How do you scale Percona XtraDB Cluster with ProxySQL in Kubernetes?

In my previous post I looked how to run Percona XtraDB Cluster in a Docker Swarm orchestration system, and today I want to review how can we do it in the more advanced Kubernetes environment.

There are already some existing posts from Patrick Galbraith (https://github.com/kubernetes/kubernetes/tree/release-1.2/examples/mysql-galera) and Raghavendra Prabhu (https://github.com/ronin13/pxc-kubernetes) on this topic. For this post, I …

Cross-posted on Google Cloud Platform Blog.

Cloud native technologies like Kubernetes help you compose scalable services out of a sea of small logical units. In our last post, we introduced Vitess (an open-source project that powers YouTube's main database) as a way of turning MySQL into a scalable Kubernetes application. Our goal was to make scaling your persistent datastore in Kubernetes as simple as scaling stateless app servers - just run a single command to launch more …

Cross-posted on Google Cloud Platform Blog. Cloud native technologies like Kubernetes help you compose scalable services out of a sea of small logical units. In our last post, we introduced Vitess(an open-source project that powers YouTube's main database) as a way of turning MySQL into a scalable Kubernetes application. Our goal was to make scaling your persistent datastore in Kubernetes as simple as scaling stateless app servers - just run a single command to launch more pods.

Cross-posted on Google Cloud Platform Blog.

Your new website is growing exponentially. After a few rounds of high fives, you start scaling to meet this unexpected demand. While you can always add more front-end servers, eventually your database becomes a bottleneck, which leads you to . . .

• Add more replicas for better read throughput and data durability
• Introduce sharding to scale your write throughput and let your data set grow beyond a single machine
• Create separate replica pools for batch jobs and backups, to isolate them from live traffic
• Clone the whole deployment into multiple datacenters worldwide for disaster recovery and lower latency

At YouTube, we went on that  …

Cross-posted on Google Cloud Platform Blog. Your new website is growing exponentially. After a few rounds of high fives, you start scaling to meet this unexpected demand. While you can always add more front-end servers, eventually your database becomes a bottleneck, which leads you to... Add more replicas for better read throughput and data durability Introduce sharding to scale your write throughput and let your data set grow beyond a single machine Create separate replica pools for batch jobs and backups, to isolate them from live traffic Clone the whole deployment into multiple datacenters worldwide for disaster recovery and lower latency At YouTube, we went on thatjourney as we scaled our MySQL deployment, which today handles the metadata for billions of daily video views and 300 hours of new video uploads per minute.