TechPrufer
Get started
Dropbox
Dropbox logoDropbox · Feature trail

Gradle Build Performance Optimization

2 posts · Nov 2020 – Mar 2022

What this trail covers

Dropbox is actively working on improving build performance for large monorepos, particularly for Android development. This includes developing and open-sourcing tools and plugins that allow engineers to focus on specific modules, reducing IDE sync times and overall build times. The goal is to enable faster iteration cycles and improve developer productivity by minimizing the amount of code Gradle and Android Studio need to process. This post details how the team reduced CI runtime from an average of 75 minutes to 25 minutes by optimizing Gradle, offloading work to Firebase Test Lab, and rearchitecting CI jobs for parallelism, specifically by implementing an Affected Module Detector to run only necessary tests.

All Dropbox trails
  1. Introducing Focus, a new open source Gradle pluginDropbox developed and open-sourced the 'Focus' Gradle plugin. This plugin allows engineers to specify a target module within a large monorepo, and it dynamically generates a settings file that only includes the dependencies required for that module. This significantly reduces the number of projects Gradle needs to configure, leading to faster IDE sync times (e.g., from 1 minute to 15 seconds for UI Components Playground) and improved build performance. The plugin also provides tasks to easily clear the focus and revert to the full project configuration.
  2. Revamping the Android testing pipeline at DropboxThis post details the revamp of Dropbox's Android testing pipeline, reducing CI runtime from 75 minutes to 25 minutes. Key contributions include: implementing an Affected Module Detector to run only tests relevant to code changes, migrating from custom tooling to industry-standard tools like Gradle and Firebase Test Lab, and rearchitecting CI jobs for better parallelism. The team learned from the AndroidX team's open-source implementation of an Affected Module Detector to solve the problem of selectively scaling the pipeline based on module dependencies.