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Grab
Southeast Asia's leading everyday "super-app" that provides ride-hailing, food delivery, grocery shopping, and digital financial services.
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Latest articles
Grab ·
New zoom freezing feature for Geohash plugin
Grab introduced a zoom freeze capability to its Geohash plugin for the Java OpenStreetMap Editor (JOSM). Previously, zooming in or out forced the plugin to calculate and render new geohashes automatically, causing visual clutter when users needed to focus on a specific region. The updated interface introduces a toggle label allowing editors to switch between enabling and disabling zoom freezing. While frozen, the current geohash boundaries remain locked during map scaling, and options to show smaller or larger geohashes are disabled. Disabling the feature instantly updates the display with the appropriate geohash subdivisions for the current zoom level.
Maria MitisorGrab ·
Graph service platform
Grab's GrabDefence team required a dedicated graph infrastructure to proactively identify mobile fraud patterns, such as multiple accounts operating on shared physical devices and suspicious financial loops. To address this, the team built a four-layer Platform as a Service that encapsulates graph database operations behind uniform RESTful APIs for OLTP search and OLAP analysis. The architecture utilizes Amazon S3 for raw data files, Amazon Neptune for graph storage, DynamoDB for schema and metadata configurations, and Kafka for streaming ingestion. When users trigger data loading tasks, the service validates entity attributes against schemas stored in DynamoDB before importing records into Neptune. This infrastructure allows investigators to traverse adjacent account IDs and visualize complex entity relationships without managing underlying database runtimes.
Wenxiang LuGrab ·
Zero trust with Kafka
Grab's real-time data platform team transitioned their large-scale Kafka infrastructure from basic network access controls to a zero-trust architecture. The platform implements mutual Transport Layer Security (mTLS) for offline peer authentication and encryption, driven by HashiCorp Vault's PKI engine and Strimzi on Kubernetes. Policy-Based Access Control is enforced using dedicated Open Policy Agent deployments per cluster, backed by GitOps workflows where topic owners approve JSON authorization rules. To simplify client integration, the team enhanced their Go SDK to handle ephemeral in-memory certificates, automatic renewals, and configurable retries. While the security posture improved, the Java encryption and decryption overhead caused a drop in streaming throughput.
Fabrice HarbulotGrab ·
How KartaCam powers GrabMaps
Grab's Geo team required an efficient, low-cost way to collect fresh street-level imagery across Southeast Asia, where professional mapping equipment is prohibitively expensive and smartphone crowdsourcing yields inconsistent quality. To address this, Grab built KartaCam, a custom mapping device equipped with a 12MP sensor, dual-band GNSS, 4G LTE, and onboard edge AI. Edge machine learning models evaluate scene suitability, check image quality, filter for map-relevant objects, and blur personal data directly on the device prior to upload. Deploying a four-camera KartaCam 360 array delivers panoramic coverage and point-of-interest data comparable to commercial mapping rigs at roughly one-twentieth the hardware cost.
Shuangquan HouGrab ·
Graph for fraud detection
Rapid expansion across multiple business verticals introduced diverse, evolving fraud patterns at Grab, making traditional rule engines and decision trees inadequate due to their heavy reliance on extensive feature engineering and labeled data. To address cold starts and detect unknown fraud, the engineering team implemented a semi-supervised Relational Graph Convolutional Network (RGCN) across millions of connected ecosystem entities. The model propagates information along neighborhood nodes through graph convolutional layers to produce node embeddings and output fraud probabilities. Trained on graphs where only a small percentage of nodes are labeled, the RGCN achieved an AUROC close to 1 and distinctly separated fraudulent embeddings from genuine ones. The approach also offers high explainability by surfacing dense clusters of shared physical devices and addresses ongoing challenges in real-time prediction and noisy connections.
Min ChenGrab ·
Query expansion based on user behaviour
Grab developed a query expansion framework to resolve search issues such as typos, multi-language terms, missing alternatives, and word spacing. The corpus is generated through manual annotation for the top thousand fat-head queries and session-based data mining of user rewrite patterns. Offline pipelines extract adjacent query rewrites occurring within 30 seconds where only the second query generated a click, limiting candidates to three per query for serving efficiency. In production, expanded keywords pass through a query understanding, multi-recall, and result fusion flow with automated pruning of underperforming pairs. Online A/B testing across six Southeast Asian countries showed improvements in click-through and conversion rates, with data-mined expansions outperforming manual annotations alone.
Shuailong LiangGrab ·
Using mobile sensor data to encourage safer driving
Dedicated vehicular inertial measurement units are prohibitively expensive to install across large fleets, while customer feedback lacks the coverage required to assess driving safety comprehensively. To monitor driver behavior at scale, Grab processes smartphone sensor data collected via a mobile telematics SDK at high sampling frequencies. A custom orientation algorithm calculates Euler roll and pitch angles from gravity and resolves yaw through principal component analysis to align phone measurements with vehicle axes. The pipeline applies low-pass or Kalman filtering to handle varying noise levels, followed by threshold peak-finding algorithms to detect harsh acceleration, braking, and cornering. Speeding events are identified by comparing GPS speeds against known map limits or aggregated speed profiles derived from free-flowing traffic.
Laiyi LinGrab ·
Automatic rule backtesting with large quantities of data
Evaluating new or modified risk rules previously required Grab analysts to run slow offline Presto queries, manually construct payloads, or run rules in shadow mode for days. To standardize and accelerate this workflow, Grab developed an automated backtesting system powered by an AWS EMR Spark pipeline. Historical events are continuously ingested via Kafka and a Kubernetes stream pipeline into S3 using Snappy-compressed Parquet. Users configure replay intervals and rule definitions directly in the rule engine UI, which triggers asynchronous Spark jobs through Amazon SQS and Lambda. This automated simulation replaces multi-week shadow mode runs and generates downloadable aggregation metrics on transactions, user counts, and treatment outcomes.
Chao WangGrab ·
How we store and process millions of orders daily
The Grab Order Platform processes millions of food and mart transactions daily, requiring high throughput, fault tolerance, and reduced cloud costs across transactional and analytical workloads. To meet these demands, the engineering team decoupled their database architecture by using Amazon DynamoDB for critical OLTP queries and MySQL RDS for historical OLAP queries. DynamoDB handles online order lifecycles with strong consistency, utilizing sparse Global Secondary Indexes for ongoing orders and TTL configurations to limit storage growth. Updates propagate asynchronously to MySQL RDS through a Kafka ingestion pipeline backed by Amazon SQS retries and timestamp-based version checks. This dual-database approach isolated core transaction availability from analytical queries and delivered significant cloud cost savings.
Xi ChenGrab ·
How we automated FAQ responses at Grab
Internal engineering on-call engineers at Grab spent substantial working hours handling repetitive questions in Slack channels, such as how-to inquiries and access permission requests. To resolve this without building an in-house tool, the team conducted an anonymized vendor comparison and selected OneBar through an employee voting process and a phased proof-of-concept. Initial rollouts were restricted by contract to 20 channels, leading the team to prioritize deployment based on Slack message volume and member counts. Populating the knowledge base required roughly a quarter of consistent updates alongside tech talks, while a targeted crowdsourcing campaign among new onboarders expanded the glossary and grew usage to approximately 3,000 users.
Preeti KarkeraGrab ·
Graph Networks - 10X investigation with Graph Visualisations
Fraud detection traditionally required investigators to manually combine large datasets from disparate anti-fraud systems using statistical methods, which proved slow and inefficient. Grab built an interactive Graph Visualisation platform to transform raw records into connected visual maps without requiring manual queries or switching tools. The platform manages over three billion nodes and edges, allowing investigators to selectively expand data points and replay chronological events using temporal filters. Visual relationship mapping helps teams verify account appeals, uncover device-sharing rings, and spot anti-money laundering behavior through transaction density patterns.
Fujiao LiuGrab ·
How facial recognition technology keeps you safe
Grab utilizes facial recognition technology across its platform for driver authentication, passenger verification, and digital electronic Know Your Customer (e-KYC) processes. The core pipeline consists of image preprocessing through face detection and alignment, anti-spoofing checks, feature extraction into high-dimensional vector embeddings, and downstream verification or search. To counter spoof attacks like screen replays, synthetic moiré patterns are generated and cropped face patches are used during training and inference to focus on local structures rather than global semantic noise. Face verification challenges involving shallow ID datasets and masked faces are resolved using semi-Siamese training architectures and masked data augmentations.
Kai Feng TeeGrab ·
Graph concepts and applications
Real-world systems generate dynamic, non-random connections that traditional statistical approaches fail to characterize or forecast. Graph models represent these structures through vertices and edges, abstracting complex networks into mathematically tractable relationships. Common data representation formats include the Resource Description Framework (RDF), which models subject-predicate-object triples with IRIs, literals, and blank nodes, and Labeled Property Graphs (LPGs), which store arbitrary key-value properties directly on nodes and edges. Graph databases, derived from the LPG model, treat relationships with equal weight to entities, delivering responsive traversals for highly interconnected systems. While they offer agility and explicit relationship modeling, graph databases lack a standardized query language and remain poorly suited for standard transaction-focused workloads compared to relational databases.
Wenxiang LuGrab ·
Automated Experiment Analysis - Making experimental analysis scalable
Manual ad-hoc analysis of online controlled experiments at Grab introduced operational inefficiencies, inconsistent quality control, and scalability barriers across teams. To resolve these issues, Grab extended its GrabX experimentation platform with an Automated Experiment Analysis system that standardises metrics and automates statistical evaluations. The architecture stores experiment configurations and metric definitions from Cosmos DB into Azure Data Lake as bronze datasets, uses Spark on Databricks via Azure Data Factory to process subjects into silver datasets, and applies an internal Python Decision Engine to generate final gold results. These gold datasets are stored in star-schema fact and dimension tables and presented directly in the GrabX interface using embedded Power BI visualisations. The automation eliminates repetitive data pipeline construction for analysts, ensures reproducible findings aligned with initial hypotheses, and accelerates product launch decisions.
Albert ChengGrab ·
Embracing a Docs-as-Code approach
Engineers at Grab faced difficulties finding and writing technical documentation because materials were fragmented across various platforms and disconnected from daily workflows. To establish a single source of truth, the organisation adopted a Docs-as-Code approach starting in 2021. Under this system, documentation is written in plaintext Markdown within dedicated folders inside each service's Git repository. An internal developer portal automatically builds and publishes documentation whenever updates are committed, integrating documentation maintenance into the standard code development and review workflow. By April 2022, approximately 80% of Grab services were projected to host their documentation on the internal portal, with the model also expanding to technical training content.
Shu Juan CheongGrab ·
Graph Networks - Striking fraud syndicates in the dark
Grab's Integrity team faced escalating threats from organized fraud syndicates operating device farms and GPS spoofing tools that mimic normal user behavior. To overcome the limitations of traditional rule-based systems, Grab built a Graph-based Prediction Platform that links entities across shared attributes to expose hidden network structures. The architecture includes a scalable graph database platform holding over one billion nodes, powering real-time graph visualization for analysts alongside a configurable network feature extraction system. In addition, the team deployed Graph Neural Networks to capture structural correlations across transaction networks. These systems collectively help detect coordinated promo abuse, payment fraud, and complex money laundering schemes.
Muqi LiGrab ·
How we reduced our CI YAML files from 1800 lines to 50 lines
Grab's Cauldron Machine Learning Platform team managed continuous delivery across multiple pipelines by using nested GitLab CI configurations, but soon encountered platform limitations such as the 100-include ceiling and bloated 1,800-line YAML files. To address these constraints, the team implemented GitLab Dynamic Child Pipelines to programmatically create execution stages on the fly. They built a command-line utility in Rust that runs git diff against the base branch, extracts pipeline and stage metadata using configurable stop patterns, and applies a template to produce a dynamic CI definition. On the master branch, the tool fetches the diff artifact from the source branch through the GitLab API to execute identical generation logic. This architectural shift reduced the root CI file from 1,800 lines down to a constant 50 lines, allowing configuration size to remain stable regardless of repository growth.
Jia Long LohGrab ·
How Kafka Connect helps move data seamlessly
Grab's real-time data platform team, Coban, implemented a managed Kafka Connect ecosystem on Kubernetes to streamline moving data in and out of Apache Kafka. To resolve dual-write consistency issues and capture pre- and post-change data, the team integrated Debezium connectors to capture MySQL binlog events and accommodate database DDL migrations. For disaster recovery and stream migrations, Coban deployed MirrorMaker2 connectors managed via Terraform to handle message mirroring and consumer offset translation across AWS regions. Additionally, they developed a custom converter utilizing Confluent Schema Registry to transform Protobuf-serialized Kafka records into JSON for ingestion into Azure Event Hubs. This architecture enabled zero-downtime cluster migrations and robust cross-region disaster recovery.
Wenli WanGrab ·
Supporting large campaigns at scale
Grab developed a batch job service within its Trident automation engine to execute multi-step marketing campaigns for millions of users simultaneously. The system replaces sequential, single-server execution with a distributed architecture powered by Apache Kafka, which distributes batches of 100 users across server clusters using hashed partition keys. To reduce network overhead and queries per second, downstream reward and messaging services introduced batch endpoints backed by bulk database queries, decreasing API latency by up to 85%. Grab further optimized performance by sharding Kafka topics by country and action type to prevent long-running reward tasks from blocking time-sensitive messaging workloads. Additionally, making terminal messaging calls asynchronous allows subsequent batch processing to proceed without waiting for message delivery confirmations.
Jie ZhangGrab ·
How telematics helps Grab to improve safety
Grab developed an in-house telematics engine to monitor driver-partner habits, detect crashes, and improve ride safety beyond passenger feedback. The mobile telematics SDK collects accelerometer, gyroscope, and GPS readings, running on-device processing for immediate accident detection while batching ride data for post-trip safety reports. Because triaxial sensor sampling rates vary independently across diverse devices, the SDK synchronizes data through interpolation to a uniform time grid followed by decimation to an output data rate. Telematics signals are transformed to the frequency domain using Fourier Transform for local compression before backend upload. Grab also combines telemetry with spatial data to identify dangerous road zones and plans to expand on-device event detection across all platform verticals.
Wilson Burhan