Scaling Temporal: The Basics

Scaling a Temporal Cluster involves addressing various workload patterns and operational goals, and this guide simplifies the process by focusing on key metrics and terminology. While using Temporal Cloud offers an easy scaling solution, the guide details a step-by-step approach for scaling a self-hosted Temporal Cluster on Kubernetes. This involves iterative cycles of loading, measuring, and scaling to transition from development to production-level configurations, with attention to aspects like Kubernetes resource management, shard count configuration, and polling optimization. The process includes adjusting the number of shards to reduce lock contention, optimizing CPU and memory usage, and improving poll sync rates to enhance throughput. The guide also emphasizes the importance of maintaining Service Level Objectives (SLOs) for request latency and Schedule-to-Start latency, using Prometheus and Grafana for monitoring. Through these adjustments, the cluster's performance significantly improves, achieving a substantial increase in State Transitions per second while maintaining database efficiency.

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