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824 posts about Architecture. Every summary links to the original.
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Argo CD is a GitOps controller that treats Git as the desired state and continuously reconciles it against what actually runs in each cluster. It maps the parts of that loop and why pull-based agents change its scale.
AI data assimilation fuses a model's prior state with fresh, uneven observations into one consistent state. See the four-layer architecture and where each layer can break.
See how Bazel’s dependency graph can separate build scope from IDE scope, helping engineers move through large monorepos without syncing every module.
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pg_graphql 1.0 marks the general availability of a PostgreSQL extension that lets databases be queried with GraphQL and formalizes a SemVer-based guarantee against backward-incompatible changes. It was designed for Supabase Free Plan servers with 1 GB of memory, where alternatives such as Hasura and Graphile consumed more memory than the platform could sacrifice. The implementation began with a C parser and SQL transpiler exposed through PostgREST RPC, then moved from SQL to Rust after nested SQL functions, copied state, and JSONB AST processing slowed schema introspection; v0.5.0 completed the rewrite. On Free Plan hardware, version 1.0 adds less than 300 microseconds per request, reaches about 1,060 SQL queries per second per connection, and the full Auth and HTTP stack handles about 645 requests per second, while larger instances exceed 10,000 requests per second.
Point-in-Time Recovery (PITR) is now available to eligible Pro Plan projects, expanding access beyond Enterprise customers and adding a Dashboard UI for enabling it. Projects created from August 2022 onward can enable PITR except in Northeast Asia / Seoul, and they must use the Small compute add-on; older projects are not yet generally eligible. The system combines daily physical database snapshots with archived Write Ahead Log (WAL) records, using the open-source WAL-G tool; snapshots are uploaded daily and WAL files are archived as transactions occur. WAL files are backed up at two-minute intervals, potentially faster when a file threshold is reached, reducing the recovery point from the up-to-24-hour exposure of daily backups. PITR backups retain up to seven days by default, with self-serve expansion to 28 days, and restoration can target a specified point in time.
Supabase Wrappers is an Alpha framework for building PostgreSQL Foreign Data Wrappers that connect databases with external systems, including third-party APIs, through SQL. It initially supports Firebase and Stripe, while wrappers for ClickHouse, BigQuery, and Airtable are under development. The framework extends pgx, a Rust Postgres extension framework, with async support for RESTful API-based wrappers and limited push-down support currently provided as a proof of concept. The post presents Wrappers as a basis for QETL, allowing teams to query, extract, transform, and load remote data through Postgres, either on demand or into local tables, while joining it with operational data. It is released outside official platform support, with potential breaking changes.
Supabase Storage introduces image resizing, webhooks, and a Smart CDN, positioning them as components of an on-demand image delivery system. Resizing uses imgproxy and supports width, height, and contain, cover, or fill modes through JavaScript clients or direct Storage API requests; images are not enlarged beyond their original dimensions, and aspect ratio is preserved by default. The Smart CDN stores resized results using cache keys based on an Etag, tenant ID, bucket, and image name, while webhook-driven metadata updates propagate invalidations globally within about 60 seconds. Image Resizing is beta for Pro and Enterprise customers, with 100 transformed images per project at no additional cost and $5 per additional 1,000; self-hosting is free.
Supabase released a new version of its documentation, rebuilding the site with Next.js after outgrowing Docusaurus. The earlier setup became harder to maintain as client-library versioning expanded, contributions grew more difficult, and the docs remained separate from Supabase’s Next.js and Turborepo monorepo. To reduce migration risk, the team first created a one-week Next.js MVP that copied Docusaurus, then spent a month developing the new release with two full-time developers. It introduces a new design, a single sidebar for broader discoverability, and reference pages that group all functions in a library while improving TypeDoc-based generation. Future work described includes unified authentication, project-specific documentation, runnable Postgres WASM examples, interactive tutorials, improved Postgres full-text search, and balancing sparse and overly verbose content.
Supabase announces pg_crdt, an experimental Postgres extension that adds CRDT support as a data type for collaborative and offline-oriented applications. It frames CRDTs as structures whose updates can be merged in any order to reach the same final state, avoiding the last-write-wins behavior of ordinary arrays. A content column can use crdt.ydoc, backed by Yjs, while the extension also supports Automerge through Rust libraries wrapped with pgx. The post contrasts storing CRDTs directly in Postgres with making Realtime an authoritative server, favoring the database-centered approach for simpler architecture and broader tooling, while noting that the decision is unsettled and pg_crdt is not released on the Supabase platform. Known limitations include full-document WAL broadcasts, increased WAL and dead tuples for frequently updated documents, and serialization overhead for large CRDT values.
Supabase describes the hiring traits and operating model it believes support its growth, arguing that people matter more than methodology. It looks for extreme asynchronous effectiveness, egoless execution, continuous incremental improvement, a bias toward immediate action, ambitious problem-solving, and a founder mindset; seniority does not exempt anyone from independent-contributor work, self-management, or maintenance. The 60-person team spans 25 countries, uses meetings as a last resort, and gives autonomous product teams access to shared metrics, user feedback, and equity through an ESOP. Teams are organised around products such as Postgres, Auth, Storage, Realtime, and Functions, while cross-cutting groups consult rather than merely receive delegated tasks. Three-month product cycles set ambitious goals, and Kaizen Weeks focused on QA, documentation, and performance help address technical debt and incorporate improvements into ongoing workflows.
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.
Preparing for BFCM and other high-traffic events, Shopify describes risk mitigation for a globally distributed, complex, interconnected infrastructure platform that peaked at 75.98M requests per minute, or 1.27M per second. At this scale, identifying and mitigating every possible failure is impossible, so the process starts with “what could go wrong” exercises, then uses voting, expert review of likelihood and severity, and assigned ownership to prioritize action. Decision makers are empowered to weigh risks and rewards, while summarized findings, including key risks and single points of failure, are shared to maintain alignment and awareness. The post frames engineering choices as probabilistic bets: decision quality should be judged separately from outcomes, and uncertainty cannot be eliminated, only addressed through thoughtful prioritization and communication.
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.
Distributed real-time collaboration becomes difficult when a single server coordinating document changes is overloaded and documents must be split across machines without exposing routing complexity to clients. This demo combines Replicache, a custom WebSocket endpoint, Fly Machines, and the Fly-Replay header to route each document’s connection through a shared router to the machine responsible for that document. The server keeps connections by spaceID, sends initial and incremental state, and tracks messages already delivered; the client overrides Replicache’s push and pull handlers to exchange data through the persistent socket. The resulting application has React frontend, Express router, and Replicache/WebSocket backend layers, with each todo list assigned to one backend machine. The author cautions that the approach is demonstrated only in a basic demo and should receive serious production testing and tuning.
The Shop Store team implemented a server-driven UI architecture for the Shop App’s Store Screen to personalize sections and layouts for different merchants. Previously, the client rendered a fixed layout, limiting customization, experiment timing, and the speed of fixes after weekly app releases. The new design uses template processing, orchestration, data loading, and a shop-server GraphQL layer, with ProductsSection and CollectionsSection types and configurable GridLayout or ShelfLayout options. On the client, ServerDrivenStoreScreen and StoreSectionContainer render the sections returned by the server, while default layouts allow older app versions to handle unfamiliar section or layout types. The architecture enables backend-controlled experiments and section changes without requiring an app release, while giving the product team a system intended to grow with the store experience.
The article demonstrates data fetching and caching with Supabase in Next.js 13’s experimental app directory, focusing on Server Components and Suspense. It creates a posts table, configures a Supabase client, and uses async components to fetch data without manually managing loading states. The examples cover static generation, dynamic routes with generateStaticParams, time-based revalidation, client-side fetching, and a realtime pattern that combines server-rendered initial data with a client subscription to Postgres changes. The article notes that Next.js 13 is stable while the app directory remains in beta, and concludes that separating caching from fetching lets applications change freshness requirements without replacing their data-fetching logic.
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.
Rails caching relied heavily on Marshal, but cached Ruby-specific objects could break after code changes when old payloads were loaded by code without the former classes. MessagePack provided a generic binary format whose core types exclude Ruby-specific Object and instance-variable encodings, while extension types allowed production-specific serializers such as the compact Date representation. During a roughly six-month migration, the team ran both formats, prefixed MessagePack payloads with a version byte, and used encoding failures to identify unsupported cache values. To scale beyond 128 extension types, they added an Object catchall based on as_pack and from_pack, plus Struct and T::Struct modules whose attribute digests detect refactors and turn stale data into cache misses. After logs showed that Marshal was no longer used, the core monolith moved exclusively to MessagePack, with the Paquito gem extracting much of the migration work.
Rails caching commonly relies on Ruby’s Marshal serialization, which can encode complex objects but also embeds their class names in cache entries. Shopify describes an incident in which a beta-flag refactor changed classes while old and new code overlapped during deployment; old code then failed on cached instances containing unfamiliar class names and methods, despite passing CI. The article examines Marshal through Ruby’s marshal.c implementation, covering atomic and composite types, instance variables, object references, circularity via TYPE_LINK, and core-type subclasses via TYPE_UCLASS. It presents MessagePack as a more compact, stricter, and controllable alternative intended to make caching safer by default, while deferring the cache migration and implementation details to the series’ next part.
The tutorial builds a simple realtime messaging app with Ionic Angular and Supabase, covering account authentication and database protection. It defines Postgres tables for users, groups, and messages, enables Row Level Security with policies for public reads and authenticated operations, and adds a trigger to copy newly registered users into a public users table. Angular services, route guards, and Supabase client logic handle email/password registration and login, session state, protected navigation, and magic links. For native apps, Capacitor URL events extract access and refresh tokens from a custom-scheme callback before establishing the session and navigating to the groups page. The tutorial concludes that Supabase can support Ionic Angular authentication with magic links, while phone authentication through Twilio is another possible option.
Developer infrastructure managers must balance three poles: management support, system and domain expertise, and organization-wide road maps, with the tension intensified by teams that often lack dedicated product managers and support critical systems for hundreds or thousands of users with six to eight developers. Domain-focused teams build deep knowledge needed for maintenance, migrations, incident investigation, on-call coverage, and developer-experience improvements, but their local road maps can diverge from the department’s highest-impact opportunities. The proposed responses are temporary individual assignments, having a whole team contribute to another team’s goals, and forming a cross-functional tiger team; each shifts trade-offs among focus, maintenance, expertise, motivation, and management oversight. A month-long tiger team built the first Spin proof of concept, which led to a dedicated team, but no structure eliminates the tensions, so choices must be judged according to context.
Shopify’s Trust team faced rising complexity in securing a distributed device fleet, including many tools, provisioning and patching work, varied networks, and the loss of in-person remediation. Hubble was built as a unified layer over mobile device management and other security systems, ingesting and standardizing their data while sending commands back with granular access controls and centralized auditing. IT staff use it for inventory, device management, and security, while employees can view device health and compliance, receive remediation guidance, and manage test-device or beta participation. The platform also serves as a standardized source for automation, and the post presents encapsulation and cross-team investment as ways to reduce administrative overhead, support proactive security, and limit risk.
Embedded data science leaders may support multiple product and business areas, creating competing work streams, specializations, and stakeholder relationships. The post compares swim lanes and stochastic process assignments against principles of efficiency, influence, stakeholder clarity, stability, growth, and flexibility. It recommends a hybrid “diamond defense” in which scientists are loosely assigned to zones for specialization while a bullpen, including the manager and an additional scientist, supplies reserve capacity. Resources can be redirected to overloaded areas, and team members can rotate into vacancies or cover leave. The framework is presented as combining clear ownership and subject-matter depth with the flexibility to handle change, though the post warns that excessive rotation can create volatility and turnover.
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