1/17/2024 0 Comments Airflow kubernetes github![]() ![]() | | Main version (dev) | Stable version (2.6.0) | **Scalable**: Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Parameterizing your scripts is built into the core of Airflow using the powerful **Jinja** templating engine. **Elegant**: Airflow pipelines are lean and explicit. **Extensible**: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment. This allows for writing code that instantiates pipelines dynamically. **Dynamic**: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. For high-volume, data-intensive tasks, a best practice is to delegate to external services specializing in that type of work.Īirflow is not a streaming solution, but it is often used to process real-time data, pulling data off streams in batches. Other similar projects include (), () and ().Īirflow is commonly used to process data, but has the opinion that tasks should ideally be idempotent (i.e., results of the task will be the same, and will not create duplicated data in a destination system), and should not pass large quantities of data from one task to the next (though tasks can pass metadata using Airflow's ()). When the DAG structure is similar from one run to the next, it clarifies the unit of work and continuity. (#airflow-merchandise)Īirflow works best with workflows that are mostly static and slowly changing. (#can-i-use-the-apache-airflow-logo-in-my-presentation) (#base-os-support-for-reference-airflow-images) (#support-for-python-and-kubernetes-versions) The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed. Rich command line utilities make performing complex surgeries on DAGs a snap. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Change start_date to today's date, but keep it static default_args = with DAG ( ' cloudquery_sync ', default_args = default_args, schedule_interval = timedelta ( days = 1 ), ) as dag : cloudquery_operator = KubernetesPodOperator ( task_id = ' cloudquery_sync ', name = ' cloudquery-sync ', namespace = ' airflow ', image = ' ghcr.() (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. client import models as k8s # Change these to match your requirements. kubernetes_pod import KubernetesPodOperator from kubernetes. ![]() python_operator import PythonOperator from airflow. For example, to map a local directory at /data/airflow/dags (inside the Minikube container, if not running on bare metal), you can use the following configs and commands to create the Persistent Volume and Persistent Volume Claim:Ĭloudquery.py from datetime import datetime, timedelta from airflow import DAG from airflow. When deployed to Kubernetes, this is done with Persistent Volumes (opens in a new tab) and Persistent Volume Claims. You will need to have the ability to set up DAGs. If you decide to proceed with Airflow, you can install it locally on Kubernetes using Minikube (opens in a new tab) and the Airflow Helm chart (opens in a new tab). If you don't, you should consider some simpler orchestration options to get started, such as GitHub Actions, Kestra, or even a simple cron-based deployment. This guide assumes that you have a working Airflow installation and an available Kubernetes cluster, and experience with operating both of these. We will use the KubernetesOperator (opens in a new tab), which allows us to run tasks in Kubernetes pods. In this guide, we will show you how to get started with Airflow and CloudQuery. It can be used to schedule CloudQuery syncs, optionally retry them and send notifications when syncs fail. Apache Airflow Orchestrating CloudQuery Syncs with Apache Airflow and KubernetesĪpache Airflow is a popular open source workflow management tool. ![]()
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