TechPrufer
Get started
Grab
Grab logoGrab · Feature trail

Android Studio IDE Performance in Monorepo

2 posts · Dec 2020 – Jul 2026

What this trail covers

Grab has evolved its Android Studio IDE performance by developing a custom Focus plugin. This plugin leverages pre-computed dependency graphs from the Gradle-to-Bazel migration workflow to allow developers to selectively sync only the modules they are actively working on, drastically reducing sync times and memory consumption. The plugin integrates seamlessly with Android Studio via an IntelliJ plugin, providing a user-friendly interface for module selection and dependency visibility, thereby improving developer productivity in large monorepos.

All Grab trails
  1. Scaling developer experience: How we improved Android Studio in a large monorepoThis post details the development of a custom Gradle plugin and an accompanying Android Studio plugin to address slow IDE sync times in a large Android monorepo. The solution pre-computes dependency graphs during the Gradle-to-Bazel migration process, enabling selective module syncing. A Gradle plugin intercepts `settings.gradle`'s `include` calls to filter modules based on user selection, and an Android Studio plugin provides a UI for module selection and automatically injects necessary Gradle properties. This approach eliminates the need for manual Gradle property management and reduces sync times by focusing on only the required modules and their transitive dependencies.
  2. How Grab is Blazing Through the Superapp Bazel MigrationThis post details Grab's efforts to address build time challenges in its large Android and iOS applications. It introduces the concept of the 'critical path' in build graphs and the development of a tool to analyze it, leading to a 7-10% improvement in CI and local build times through module splitting and dependency optimization. For Android, a Kubernetes-based Mainframer infrastructure was implemented, resulting in a 40-50% improvement in local build times. For iOS, remote builds were explored but abandoned due to Apple's hardware restrictions and scalability issues, leading to a focus on other optimization strategies.