Intelligent automation is critical under these circumstances, which is why we developed Cruise Control: a general-purpose system that continually monitors our clusters and automatically adjusts the resources allocated to them to meet pre-defined performance goals. In essence, users specify goals, Cruise Control monitors for violations of these goals, analyzes the existing workload on the cluster, and automatically executes administrative operations to satisfy those goals. You can see a video here about Cruise Control at the Stream Processing Meet Up last fall.
Today we are pleased to announce that we have open sourced Cruise Control and it is now available on Github. In this post, we’ll describe Cruise Control’s uses both generally and at LinkedIn, its architecture, and some unique challenges we faced when creating it. For further details about Kafka terminology used throughout this post, this reference can be a helpful guide.
This isn’t a monitoring tool per se, but rather a resource balancing tool. And it’s now freely available to all.