The User Experience Will Our Airflow Users Notice Any Difference? So once you’ve downloaded the KEDA pod and given it proper permissions, it acts like a native piece of your Kubernetes cluster. Custom controllers are lightweight and only require a single pod per-cluster. The Spark on k8s and Airflow on K8s Operator are both examples of custom controllers. Custom controllers are a common concept in Kubernetes, where users create features that Kubernetes treats like native features. KEDA is what’s called a custom controller.
Not at all! KEDA is pretty nifty in that the entire program lives on a single pod. CEIL(32 RUNNING + 30 QUEUED/16) = 4 WORKERS This consistency means that these Celery + KEDA workers are significantly faster than KubernetesExecutor workers while having the same scale-to-zero efficiency. There is no loading time as the Celery worker maintains a python environment between task executions. If there is a period of high load, KEDA will be able to launch new Celery workers, all of which are pulling tasks from the Celery Queue as quickly as possible. Even as Airflow adds tasks, as long as old tasks finish before the number of running + queued tasks rise above 16, KEDA will only run a single worker! CEIL(0 RUNNING + 1 QUEUED/16) = 1 WORKERS Using the equation CEIL(RUNNING + QUEUED)/worker_concurrency, KEDA launches a single worker that will handle the first 16 (our default concurrency) tasks in parallel. CEIL(0 RUNNING + 0 QUEUED/16) = 0 WORKERS In the following example, we start with an Airflow cluster that has zero Celery workers as it is running no tasks. Given that all metadata regarding an airflow cluster lives in the backend SQL database, we can now autoscale the number of Celery workers based on the number of running and queued tasks! Using Keda’s flexible scaler system, we created an autoscaler capable of scaling celery workers based on data stored in the Airflow metadata database. So What Does This Have to Do With Airflow? We are happy to say that the results have been fantastic.
Astronomer kubernetes how to#
Over the past few months, we’ve teamed up with our friends at Polidea to investigate how to use KEDA as an autoscaler for Apache Airflow. KEDA allows users to define autoscaling using external state of things like Kafka Topics, RabbitMQ queue size, and PostgreSQL queries. KEDA stands for Kubernetes Event-Driven Autoscaler. With these factors in mind, we feel that with KEDA we might finally have our optimal solution! What Is KEDA? Any autoscaling system for the Airflow community should scale to zero and be reactive to Airflow’s scaling needs.Any autoscaling system for the Airflow community should maximize efficiency and allow multiple tasks to run per-worker.
Astronomer kubernetes install#
Any autoscaling system for the Airflow community should be easy to install and easy to maintain.
In continuing our search for an ideal autoscaling solution, we felt that these were the three most important factors: So while the KubernetesExecutor is valuable for users who want per-task configurations and don’t want to use Celery, it is not our ideal autoscaling solution. Launching an entire Airflow virtual environment for each task leads to a fair amount of wasted resources, and running hundreds or thousands of tasks in parallel can place significant pressure on a Kubernetes cluster. While this system has huge benefits for users, it is not without its drawbacks. The executor can launch a pod for each task, and shrink back down to a single instance when all tasks complete. When the KubernetesExecutor was first released, it was the first time that Airflow had a working scale-to-zero solution. With the release of KEDA (Kubernetes Event-Driven Autoscaler), we believe we have found a new option that merges the best technology available with an architecture that is both efficient and easy to maintain. This journey has taken us through multiple architectures and cutting edge technologies. As some of you may know, we’ve been busy for the past few years trying to find an ideal autoscaling architecture for Apache Airflow.
0 kommentar(er)
