# Grab > Southeast Asia's leading everyday "super-app" that provides ride-hailing, food delivery, grocery shopping, and digital financial services. ## Articles ### [Mockers - Overcoming Testing Challenges at Grab](https://yomu.fyi/post/mockers-overcoming-testing-challenges-at-grab.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Mayank Gupta - Published: Sep 18, 2018 Grab operates over 250 microservices communicating over HTTP and gRPC, making shared staging environments costly, ambiguous in ownership, and fragile due to inconsistent data and uncoordinated deployments. To address these testing bottlenecks, Grab created Mockers, a Go SDK and CLI tool backed by a central monorepo of mock servers for local-box and CI testing. Mockers automatically generates HTTP and gRPC mock servers from Swagger specifications and protobuf files, returning configured network responses without internal business logic. By incorporating Grab's in-house chaos SDK middleware, Mockers also enables repeatable resiliency and contract testing locally without relying on code-level mocks. While Grab still mandates integration testing on distributed staging environments with live data, Mockers enables developers to detect complex defects and contract mismatches earlier. ### [Journey of a Tourist via Grab](https://yomu.fyi/post/journey-of-a-tourist-via-grab.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Lara PuReum Yim - Published: Sep 11, 2018 Grab analyzed platform ride data from millions of tourist passengers representing over 150 countries visiting Singapore. Over 60% of these tourist riders originated from Southeast Asia, while non-regional visitors mainly came from China, the United States, and India. Seasonal demand revealed a trimodal distribution for tropical travelers aligning with holiday periods, contrasting with a September-to-January peak for visitors escaping winter in four-season climates. Airport trips showed that nearly 90% of tourist passengers headed directly to hotels, concentrated heavily in central areas like Orchard, Bugis, Downtown Core, and Kallang. Additional key destinations included major shopping districts, iconic dining locations like Newton Food Centre and Chijmes, and medical centers, which saw tourist ride volume grow over 500% between 2015 and 2017. ### [How We Designed the Quotas Microservice to Prevent Resource Abuse](https://yomu.fyi/post/how-we-designed-the-quotas-microservice-to-prevent-resource-abuse.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Jim Zhan - Published: Aug 10, 2018 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. ### [Grab Senior Data Scientist Liuqin Yang Wins Beale-Orchard-Hays Prize](https://yomu.fyi/post/grab-senior-data-scientist-liuqin-yang-wins-beale-orchard-hays-prize.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Yang Liuqin - Published: Jul 20, 2018 Grab Senior Data Scientist Dr. Liuqin Yang, Professor Defeng Sun, and Professor Kim-Chuan Toh received the 2018 Beale-Orchard-Hays Prize for their research paper introducing SDPNAL+. The software employs a majorised semismooth Newton-CG augmented Lagrangian method to solve large-scale semidefinite programming problems with nonnegative constraints. While traditional methods struggled beyond matrix dimensions of 2,000 and 5,000 constraints, SDPNAL+ successfully scales to matrix dimensions of 9,261 and over 12 million constraints. In benchmark testing, the software solved a problem on a desktop PC in 1.5 hours that required 122 hours on a 56-core CPU and 128-GPU cluster using a traditional solver. Grab implements these optimisation techniques to accelerate its passenger-driver allocation algorithms by hundreds of times. ### [Building Grab’s Experimentation Platform](https://yomu.fyi/post/building-grab-s-experimentation-platform.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Abeesh Thomas - Published: Jul 13, 2018 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. ### [Introducing Grab-Kit: Distributed Service Design at Grab](https://yomu.fyi/post/introducing-grab-kit-distributed-service-design-at-grab.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Karen Kue - Published: Jun 8, 2018 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. ### [How Grab Experimented with Chat to Drive Down Booking Cancellations](https://yomu.fyi/post/how-grab-experimented-with-chat-to-drive-down-booking-cancellations.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Ishita Parbat - Published: Mar 1, 2018 Post-allocation ride cancellations at Grab degrade the booking experience and create costly inefficiencies for both passengers and driver-partners. Internal user research and platform data confirmed that rides involving GrabChat conversations had significantly lower cancellation rates by reducing perceived wait times. To scale this interaction without extra cost, the team tested system-generated automated messages sent at varying delay intervals, styles, tones, and localized verbiage across different cities. Faster message delivery outperformed longer delays, and tailored prompts reduced booking cancellations by up to two percentage points across tested markets. The experiment demonstrated that high-quality, directed prompts solicited quick responses and improved pick-up efficiency even when overall message volume was lower than control groups. ### [Deep Dive into Database Timeouts in Rails](https://yomu.fyi/post/deep-dive-into-database-timeouts-in-rails.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Jia Hao Goh - Published: Jan 29, 2018 Following a production outage where a database failover caused a Ruby on Rails application to exhaust its Puma server threads, an investigation was conducted to understand how ActiveRecord and MySQL timeout settings behave. A reproduction environment using Docker, Puma, and Toxiproxy replicated how hanging requests to a failing database consume all available server threads, ultimately starving unrelated endpoints. The analysis breaks down ActiveRecord connection pooling mechanics alongside underlying mysql2 and libmysqlclient settings, specifically checkout\_timeout, connect\_timeout, and read\_timeout. Testing confirmed how existing and new TCP connections transition through socket states during network interruptions while waiting on configured timeout intervals. ### [Dealing with the Meltdown Patch at Grab](https://yomu.fyi/post/dealing-with-the-meltdown-patch-at-grab.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Althaf Hameez - Published: Jan 7, 2018 AWS infrastructure maintenance related to Meltdown patches led to severe CPU utilization spikes across Grab's ElastiCache Redis instances. Because Redis is single-threaded, spikes past 50% CPU on two-vCPU instances threatened service capacity, and initial Multi-AZ failovers only provided temporary relief until the new master nodes received rolling patches. To handle the increased overhead before their peak traffic window, the engineering team horizontally scaled both clustered and non-clustered Redis fleets. For Redis 3.2.4 clusters lacking live re-sharding support, they provisioned larger clusters, warmed caches, and redirected traffic. Non-clustered workloads were resolved by provisioning extra nodes, migrating compatible services to Redis Cluster, or updating application code to shard data across multiple instances. ### [GrabShare at the Intelligent Transportation Engineering Conference](https://yomu.fyi/post/grabshare-at-the-intelligent-transportation-engineering-conference.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Dominic Widdows - Published: Dec 13, 2017 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. ### [Grabbing Growth: A Growth Hacking Story](https://yomu.fyi/post/grabbing-growth-a-growth-hacking-story.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Gaurav Sachdeva - Published: Dec 8, 2017 Grab established a dedicated Growth Hacking team within its Technology organization to pursue high-risk, niche initiatives and scale impact across 68 million regional users. Positioning the team within engineering allowed rapid A/B testing and simultaneous multi-market deployments driven by structured growth loops. The team prioritizes and evaluates all initiatives using a Growth Factor metric, calculated as the increase in rides divided by the increase in costs. To improve driver engagement, the team implemented a Spin-to-Win game based on B.F. Skinner's variable ratio reinforcement principles, delivering probabilistic monetary and merchandise rewards upon meeting daily ride thresholds. Ongoing regional experiments continue to evaluate metrics including driver acceptance, cancellation rates, and driver ratings. ### [The Data and Science Behind GrabShare Part I: Verifying Potential and Developing the Algorithm](https://yomu.fyi/post/the-data-and-science-behind-grabshare-part-i-verifying-potential-and-d.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Tang Muchen - Published: Oct 20, 2017 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. ### [The Art of Hiring Good Engineers](https://yomu.fyi/post/the-art-of-hiring-good-engineers.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Rachel Lee - Published: Oct 4, 2017 Building and scaling engineering teams requires adapting recruitment strategies across different organizational sizes, from initial hires to hundreds of engineers. Teams often begin with small groups of battle-tested engineers before designing formal hiring pipelines. The recruitment approach outlines four key steps: sourcing top-tier talent from platforms like GitHub and LinkedIn, assessing technical fit through tailored screens or assignments, evaluating cross-functional and culture fit via group interviews, and optimizing the process. This optimization includes routing candidates strategically to strict bar-raiser interviewers to prevent burnout and expedite decisions. ### [Migrating Existing Datastores](https://yomu.fyi/post/migrating-existing-datastores.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Nishant Gupta - Published: Aug 8, 2017 Grab's Identity team faced imminent memory exhaustion on a single Redis node used to cache mobile authentication tokens under rapid user growth. Because read traffic outweighed write traffic by roughly 200 times, the team opted for an AWS ElastiCache cluster with three shards and two read replicas per shard. They executed a zero-downtime, six-phase migration plan while handling a peak load of 20,000 queries per second. The migration transitioned through initial one-time data replication, asynchronous shadow writes, synchronous dual writes, asynchronous read validation, switching primary reads, and final write cleanup. Controlled by feature flags and monitored with metrics at every stage, the migration completed without invalidating tokens or causing service disruptions. ### [So You Need to Hire Good Engineers](https://yomu.fyi/post/so-you-need-to-hire-good-engineers.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Rachel Lee - Published: Jul 24, 2017 Hiring strategies in fast-growing technology startups dictate the quality and scalability of expanding engineering teams. Insights gathered from conversations with over one hundred engineering leaders identify crucial evaluation criteria across technical competence, cultural alignment, and problem-solving capability. Candidates rarely present a perfect technical fit, requiring hiring managers to weigh passion, willingness to learn, and risk appetite against baseline technical requirements. Rather than focusing solely on static factual questions, interviewers gain deeper insights by probing the rationale behind candidates' architectural and implementation choices. High-caliber engineers ultimately elevate team capability by simplifying complex challenges, writing cleaner code, and learning quickly. ### [Come and #hackallthethings at Grab](https://yomu.fyi/post/come-and-hackallthethings-at-grab.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Grab Engineering - Published: Jul 11, 2017 Grab has officially launched a public bug bounty program in partnership with HackerOne to strengthen the security of its platform. This rollout follows a private bounty initiative operated over the previous year, during which the organization worked with over 350 security researchers and resolved nearly 200 awarded bug reports. The new public program invites external researchers to scrutinize Grab's code for critical flaws, including remote code execution, SQL injections, and exportable cross-site scripting vulnerabilities. To support ethical and responsible disclosure, Grab offers payouts reaching up to $10,000 per valid vulnerability report based on severity and impact. ### [How We Scaled Our Cache and Got a Good Night's Sleep](https://yomu.fyi/post/how-we-scaled-our-cache-and-got-a-good-night-s-sleep.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Gao Chao - Published: Jun 19, 2017 Growing business load on the Common Data Service (CDS) created potential bottlenecks for its single-threaded Redis cache on ElastiCache, necessitating horizontal scaling for greater capacity and throughput. After ruling out master-slave replication and intermediate Twemproxy setups due to memory constraints and proxy I/O bottlenecks, the team implemented client-side sharding. Using an internal Go package for consistent hashing, CDS instances hash cache keys locally to determine the target shard. The implementation encapsulates hashing inside a thin \`ShardedCache\` wrapper sharing the original cache interface while supporting Ketama and custom hash functions. Deploying via double-writing cron jobs during off-peak hours reduced database read pressure and improved P99 latency. ### [Grab's Front End Study Guide](https://yomu.fyi/post/grab-s-front-end-study-guide.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Yangshun Tay - Published: Jun 3, 2017 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. ### [DNS Resolution in Go and Cgo](https://yomu.fyi/post/dns-resolution-in-go-and-cgo.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Ryan Law - Published: May 24, 2017 Go applications experiencing load balancing issues across AWS Elastic Load Balancer (ELB) nodes trace uneven traffic distribution to IP address sorting defined in RFC 6724. Comparing Go's native DNS resolver with Cgo and glibc's getaddrinfo shows that both initially sort destination addresses using Rule 9 longest matching prefix rules. Disabling IPv6 on the network interface causes C and Cgo resolvers to return IP addresses in randomized order, while the native Go resolver continues deterministic sorting. Examination of net/addrselect.go reveals that Go's native resolver implements only a subset of the RFC rules and omits dynamic source address selection. Achieving permanent parity requires modifying the Go source code directly. ### [Driving Southeast Asia Forward with AWS](https://yomu.fyi/post/driving-southeast-asia-forward-with-aws.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Arul Kumaravel - Published: May 21, 2017 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%. [Newer posts](https://yomu.fyi/company/grab/page/10.md) · [Older posts](https://yomu.fyi/company/grab/page/12.md)