Databricks releases four-phase guide for scaling Apps
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Databricks has released the "Databricks Apps Scaling Guide," which is organized into four phases—design, build, test and deploy, and operate—and explains how to keep applications low-latency and highly available as users grow from hundreds to thousands. The guide is aimed at developers using Databricks Apps and organizes scaling practices around the above phases, with the goal of maintaining low latency and high availability as user scale grows.
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- Databricks BlogA builder’s guide to scaling Databricks Apps
This guide outlines scaling Databricks Apps across four stages—design, build, test and deploy, and operate—starting with agreed latency and peak-load targets before any code is written. It recommends keeping app compute for serving the UI, sending heavy work to purpose-built services, using async code, splitting soft and hard state so sessions can be rebuilt on any instance, and letting load tests and telemetry determine instance size, worker count and instance count. It also covers connection budgeting for Lakebase, graceful shutdown within the 15-second SIGTERM window, and monitoring and alerting in production.
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