This case is primarily recommended for development/testing, yet will still work for small production cases. This system will simply pull your DAGS from github in an init container for usage by the airflow pod. Git mode is the least scalable, yet easiest to setup DAG storage system. We will offer two modes for DAG storage: git-mode and persistent volume mode. The PV/PVC abstractions allow Kubernetes to encapsulate a wide variety of distributed storage options. To encourage a wide array of potential storage options for airflow users, we will take advantage of Kubernetes Persistent Volumes. We can contain the watchers on separate threads which can use event handling to handle failures from airflow pods. This API will allow us to passively watch all events on a namespace, filtered by label. We will watch jobs using the Kubernetes Watch API. The second benefit is that dynamically creating pods allows for a highly elastic system that can easily scale to large workloads while not wasting resources during periods of low usage. Users will not need to pre-build airflow workers or consider how the nodes will communicate with each other. The first benefit is that dynamically creating airflow workers simplifies the cluster set-up. This design has two major benefits over the previous system. Each job will have contain a full airflow deployment and will run an airflow run command. Unlike the current MesosExecutor, which uses pickle to serialize DAGs and send them to pre-built slaves, the KubernetesExecutor will launch a new temporary worker job for each task. Users will be required to either run their airflow instances within the kubernetes cluster, or provide an address to link the API to the cluster. This client will allow us to create, monitor, and kill jobs. We will communicate with Kubernetes using the Kubernetes python client. Adding native Kubernetes support into Airflow would increase the viable use cases for airflow, add a mature and well understood workflow scheduler to the Kubernetes ecosystem, and create possibilities for improved security and robustness within airflow in the future. While traditional environments like YARN-based hadoop clusters have used Oozie, newer data and ML pipelines built on Kubernetes are increasingly using Airflow for orchestrating and scheduling DAGs. Kubernetes is an open-source platform designed to automate deploying, scaling, and operating application containers, and is widely used by organizations across the world for a variety of large-scale solutions including serving, stateful applications, and increasingly - data science and ETL workloads. Kubernetes has first class support on Google Cloud Platform, Amazon Web Services, and Microsoft Azure. Conceived by Google in 2014, and leveraging over a decade of experience running containers at scale internally, it is one of the fastest moving projects on GitHub with 1000+ contributors and 40,000+ commits. Kubernetes is a fast growing open-source platform which provides container-centric infrastructure.
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