having a higher number of concise, smaller pipelines that you can easily monitor and troubleshoot individually is much better than having one mega-pipeline that takes a day to complete, even if all the pipelines are doing similar functions. That way, if one pipeline fails, you have a much smaller blast radius, and you have a clear picture of exactly where the problem arose. By breaking your pipelines up, you can also organize them more easily with things like folders and tags, instead of keeping everything in a monolithic DAG file.

Optimizing Task Execution

Task Optimized Compute: One great way to optimize task execution time for ETL DAGs, where different tasks may require vastly different amounts of compute, is to set up an Airflow environment that has access to a variety of different compute nodes. This can be accomplished through worker queues, which allow your CeleryExecutor to assign tasks to different pools of worker nodes.

Setting this up on your own with open-source Airflow is a very daunting task, but Astronomer is the only hosted Airflow solution that comes with worker queues. By implementing Astronomer’s worker queues, you can optimize your ETL processes by designating specific tasks to the most suitable types of machines. For instance, the “Extract” and “Load” steps, which are typically I/O intensive operations, can be assigned to machines optimized for disk I/O operations. On the other hand, the “Transform” step, which is often compute-intensive, can be assigned to compute-optimized machines. This ensures efficient resource utilization while managing ETL workloads.

Parallelism: One of the key advantages of Airflow is that it can use parallelism to execute multiple tasks at the same time, rather than forcing data pipelines to run sequentially one at a time. Many hosted Airflow solutions like Astronomer come with worker pools built in.

Not only does this allow you to run many data pipelines at the same time, but it’s especially useful for ETL workflows as it allows you to extract, transform, load many different data files in parallel, all within the same pipeline. This can help cut execution time way down, resulting in faster data updates and more timely insights.

Task Duration: Keep your tasks short. Long-running tasks can hold up resources and slow down your pipeline. If a task takes a long time to execute, consider breaking it up into smaller tasks that can then run in parallel, or assigning larger compute nodes to that task to allow it to complete quicker.

One advantage of breaking up large tasks into smaller chunks is that you can then execute those smaller tasks in parallel, allowing you to extract, load, and transform files in only the time it takes to execute the smaller tasks. By cutting down on processing time, your ETL team will have an easier time meeting their SLAs and optimizing resource consumption.

Data Processing

Batch Processing: When dealing with large volumes of data, it’s often more efficient to process data in batches in parallel rather than one large dataset at a time. A great way to do this is by splitting up your dataset into X amount of files and leveraging dynamically task mapping to create X amount of task instances for them. By splitting them up, you can then process all of the data files in parallel, so processing the entire dataset only takes as long as it takes to process one of the smaller data files.

Data Formats: Use efficient data formats like Parquet or Avro for storing and processing data. These formats are optimized for performance and can significantly speed up your ETL pipeline.

Error Handling and Monitoring

Retries and Backoff: Airflow allows you to set a retries parameter on your tasks. If a task fails, Airflow will retry it the specified number of times. You can also set a retry_delay parameter to specify how long Airflow should wait between retries. This is really useful where you have tasks that rely on external data being available at runtime, so that if the data isn’t available when the task is run, you can run it again slightly later when it has arrived, instead of causing a failure and requiring an engineer to come in and manually restart it.

Alerts: Set up alerts to notify you when a task or DAG fails. You can use Airflow’s built-in email alerts, or integrate with a third-party service like PagerDuty or Slack by adding notifier classes to your DAGs. Astronomer also provides a way to set up Slack and PagerDuty alerts through the GUI across all of your DAGs and tasks, eliminating the need to manually code alerting mechanisms into your DAGs.

Environment and Infrastructure

Scaling: Use a scalable backend like Celery or KubernetesExecutor to execute your tasks, as this allows you to scale your Airflow setup to handle larger workloads. While this can be a daunting task to set up on your own, Astronomer offers a fully-managed Airflow service that comes with auto-scaling capabilities built in. Auto-scaling will scale the compute for your environment dynamically up and down based on your current needs, giving you the capacity you need to handle your workloads at peak times, and then killing any excess compute capacity during downtime to keep compute costs as low as possible.

Resource Allocation: Make sure to allocate enough resources (CPU, memory, disk space) to your Airflow setup. Monitor your resource usage and adjust as necessary to make sure resources aren’t over or under utilized. Worker queues and task optimized compute nodes are a great way to accomplish this, as discussed previously.

Remember, the key to creating efficient ETL pipelines in Airflow is to follow best practices, monitor your pipelines, and continuously optimize them based on your observations and requirements. Happy developing! If you’re interested in getting started with Airflow in less than 5 minutes to orchestrate your ETL pipelines, check out a free trial of Astro.