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Architecture
326 posts about Architecture. Every summary links to the original.
Grab ·
Real-time data ingestion in Grab
Service teams at Grab historically had to dual-write transactional data into databases and Kafka, creating data integrity issues during transaction failures alongside substantial schema maintenance overhead. To overcome these limitations and eliminate burst reads from SQL-based queries, the Caspian team built a real-time ingestion platform synchronising MySQL, Aurora, and DynamoDB directly to Kafka. For MySQL and Aurora, the platform uses Debezium with Kafka Connect on ROW-format binlogs, while DynamoDB changes are captured via DynamoDB streams with auto-scaling AWS Lambda functions. Messages encoded in Protobuf are transported via Kafka and ingested into Amazon S3 using a Golang stream processor. This architecture supports search indexing in Elasticsearch, automated data lake pipelines, cross-region disaster recovery replication, and audit trails.
Shuguang XiangGrab ·
Abacus - Issuing points for multiple sources
Grab needed a centralised points management architecture to issue loyalty points across a growing catalog of products, membership tiers, and external partner exchanges. To address this, the engineering team built Abacus, an issuance platform designed to process millions of daily transactions with high availability. The system ingests completed transaction streams or API calls, dynamically computes points via configured multipliers, and passes calculations through Amazon Simple Queue Service queues. Once the Point Awarding module updates a persistent ledger, Abacus notifies consumers, emits events to Kafka for downstream consumers, and recalculates rolling point expiration dates.
ChandrakanthGrab ·
Exposing a Kafka Cluster via a VPC Endpoint Service
To replace VPC peering and reduce attack surfaces, Grab exposed a multi-Availability Zone Apache Kafka cluster in its main AWS VPC to clients in a separate GrabKios VPC using AWS VPC Endpoint Service. Because Kafka requires clients to establish deterministic connections to individual brokers, the team configured a Network Load Balancer with unique TCP ports and dedicated target groups for each broker alongside a shared bootstrap port. They added custom listeners on the Kafka brokers to advertise endpoints using private Route 53 CNAMEs rather than raw interface hostnames. To eliminate unnecessary cross-AZ network latency and data transfer costs, the architecture was refined to advertise AZ-specific private CNAMEs mapped directly to zonal endpoint interfaces.
Fabrice HarbulotGrab ·
How Grab built a scalable, high-performance ad server
Grab transitioned from an off-the-shelf MVP to an in-house ad serving system to accommodate business scale, hyperlocal requirements, and machine learning personalization. The architecture orchestrates core microservices and data pipelines across sequential steps: targeting, capping, pacing, scoring, ranking, pricing, and tracking. ElasticSearch serves as the targeting ads repository, while ScyllaDB acts as the high-throughput stats store fed by Kafka streams and data pipelines. The system operates on key engineering principles including parallelization and tuned latency limits, graceful fallbacks for slow dependency calls, and a unified server serving all ad types across the superapp.
Anthony McCallumGrab ·
Biometric authentication - Why do we need it?
Grab addressed the vulnerabilities and costs associated with SMS one-time passwords and PINs by implementing device-level biometric authentication. The architecture pairs device biometric sensors with hardware secure enclaves to protect private keys separately from the main operating system. During enrollment, Grab generates a public-private key pair using SHA512withECDSA, authenticates the user locally, and stores reference identifiers in encrypted device storage. HellfireSDK verifies that the device is not rooted, ensuring raw biometric data never leaves the handset. Early experimental runs indicate an adoption rate exceeding 90% and a login success rate near 90%.
Chad Burgesshuggingface.co ·
Perceiver IO: a scalable, fully-attentional model that works on any modality
Standard Transformer architectures scale poorly in compute and memory because pairwise dot-product self-attention depends quadratically on input size. Perceiver IO addresses this constraint by computing self-attention across a small set of latent variables rather than directly on high-dimensional inputs. Inputs and outputs interact with the model via cross-attention operations, decoupling compute and memory costs from input and output dimensions. Integrated into Hugging Face Transformers via the PerceiverModel class, the architecture supports diverse data types using optional preprocessors, decoders, and postprocessors. Experiments demonstrate competitive performance across text, multimodal video classification, 3D point cloud classification on ModelNet40, and StarCraft II reinforcement learning in AlphaStar.
Niels RoggeSupabase ·
Realtime Postgres RLS now available on Supabase
Supabase updated its Realtime server to enforce PostgreSQL Row Level Security (RLS) policies when broadcasting database changes over websockets. Previously, Realtime operated as an opt-in beta feature that sent all replication changes to every client regardless of user authorization. To enforce RLS per subscriber without heavy performance overhead, Supabase introduced WALRUS, a security engine colocated inside PostgreSQL. For each replication change, WALRUS looks up active subscribers, assumes their identities, and evaluates row visibility using prepared statements queried by primary key. This in-database evaluation avoids external network round trips and single-query planning overhead while returning an authorized subscriber list to Realtime.
Oliver RiceGrab ·
Using real-world patterns to improve matching in theory and practice
Continuous ride-hailing assignment relies on solving the minimum weight bipartite matching problem between passengers and driver-partners. While traditional implementations assume a precalculated cost matrix, computing shortest-path travel times across large road networks dominates total execution time. Researchers introduced an Incremental Kuhn-Munkres algorithm that leverages the spatial locality of optimal matches to compute edge costs on demand. The approach integrates priority queues and lower-bounding techniques with refinement rules to avoid evaluating distant pairs while guaranteeing the same optimal assignment. Evaluated on Singapore road network data and real Grab production workloads, the incremental techniques reduced exact cost calculations and decreased assignment running times by over an order of magnitude.
Tenindra Abeywickramahuggingface.co ·
The Age of Machine Learning As Code Has Arrived
Recent findings from the 2021 State of AI Report and Kaggle State of Machine Learning and Data Science Survey indicate that machine learning is expanding into critical infrastructure while Transformers become general-purpose architectures across text, vision, and audio. In response, organizations face questions about scaling infrastructure, team composition, and engineering maturity. Rather than treating machine learning as isolated sandbox experiments or hiring solely data scientists, teams benefit from adopting established software engineering and DevOps principles like versioning, testing, automation, and continuous deployment. Furthermore, the rise of pre-trained Transformer architectures enables practitioners to fine-tune existing off-the-shelf models rather than training from scratch, reducing compute costs and training duration. Tools from platforms such as Hugging Face streamline model deployment, latency optimization, and infrastructure abstraction.
Julien SimonGrab ·
Automating Multi-Armed Bandit testing during feature rollout
Traditional feature rollouts and Multi-Armed Bandit testing operate as separate workflows that often depend on delayed offline analysis. To eliminate manual intervention, the Multi-Armed Bandit Optimiser automates testing concurrently during feature rollouts by responding to minute-level feedback metrics. The architecture connects Kafka Streams data processing, a metrics server with Spark jobs, and an adaptive rollout module updating online experimentation configurations. Candidate models are evaluated via Thompson Sampling on Beta distributions, with Monte Carlo simulations determining traffic allocation across user entities. In production for the GrabFood recommendation widget, the system optimizes the Effective Conversion Rate over a 30-minute window and includes fallback distribution logic.
Weicheng ZhuSupabase ·
Epsilon3 Self-Host Supabase To Revolutionize Space Operations
Epsilon3 digitizes paper procedures and telemetry data for high-stakes space missions and complex operational testing. Operating in heavily regulated environments requires strict security, ITAR compliance, and flexible deployment options ranging from AWS GovCloud to on-premises installations. To meet these demands without sacrificing developer velocity, Epsilon3 deployed a self-hosted Supabase configuration running against an Amazon RDS Postgres instance on AWS. This architecture gives the team real-time data streaming capabilities over Postgres so operators can track live procedure changes as they happen. In addition to reducing DevOps complexity, self-hosting Supabase enables Epsilon3 to leverage Postgres Row Level Security while ensuring full compliance across diverse deployment environments.
Grab ·
App Modularisation at Scale
Grab transitioned its monolithic mobile application into a modular architecture to resolve increasing code conflicts, slow releases, and difficult team collaboration. The team decomposed the single module by establishing base infrastructure modules, shared UI and utility libraries, discrete feature modules, and bridge kit modules for inter-module communication. Dependency injection using Dagger ties these components together in the main app module while preventing feature modules from directly depending on one another. The architecture spans over 1,000 modules across the app, with more than 200 modules in the Grab Financial Group payments domain where over 95% of modules build in under 15 seconds. This approach accelerated Gradle CI and local builds through parallel compilation and caching, though it increased Gradle sync times, IDE memory usage, and configuration maintenance overhead.
Amar JainGrab ·
Reshaping Chat Support for Our Users
Grab transitioned from voice hotlines and third-party tools to an in-house native chat support system integrated into their CRM. The team validated the platform through an MVP and user shadowing to address session disconnections, agent context switching, and routing bottlenecks. To optimize support operations at scale, they introduced dynamic queue limits based on Little's law, machine learning autocomplete suggestions for agents, and duration timers with visual nudges. These enhancements reduced chat waiting times by 30%, unresponsive users by 7%, and overall chat handling duration by 22%.
Elisa MonacchiGrab ·
Building a Hyper Self-Service, Distributed Tracing and Feedback System for Rule & Machine Learning (ML) Predictions
Grab's Trust, Identity, Safety, and Security team processes billions of daily rule and machine learning decisions for fraud detection, safety, and identity checks. Earlier logging approaches using plain text Kibana logs and the ActionTrace library lacked structured formats, dynamic entity customization, and fine-grained access controls. To resolve these limitations, the team built Archivist, a centralized tracing, statistics, and feedback system. Archivist ingests events through an SDK into Kafka streams, buffers and routes data into Elasticsearch indices and Amazon S3, and provides a role-based user portal. The platform handles 80 million daily logs across roughly 50 business scenarios, reducing scenario onboarding times from days to minutes.
Warren ZhouGrab ·
How We Improved Agent Chat Efficiency with Machine Learning
Agent typing time represented a large portion of Grab's chat support journey, and 85% of messages were still free typed because agents customized static templates to fit their personal style. To accelerate typing across multilingual markets without robotic templates, Grab built SmartChat, a machine learning feature that provides contextual sentence completion. The team opted for a lightweight seq2seq architecture using single-layered GRU encoder-decoders in TensorFlow instead of bulky attention models to keep model latency under 100ms. The user interface was implemented in React using a content-editable div with inline typeahead suggestions activated via keyboard shortcuts.
Suman AnandSupabase ·
PgBouncer is now available in Supabase
Serverless JavaScript frameworks and developer tools frequently establish separate database connections during bursty traffic, rapidly exhausting PostgreSQL connection limits in the absence of traditional middleware. To resolve connection surges, Supabase integrated the open-source connection pooler PgBouncer directly onto the PostgreSQL server across all newly created projects. Rather than increasing the total number of connections PostgreSQL can open, PgBouncer recycles open connections and queues excess requests until active connections become available. Developers can manage pooling through the dashboard across Session, Transaction, and Statement modes, with Transaction mode recommended for serverless functions despite disabling session features like prepared statements. The default pool size is initially configured to 15 connections.
Angelico de los ReyesSupabase ·
Workflows are coming to Supabase
Supabase is developing Workflows, an Elixir-based orchestration engine designed to coordinate complex serverless tasks and event-driven logic. Standard serverless and database-triggered functions often struggle with delayed execution and queuing without external cron processes. To solve this with native Postgres integration, the new engine adopts the open-source Amazon States Language specification to orchestrate functions across platforms including AWS, GCP, Azure, OpenFaaS, and Postgres itself. Execution states, jobs, queues, and logs are deeply integrated with Postgres using the Oban job processing library, though transient in-memory workers are also supported. The engine responds directly to HTTP calls or database change events delivered through Supabase Realtime.
Francesco CecconSupabase ·
Supabase Launches NFT Marketplace
Non-fungible tokens frequently suffer from the copy-paste problem, where public URLs allow unauthorized users to download underlying media directly from web hosts or IPFS. To address this issue, BuyMeth proposes combining public blurhash thumbnails with full image files encrypted under the active owner's cryptographic key and hosted on IPFS. Completed sales trigger Metamask to re-encrypt the file with the buyer's public key, followed by a one-week escrow challenge window where automated verification matches decrypted image hashes against public thumbnails. Ongoing royalty distributions to previous owners provide a financial mechanism to disincentivize leaking unencrypted original image files. The announced platform serves as an April Fools joke rather than an active Supabase product release.
Ant WilsonSupabase ·
Storage is now available in Supabase
Supabase launched Storage, adding a scalable object store to its existing Postgres, authentication, and API services. Existing open-source storage servers like Ceph, Swift, Minio, and Zenko were evaluated but rejected due to auth incompatibilities and external dependencies like etcd, MongoDB, and Kafka. Consequently, the team implemented a custom Storage API server built with Fastify and TypeScript behind the Kong gateway. Object metadata and access control reside directly in Postgres, leveraging Row Level Security policies written in SQL rather than a proprietary domain-specific language. Objects stream directly to managed backends like AWS S3 using Node streams with minimal in-memory buffering, accompanied by a default one-hour Cache-Control header.
Inian ParameshwaranGrab ·
Customer Support Workforce Routing
Grab replaced its third-party customer support routing software with an in-house workforce routing system for Livechat to gain better priority controls, bespoke configurations, and deeper analytics. The platform separates requests into distinct priority and business queues, using parallel workers that spend varied time slices dequeuing higher-priority issues like safety concerns. To prevent request starvation, workers operate out of sync across queue priority levels while dynamic queue limits cap incoming volume based on agent availability and performance. The system routes requests through an intermediate Agent Group layer, calculating eligibility scores from proficiency and concurrency metrics while managing per-agent locks to prevent over-allocation.
Suman Anand