Using a last modified date is recommended for incremental loads. There are multiple ways you can achieve incremental pipelines. If your DAGs are idempotent, you can rerun a DAG for only the data that failed rather than reprocessing the entire dataset. When the results in each DAG run represent only a small subset of your total dataset, a failure in one subset of the data won't prevent the rest of your DAG Runs from completing successfully. For example, if you have a DAG that runs hourly, each DAG run should process only records from that hour, rather than the whole dataset. You should break out your pipelines into incremental extracts and loads wherever possible. For more information on this topic, see templating and macros in Airflow. You can use one of the Airflow built-in variables and macros, or you can create your own templated field to pass information at runtime.
EXAMPLE AIRFLOW DAG CODE
See Avoid top level code in your DAG file.Ĭontrary to our best practices, the following example defines variables based on datetime Python functions: Compared to using Python functions, using templated fields helps keep your DAGs idempotent and ensures you aren't executing functions on every Scheduler heartbeat.
Use template fields, variables, and macros īy using templated fields in Airflow, you can pull values into DAGs using environment variables and jinja templating. Atomizing these tasks allows you to rerun each operation in the pipeline independently, which supports idempotence. In an atomized task, a success in part of the task means a success of the entire task.įor example, in an ETL pipeline you would ideally want your Extract, Transform, and Load operations covered by three separate tasks. When organizing your pipeline into individual tasks, each task should be responsible for one operation that can be re-run independently of the others. The following DAG design principles will help to make your DAGs idempotent, efficient, and readable. Designing idempotent DAGs and tasks decreases recovery time from failures and prevents data loss. This can be achieved by designing each individual task in your DAG to be idempotent. In the context of Airflow, a DAG is considered idempotent if rerunning the same DAG Run with the same inputs multiple times has the same effect as running it only once.
A program is considered idempotent if, for a set input, running the program once has the same effect as running the program multiple times. Idempotency is the foundation for many computing practices, including the Airflow best practices in this guide. To get the most out of this guide, you should have an understanding of: In general, best practices fall into one of two categories:įor an in-depth walk through and examples of some of the concepts covered in this guide, it's recommended that you review the DAG Writing Best Practices in Apache Airflow webinar and the Github repo for DAG examples. In this guide, you'll learn how you can develop DAGs that make the most of what Airflow has to offer. However, writing DAGs that are efficient, secure, and scalable requires some Airflow-specific finesse. DAG writing best practices in Apache Airflowīecause Airflow is 100% code, knowing the basics of Python is all it takes to get started writing DAGs.
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