Celery Executor Vs Kubernetes Executor at Ernestine Verna blog

Celery Executor Vs Kubernetes Executor. Airflow kubernetes executor is more efficiently scalable than celery even when we using keda for scaling celery (subject for another article). Kubernetesexecutor is where airflow spins up a new pod to run an airflow task. The celerykubernetesexecutor allows users to run simultaneously a celeryexecutor and a kubernetesexecutor. An executor is chosen to run. With kubernetesexecutor, for each and every task that needs to run, the executor talks to the kubernetes api to dynamically launch an. Unlike celery executor the advantage is you don't have a bunch of workers. Celeryexecutor requires a message broker like redis or rabbitmq to queue tasks, whereas kubernetesexecutor leverages kubernetes to. Between using a celeryexecutor and kubernetesexecutor, the latter saves you from setting up extra stack for message broker (such as rabbitmq) and celery worker nodes that would need to be scaled up or down based on your workloads, since everything would be handled by the kubernetes cluster. In summary, the celery executor is a great fit for any environment where the tasks are “similar” and you can find a configuration for the worker that.

An executor is chosen to run. Kubernetesexecutor is where airflow spins up a new pod to run an airflow task. The celerykubernetesexecutor allows users to run simultaneously a celeryexecutor and a kubernetesexecutor. Between using a celeryexecutor and kubernetesexecutor, the latter saves you from setting up extra stack for message broker (such as rabbitmq) and celery worker nodes that would need to be scaled up or down based on your workloads, since everything would be handled by the kubernetes cluster. In summary, the celery executor is a great fit for any environment where the tasks are “similar” and you can find a configuration for the worker that. Unlike celery executor the advantage is you don't have a bunch of workers. Celeryexecutor requires a message broker like redis or rabbitmq to queue tasks, whereas kubernetesexecutor leverages kubernetes to. Airflow kubernetes executor is more efficiently scalable than celery even when we using keda for scaling celery (subject for another article). With kubernetesexecutor, for each and every task that needs to run, the executor talks to the kubernetes api to dynamically launch an.

Airflow executor & persistent volume by Ibnu Bayhaqi Medium

Celery Executor Vs Kubernetes Executor An executor is chosen to run. In summary, the celery executor is a great fit for any environment where the tasks are “similar” and you can find a configuration for the worker that. Between using a celeryexecutor and kubernetesexecutor, the latter saves you from setting up extra stack for message broker (such as rabbitmq) and celery worker nodes that would need to be scaled up or down based on your workloads, since everything would be handled by the kubernetes cluster. Celeryexecutor requires a message broker like redis or rabbitmq to queue tasks, whereas kubernetesexecutor leverages kubernetes to. With kubernetesexecutor, for each and every task that needs to run, the executor talks to the kubernetes api to dynamically launch an. An executor is chosen to run. Kubernetesexecutor is where airflow spins up a new pod to run an airflow task. Unlike celery executor the advantage is you don't have a bunch of workers. Airflow kubernetes executor is more efficiently scalable than celery even when we using keda for scaling celery (subject for another article). The celerykubernetesexecutor allows users to run simultaneously a celeryexecutor and a kubernetesexecutor.