![]() The above exception was the direct cause of the following exception:įile "/usr/local/lib/python3.6/multiprocessing/process.py", line 258, in _bootstrapįile "/usr/local/lib/python3.6/multiprocessing/process.py", line 93, in run We will see how the operators in the apache-airflow-providers-google package make it easy to integrate a wide range of GCP services such as GCS.Forkers bgaechter toneill818 PierreC1024 kangmaoyin vincentbellitto pkuong Isaiah-Turner CapChrisCap pedaling flavio-assis DavidChenLondon chris-vest hedrickw KimKiHyuk alexbegg michael-j-thomas mgvalverde vizbeth-oss ectogon ArielShup ankurshaswat kot-behemoth MingLunWu sisandi dstandish adanque dre1992 naturalett Swalloow shoman2 makramjandar datGnomeLife gfeldman sparbz hudsondba razius kulkinp dgomex bigsharkie rodolfo-picoreti Patricol phobot povode billmcmath atulprusty thewaisy srnbckr Bhavishya101 jessicastewart-udemy abrahamgv Tiqets chakir-alaoui darsi-an scheung38 abhaypartap edenbaus spagarwa bensta devkda franchev cfraleig justincmoy LeoTSH kurshi JohnsonHeather odenio bledogit rafaelkonrath Piboonsak HarryDunham steve148 dominique-vassard dgadiraju meseta sk1tter rolanddb karakanb fernbach thetko85 maxcrom Lord-Y louison jgilme1 brain-buster roopainfoworks luozhaoyu davido912 griseau Tonkonozhenko pindge yehlo matalo33 ValentinoVizner-SBT MevinFernando enniomorricone123 cocampbe jrggggg Sri-nidhi hussainsaify hanzala1234 In the next part we will further extend our deployment by adding a DAG. Likewise, by changing `CeleryExecutor` to `LocalExecutor` GCP takes care of terminating the redundant flower and redis services and spinning up a scheduler service. For instance, merely replacing `ClusterIP` with `LoadBalancer` is sufficient to make GCP spin up and configure a load balancer for the Airflow web server. We saw how Helm and GCP abstract away a lot of the intricacies in configuring Kubernetes. It’s therefore recommended to set up your project over the command line (or with tools like Terraform) and use the GCP interface to inspect our work. We can always recreate it thanks to the values.yaml file that stores our configuration. We now have a fully functional Airflow deployment on GKE. We will learn how to add a DAG along with its dependencies in the second part of this article. Creating a Kubernetes cluster on GKEīefore we can initialize a Kubernetes cluster on GKE we must first set the project in the gcloud CLI using its Project ID:Ĭurrently there are no DAGs. If you need a quick introduction to Kubernetes watch this light-hearted video. The CLI-tools gcloud, kubectl and helm.A GCP project named ‘airflow-gke’ with an active billing account (potentially with free trial credit).This article assumes that the prerequisites have been met on your workstation: This allows us to optimize the infrastructure for our specific use case and lower the cost. However, by managing our own deployment on Kubernetes we maintain more granular control over the underlying infrastructure. It’s worth noting that GCP offers its own managed deployment of Airflow called Cloud Composer. This makes integrating Airflow with the many GCP services such as BigQuery and GCS a breeze. The apache-airflow-providers-google Python package provides a larger number of Airflow operators, hooks and sensors. GCP is an excellent cloud provider choice for Airflow. Integrate other GCP services such as Google Cloud Storage.Īfter part two we will have extended our Airflow deployment with a DAG that writes a daily batch of data to a Google Cloud Storage bucket.Automatically pull Airflow DAGs from a private GitHub repository with the git-sync feature.Install Airflow dependencies and custom operators via a Docker image loaded from the Artifact Registry. ![]() Manage Airflow Connections using GKE Secrets.Expose the Airflow web server on GKE through a GCP LoadBalancer.Īt the end of this part we will have an Airflow deployment running the LocalExecutor and an Airflow web server accessible through a GCP LoadBalancer.Deploy and configure Airflow using Helm and the values.yaml file.This two-part article will demonstrate how to deploy and configure Apache Airflow on the Google Kubernetes Engine on GCP using the official Helm chart. Photo by Reza Rostampisheh on Unsplash Objectives
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