![]() it’s not serverless - and we LOVE serverless in our team. It’s got a huge infra footprint, and you need to pay for it twenty-four-seven i.e. However, it’s a heavyweight solution to a very simple problem. It’s enterprise grade, which will also keep those snazzy architects up in their ivory towers real happy. Sure, that will work fine as long as you have deep pockets. But, what if you need to run N queries sequentially and they are dependant on each other? Or if you want to run other commands like a gsutil? Or even just some plain bash? Oh, the humanity!įinally, there’s the Cloud Composer option. Also, it only allows you to schedule one query at a time. Most notably it’s tied to a user account, and this ain’t gonna fly with your boss when staff offboard. There’s also BigQuery’s scheduling feature, but we see a few limitations with that. Currently there’s a few ways (hacks) to do this. ![]() Nothing fancy, just a good ‘auld fashioned SQL pipeline really. Recently, our team needed a simple way to schedule a bunch of BigQuery SQL queries to run. However, if you continue reading to the end you’ll find a wee little surprise waiting for you. Indeed I did, and for this I’m truly sorry. Now, I know what you’re thinking: _“You promised us that your first post on your shiny new blog would be pictures of your dog!"_. But, somehow, between the nappy changes, the cleaning up of puke and the endless interruptions, I finally managed to get pen to paper - albeit it took about thirty attempts to get there. The reality is that between work and two young children, I can barely find time to scratch my arse, let alone write a technical blog post. Nor the fact that it’s the first time I’m posting my drivel on a new platform and I’m still learning how to use Hugo. The code and example provided above are easy to use and help avoid the need of API level integration to schedule commands like gsutil, gcloud etc.This has probably been one of the hardest blog posts that I’ve ever tried to write. The steps described in the blog present a simplified method to invoke the most commonly used developer-friendly CLI commands on a schedule, in a production setup. Follow these steps to create and view metrics on Cloud Run. Cloud Monitoring provides more charting and filtering options. You can view metrics either in Cloud Monitoring or in the Cloud Run page in the console. This means that metrics of your Cloud Run services are captured automatically when they are running. Step 5: Create monitoring and alerting to check if the cloud run failed.Ĭloud Run is automatically integrated with Cloud Monitoring with no setup or configuration required. Step 1: Enable services (Cloud Scheduler, Cloud Run) and create a service account export REGION=> You can also build monitoring for the job and create alerts. This example requires you to set up your environment for Cloud Run and Cloud Scheduler, create a Cloud Run job, package it into a container image, upload the container image to Container Registry, and then deploy to Cloud Run.
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