![]() ![]() ![]() Switching to a more scalable solution has been a momentous effort, and this article explains how Docker helped us get here. Schemadock has many components, and Docker is a small but significant one. A centralized service maintains and monitors the goal state for each instance and reacts to any deviations. Agents then apply those topologies on the individual databases. Cluster topologies specify how MySQL clusters should look for example, that there should be a Cluster A with 3 databases, and which one should be the master. We run MySQL in Docker containers, which are managed by goal states that define cluster topologies in configuration files. The solution we came up with is a design called Schemadock. Have a single entry point to manage and monitor all clusters across all data center.Run multiple database processes on each host.When we began looking for a better way to manage our increasing number of MySQL clusters, we had a few basic requirements: Initially, all our clusters were managed by Puppet, a lot of ad hoc scripts, and manual operations that couldn’t scale at Uber’s pace. These days, we have more than 1,000 clusters containing more than 4,000 database servers, and that requires a different class of tooling. Managing these clusters was fairly easy when we had 16 clusters. Schemaless is a scalable and highly available datastore on top of MySQL¹ clusters. Uber Engineering’s Schemaless storage system powers some of the biggest services at Uber, such as Mezzanine. ![]()
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