Managing large stateful applications is tough.
Matt Schallert outlines how, using Kubernetes, Uber automated managing a challenging stateful workload—M3DB, its sharded, replicated, multizone time series database—and examines the operational challenges the company faced while scaling M3DB from a handful of clusters to over 40 clusters across multiple data centers and cloud providers, all while trying to create an environment-agnostic solution for open source users.
Matt then demonstrates methods of managing stateful workloads in a declarative manner to ease operational burden. You’ll see how M3DB’s declarative approach to cluster management can be extended to other workloads using its common set of open source libraries. This approach made orchestrating M3DB easier.
Along the way, Matt shares lessons learned that you can apply to a variety of stateful workloads across bare metal and cloud environments, regardless of whether it’s running under an orchestration system or managing instances directly. You’ll walk away with advice for managing stateful systems at scale and lessons to bear in mind when considering using an orchestration system for state management.
Matt is a site reliability engineer at Uber, where he works on its open source metrics platform, M3. His efforts have been focused on improving the operational experience for M3 users and making it Kube native. Previously, Matt was an SRE at Tumblr, where he spent his time improving the reliability of Tumblr’s infrastructure. In his spare time, Matt can be found hiking, skiing, and building data centers in his apartment.
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