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Architecture
335 posts about Architecture. Every summary links to the original.
Grab ·
Orchestrating Chaos Using Grab's Experimentation Platform
Grab operates hundreds of microservices where failures in non-critical components can cause outages in critical user flows if fallback mechanisms are improperly configured. To validate system resilience, Grab built Chaos ExP by layering a chaos engineering SDK and dedicated web UI on top of its existing Experimentation Platform. Integrated directly into the Grab-Kit server middleware, the framework intercepts incoming requests and evaluates whether to inject failures using local variable resolution. Supported failure primitives include latency, errors, panics, rate throttling, and resource leaks to test dependent services. Combining chaos testing with experimentation telemetry enables engineers to correlate injected infrastructure disruptions with business metric impacts.
Roman AtachiantsGrab ·
Reliable and Scalable Feature Toggles and A/B Testing SDK at Grab
Grab previously managed experiments using custom service-level code and a toggling library that queried a shared Redis instance, creating latency risks and a single point of failure across backend microservices. To achieve reliable, sub-microsecond feature evaluations, the team designed a Go SDK that resolves rollouts and A/B tests entirely in memory without runtime network I/O. Backend services periodically poll JSON-defined configuration schemas stored in Amazon S3 through a Universal Configuration Manager. The SDK evaluates contextual attributes called facets locally and pushes decision telemetry asynchronously to an S3 and Presto data lake. This architecture allows engineering and product teams to gate deployments and run server-side experiments safely without service disruption.
Roman AtachiantsGrab ·
How We Designed the Quotas Microservice to Prevent Resource Abuse
As Grab migrated from a monolith to hundreds of microservices, managing global rate limiting became essential to prevent cascading failures and resource exhaustion. To avoid putting a rate limiting service on the critical path of every API call, Grab built Quotas, an asynchronous rate limiting system. Client services use a lightweight SDK and middleware to read rate limiting decisions from local in-memory caches and stream usage metrics asynchronously via Apache Kafka. The Quotas service aggregates usage data locally, flushes stats to Redis periodically, and publishes updated rate limiting decisions back over Kafka topics. In production, Quotas successfully handles 200k peak transactions per second with decision enforcement delays capped at 200 milliseconds.
Jim ZhanGrab ·
Building Grab’s Experimentation Platform
Grab built its internal Experimentation Platform (ExP) to replace a manual, expensive testing process that required bespoke meetings, custom logging pipelines, and service modifications for each experiment. ExP provides a unified infrastructure featuring a centralized management UI, automated real-time data streaming to S3, and SDKs for Android, iOS, and Go. The platform leverages JSON-based experiment definitions delivered through dynamic configuration management, enabling client-side evaluation without costly network calls. It addresses marketplace network effects and inter-experiment interference through mechanisms such as geo-temporal segmentation and domain-layer models. The platform has scaled to run approximately 25 concurrent experiments while computing roughly 2,500 metrics and 50,000 experiment-metric combinations daily.
Abeesh ThomasGrab ·
Introducing Grab-Kit: Distributed Service Design at Grab
As Grab migrated from a monolith to microservices, maintaining consistency, coordination, and code quality across rapidly expanding teams became a major engineering challenge. To address this, the Developer Experience team built Grab-Kit, a Go framework that automates service scaffolding, code generation, and distributed system design patterns. The framework uses Protocol Buffer definition files as a single source of truth to generate data transfer objects, communication bindings, and standardized middleware for logging and profiling. Grab-Kit also features declarative metrics definitions that synchronize with the DataDog API to build and update service dashboards automatically. Adopting the framework reduced development time for creating new services by up to 70% in teams such as GrabFood while improving overall system stability.
Karen KueGrab ·
GrabShare at the Intelligent Transportation Engineering Conference
Grab presented a technical paper on the construction of its real-time ridesharing service, GrabShare, at the Intelligent Transportation Engineering Conference in Singapore. The platform pairs passengers heading along similar routes with drivers immediately while handling network drops, volatile supply and demand, and heavy traffic conditions in Southeast Asian cities. To deliver accurate pairings, the scheduling system generates and filters through hundreds of travel time estimates for each candidate match before finalizing an itinerary. Operational teams on the ground evaluate complaints about poor matches, enabling engineers to refine the online matching systems. Over the course of one month, the service cut more than 4.5 million kilometers of driving distance and brought in over 100,000 new users within two weeks.
Dominic WiddowsGrab ·
The Data and Science Behind GrabShare Part I: Verifying Potential and Developing the Algorithm
Expanding from point-to-point dispatch services to dynamic carpooling requires matching independent passenger requests traveling in similar directions without causing unacceptable delays. Grab evaluated the feasibility of its GrabShare service by analyzing historical trip data with DBSCAN clustering on coordinates projected into a Universal Transverse Mercator system. This analysis demonstrated that 35% to 46% of rides across typical daytime windows fell into tight geographic clusters with near-identical pickup and drop-off coordinates. The resulting matching framework adapts the baseline dispatch flow by searching for in-transit drivers and enforcing real-time seat reservation constraints. Route assignment decisions subsequently evaluate detour times, trip angles, and expected arrival times to ensure driver utilization improves while total driving distance decreases.
Tang MuchenGrab ·
Grab's Front End Study Guide
Rapid hiring and engineering expansion created a steep learning curve for newcomers and backend engineers unfamiliar with modern web architecture. Grab created an opinionated front-end study guide to help engineers navigate JavaScript tooling, state management, and modern application practices. The guide frames client-side single-page applications as a scalable alternative to traditional server-side rendering, enabling clear client-server separation for multiple client apps hitting the same backend API. It establishes core competencies in ECMAScript 2015 via Babel and explains how component-driven interfaces in React improve maintainability and performance through virtual DOM reconciliation. Ultimately, the resource aims to ramp up developer onboarding and accelerate shipping speeds across web platforms.
Yangshun TayGrab ·
Driving Southeast Asia Forward with AWS
Grab transitioned its transportation platform from a single Ruby on Rails monolith on Amazon EC2 and Amazon RDS MySQL to a microservices architecture hosted on Amazon Web Services. The platform processes multi-petabyte real-time data flows and hundreds of millions of GPS data points to match drivers with passengers and push proactive demand heat maps. Operational efficiency is sustained with fewer than ten full-time infrastructure engineers, supported by AWS managed services. The analytics backend also transitioned from MySQL to Amazon Redshift, eventually moving to an Amazon S3 data lake using Amazon EMR and Presto. These data-driven matching systems improved driver-passenger allocation rates by up to 30%.
Arul KumaravelGrab ·
How to Go from a Quick Idea to an Essential Feature in Four Steps
Grab engineered an in-app messaging platform, GrabChat, to help drivers and passengers coordinate pickups across Southeast Asian markets characterized by weak 2G connectivity and high packet loss. The team developed an in-house TCP messaging architecture consisting of a TCP gateway named Gundam and a message dispatcher named Hermes connected to internal backend services over HTTPS. To protect backend server resources from resend loops during poor connection states, the communication protocol adopts a "server only push once" model that delegates retry handling to the client. Data science evaluations using a pre-trained cancellation prediction model confirmed that GrabChat adoption correlated with reduced booking cancellations. Following early usage feedback, the team further iterated on the feature by introducing pre-written message templates to reduce driver distraction on the road.
Da HuangGrab ·
Scaling Like a Boss with Presto
Grab experienced severe performance degradation, long queue times, and connection timeouts on its Amazon Redshift analytics cluster as user concurrency and reporting workloads expanded. Although an initial Amazon S3 data lake decoupled storage from compute, business users required standard SQL interfaces rather than Spark data pipelines. The team deployed Presto clusters on AWS EMR, switching their storage format from AVRO to Parquet to support ANSI SQL querying directly against S3. Utilizing a shared Hive metastore on Amazon RDS allowed Grab to adopt a shared-data multi-cluster architecture that isolated distinct workloads across dedicated compute clusters. This setup enabled rapid cluster scaling, streamlined failover, and matched Redshift performance on partitioned time-range queries.
Aneesh ChandraGrab ·
Grab Vietnam Careers Week
Grab announced its first Grab Vietnam Careers Week, held in Ho Chi Minh City from 22 to 26 October 2016, aimed at hiring engineers to improve regional transportation and reduce congestion. At the time, Grab operated on 23 million mobile devices supported by 460,000 drivers across Southeast Asia. The announcement features an interview with Singapore-based iOS engineer Hai Pham and Android engineer Son Nguyen discussing team culture, regular hackathons termed "Grabathons", and relocation to Singapore. Technical candidates undergo a Codility test and are evaluated on clean mobile architecture, testing, and performance maintenance. The engineers also offer advice on apartment hunting through online community forums and adapting to life in Singapore.
Grab EngineeringGrab ·
Round-robin in Distributed Systems
Building client-side load balancing for Grab's Common Data Service prompted a move from AWS Elastic Load Balancers to DNS discovery due to persistent connection issues and unpredictable scaling events. After patching an open-source library that failed to rotate IP sequences properly, the author evaluated different Go patterns for round-robin routing. A mutex-protected array counter provides the simplest model for basic retrieval, though adding mutations requires careful lock coordination. Alternatively, a dedicated balancer goroutine receiving requests over nested channels enables explicit operation timeouts and centralized event handling at the cost of higher code complexity and channel creation overhead. The author recommends the mutex approach for resource fetching and the goroutine-based design for workload balancing.
Gao ChaoGrab ·
Programmers Beware - UX is Not Just for Designers
Software engineers frequently overlook user experience when designing APIs, SDKs, and code-level functions. Usability issues arise across various technical interfaces, from mobile apps forced to coordinate multiple round-trip network calls to ambiguous function signatures that obscure boolean arguments. To counter this, engineers can apply a five-question discovery framework to identify user identity, core objectives, user capabilities, ways to reduce user burden, and familiar paradigms. Practical remedies include sacrificing strict RESTful separation to merge mobile endpoints, placing validation logic inside internal RPC servers, and replacing boolean arguments with explicitly named helper functions. Ultimately, treating calling systems, end users, and fellow programmers as users shifts implementation complexity from consumers to servers.
Corey ScottGrab ·
Grab You Some Post-Mortem Reports
Grab uses a Service-Oriented Architecture to deploy features quickly, but unfamiliarity across teams makes cross-service production debugging difficult. Historical incident reports lacked context, diagnostic details, impact data, and timelines, leaving outside engineers unable to learn from past outages. To address this, Grab established a four-pillar framework for post-mortem reports covering chronology, context, empowerment, and solutions. The approach mandates blameless, educational write-ups that categorize post-incident improvements across people, product, and process dimensions. Final reports undergo peer reviews by engineers from external teams to ensure clarity and remove bias.
Lian Yuanlin