Pinterest’s moves to Kubernetes
What is Kubernetes?
Kubernetes also known as k8s or “Kube” is an open-source container orchestration platform that automates many of the manual processes involved in deploying, managing, and scaling containerized applications. It was originally designed by Google and is now maintained by the Cloud Native Computing Foundation
The name Kubernetes originates from Greek, meaning helmsman or pilot. K8s as an abbreviation results from counting the eight letters between the “K” and the “s”. Google open-sourced the Kubernetes project in 2014.
The purpose of Kubernetes is to manage a containerized application in various types of physical, virtual, and cloud environments. Google Kubernetes is a highly flexible container tool to deliver even complex applications, consistently. Applications run on clusters of hundreds to thousands of individual servers.
Why you need Kubernetes and what it can do
Containers are a good way to bundle and run your applications. In a production environment, you need to manage the containers that run the applications and ensure that there is no downtime. For example, if a container goes down, another container needs to start. Wouldn’t it be easier if this behaviour was handled by a system?
That’s how Kubernetes comes to the rescue! Kubernetes provides you with a framework to run distributed systems resiliently. It takes care of scaling and failover for your application, provides deployment patterns, and more. For example, Kubernetes can easily manage a canary deployment for your system.
Features of Kubernetes
Automated rollouts and rollbacks
Kubernetes progressively rolls out changes to your application or its configuration, while monitoring application health to ensure it doesn’t kill all your instances at the same time. If something goes wrong, Kubernetes will rollback the change for you. Take advantage of a growing ecosystem of deployment solutions.
Storage orchestration
Automatically mount the storage system of your choice, whether from local storage, a public cloud provider such as GCP or AWS, or a network storage system such as NFS, iSCSI, Gluster, Ceph, Cinder, or Flocker.
Service discovery and load balancing
No need to modify your application to use an unfamiliar service discovery mechanism. Kubernetes gives Pods their own IP addresses and a single DNS name for a set of Pods and can load-balance across them.
Automatic bin packing
Automatically places containers based on their resource requirements and other constraints, while not sacrificing availability. Mix critical and best-effort workloads in order to drive up utilization and save even more resources.
Secret and configuration management
Deploy and update secrets and application configuration without rebuilding your image and without exposing secrets in your stack configuration.
Batch execution
In addition to services, Kubernetes can manage your batch and CI workloads, replacing containers that fail, if desired.
Horizontal scaling
Scale your application up and down with a simple command, with a UI, or automatically based on CPU usage.
Designed for extensibility
Add features to your Kubernetes cluster without changing upstream source code.
Self-healing
Restarts containers that fail, replaces and reschedules containers when nodes die, kills containers that don’t respond to your user-defined health check, and doesn’t advertise them to clients until they are ready to serve.
Kubernetes Components
When we deploy Kubernetes, we get a cluster.
A Kubernetes cluster consists of a set of worker machines, called nodes, that run containerized applications. Every cluster has at least one worker node.
The worker node(s) host the Pods that are the components of the application workload. The control plane manages the worker nodes and the Pods in the cluster. In production environments, the control plane usually runs across multiple computers and a cluster usually runs multiple nodes, providing fault-tolerance and high availability.
To learn more about Kubernetes in detail you can refer to the mention documents URLs.
Now let's come to the industrial case-studies
Pinterest’s Kubernetes story
Pinterest is an American image sharing and social media service designed to enable the saving and discovery of information (specifically “ideas”) on the internet using images and, on a smaller scale, animated GIFs and videos, in the form of pinboards. The site was created by Ben Silbermann, Paul Sciarra, and Evan Sharp and had over 478 million global monthly active users as of March 2021. It is operated by Pinterest, Inc., based in San Francisco.
Why Kubernetes?
With over 478 million monthly active users and serving over 10 billion recommendations every single day, that is huge. (The numbers might have changed now) As they knew these numbers are going to grow day by day, they began to realize the pain of scalability and performance issues.
Their initial strategy was to move their workload from EC2 instances to Docker containers; hence they first moved their services to Docker to free up engineering time spent on Puppet and to have an immutable infrastructure.
And then the next strategy was to move to Kubernetes:) Now they can take ideas from ideation to production in a matter of minutes whereas earlier they used to take hours or even days. They have cut down so much overhead cost by utilizing Kubernetes and have removed a lot of manual work without making engineers worry about the underlying infrastructure.
How Kubernetes?
According to lead author Lida Li and team, the Cloud Management Platform team started their journey with Kubernetes in 2017 by dockerizing their production workloads and evaluating different container orchestration systems. The Kubernetes native workload model covered deployment, jobs and daemon sets but the team needed more to model their workloads.
Pinterest designed its custom resource definitions (CRDs) to achieve various ends that may also be informative for engineers considering Kubernetes adoption. Firstly, they wanted to bundle various native Kubernetes resources to work as a single workload, which saved their engineers from doing this piece by piece. Secondly, they wanted to inject necessary runtime support for their applications by adding the necessary sidecars, init containers, equipment variables and volumes into the specification. Lastly, these definitions were used to perform the life cycle management for native resources, such as reconciling the specifications and updating the event record. The Pinterest team surmised that this evolution significantly reduced the workload on engineers and therefore the risk of error.
Another consideration is that the Pinterest team built an end-to-end test pipeline on top of the native Kubernetes test infrastructure with tests deployed to all clusters. This mitigated risks associated with going beyond the Kubernetes native workflow model and the engineers stated it caught many regressions before they reached production. The Pinterest team was also integrating their deployment workflow into their new CI|CD platform.
Kubernetes Impact on Pinterest
“By moving to Kubernetes the team was able to build on-demand scaling and new failover policies, in addition to simplifying the overall deployment and management of a complicated piece of infrastructure such as Jenkins,” says Micheal Benedict, Product Manager for the Cloud and the Data Infrastructure Group at Pinterest.
“We not only saw reduced build times but also huge efficiency wins. For instance, the team reclaimed over 80 per cent of capacity during non-peak hours. As a result, the Jenkins Kubernetes cluster now uses 30 per cent fewer instance-hours per day when compared to the previous static cluster.”
For more information, you may refer to the below link of the Pinterest-Kubernetes case study.
Thank You!
Keep Learning & Sharing…
If this article is useful for you then don’t forget to press the clap 👏 icon and also follow me on medium for more such amazing articles.
