We’ll use a Helm chart to set up Airflow on minikube, but you can deploy it on any cloud provider if you want. The remainder of the post walks through a simple example. Your cluster can use its resources for other applications. When Airflow has no more jobs to run, only the scheduler, web server and database remain alive. As long as you have enough CPU and memory available, Airflow can keep scheduling more tasks. Scaling is only limited to the size of the cluster. There’s no more need for additional components.
AIRFLOW HELM CHART FULL
Now we leverage the full potential of Kubernetes. Airflow cleans up the resource as soon as the job finished. Instead, it spawns a new worker pod for every job. In contrast, the KubernetesExecutor runs no workers persistently. The queue will then schedule tasks across them. With Celery, you deploy several workers up front. Perhaps these many components add too much overhead for the task at hand? Can’t we have something more simple? Yes! Airflow 1.10 introduced a new executor to scale workers: the Kubernetes executor. Additionally, you probably want Flower, a web interface, to monitor Celery. And Celery requires a broker such as RabbitMQ or Redis as back-end. You need to deploy Celery as an extra component in your system. But if using Celery works so well, then why would we need another executor for Kubernetes? Because celery is quite complex. Here you can find a Helm chart to automate the deployment with the CeleryExecutor. Using Celery to schedule jobs on worker nodes is a popular approach to scale Airflow. Airflow has two popular executors available that deploy workers at scale - the CeleryExecutor and KubernetesExecutor.Ĭelery is a distributed task queue that balances the workload across multiple nodes. It’s the worker nodes that do the heavy duty. The scheduler, web server and database usually don’t need to scale. Finally, Airflow has workers that run the tasks. A database keeps track of the state of the current and past jobs. The web server enables users to interact with DAGs and tasks. At its core, a scheduler decides which tasks need to run next. Kubernetes duplicates those pods when the application needs more power.Īirflow’s architecture fits perfectly in this paradigm. Individual pieces of an application run as isolated containers in so-called pods. Kubernetes is one of the leading technologies to scale applications. That opens a world of opportunities! Before you know, your Airflow installation becomes crowded with pipelines. Here’s how such an Airflow workflow looks like.Īirflow can run more than just data pipelines.
At the same time we can close the Dataproc cluster to clean up our resources. Finally, a BigQuery job loads the data from GCS into a table. Then the Spark job fetches the data from GCS, processes it and dumps it back to GCS. We first want to create a new Dataproc cluster (Dataproc is Hadoop on Google Cloud).
The budget is tight, so we don’t have a Hadoop cluster running 24/7. We want to use Spark to clean the data and move it to BigQuery for analysis. Imagine we have a pile of data sitting somewhere in Google Cloud Storage (GCS), waiting to be processed. Airbnb started the project and open-sourced it as an Apache incubator. Airflow gained a lot of support from the community because of its rich UI, flexible configuration and ability to write custom extensions. Well-known schedulers are Airflow, Luigi, Oozie and Azkaban. So how can we sew these components together into a reliable workflow?Ī workflow scheduler manages dependencies between tasks and orchestrates their execution. Data pipelines often consist of many steps and many tools to move data around. Or run a query on a data warehouse and store the results in a new table. You can use tools such as Spark, Flink, Storm or Beam to process these humongous amounts of data. Think of open-source platforms - Hadoop, Kafka, Druid, Ceph - or cloud-native solutions - Amazon Redshift and S3, Google BigQuery and GCS, Azure Data Warehouse and Data Lake. Lots of technologies exist today to store and query petabytes of raw data in so-called data lakes or data warehouses. The data engineering space rapidly evolves to process and store ever-growing volumes of data.
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