Netflix runs thousands of microservices to serve more than 100M users everyday. These services are backed by large fleet of data store instances running on the public cloud. It is nearly impossible to predict the traffic patterns imposed by our architecture upon our data stores. We needed a framework that would help us determine the behavior of our platform systems under various workloads. We wanted to be mindful of provisioning our clusters, scaling them either horizontally (by adding nodes) or vertically (by upgrading the instance types), and operating under a variety of conditions, such as node failures, network partitions, etc.
To address those complexities we designed a benchmarking system for Netflix's Cloud platform that can mimic the performance of production use cases. By integrating dynamic configuration management, middle-tier load balancing, and metrics, we can study the effect of different workload parameters. This helped us identify potential memory leaks and garbage collection issues. In addition it allowed us to test the impact of long running maintenance jobs such as database repairs or reconciliation. We are going to showcase how the deployment, management, and monitoring of multiple instances can be done from a single entry-point (UI). We finally going to show how we integrated a benchmarking tool into our release lifecycle.