XComs (short for “cross-communications”) are a mechanism that let Tasks talk to each other, as by default Tasks are entirely isolated and may be running on entirely different machines.

An XCom is identified by a key (essentially its name), as well as the task_id and dag_id it came from. They can have any (serializable) value, but they are only designed for small amounts of data; do not use them to pass around large values, like dataframes.

XComs are explicitly “pushed” and “pulled” to/from their storage using the xcom_push and xcom_pull methods on Task Instances. Many operators will auto-push their results into an XCom key called return_value if the do_xcom_push argument is set to True (as it is by default), and @task functions do this as well.

xcom_pull defaults to using this key if no key is passed to it, meaning it’s possible to write code like this:

# Pulls the return_value XCOM from "pushing_task"
value = task_instance.xcom_pull(task_ids='pushing_task')

You can also use XComs in templates:

SELECT * FROM {{ task_instance.xcom_pull(task_ids='foo', key='table_name') }}

XComs are a relative of Variables, with the main difference being that XComs are per-task-instance and designed for communication within a DAG run, while Variables are global and designed for overall configuration and value sharing.


If the first task run is not succeeded then on every retry task XComs will be cleared to make the task run idempotent.

Custom XCom Backends

The XCom system has interchangeable backends, and you can set which backend is being used via the xcom_backend configuration option.

If you want to implement your own backend, you should subclass BaseXCom, and override the serialize_value and deserialize_value methods.

There is also an orm_deserialize_value method that is called whenever the XCom objects are rendered for UI or reporting purposes; if you have large or expensive-to-retrieve values in your XComs, you should override this method to avoid calling that code (and instead return a lighter, incomplete representation) so the UI remains responsive.

You can also override the clear method and use it when clearing results for given DAGs and tasks. This allows the custom XCom backend to process the data lifecycle easier.

Working with Custom XCom Backends in Containers

Depending on where Airflow is deployed i.e., local, Docker, K8s, etc. it can be useful to be assured that a custom XCom backend is actually being initialized. For example, the complexity of the container environment can make it more difficult to determine if your backend is being loaded correctly during container deployment. Luckily the following guidance can be used to assist you in building confidence in your custom XCom implementation.

Firstly, if you can exec into a terminal in the container then you should be able to do:

from airflow.models.xcom import XCom


which will print the actual class that is being used.

You can also examine Airflow’s configuration:

from airflow.settings import conf

conf.get("core", "xcom_backend")

Working with Custom Backends in K8s via Helm

Running custom XCom backends in K8s will introduce even more complexity to you Airflow deployment. Put simply, sometimes things go wrong which can be difficult to debug.

For example, if you define a custom XCom backend in the Chart values.yaml (via the xcom_backend configuration) and Airflow fails to load the class, the entire Chart deployment will fail with each pod container attempting to restart time and time again.

When deploying in K8s your custom XCom backend needs to be reside in a config directory otherwise it cannot be located during Chart deployment.

An observed problem is that it is very difficult to acquire logs from the container because there is a very small window of availability where the trace can be obtained. The only way you can determine the root cause is if you are fortunate enough to query and acquire the container logs at the right time. This in turn prevents the entire Helm chart from deploying successfully.

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