What is Google Kubernetes Engine?
Who uses Google Kubernetes Engine?
Google Kubernetes Engine Integrations
Why developers like Google Kubernetes Engine?
Here are some stack decisions, common use cases and reviews by companies and developers who chose Google Kubernetes Engine in their tech stack.
At Shopify, over the years, we moved from shards to the concept of "pods". A pod is a fully isolated instance of Shopify with its own datastores like MySQL, Redis, Memcached. A pod can be spawned in any region. This approach has helped us eliminate global outages. As of today, we have more than a hundred pods, and since moving to this architecture we haven't had any major outages that affected all of Shopify. An outage today only affects a single pod or region.
As we grew into hundreds of shards and pods, it became clear that we needed a solution to orchestrate those deployments. Today, we use Docker, Kubernetes, and Google Kubernetes Engine to make it easy to bootstrap resources for new Shopify Pods.
We are hardcore Kubernetes users and contributors. We loved the automation it provides. However, as our team grew and added more clusters and microservices, capacity and resources management becomes a massive pain to us. We started suffering from a lot of outages and unexpected behavior as we promote our code from dev to production environments. Luckily we were working on our AI-powered tools to understand different dependencies, predict usage, and calculate the right resources and configurations that should be applied to our infrastructure and microservices. We dogfooded our agent (http://github.com/magalixcorp/magalix-agent) and were able to stabilize as the #autopilot continuously recovered any miscalculations we made or because of unexpected changes in workloads. We are open sourcing our agent in a few days. Check it out and let us know what you think! We run workloads on Microsoft Azure Google Kubernetes Engine and Amazon EC2 and we're all about Go and Python!
We recently moved our main applications from Heroku to Kubernetes . The 3 main driving factors behind the switch were scalability (database size limits), security (the inability to set up PostgreSQL instances in private networks), and costs (GCP is cheaper for raw computing resources).
We prefer using managed services, so we are using Google Kubernetes Engine with Google Cloud SQL for PostgreSQL for our PostgreSQL databases and Google Cloud Memorystore for Redis . For our CI/CD pipeline, we are using CircleCI and Google Cloud Build to deploy applications managed with Helm . The new infrastructure is managed with Terraform .
Read the blog post to go more in depth.
So, the shift from Amazon EC2 to Google App Engine and generally #AWS to #GCP was a long decision and in the end, it's one that we've taken with eyes open and that we reserve the right to modify at any time. And to be clear, we continue to do a lot of stuff with AWS. But, by default, the content of the decision was, for our consumer-facing products, we're going to use GCP first. And if there's some reason why we don't think that's going to work out great, then we'll happily use AWS. In practice, that hasn't really happened. We've been able to meet almost 100% of our needs in GCP.
So it's basically mostly Google Kubernetes Engine , we're mostly running stuff on Kubernetes right now.
#AWStoGCPmigration #cloudmigration #migration
I was thinking what could be the best option for deploying Daily's #frontend applications. On one side there is Netlify , promoting and encouraging the JAM stack, very easy to deploy and manage your deployments. And on the other side, Kubernetes , specifically Google Kubernetes Engine as I don't like to manage my own clusters. Kubernetes provides much more options, in terms of deployment strategy, networking, etc but requires far more configurations. As I don't have any SSR on my applications, I decided that the ease of use of Netlify is the number one priority for the project.
Google Kubernetes Engine no-ops. Maintaining reliable Kubernetes setup is not priority for the product driven engineering team. GKE makes it incredibly simple and cost-effective to run our container applications
Google Kubernetes Engine's Features
- Docker support - Improve the predictability of your deployments with Docker containers. Containers make it easy to deploy applications across environments.
- Better ops - Give ops a better system, starting with a managed compute cluster. Container Engine takes care of provisioning and maintaining the underlying virtual machines and operational logistics like logging, monitoring, and health management.
- Declarative management - Use declarative syntax to define your application requirements. Container Engine will actively manage your application, ensuring your containers are running and scheduling additional as needed.
- Scalable - Run multiple containers in a single virtual machine, or scale to many as your application grows. Container Engine makes it easy to manage your containers across a group of virtual machines.
- Powered by Kubernetes - Container Engine is powered by the open source Kubernetes technology. Join the discussion on Kubernetes and be part of the growing community.
- Decoupled apps - Let developers focus on code, with very few constraints. Create loosely coupled microservice apps that are more robust and easier to maintain and extend.