# Grab
> Southeast Asia's leading everyday "super-app" that provides ride-hailing, food delivery, grocery shopping, and digital financial services.

## Articles

### [An elegant platform](https://yomu.fyi/post/an-elegant-platform.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Fabrice Harbulot
- Published: Nov 30, 2023

Grab’s Coban real-time data streaming team initially relied on direct Terraform merge requests for managing self-served resources like Kafka topics and CDC pipelines. This pure Infrastructure-as-Code workflow faced challenges with CI pipeline failures from manual errors, security risks from lacking access controls, and platform team review bottlenecks. To resolve this, Coban implemented a three-tier control plane comprising the Coban UI, a Go backend named Heimdall, and a Git storage and provisioner repository named Khone. Heimdall translates user form submissions into validated merge requests containing Terraform and metadata files, polling Khone's CI status and alerting users. This architecture abstracts infrastructure code behind a graphical self-service portal while preserving underlying Git auditing and automation.


### [Road localisation in GrabMaps](https://yomu.fyi/post/road-localisation-in-grabmaps.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Roxana Crisan
- Published: Nov 17, 2023

Grab needed to localise nearly 30 million road segments across more than 10,000 area boundaries in Southeast Asia to attach hyperlocal attributes like language, driving side, and vehicle access rules. Testing direct geometric inclusion between complex road polylines and boundary polygons proved computationally prohibitive for daily map generation pipelines. To optimize this process, Grab introduced geohashes as rectangular proxies, precomputing geohash coverage across borders and road segments before joining the datasets in parallel. To resolve misclassification errors near borders without sacrificing performance, the team evaluated geohash coverage percentages and refined boundary-touching geohashes with polygon clipping. A laptop benchmark on a subset of data reduced runtime from 38 minutes with geometric intersection to 78 seconds using the geohash approximation.


### [Graph modelling guidelines](https://yomu.fyi/post/graph-modelling-guidelines.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Wenxiang Lu
- Published: Nov 8, 2023

Graph modelling uses graph theory to represent real-world entities, relationships, and properties through nodes and edges, unlocking insights across interconnected datasets. The process begins with defining domain concepts, mapping entities and relationships, and assigning relevant descriptive properties to both nodes and edges. Practitioners choose between graph structures such as property graphs or Resource Description Framework models before optionally developing a structural schema. Data is then imported or generated to populate storage systems such as Neo4j, Amazon Neptune, Azure Cosmos DB, or in-memory data structures. Finally, the graph is visualised and analysed using algorithmic methods like shortest path calculation, centrality measurement, and community detection.


### [Scaling marketing for merchants with targeted and intelligent promos](https://yomu.fyi/post/scaling-marketing-for-merchants-with-targeted-and-intelligent-promos.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Sharon Teng
- Published: Oct 11, 2023

Grab previously relied on globally assigned, heuristic promotional campaigns where all users could redeem offers until limits were reached. This lack of targeting and customisation failed to optimize promo spending or meet specific merchant business objectives. To solve this, the engineering team built Bullseye, an automated AI-driven promotional assignment system that customises and assigns offers to consumers. The architecture integrates an operations user interface, backend services, Amazon S3 storage, and Spark jobs executing a suite of predictive modules including eater segmentation, campaign impact simulation, and customer response modeling. Since deploying Bullseye in 2021, the system has increased food campaign sales while decreasing promo spend across food and Mart campaigns.


### [Stepping up marketing for advertisers: Scalable lookalike audience](https://yomu.fyi/post/stepping-up-marketing-for-advertisers-scalable-lookalike-audience.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: William Wu
- Published: Sep 22, 2023

Grab's legacy lookalike audience generation platform suffered from long creation SLAs of two working days, high costs, and low weekly update frequencies. To resolve these bottlenecks, the engineering team designed an embedding-based platform powered by an in-memory retrieval service and automated update pipelines. The system creates audience representations by averaging constituent passenger embeddings, determining user membership through real-time cosine score thresholds. To eliminate feature store latency while fitting all embeddings into memory, a hash-based compression method cuts passenger embedding storage needs by roughly 90%. Consequently, audience availability dropped to within 15 minutes of campaign creation, audience generation costs fell by 98%, and ad impressions and clicks doubled.


### [Building hyperlocal GrabMaps](https://yomu.fyi/post/building-hyperlocal-grabmaps.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Adriana Lazar
- Published: Aug 30, 2023

Southeast Asia presents unique mapping challenges, including fast-changing road restrictions, new roads appearing rapidly, and severe traffic congestion. To address these conditions, GrabMaps integrates hyperlocal data derived from local team knowledge, rides and deliveries GPS pings, and KartaView imagery with OpenStreetMap data. The pipeline downloads OpenStreetMap .pbf files for Asia and converts them into Parquet format using a Parquetizer. GrabMaps processes spatial data using Apache Spark and bi-directional graphs divided by geohash to detect complex features such as intersection links based on road parallelism, naming, direction, and shape geometry. Tagging these intersection links enables downstream navigation services like GrabNav to calculate shorter routing alternatives and provide more accurate pricing.


### [Streamlining Grab's Segmentation Platform with faster creation and lower latency](https://yomu.fyi/post/streamlining-grab-s-segmentation-platform-with-faster-creation-and-low.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Jake Ng
- Published: Aug 15, 2023

Grab's Segmentation Platform previously stored user-to-segment mappings across individual rows in ScyllaDB, causing write bottlenecks during segment creation and read latencies too high for downstream consumers. To resolve these performance limitations, the team transitioned to storing segments as Roaring Bitmaps saved as single blobs in object storage. This compression strategy splits 32-bit integer user IDs into chunks across array, bitmap, and run containers based on data density, reducing a one-million-member segment to under one megabyte. An accompanying client SDK manages segment retrieval, decoding, update notifications, and least-recently-used in-memory caching. Consequently, consumers such as Grab's communications platform achieved peak throughput of 15,000 queries per second with sub-millisecond p99 read latencies.


### [Unsupervised graph anomaly detection - Catching new fraudulent behaviours](https://yomu.fyi/post/unsupervised-graph-anomaly-detection-catching-new-fraudulent-behaviour.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Rizal Fathony
- Published: Aug 2, 2023

Fraud detection systems relying on historical training data often struggle to detect new fraudulent modus operandi because emerging patterns lack labeled supervision. Grab developed GraphBEAN, an unsupervised autoencoder model designed to detect anomalous patterns across bipartite interaction graphs representing consumers and merchants. The architecture uses graph convolution layers to encode node and edge attributes, then reconstructs features and edge existence through separate feature and structure decoders. High reconstruction errors flag rare, anomalous behaviors at both the node and edge levels. An automated pipeline applies heuristic fraud tags to these scores and routes them to human analysts and automated mitigation systems.


### [Zero traffic cost for Kafka consumers](https://yomu.fyi/post/zero-traffic-cost-for-kafka-consumers.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Fabrice Harbulot
- Published: Jul 7, 2023

Grab's real-time data streaming platform team reduced infrastructure expenses by enabling Kafka consumers to fetch from the closest partition replica instead of partition leaders. The platform previously operated across three AWS Availability Zones with three-way replication, resulting in cross-AZ network fees that constituted half of the Kafka platform's total cost. To address this, the team performed a zero-downtime upgrade to Kafka 3.1, configured a rack-aware replica selector using AWS Availability Zone IDs, and updated consumer SDKs to populate the client rack parameter. The rollout produced a 25% drop in cross-AZ traffic within three months under steady data volumes. However, the change increased end-to-end latency by up to 500ms, created potential CPU load skews across zones, and removed graceful isolation during broker rotation maintenance.


### [Go module proxy at Grab](https://yomu.fyi/post/go-module-proxy-at-grab.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Jerry Ng
- Published: Jun 30, 2023

Grab's 69.3 GiB multi-module Go monorepo caused commands like go get to take over 18 minutes as Git repeatedly traversed commit history, downloaded large worktrees, and overloaded their GitLab VCS infrastructure. To bypass direct VCS queries without losing automatic updates for external repositories, the team deployed the Athens Go module proxy configured in fallback network mode. They used the GOVCS environment variable to disable Git access specifically for the monorepo path, forcing Athens to fall back to its internal object storage when resolving monorepo modules. A dedicated CI pipeline pre-populates and refreshes the Athens cache whenever new monorepo modules are released. This setup reduced monorepo go get execution times to approximately 12 seconds and allowed a 70% scale-down of the Athens proxy cluster.


### [PII masking for privacy-grade machine learning](https://yomu.fyi/post/pii-masking-for-privacy-grade-machine-learning.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Fabrice Harbulot
- Published: Jun 1, 2023

Data engineers at Grab require real-world streaming data to tune machine learning models, but user personal data cannot be exposed in non-production environments. To solve this, the data streaming team implemented an automated masking pipeline between production and staging Kafka clusters. Developers explicitly tag Personally Identifiable Information (PII) types in Protocol Buffers schemas, which a CI script validates against a keyword list before packaging the schemas into Scala JARs. An in-house Apache Flink application running in production consumes the unmasked streams, applies dynamic and consistent transformations like keyed HMACs or seeded random values, and produces sanitised records to staging. Untagged new fields in schema updates are automatically dropped by the deserialiser until the masking application is redeployed, preventing accidental data leaks.


### [Performance bottlenecks of Go application on Kubernetes with non-integer (floating) CPU allocation](https://yomu.fyi/post/performance-bottlenecks-of-go-application-on-kubernetes-with-non-integ.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Shubham Badkur
- Published: May 23, 2023

Grab's real-time stream processing platform encountered severe consumer lag and CPU throttling when running Go-based Kafka consumer pipelines on Kubernetes. The issue originated when the Vertical Pod Autoscaler (VPA) scaled pod CPU allocations down to floating-point values such as 1.94 cores. Because AUTO-GOMAXPROCS rounds non-integer CPU limits down to integers, Go runtime thread allocation dropped to 1 core, significantly throttling pipeline throughput despite available pod capacity. Setting a minimum floor of 2 cores instantly restored CPU utilization to 95% and cleared the message backlog. To prevent similar throttling, the team utilized integer CPU scaling recommendations available in VPA v0.13 on Kubernetes 1.25 and above.


### [How we improved our iOS CI infrastructure with observability tools](https://yomu.fyi/post/how-we-improved-our-ios-ci-infrastructure-with-observability-tools.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Bunty Madan
- Published: May 18, 2023

Upgrading to Xcode 13.1 introduced severe CI test instability and high CPU utilisation for Grab's iOS development team. To address this, the team integrated observability tools across their UITest pipeline to pinpoint performance bottlenecks and test flakiness. Interventions included isolating spotlight.app to curb CPU spikes, replacing Safari with a mock browser for deep link tests, and booting simulators with pre-granted permissions. The team also built custom network tracking tools to enforce resource mocking and transitioned tests away from arbitrary sleep commands to explicit wait wrappers. These combined changes cut CI runtimes, decreased CPU utilisation by over 50%, and stabilized automated test executions.


### [2.3x faster using the Go plugin to replace Lua virtual machine](https://yomu.fyi/post/2-3x-faster-using-the-go-plugin-to-replace-lua-virtual-machine.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Yonghao Hu
- Published: May 15, 2023

Talaria, an open-source distributed time-series database developed at Grab, previously allowed users to run custom data transformation scripts during ingestion using a Lua virtual machine. Launching and executing Lua scripts caused significant performance overhead when processing large volumes of events. To resolve this bottleneck, the team replaced the Lua VM with Go plugins compiled as Linux shared libraries (.so files). Benchmarks revealed that calling Go plugins achieves performance on par with native Go functions, executing roughly 2.3 times faster and consuming 2.3 times less memory than cached Lua VMs. Both execution methods conform to a unified Handler interface to load and run custom transformations.


### [Safer deployment of streaming applications](https://yomu.fyi/post/safer-deployment-of-streaming-applications.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Shi Kai Ng
- Published: May 2, 2023

Stateful stream processing frameworks like Apache Flink present unique deployment challenges because conventional canary and blue-green strategies can cause data inaccuracies or state divergence. Grab's real-time data platform team encountered risks of state loss, manual rollback overhead, and absent health checks in their Kubernetes and Spinnaker deployment pipeline. To resolve these operational issues, the team redesigned the deployment workflow around automated Flink savepointing and programmatic health monitoring. The new pipeline halts existing applications after capturing state snapshots and Kafka offsets, monitors target deployments via API health probes, and executes automated rollbacks using versioned ConfigMaps and replica metadata annotations. This automated process ensures state consistency during upgrades and eliminates manual intervention during deployment failures.


### [Message Center - Redesigning the messaging experience on the Grab superapp](https://yomu.fyi/post/message-center-redesigning-the-messaging-experience-on-the-grab-supera.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Jonathan Lee
- Published: Apr 17, 2023

Grab redesigned its messaging infrastructure from GrabChat to Message Center to overcome two-party chat limitations and support complex superapp requirements like group conversations and varied user roles. The architecture separates core processing logic from message delivery by splitting the system into a backend processor and an independently scalable postman service. Communication relies on an in-house TCP gateway named Hermes that proxies client payloads via gRPC, alongside Apache Kafka streams and Amazon SQS delay queues. Custom client-server acknowledgements and a DynamoDB event store ensure reliable message delivery even across dropped mobile TCP connections and offline reconnects.


### [Evolution of quality at Grab](https://yomu.fyi/post/evolution-of-quality-at-grab.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Abby Alcantara
- Published: Mar 31, 2023

As Grab expanded its superapp, manual QA sign-offs led to prolonged testing cycles and a peak in major production defects around 2019. To counter defect leakage and improve delivery pace, the engineering organization transitioned to a shift-left testing strategy across its software development lifecycle. Quality engineers began writing Given/When/Then acceptance tests prior to coding so that software engineers could execute them during development, accompanied by Definition of Ready and Definition of Done standards. The team also structured a multi-layer test strategy spanning unit tests, pipeline-executed UI component tests with mocked APIs, backend integration tests, and a custom internal end-to-end framework. Between 2019 and 2022, these changes reduced major and critical production issues by 60% and development-phase critical bugs by 40%.


### [How OVO determined the right technology stack for their web-based projects](https://yomu.fyi/post/how-ovo-determined-the-right-technology-stack-for-their-web-based-proj.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: George Matthew Limongan
- Published: Mar 21, 2023

OVO faced maintainability issues caused by fragmented web technology stacks, including PHP, Vue, React, Nuxt, and Go, alongside outdated documentation. This fragmentation resulted in severe context-switching costs during code reviews and prolonged onboarding times for new engineers. To establish a single primary front-end stack, the team compared React and Vue against maintainability principles encompassing operability, simplicity, and evolvability. Vue was selected because its standardized framework structure and documentation minimized architectural variations across projects. Implementation entailed creating a standardized Vue boilerplate, updating the internal UI library, and scheduling periodic upgrades for legacy React services rather than migrating them entirely.


### [Migrating from Role to Attribute-based Access Control](https://yomu.fyi/post/migrating-from-role-to-attribute-based-access-control.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Minh Khoi Nguyen
- Published: Mar 9, 2023

Grab's streaming data platform team migrated the Kafka Control Plane from Role-Based Access Control to Attribute-Based Access Control to eliminate operational bottlenecks and manual permission management. The previous model required defining hundreds of roles, permissions, and group mappings in an internal IAM service, leading to approval delays and stale memberships. Under the new architecture, user attributes sync from the HRMS and token payloads, while resource attributes are tagged upon creation or backfilled to reflect department and team ownership. Open Policy Agent evaluates access requests defined in Rego via middleware by comparing user attributes with resource metadata. This transition eliminated over 200 roles, 200 permissions, and roughly 3,000 unused IAM resources while automating access provisioning for new joiners.


### [Securing GitOps pipelines](https://yomu.fyi/post/securing-gitops-pipelines.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Thang Le
- Published: Mar 1, 2023

Grab's real-time data platform team transitioned from an Atlantis-driven Terraform workflow to an in-house GitOps platform called Khone to manage streaming infrastructure resources like Kafka topics and Flink pipelines. The earlier setup suffered from coarse-grained access controls, required manual merge request comments, and lacked flexible validation capabilities within native configuration files. Khone derives environment parameters directly from standardized directory paths and uses Python with the python-hcl2 library to inspect and validate resource definitions before executing Terraform stages in parallel. To prevent configuration tampering in merge requests, CI/CD pipeline definitions and execution scripts are isolated in a separate administrative repository and fetched during job runs using shallow Git clones.


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