Maybe you have hundreds or thousands of DAGs that do similar things with just a parameter changing between them. Sometimes, manually writing DAGs isnt practical. For example: source (could be a different FTP server, API route etc.). The simplest way to create a DAG is to write it as a static Python file. Now, lets say this DAG has different configuration settings.
#Airflow dag config code#
Where at all possible, use Connections to store data securely in Airflow backend and retrieve them using a unique connection id. Airflow executes all Python code in the dagsfolder and loads any DAG objects that appear in globals (). 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. The flow of dynamically configured DAGs (by author) The solution is composed of two DAGs: readconfig which is responsible for fetching the configuration from database dynamicdags that is responsible for creating DAGs, based on the configuration Read configuration One could ask why we need two DAGs, and why not have everything in one DAG. 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’s fine to use 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. This function should never be used inside a task, especially to do the critical computation, as it leads to different outcomes on each run. The python datetime now() function gives the current datetime object. You should follow this partitioning method while writing data in S3/HDFS, as well. You can use execution_date as a partition.
#Airflow dag config update#
Someone may update the input data between re-runs, which results in different outputs.Ī better way is to read the input data from a specific partition. This tutorial will introduce you to the best practices for these three steps. Never read the latest available data in a task. configuring environment dependencies to run your DAG. Some of the ways you can avoid producing a different result -ĭo not use INSERT during a task re-run, an INSERT statement might lead to duplicate rows in your database. Thus, the tasks should produce the same outcome on every re-run. An example is not to produce incomplete data in HDFS or S3 at the end of a task.Īirflow can retry a task if it fails. You should treat tasks in Airflow equivalent to transactions in a database.
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