# Architecture > 326 posts about Architecture, summarised, each linking to the original. ## Articles ### [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. ### [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. ### [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. ### [Graph service platform](https://yomu.fyi/post/graph-service-platform.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Wenxiang Lu - Published: Jan 5, 2023 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. ### [Zero trust with Kafka](https://yomu.fyi/post/zero-trust-with-kafka.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Fabrice Harbulot - Published: Dec 7, 2022 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. ### [Graph for fraud detection](https://yomu.fyi/post/graph-for-fraud-detection.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Min Chen - Published: Nov 24, 2022 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. ### [Query expansion based on user behaviour](https://yomu.fyi/post/query-expansion-based-on-user-behaviour.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Shuailong Liang - Published: Nov 16, 2022 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. ### [How we store and process millions of orders daily](https://yomu.fyi/post/how-we-store-and-process-millions-of-orders-daily.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Xi Chen - Published: Aug 15, 2022 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. ### [Typeahead Search at Nextdoor](https://yomu.fyi/post/typeahead-search-at-nextdoor.md) - Company: [Nextdoor](https://yomu.fyi/company/nextdoor.md) - Author: Jerry Tian - Published: Jul 6, 2022 Nextdoor built a proximity-based autocomplete service to power typeahead search and mention features across its hyperlocal platform for hundreds of millions of entities, including users and businesses. The system shards geographic data using Uber's open-source H3 geohashing library and stores prefix indexes in memory using Redis sorted sets. By adopting a Command Query Responsibility Segregation architecture, ingestion writes are processed on Redis primary nodes and replicated to read-only search nodes with under 10 milliseconds of replication lag. Dedicated APIs handle indexing, typeahead lookups, and ranking before returning hydrated results. Operating since August 2021, the service processes hundreds of millions of monthly typeahead queries while maintaining a P95 search latency below 30 milliseconds. ### [Graph Networks - 10X investigation with Graph Visualisations](https://yomu.fyi/post/graph-networks-10x-investigation-with-graph-visualisations.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Fujiao Liu - Published: Jun 30, 2022 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. ### [How facial recognition technology keeps you safe](https://yomu.fyi/post/how-facial-recognition-technology-keeps-you-safe.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Kai Feng Tee - Published: Jun 9, 2022 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. ### [Graph concepts and applications](https://yomu.fyi/post/graph-concepts-and-applications.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Wenxiang Lu - Published: Jun 2, 2022 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. ### [Automated Experiment Analysis - Making experimental analysis scalable](https://yomu.fyi/post/automated-experiment-analysis-making-experimental-analysis-scalable.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Albert Cheng - Published: May 30, 2022 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. ### [Graph Networks - Striking fraud syndicates in the dark](https://yomu.fyi/post/graph-networks-striking-fraud-syndicates-in-the-dark.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Muqi Li - Published: Apr 28, 2022 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. ### [~Don't~ Repeat Yourself](https://yomu.fyi/post/don-t-repeat-yourself.md) - Company: huggingface.co - Author: Patrick von Platen - Published: Apr 5, 2022 Hugging Face's Transformers library intentionally departs from the traditional "Don't repeat yourself" (DRY) software design principle by adopting a single model file policy. This architecture places all code required for a model's forward pass into a dedicated file, avoiding centralized shared modules for components like attention mechanisms. Because machine learning research evolves rapidly and published model architectures remain static, isolating code protects individual models from breaking changes and lowers contribution barriers for the open-source community. To maintain consistency without manually syncing duplicated code across successor models, the library uses automated tooling driven by Copied from comments. While this approach complicates unified API enforcement and component-wide research integration, Transformers validates API consistency by running approximately 20,000 automated tests daily. ### [Supabase Realtime, with Multiplayer Features](https://yomu.fyi/post/supabase-realtime-with-multiplayer-features.md) - Company: [Supabase](https://yomu.fyi/company/supabase.md) - Author: Wen Bo Xie - Published: Apr 1, 2022 Supabase announced the third major release of Supabase Realtime, evolving the system from a single-tenant PostgreSQL change listener into a multi-tenant, distributed real-time platform. Built using Elixir and the Phoenix framework across a distributed node cluster, the new architecture decouples the database listener into an extension model and opens generic WebSocket functionality. New multiplayer capabilities include Presence, which uses Conflict-free Replicated Data Types (CRDTs) to track user connection states, and Broadcast, which distributes ephemeral messages across topics without writing to disk. The modular extension design allows listening to multiple PostgreSQL instances, external systems like MySQL, and financial or blockchain feeds, as demonstrated on a globally distributed 20-node cluster. ### [Supporting large campaigns at scale](https://yomu.fyi/post/supporting-large-campaigns-at-scale.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Jie Zhang - Published: Apr 1, 2022 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. ### [Edge Functions are now available in Supabase](https://yomu.fyi/post/edge-functions-are-now-available-in-supabase.md) - Company: [Supabase](https://yomu.fyi/company/supabase.md) - Author: Inian Parameshwaran - Published: Mar 31, 2022 Supabase launched Edge Functions to execute TypeScript code close to end-users across more than 30 data centers. Built on the secure-by-default Deno runtime and hosted via Deno Deploy, the feature offers fast start-ups, scale-to-zero pricing, and native support for npm modules and Node built-in APIs. Incoming requests first reach an API gateway called Relay, which authenticates JSON Web Tokens, handles rate-limiting, and forwards requests using a Deployment ID to Deno Deploy. Observability is handled through Logflare to feed structured logs and metrics directly into the Supabase Dashboard. To mitigate abuse risks experienced during earlier product rollouts, invocations are currently limited to POST requests rather than general website hosting. ### [How telematics helps Grab to improve safety](https://yomu.fyi/post/how-telematics-helps-grab-to-improve-safety.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Wilson Burhan - Published: Mar 24, 2022 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. [Newer posts](https://yomu.fyi/topic/architecture/page/12.md) · [Older posts](https://yomu.fyi/topic/architecture/page/14.md)