Event-driven scheduling¶
Added in version 3.0.
Apache Airflow allows for event-driven scheduling, enabling dags to be triggered based on external events rather than predefined time-based schedules. This is particularly useful in modern data architectures where workflows need to react to real-time data changes, messages, or system signals.
By using assets, as described in Asset-Aware Scheduling, you can configure dags to start execution when specific external events occur. Assets provide a mechanism to establish dependencies between external events and DAG execution, ensuring that workflows react dynamically to changes in the external environment.
The AssetWatcher class plays a crucial role in this mechanism. It monitors an external event source, such as a
message queue, and triggers an asset update when a relevant event occurs.
The watchers parameter in the Asset definition allows you to associate multiple AssetWatcher instances with an
asset, enabling it to respond to various event sources.
See the common.messaging provider docs for more information and examples.
Supported triggers for event-driven scheduling¶
Not all triggers in Airflow can be used for event-driven scheduling. As opposed to all triggers that
inherit from BaseTrigger, only a subset that inherit from BaseEventTrigger are compatible.
The reason for this restriction is that some triggers are not designed for event-driven scheduling, and using them to
schedule dags could lead to unintended results.
BaseEventTrigger ensures that triggers used for scheduling adhere to an event-driven paradigm, reacting appropriately
to external event changes without causing unexpected DAG behavior.
Writing event-driven compatible triggers¶
To make a trigger compatible with event-driven scheduling, it must inherit from BaseEventTrigger. There are three
main scenarios for working with triggers in this context:
1. Creating a new event-driven trigger: If you need a new trigger for an unsupported event source, you should create
a new class inheriting from BaseEventTrigger and implement its logic.
2. Adapting an existing compatible trigger: If an existing trigger (inheriting from BaseEvent) is proven to be
already compatible with event-driven scheduling, then you just need to change the base class from BaseTrigger to
BaseEventTrigger.
3. Adapting an existing incompatible trigger: If an existing trigger does not appear to be compatible with
event-driven scheduling, then a new trigger must be created.
This new trigger must inherit BaseEventTrigger and ensure it properly works with event-driven scheduling.
It might inherit from the existing trigger as well if both triggers share some common code.
Avoid infinite scheduling¶
The reason why some triggers are not compatible with event-driven scheduling is that they are waiting for an external resource to reach a given state. Examples:
Wait for a file to exist in a storage service
Wait for a job to be in a success state
Wait for a row to be present in a database
Scheduling under such conditions can lead to infinite rescheduling. This is because once the condition becomes true, it is likely to remain true for an extended period.
For example, consider a DAG scheduled to run when a specific job reaches a “success” state. Once the job succeeds, it will typically remain in that state. As a result, the DAG will be triggered repeatedly every time the triggerer checks the condition.
Another example is the S3KeyTrigger, which checks for the presence of a specific file in an S3 bucket.
Once the file is created, the trigger will continue to succeed on every check, since the condition
“is file X present in bucket Y” remains true.
This leads to the DAG being triggered indefinitely every time the trigger mechanism runs.
When creating custom triggers, be cautious about using conditions that remain permanently true once met. This can unintentionally result in infinite DAG executions and overwhelm your system.
Use cases for event-driven dags¶
Data ingestion pipelines: Trigger ETL workflows when new data arrives in a storage system.
Machine learning workflows: Start training models when new datasets become available.
IoT and real-time analytics: React to sensor data, logs, or application events in real-time.
Microservices and event-driven architectures: Orchestrate workflows based on service-to-service messages.