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68 articles tagged #Architecture.
P.01Run three copies of a service for availability and you've created a new problem: only one of them should do certain jobs at a time. Here's how leader election actually works, from Raft's term-based voting to the etcd lease pattern most teams use in practice.
P.02A webhook is a promise your server makes to accept a request it can't control the timing, order, or retry count of. Here's how to verify signatures correctly, handle retries without duplicating side effects, and design a receiver that survives real-world delivery chaos.
P.03When something produces work faster than something else can handle it, the system has to do one of three things: buffer, drop, or push back. Here's how backpressure actually works across queues, streams, and APIs, with real implementation patterns.
P.04Integration tests that spin up every dependent service are slow and flaky. Contract testing checks that a consumer and provider agree on a shape without either one running, and catches the breaking changes end-to-end tests miss. Here's how to set it up with Pact.
P.05A message that fails every time it's retried shouldn't loop forever or vanish silently. Here's how dead letter queues catch it, how to configure retry limits properly, and the reprocessing workflow that actually closes the loop.
P.06VectorWare mapped Rust's std::simd types directly onto GPU warps, letting the same vector code run on CPU and GPU without a rewrite. What it does, why it's hard, and where it still breaks.
P.07The algorithm that decides which elevator picks you up is the same family of algorithm your kernel uses to schedule disk reads and your backend uses to schedule background jobs. Here's how SCAN, LOOK, and destination dispatch actually work.
P.08A single checkout request might touch five services and nobody can say which one is slow. Distributed tracing fixes that by tagging every hop with one trace ID. Here's how spans, trace IDs, and context propagation actually work, with OpenTelemetry examples.
P.09An SLA is a promise with a penalty. An SLO is the internal target that keeps you inside that promise. An error budget is what's left before you breach it. Here's how the three fit together, with the math worked through on a real example.
P.10A circuit breaker stops you from calling a failing service. The bulkhead pattern is the piece that comes before that: making sure a slow, not-yet-failing service can't consume every thread and starve every other feature in the meantime.
P.11Four different philosophies for the same problem: getting a database schema from what it is to what it should be, safely, in a team. Here's how Flyway, Liquibase, Prisma Migrate, and Atlas actually differ, and which one fits your stack.
P.12Domain-driven design gets a reputation for ceremony: ubiquitous language, aggregates, bounded contexts, a vocabulary that scares teams off before they see the actual idea. The actual idea is simple and it solves a real problem. Here's what it is and when to reach for it.
P.13Almost every message queue that advertises 'exactly-once' delivery is actually giving you at-least-once delivery plus a mechanism for you to make your own handler idempotent. Here's the real distinction, why true exactly-once is so hard, and how to design for what you actually have.
P.14Two requests try to update the same row at the same time. Pessimistic locking stops one of them from starting; optimistic locking lets both run and catches the conflict at the end. Here's how each works, with real SQL, and how to pick.
P.15Soft delete keeps a deleted_at column and hides rows instead of removing them. It sounds like the safe default, but it quietly breaks unique constraints, foreign keys, and query performance if you don't design for it up front. Here's the real tradeoff.
P.16A full rewrite of a legacy system is the option that fails most often, not because the new code is bad, but because the business can't stand still for two years. The strangler fig pattern routes traffic to new code piece by piece instead. Here's how it actually works.
P.17Partitioning and sharding both split data up, and people mix up the terms constantly. Partitioning stays on one database and one server. Here's how range, list, and hash partitioning actually work, and when a partitioned table solves your problem before you need sharding at all.
P.18Read Committed, Repeatable Read, Serializable: most developers pick whatever their ORM defaults to and never ask why. Here's what each isolation level actually prevents, what it still allows, and how to pick one on purpose.
P.19A background task that outlives the function that spawned it is a real, recurring production bug. Structured concurrency fixes it by tying a task's lifetime to a scope. Here's the pattern across Python, Kotlin, Swift, and Java.
P.20A practical comparison of the four vector databases most teams actually shortlist for RAG and semantic search, with real pricing models, when each one wins, and the one question that should decide it for you.
P.21A 3-hour-33-minute CloudFront VPC Origins failure on July 16, 2026 knocked out at least ten unrelated services worldwide. The root cause was a single-ingress design decision, and it's a pattern worth checking your own architecture for.
P.22Chaos engineering means deliberately injecting failure into a running system to find weaknesses before an outage does. Here's what it actually involves, what tools make it practical, and when a smaller team should and shouldn't bother.
P.23Event sourcing stores every change to your data as an immutable event, then derives current state by replaying them, instead of overwriting a row each time something changes. Here's what that actually buys you, and the real operational cost it isn't worth paying in most systems.
P.24Hexagonal architecture keeps your business logic from importing your database driver, your HTTP framework, or your payment SDK. It also adds files and indirection you don't always need. Here's how it works, a working example, and how to tell if your project actually needs it.
P.25Most teams reach for cache-aside by default and never ask if it's actually the right pattern. Here's how the three main caching strategies behave under real traffic, the consistency gap each one leaves open, and how to pick between them.
P.26CAP theorem gets summarized as 'pick two of three' so often that the summary has replaced the theorem. Here's what it actually says, why the real constraint only bites during a network partition, and how to pick a consistency model for a system you're actually building.
P.27Webhooks push data to you the moment something happens; polling has you ask repeatedly whether anything changed. Here's how to decide which fits your integration, plus real code for both and the hybrid pattern most production systems actually need.
P.28A distributed lock keeps two processes on different machines from touching the same resource at once, but the naive Redis implementation has a gap that lets it fail silently. Here's how the pattern actually works, and the fencing token that closes the gap.
P.29Round robin isn't wrong, but it's the wrong default more often than teams realize. Here's how the main load balancing algorithms actually behave under uneven traffic, when each one earns its complexity, and a working consistent hashing implementation.
P.30A naive retry loop can turn a brief blip into a full outage by hammering a recovering service the instant it comes back. Here's how exponential backoff and jitter actually prevent that, with working code, not just the formula.
P.31Sharding splits one database across many machines when a single server can't hold or serve your data anymore. The strategy you pick determines whether you get hot spots, painful resharding, or a system that actually scales. Here's how each one works.
P.32Queues, pub/sub, and event streaming all move messages between services, but they answer different questions about delivery, replay, and who's allowed to read what. Here's the decision framework and where each one breaks down.
P.33Writing to your database and publishing an event are two separate operations, and a crash between them silently loses data. The transactional outbox pattern closes that gap. Here's how it works and how to implement it.
P.34A single GraphQL schema works until multiple teams own different parts of your data. Here's what federation actually solves, how Apollo Federation composes subgraphs, and when the added complexity is worth it.
P.35When a business transaction spans multiple services, you can't wrap it in one database transaction anymore. The saga pattern solves this with a chain of local transactions and compensating actions. Here's how it works and when it earns its complexity.
P.36A growing thread of developer discussion is pushing back on fashion-driven stack churn, favoring Rails, Elixir, and calmer backend systems over the next new framework. Here's why boring is winning arguments it used to lose, and when it's still the wrong call.
P.37Idempotency keys let a client safely retry a request that might have already succeeded, without double-charging a card or double-creating an order. Here's how the pattern works, with a real implementation against Postgres and Redis.
P.38CQRS separates the code path that changes data from the code path that reads it. It solves real scaling and modeling problems, and it's also one of the most over-applied patterns in backend architecture. Here's when it earns its complexity.
P.39A circuit breaker stops your application from hammering a failing dependency until it recovers. Here's how the pattern works, the three states that make it up, and how to implement one without over-engineering a simple retry.
P.40TC39 is standardizing signals as a native JavaScript primitive, the same reactive pattern already inside Vue, Solid, Angular, and Preact. It won't ship in browsers for years, but it changes what's worth building a library around today.
P.41Acquiring a product, taking on a legacy project, or partnering with a technical team? Here's how to assess a codebase systematically — what to look for, what kills deals, and what's fixable.
P.42Three tools, three different bets on where complexity belongs. Here's how to choose between BullMQ, Inngest, and Temporal based on what your system actually needs — not what sounds most impressive.
P.43The fear of vendor lock-in leads to over-engineering. Here's a practical framework for deciding when to abstract cloud dependencies and when accepting lock-in is the right call.
P.44An Architecture Decision Record captures why a technical choice was made, not just what was decided. One page per decision, stored in the repo, reviewed in PRs. Here's the format and how to make it stick.
P.45MCP has become the standard for connecting AI models to external systems. Here's how it works, how to implement an MCP server, and what to watch out for before going to production.
P.46Most slow queries come from a small set of fixable problems: missing indexes, N+1 patterns, and over-fetching. This is the practical diagnostic and fix guide.
P.47Blue-green and canary deployments give you a way to release software without taking down your service or discovering a bug when it's already affecting everyone. Here's how they work and when to use each.
P.48NestJS has been growing quietly for years. If your Node.js backend is a pile of Express middleware with no clear structure, NestJS offers a path forward without changing languages.
P.49Local-first means your app works offline and syncs when connected. The technology (CRDTs and sync engines) is mature enough to use. The question is whether your use case actually needs it.
P.50Every agency project with a marketing team needs a CMS. The options have changed a lot. Here's how Sanity, Contentful, Strapi, and Payload stack up on the criteria that actually matter.
P.51Row Level Security moves data isolation into the database where it belongs. Here's how to set it up for a multi-tenant SaaS, handle common edge cases, and avoid the traps that break it.
P.52Multi-cloud gets pitched as the default choice for serious companies. The reality is more nuanced — and for most teams, the complexity cost is higher than the lock-in risk it's supposed to prevent.
P.53Three leading agent orchestration frameworks, three different mental models. Here's when each one earns its place, what each costs you in complexity, and what the choice looks like when you're debugging at 2am.
P.54An agent that forgets everything when the session ends is a limited tool. Here are the practical patterns for building different kinds of memory into your agents.
P.55JavaScript error handling is broken by default — exceptions are untyped, async errors are easy to miss, and side effects are invisible in function signatures. Effect TS fixes all three. Here is how it works.
P.56gRPC has been available for years but many teams default to REST without thinking through the tradeoffs. Here's how gRPC works, where it fits, and where it doesn't.
P.57Breaking API changes are inevitable. How you handle versioning determines whether clients trust your platform or avoid integrating with it. Here's a practical comparison of URL versioning, header versioning, and content negotiation.
P.58Message queues and event streams solve different problems. Kafka is not always the right answer. Here's how to think through event-driven patterns and choose the right tool for your production workload.
P.59Every SaaS team eventually faces the multi-tenancy decision. The wrong choice creates migration pain later. Here's how to think through database-per-tenant, schema-per-tenant, and row-level security based on what your product actually needs.
P.60Every public API needs rate limiting, but the algorithm you choose shapes the user experience and the failure modes. Here's how each approach works and when to use it.
P.61EditorPickHow we built production multi-agent systems using Claude Agent SDK and Model Context Protocol. Covers orchestrator-worker patterns, handoff strategies, error handling, and real-world architecture decisions.
P.62EditorPickWe migrated three production projects from Celery to Django's new Tasks framework. Two went smoothly. One was a disaster. Here is everything we learned.
P.63EditorPickA deep dive into how we built codercops.com with Astro 5 SSR, Supabase as a CMS, Git-based content, and Edge Functions. Architecture decisions and lessons learned.
P.64How we built a Git-to-Supabase content pipeline with SHA-256 delta sync, Edge Functions, and GitHub Actions. Full architecture and code walkthrough.
P.65Every engineering team faces build vs buy decisions constantly. Here's a practical framework for making these decisions without analysis paralysis.
P.66Choosing a tech stack is one of the earliest and most consequential decisions for a startup. Here's a practical guide to making choices you won't regret.
P.67How we split our agency website into two repos — one for code, one for content — and why this architecture scales better than a monolith. With our exact Git submodule setup, GitHub Actions validation, and content workflow.
P.68EditorPickLearn the 5 SOLID principles of clean code through real-world analogies, humor, and simple examples. No PhD required - just common sense and a sense of humor.