Loading…
The GrabMart Journey
GrabClarisse Peralta
Summary
During the COVID-19 pandemic, Grab accelerated the rollout of its GrabMart on-demand grocery delivery service to meet surging regional demand. Early pilot testing within the existing GrabFood interface revealed that grocery shopping fundamentally diverges from meal ordering, as users search at an item level rather than browsing merchants. To support grocery workflows, the team restructured the user experience with item and store categories, one-click add-to-cart functionality, and scheduled delivery options. Engineering teams simultaneously updated core infrastructure across order management, driver allocation systems, and merchant Partner APIs to handle specialized mart bookings. Rollouts were validated across two main phases using the internal Grab Early Access program to catch issues before regional release.
Context
Rapid adoption of online delivery during COVID-19 and early pilot results revealed that grocery shopping differs from food delivery, with users struggling to find specific items within GrabFood's merchant-first flow and drivers facing unsegregated mart orders.
Approach / What changed
The team redesigned GrabMart around item-level search and categories, split delivery into on-demand and scheduled options, updated order management and driver allocation systems, enhanced merchant Partner APIs, and tested releases via the Grab Early Access program.
Takeaways
- Grocery shopping behavior begins at the item level rather than the merchant level, requiring item-focused search and category navigation rather than restaurant-centric listings.
- Scaling GrabMart required cross-system changes across the order management system, driver allocation algorithms, merchant apps, and Partner APIs.
- Splitting feature rollouts into two release phases validated by the Grab Early Access internal testing program helped surface and resolve defects before regional launch.
Related reading
Grab ·
How We Improved Agent Chat Efficiency with Machine Learning
Agent typing time represented a large portion of Grab's chat support journey, and 85% of messages were still free typed because agents customized static templates to fit their personal style. To accelerate typing across multilingual markets without robotic templates, Grab built SmartChat, a machine learning feature that provides contextual sentence completion. The team opted for a lightweight seq2seq architecture using single-layered GRU encoder-decoders in TensorFlow instead of bulky attention models to keep model latency under 100ms. The user interface was implemented in React using a content-editable div with inline typeahead suggestions activated via keyboard shortcuts.
Suman AnandGrab ·
How We Cut GrabFood.com’s Page JavaScript Asset Sizes by 3x
GrabFood.com faced high cloud costs while serving over 1 TB of weekly network egress across 175 million requests. To minimize egress and improve page performance, the engineering team audited and reduced their webpack dependencies using tools such as webpack-bundle-analyzer and dependency-cruiser. Their strategies included lazy loading rarely used libraries, unifying duplicate modules under single entry points, and substituting utility libraries like axios with native browser Web APIs. Additionally, altering implementation approaches, such as replacing client-side signed JWT cookie encoding with plain JSON strings, eliminated heavy Node dependencies like crypto. These optimizations reduced JavaScript static assets from 750 KB to 250 KB, decreased CloudFront costs by 20%, and accelerated build times by 3.6x.
Gibson ChengGrab ·
App Modularisation at Scale
Grab transitioned its monolithic mobile application into a modular architecture to resolve increasing code conflicts, slow releases, and difficult team collaboration. The team decomposed the single module by establishing base infrastructure modules, shared UI and utility libraries, discrete feature modules, and bridge kit modules for inter-module communication. Dependency injection using Dagger ties these components together in the main app module while preventing feature modules from directly depending on one another. The architecture spans over 1,000 modules across the app, with more than 200 modules in the Grab Financial Group payments domain where over 95% of modules build in under 15 seconds. This approach accelerated Gradle CI and local builds through parallel compilation and caching, though it increased Gradle sync times, IDE memory usage, and configuration maintenance overhead.
Amar JainGrab ·
How Grab is Blazing Through the Superapp Bazel Migration
Grab's mobile superapp scaled past 2.5 million lines of code across both Android and iOS, leading to unsustainable local and CI build times under Gradle and Xcode. To address these bottlenecks, the engineering team analyzed their dependency trees and introduced an internal tool to calculate and optimize the build critical path. They also deployed a Kubernetes-autoscaled remote build system using Mainframer for Android and implemented Test Impact Analysis to run only affected tests in pre-merge validation. While dependency decoupling yielded modest 7% to 10% gains and iOS remote builds proved unscalable on Apple hardware, Android remote builds reduced local compile times by up to 50%, and targeted test execution reduced pre-merge pipeline durations by more than 30%.
Sergii Grechukha