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Auto-sizing for Stream Processing Applications at LinkedIn

Stream processing as a platform-as-a-service (PaaS) offering is used at LinkedIn to host thousands of business-critical applications. This requires service owners to manage applications’ resource sizing and tuning. Unfortunately, applications have diverged from their conventional model of a directed acyclic graph (DAG) of operators and incorporate multiple other functionalities, which presents numerous challenges for sizing. We present a controller that dynamically controls applications’ resource sizing while accounting for diverse functionalities, load variations, and service dependencies, to maximize cluster utilization and minimize cost. We discuss the challenges and opportunities in designing such a controller.