Resumable Tasks

Added in version 3.3.0.

Many data engineering workflows involve submitting work to an external system and waiting for it to complete. A Spark job, a BigQuery query, a Kubernetes batch pod, an EMR step: these are all tasks where the real work happens outside Airflow, and the operator’s job is mostly to submit, poll, and collect the result.

These tasks share a common failure mode. In classic operator cases, the worker slot is held for the entire polling duration, and if the worker process is restarted or the host is preempted, the task retries from scratch, losing all the progress made. Depending on the operator, that means the external job is submitted again, creating a duplicate run in context of the external system.

Airflow recommends three approaches for handling long-running external work. Understanding the trade-offs between them helps you choose the right one for your situation.

Deferrable Operators

A deferrable operator pauses itself at the point where it would otherwise start polling, hands the polling work to the Triggerer component, and releases its worker slot. When the external condition is met, the Triggerer wakes the task and the worker resumes from where the operator left off.

This is the most resource-efficient option. A single Triggerer process can concurrently watch thousands of conditions, so the rest of the worker pool stays free for other tasks.

The trade-offs are:

A Triggerer component must be running. Deployments that do not include a Triggerer cannot use this pattern.
Writing a custom deferrable operator requires implementing a dedicated Trigger class in addition to the operator itself.
The polling logic runs inside the Triggerer’s async event loop. Blocking calls inside a Trigger stall the entire Triggerer process.

If a deferrable operator already exists for your use case, or your team is comfortable implementing one, this is the recommended path considering its resource efficiency.

For more details, see Deferrable Operators & Triggers.

Resumable Tasks

A resumable task uses the task state store to persist a checkpoint before it would otherwise lose progress. On retry, the task reads that checkpoint and continues from where it left off rather than starting over.

The worker slot is held for the full duration of the task, the same as a standard synchronous operator. The benefit is crash safety and continuity, not resource efficiency.

Resumable tasks are useful when:

No deferrable operator exists for your external system and writing one is not practical.
You want crash recovery without operating a Triggerer.
The task processes work incrementally (for example, reading files from a list or paginating through API results) and should be able to resume from its last completed batch.

General pattern

The task reads a checkpoint from task_store at the start, does some work, writes an updated checkpoint, and either continues or finishes. On the next run (whether due to a retry after a crash or a deliberate reschedule), it reads the checkpoint again and picks up from there.

from airflow.sdk import dag, task


@dag(schedule=None)
def process_files_dag():

    @task(retries=5)
    def process_files(context=None):
        task_store = context["task_store"]
        files = ["a.csv", "b.csv", "c.csv", "d.csv"]

        last_processed = task_store.get("last_processed")
        start_index = 0
        if last_processed is not None:
            start_index = files.index(last_processed) + 1

        for file in files[start_index:]:
            # ... process the file ...
            task_store.set("last_processed", file)

    process_files()


process_files_dag()

This pattern works without any additional work, relying only on context. The state store is just a key-value store scoped to the task instance, and what you checkpoint is up to you.

Resumable operators for external jobs

When the task submits a job to an external system and then polls for completion, there is an additional problem: on retry, the task would submit a second job even though the first one may still be running. Instead of having to handle this manually, the ResumableJobMixin solves this by persisting the external job identifier before polling starts, and reconnecting to the existing job on retry instead of submitting a new one.

For more details and a working example, see ResumableJobMixin.

Asynchronous Tasks

Note

Async task support applies to Python tasks only: @task decorated async def functions and class-based operators that subclass BaseAsyncOperator. It is not available for other operator types.

Python tasks support async/await syntax. When the decorated callable is an async function, Airflow runs it inside an event loop, which lets you fan out many concurrent I/O operations (HTTP requests, database queries, file reads) within a single task execution without blocking the event loop while waiting for each one.

The worker slot is held for the full duration of the task. Async tasks are not designed for long external waits or crash recovery; they are designed for high-throughput I/O work that completes within a single execution.

For guidance on when to use async tasks versus deferrable operators, see Deferred vs Async Operators.

Comparison

Characteristic	Deferrable operator	Resumable task	Async task
Worker slot during external wait	Freed	Held	Held
Requires Triggerer	Yes	No	No
Handles crash recovery	Yes (via Triggerer)	Yes (via task store checkpoint)	No
Prevents duplicate job submission	Depends on operator implementation	Yes (with `ResumableJobMixin`)	Not applicable
Suitable for concurrent I/O fan-out	No	No	Yes
Available from	Airflow 2.2	Airflow 3.3	Airflow 3.2