# Grab > Southeast Asia's leading everyday "super-app" that provides ride-hailing, food delivery, grocery shopping, and digital financial services. ## Articles ### [Returning 575 Terabytes of Storage Space to Our Users](https://yomu.fyi/post/returning-575-terabytes-of-storage-space-to-our-users.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Lucas Nelaupe - Published: Feb 25, 2020 Android Vitals data revealed that 15.7% of Grab users had less than 1GB of free device storage and uninstalled the app at 1.2 times the normal rate. To understand on-device storage consumption, the team instrumented session launches using the Android StorageManager API to collect binary size, cache folder size, and total footprint metrics. Analysis showed unusually large cache sizes driven by orphaned cache folders from discontinued third-party libraries, including an image library replacement of Picasso by Glide. An automated cleanup routine deployed in app updates purged legacy cache directories upon launch. This mechanism reclaimed 575 terabytes of junk data across more than 13 million devices, averaging 40MB per user. ### [Grab-Posisi - Southeast Asia’s First Comprehensive GPS Trajectory Dataset](https://yomu.fyi/post/grab-posisi-southeast-asia-s-first-comprehensive-gps-trajectory-datase.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Zhengmin Xu - Published: Feb 20, 2020 Grab released Grab-Posisi, a GPS trajectory dataset covering Singapore and Jakarta designed to overcome the poor coverage, low sampling rates, and missing contextual metadata common in existing public datasets. Captured in April 2019 from drivers in transit, the dataset comprises 84,000 trajectories, over 80 million GPS pings, and more than 1 million kilometres recorded at a one-second sampling frequency. Each entry in the 2 GB Apache Parquet dataset records geographic coordinates, timestamp, accuracy radius, bearing, speed, mobile operating system, and vehicle mode. To protect privacy, driver personal information is encrypted and trip start and end locations are removed. The data enables applications including automated road network reconstruction, map matching, real-time traffic forecasting, and municipal infrastructure planning. ### [How We Prevented App Performance Degradation from Sudden Ride Demand Spikes](https://yomu.fyi/post/how-we-prevented-app-performance-degradation-from-sudden-ride-demand-s.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Corey Scott - Published: Jan 8, 2020 Grab experienced severe system strain when sudden localized spikes in ride demand, triggered by events like heavy rain or concert dismissals, coincided with driver shortages. These localized bursts overloaded the platform and degraded the experience for users outside the affected areas. To mitigate this, engineers created the Spampede filter, a circuit-breaker mechanism placed at the start of the booking pipeline. The filter converts pickup locations into Geohash Integer buckets and partitions time using Unix timestamps, tracking unfulfilled requests in Redis with atomic increments and time-to-live expirations. When unallocated requests exceed configured thresholds within a specific bucket, the system immediately short-circuits new incoming bookings to protect overall platform stability. ### [Plumbing At Scale](https://yomu.fyi/post/plumbing-at-scale.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Karan Kamath - Published: Jan 6, 2020 Grab's backend services process terabytes of data ingress per hour, generating recurring needs for stream transformations, joins, and time-windowed aggregations across diverse workloads. To support these asynchronous processing patterns across their Go ecosystem, the Coban team developed a managed, NoOps event sourcing and stream processing platform. The architecture packages stateless processing pipelines as Kubernetes deployments on AWS, polling Kafka event logs and using ScyllaDB as a shared metastore for stateful needs like deduplication and windowing. Stream processing pods combine ingestion triggers, a worker pool runtime, and user-provided domain logic plugins with customizable failure handling. This infrastructure scales to handle over 300 billion events weekly while maintaining workload isolation and elastic autoscaling. ### [Journey to a Faster Everyday Superapp Where Every Millisecond Counts](https://yomu.fyi/post/journey-to-a-faster-everyday-superapp-where-every-millisecond-counts.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Renu Yadav - Published: Dec 26, 2019 Grab undertook an initiative to reduce startup time and improve time to interactive (TTI) on its passenger mobile app. Because local benchmarks failed to simulate real device and network conditions, the team instrumented code in production across 8–9 million daily users to capture p50 and p95 metrics. Initial gains came from caching service tiles between sessions and removing a startup animation, saving four seconds. Architectural changes followed, including converting iOS dynamic frameworks to static linking and merging others, while Android initialisation was refactored with Kotlin coroutines. Replacing a heavy third-party analytics library with an internal experimentation platform yielded further startup reductions. ### [Marionette - Enabling E2E User-scenario Simulation](https://yomu.fyi/post/marionette-enabling-e2e-user-scenario-simulation.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Anish Jha - Published: Dec 23, 2019 Conducting end-to-end testing across Grab's transport microservices became difficult due to service availability, environment construction, cross-service authentication, and complex data setups for real-world user accounts. To address these challenges without relying on physical mobile devices or emulators, Grab built Marionette, an internal simulation platform for passenger and driver interactions. The platform provisions required test data, coordinates booking lifecycles, and isolates test executions across distinct user groups using localized cohorts. Engineers can configure driver and passenger behaviors, execute workflows, and run load or integration tests through a dedicated user interface, a Go SDK, and RESTful APIs. ### [How We Implemented Domain-Driven Development in Golang](https://yomu.fyi/post/how-we-implemented-domain-driven-development-in-golang.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Kapil Chaurasia - Published: Nov 21, 2019 Building GrabPlatform's partner integration self-service portal initially resulted in an unstructured codebase where individual files exceeded 500 lines and lacked proper segregation. Modifying existing functions carried high risks of breaking functionality across imported source collections. To resolve this, the team restructured the Go application using Domain-Driven Design principles in coordination with product domain experts. They mapped business rules into bounded contexts, identified entities and aggregate roots, introduced repository interfaces, and utilized domain events for cross-context communication. The refactoring distributed core functionality evenly, simplified onboarding, and aligned technical terminology with business concepts. ### [Driving Southeast Asia Forward Through People-Focused Design](https://yomu.fyi/post/driving-southeast-asia-forward-through-people-focused-design.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Philip Madeley - Published: Nov 5, 2019 Designing digital products for Southeast Asia requires tailoring user experiences to unique regional constraints and consumer behaviors across diverse populations. Users in the region often operate low-end mobile hardware on congested networks while carefully rationing prepaid mobile data. Grab addresses these challenges by designing comprehensively for non-ideal UI stacks, implementing loading skeletons, and replacing heavy video tutorials with lightweight SVG animations to minimize bandwidth consumption. Furthermore, product teams adapt to mobile-only environments by prioritizing phone number and one-time-password registrations while avoiding legacy desktop-era iconography. Visual accessibility is validated by testing UI readability on dimmed, low-resolution screens under bright ambient sunlight. ### [Griffin, an Anti-fraud Risk Rule Engine Making Billions of Predictions Daily](https://yomu.fyi/post/griffin-an-anti-fraud-risk-rule-engine-making-billions-of-predictions.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Muqi Li - Published: Oct 28, 2019 Grab's Trust/Identity/Safety team built Griffin, an in-house anti-fraud risk rule engine designed to process billions of daily predictions across multiple business verticals. Initially, Grab managed fraud rules directly within backend service code, but escalating rule complexity, tight rule interdependencies, and translation gaps between data scientists and developers caused deployment delays and misfiring errors. To overcome the limitations and steep learning curves of third-party engines like Drools, the team separated the workflow into data orchestration and rule-based prediction. Griffin enables analysts and data scientists to author Python-based rules directly via a web portal and reload updated logic into memory without manual developer intervention. By eliminating I/O during rule evaluation and leveraging Gunicorn multi-processing, the engine handles over 100,000 queries per second at peak on six EC2 instances with single-prediction latencies under six milliseconds. ### [Using Grab’s Trust Counter Service to Detect Fraud Successfully](https://yomu.fyi/post/using-grab-s-trust-counter-service-to-detect-fraud-successfully.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Chao Wang - Published: Oct 21, 2019 Grab's Trust Platform team built the Counter service to detect fraud across business verticals like transportation, food, and payments. The platform replaces manual, multi-week engineering cycles with a self-service UI where data analysts can define and experiment with counters independently. Operating on an asynchronous ingestion and synchronous transaction model, the architecture evaluates incoming stream data, enriches it via internal services, and persists aggregated signals to ScyllaDB through Grab-Stats. A multi-bucket strategy partitions queries into fifteen-minute, hourly, and daily granularities to maintain low-latency query aggregations across wide time ranges under strict SLAs. ### [Being a Principal Engineer at Grab](https://yomu.fyi/post/being-a-principal-engineer-at-grab.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Roman Atachiants - Published: Sep 25, 2019 Grab's rapid growth resulted in roughly 350 microservices powering its superapp, creating the need for defined individual contributor career milestones. At Grab, a principal engineer oversees the architecture of an entire Tech Family, a sub-organisation containing over 50 engineers and 20 or more microservices. Responsibilities include translating broad, ambiguous problems into concrete projects, managing technical debt, and aligning multiple engineering teams across global R&D centres. The role demands continuous technical leadership through RFC design reviews, cross-functional communication, mentorship, and self-directed prioritization alongside engineering leadership. Ultimately, principal engineers amplify engineering quality and operational stability without directly managing people. ### [Data First, SLA Always](https://yomu.fyi/post/data-first-sla-always.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Johan Kok - Published: Aug 1, 2019 Grab's Data Engineering team transitioned from periodic batch ETL ingestion to a real-time change data capture architecture called Trailblazer after dataset sizes exceeded the petabyte mark. The previous system caused severe JDBC timeouts and heavy CPU loads when executing chunked or full-scan queries on unindexed upstream MySQL tables. To solve this, MySQL binary logs are captured via Debezium on Kafka Connect, buffered in Kafka, and ingested into a data lake using Spark Structured Streaming. Checkpoints are decoupled from local storage and persisted in a Redis cluster to simplify ingestion offset overrides and handle ephemeral compute clusters. The system incorporates extensive health monitoring across Airflow, Datadog, and custom services to maintain stream liveliness and avoid Kafka retention breaches. ### [Save Your Place with Grab!](https://yomu.fyi/post/save-your-place-with-grab.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Summit Saurav - Published: Aug 1, 2019 Grab introduced Saved Places across Southeast Asia to eliminate the friction of repeatedly typing addresses and prevent selection errors between similarly named locations. Data analysis across transport and food orders revealed that consumers consistently visit only five to seven unique locations and order food to one or two addresses. To streamline repeat bookings, cross-functional teams designed a feature allowing users to bookmark locations under custom labels such as Home and Work. Following usability testing and release, more than 14 million users stored nearly 45 million addresses across the platform. Platform metrics also showed that while office destinations clustered in central districts across major cities, residential distributions varied significantly between markets like Singapore and Jakarta. ### [No More Forgetting to Input ERP Charges - Hello Automated ERP!](https://yomu.fyi/post/no-more-forgetting-to-input-erp-charges-hello-automated-erp.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Garvee Garg - Published: Jul 31, 2019 Grab launched an automated Electronic Road Pricing (ERP) fare calculation feature in Singapore to eliminate the need for driver-partners to manually track gantries and enter toll charges. Because Singapore gantries frequently adjust fares based on time and road conditions, manual entry often caused driver errors and revenue loss. Grab solved this by mapping precise geographical coordinates for every toll gate using satellite imagery and open data, matching frequent driver GPS pings against road layers and gantry locations. The engineering and operations teams also built an internal ERP Workflow tool to map ride trajectories and resolve driver dispute feedback within an average of one day. Following its rollout in Singapore, Grab began testing and planning regional expansion to Indonesia, Thailand, Malaysia, and the Philippines. ### [How We Built a Logging Stack at Grab](https://yomu.fyi/post/how-we-built-a-logging-stack-at-grab.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Daniel Kasen - Published: Jul 31, 2019 Grab needed a scalable logging platform to replace slow, fragmented systems that hindered debugging across their growing service fleet. Generating 25TB of daily logs, the team built a horizontally scalable Elasticsearch cluster configured via Ansible and monitored with Datadog. Although the initial proof of concept assigned all node roles (ingest, coordinator, master, and data) to every machine, operating at scale introduced major challenges with JVM heap exhaustion and cluster stability. The team resolved memory pressure and performance bottlenecks by tuning circuit breakers, lowering field data cache limits, adjusting shard allocations based on segment memory, and disabling translog compression during shard transfers. ### [Making Grab’s Everyday App Super](https://yomu.fyi/post/making-grab-s-everyday-app-super.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Justin Bolilia - Published: Jul 3, 2019 Grab manages an expanding superapp ecosystem comprising ride-hailing, food delivery, payments, and partner content surfaced through the Grab Feed. As content volume grows, the platform risks overwhelming users with irrelevant information. To address this, Grab built a recommendation engine that ranks cards using signals across user profiles, content metadata, and contextual factors such as time and location. The system employs multiple recommendation strategies—including popularity metrics, user favorites, collaborative filtering, habitual patterns, and cross-platform deep embeddings—which are selected or aggregated. Recommendation quality is evaluated via offline metrics like Recall@K and NDCG alongside online engagement experiments. ### [Catwalk: Serving Machine Learning Models at Scale](https://yomu.fyi/post/catwalk-serving-machine-learning-models-at-scale.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Nutdanai Phansooksai - Published: Jul 2, 2019 As machine learning adoption expanded at Grab, individual teams created fragmented model serving solutions that duplicated engineering effort and required data scientists to handle underlying infrastructure. To resolve these inefficiencies, Grab developed Catwalk, a self-service machine learning model serving platform. The system runs TensorFlow Serving containers across a managed Kubernetes cluster integrated with Grab's observability stack. Data scientists deploy or update models simply by saving files using the tf.saved\_model API to dedicated Amazon S3 buckets, while Kubernetes automates orchestration, ingress routing, and pod autoscaling. Catwalk abstracts server management away from data scientists, shortens deployment timelines, and provides high availability during model version rollouts. ### [React Native in GrabPay](https://yomu.fyi/post/react-native-in-grabpay.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Sushant Tiwari - Published: May 30, 2019 Following the release of the GrabPay Merchant App, Grab adopted React Native inside the Grab Passenger app to maintain a single cross-platform codebase across iOS and Android. Integrating the framework into native applications required establishing bridge communication guidelines and incorporating React Native modules into the Grablet architecture. To streamline network communication, API calls were migrated from axios to native bridges returning promises, which eliminated the need to pass access tokens into JavaScript. The team also established a shared internal library of approximately 20 UI components alongside Redux for state management and react-navigation for routing. Modules like BillPay and Transaction History successfully launched across Southeast Asia while maintaining the performance and feel of native software. ### [Connecting the Invisibles to Design Seamless Experiences](https://yomu.fyi/post/connecting-the-invisibles-to-design-seamless-experiences.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Stephanie Lukito - Published: May 28, 2019 Service design operates as connective tissue within complex product ecosystems by bridging digital touchpoints, physical operations, and backstage technical workflows. At Grab, placing an order on GrabFood requires coordinating driver allocation, customer support paths, and long-term data storage rather than simply transmitting information to merchants. Focusing exclusively on singular features risks breaking broader network dependencies when modifications ripple into other operational systems. Grab addresses these interdependencies through participatory design processes and visual mapping across cross-functional teams. This holistic framework evaluates whether systemic issues, such as inaccurate restaurant operating hours, are best resolved through in-app feature changes or operational adjustments. ### [Tourists on GrabChat!](https://yomu.fyi/post/tourists-on-grabchat.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Lara PuReum Yim - Published: May 22, 2019 Grab examined more than 3.7 million tourist messages across Singapore, Malaysia, and Indonesia sent between December 2018 and March 2019 to evaluate passenger communication patterns. The platform deployed in-house translation and prewritten, auto-translated chat templates to bridge language barriers between international riders and local drivers. Analysis showed that bookings utilizing chat templates experienced a 10% higher ride completion rate than those without. Image-sharing features were most heavily used in high-traffic hubs such as airports, shopping malls, and major tourist centers to aid driver location. Passengers also consistently used messaging to clarify luggage capacity, provide identifiable passenger descriptions, and check pet policies. [Newer posts](https://yomu.fyi/company/grab/page/8.md) · [Older posts](https://yomu.fyi/company/grab/page/10.md)