Design Patterns Catalogue

Ideas you will meet

Microservices

Decompose systems into independently deployable services.

What we already know about this

Our own lessons on these ideas. Read them here before you start, so it makes sense.

• Real-World Systems · 11 minHow microservices work togetherSynchronous calls, events, sagas and discovery: how separate services cooperate without becoming one tangled system.Read hereClose

  What a microservice architecture is

  Instead of one large application, the system is split into small services, each responsible for one business capability (orders, payments, inventory, users). Services are deployed and scaled independently, often by different teams, and talk over the network.

  The benefits are independent deployment, team autonomy and targeted scaling. The cost is that everything that used to be a function call is now a network call that can be slow or fail, and data is spread across services.

  Ways services talk

  Style	How	Good for	Cost
  Synchronous request and response (REST, gRPC)	Caller waits for the answer	Queries and steps the user waits for	Coupling in time; failures propagate
  Asynchronous messages and events (queues, streams)	Caller publishes and moves on	Reactions, workflows, decoupling	Eventual consistency; harder to trace

  100%

  Figure SDA-RWS-13-1 · Services: sync calls, events and owned data · © forcsguy.com

  100%

  Figure SDA-RWS-13-2 · Two ways to run a multi-step process · © forcsguy.com

  Before: a chain of synchronous calls

  100%

  After: events between services

  100%

  What changed

  • A slow service no longer blocks the order
  • New services can listen without changing the sender
  • The result is eventually consistent, so design for that

  Data ownership

  Each service owns its own data and exposes it only through its API or events. No other service reads its tables. This keeps boundaries real, but it means you cannot join across services in SQL: you call APIs or keep read models built from events.

  Keeping things consistent: sagas

  A business action often spans services (take payment, reserve stock, create shipment). A single database transaction cannot cover them. A saga is a sequence of local transactions, each with a compensating action:

  1. Create the order (pending).
  2. Reserve stock. Compensation: release stock.
  3. Charge the payment. Compensation: refund.
  4. Confirm the order. If any step fails, run the compensations for earlier steps in reverse.

  Two ways to run a saga:

  • Choreography: services react to each other's events. Simple and decoupled, but the overall flow is spread out and hard to see.
  • Orchestration: a coordinator (an orchestrator service or workflow engine) tells each service what to do next and handles failures. The flow is explicit and easier to reason about.

  The outbox pattern

  A service must update its database and publish an event, and one may succeed without the other. Write the event to an outbox table in the same transaction, and let a separate process publish it. Consumers must be idempotent, because delivery is at least once.

  Finding and reaching services

  • Service discovery (via the platform, DNS or a registry) tells callers where healthy instances are, since addresses change constantly.
  • An API gateway is the front door for external clients: routing, authentication, rate limiting. A backend for frontend shapes responses for a specific client.
  • A service mesh adds a sidecar proxy next to each service to provide mutual TLS, retries, timeouts and metrics without changing application code.

  Resilience between services

  Reuse the reliability tools: timeouts, retries with backoff and jitter, circuit breakers and bulkheads. Prefer asynchronous communication where a user does not need an immediate answer.

  Operating many services

  • Distributed tracing with a correlation id shows a request's path across services.
  • Contract testing and careful API versioning let teams change without breaking each other.
  • Beware the distributed monolith: services so entangled they must be deployed together, which gives you all the costs and none of the benefits. Split along business boundaries, not technical layers.
  • Start with a well-structured monolith if the team is small. Split when a real need appears.

  More on this site

  Related reading

  • Pattern: Transactional outboxChris Richardson · microservices.ioDocumentation
  • Pattern: SagaChris Richardson · microservices.ioDocumentation
  • Awesome MicroservicesMehdi Fornos · mfornosGitHub
  • Microservices PatternsChris Richardson · ManningBooks

  Show 6 moreShow fewer

  • Tech Dummies: System Design Case StudiesNarendra L · Tech DummiesYouTube
  • Payment Transaction Management in Microservices at MercariMercari engineers · MercariCase Studies
  • Circuit BreakerMartin FowlerBlogs
  • Avoiding insurmountable queue backlogsAmazon Builders' Library · Amazon Web ServicesBlogs
  • Microservice Architecture at MediumMedium engineers · MediumCase Studies
  • Back-end at FlickrFlickr engineers · FlickrCase Studies

  Open this lesson with its quiz and exercise

• Messaging and Microservice Patterns · 13 minOperating microservices: config, secrets, feature flags, tracing and loggingThe operational toolkit around a service: configuration and secrets done safely, feature flags for controlled releases, and traces and logs that follow a request across services.Read hereClose

  Configuration

  Separate configuration from code. Anything that changes between environments (database address, feature limits, endpoints) should come from environment variables, config files or a config service, not be baked into the build ("build once, run anywhere").

  • Keep defaults in code and overrides in the environment.
  • Validate configuration at start-up and fail fast with a clear message.
  • For values that must change at runtime, use a config service or watch a mounted file, and treat changes like deployments (versioned, reviewed, auditable).
  • Keep config in version control (as code or templates) so changes are reviewed and reversible.

  Secrets

  Secrets (passwords, API keys, private keys, tokens) need stricter handling.

  • Never commit them, put them in images, or print them in logs.
  • Store them in a secrets manager (a cloud service, HashiCorp Vault, or an encrypted platform secret store) with access control, audit logs and encryption at rest.
  • Inject at runtime (environment, mounted file or an agent), and use short-lived credentials where possible (cloud roles, dynamic database credentials).
  • Rotate regularly and after any suspected leak; design services to reload secrets without downtime.
  • Least privilege: each service gets only its own secrets.

  Feature flags

  A feature flag is a runtime switch controlling whether code runs, separate from deployment.

  100%

  Figure SDA-MSV-05-1 · Deploy is not release · © forcsguy.com

  Uses: gradual rollout, kill switches for risky features, A/B tests, per-customer entitlements, and decoupling release from deploy (see the deployment strategies lesson). Cautions: flags multiply test combinations, can be left in place forever, and a flag service outage must fail to a safe default.

  Distributed tracing

  A request through a microservice system touches many services. A trace records the whole journey as a tree of spans (one per operation), joined by a trace id passed in request headers.

  100%

  Figure SDA-MSV-05-2 · One request traced across services · © forcsguy.com

  • OpenTelemetry is the vendor-neutral standard for instrumentation (traces, metrics, logs) and the W3C Trace Context header standardises propagation.
  • Propagate the id through every hop, including message queues (put it in message headers).
  • Add useful attributes (user type, order size) but never secrets or personal data.
  • Sampling: keep all errors and slow traces, a small share of the rest.

  Centralised logging

  Logs written on many short-lived instances are useless unless shipped to one searchable place.

  • Write structured logs (JSON with fields: time, level, service, trace id, message, key attributes) to standard output; let the platform collect them.
  • Ship to a central system (the ELK/OpenSearch stack, Loki, Splunk, cloud logging).
  • Always include the trace id so logs and traces connect.
  • Set levels sensibly; avoid logging personal data and secrets; set retention by value and legal need.
  • Alert on log-derived signals sparingly; prefer metrics for alerting.

  Health checks

  Expose liveness (the process is alive; restart if not), readiness (ready to take traffic; remove from rotation if not) and optionally startup endpoints. Readiness should check what the service truly needs without cascading (a failing optional dependency should degrade, not mark the service unready for everyone).

  A minimum for every service

  A health endpoint, structured logs with trace ids, the standard metrics (request rate, errors, latency, saturation), propagated trace headers, config and secrets from the environment, and a runbook. The next lesson turns these signals into targets and alerts.

  Real-world reading

  Related reading

  • Feature Toggles (aka Feature Flags)Pete Hodgson · Martin FowlerBlogs
  • DevOps Roadmaproadmap.sh community · roadmap.shCourses
  • Ensuring rollback safety during deploymentsAmazon Builders' Library · Amazon Web ServicesBlogs
  • Dapper: a Large-Scale Distributed Systems Tracing Infrastructure (paper)Sigelman et al. · GooglePapers

  Show 6 moreShow fewer

  • Blue Green DeploymentMartin FowlerBlogs
  • The Twelve-Factor AppAdam Wiggins · 12factor.netDocumentation
  • XCMetrics: All-in-One Tool for Tracking Xcode Build Metrics at SpotifySpotify engineers · SpotifyCase Studies
  • Going faster with continuous deliveryAmazon Builders' Library · Amazon Web ServicesBlogs
  • Unicorn: Remediation System at eBayeBay engineers · eBayCase Studies
  • Distributed Tracking, Tracing, and MeasuringOreilly engineers · OreillyCase Studies

  Open this lesson with its quiz and exercise

• Software Design Essentials · 9 minHow to learn design patternsWhat patterns are, the three families, a map of the classic catalogue, and a practical way to learn them without over-engineering.Read hereClose

  What a pattern is

  A design pattern is a named solution to a recurring design problem, together with when it applies and what it costs. The famous catalogue, Design Patterns (1994) by Gamma, Helm, Johnson and Vlissides ("the Gang of Four"), described 23 patterns in object-oriented software.

  Patterns give you three things:

  1. A shared vocabulary: "use a factory here" communicates a whole idea in a few words.
  2. Proven structures you do not have to invent under pressure.
  3. Awareness of trade-offs, since each pattern comes with consequences.

  They are not copy-paste code, and not a goal in themselves.

  The three families

  100%

  Figure SDA-SDE-01-1 · The three families · © forcsguy.com

  The catalogue at a glance

  Pattern	One-line idea	Typical use
  Factory Method	A method decides which concrete class to create	Create objects without hard-coding the class
  Abstract Factory	Create families of related objects	Themes, platform-specific widget sets
  Builder	Build a complex object step by step	Objects with many optional parts
  Prototype	Create objects by copying an existing one	Costly-to-create objects
  Singleton	Exactly one instance, globally reachable	Rare; often better as dependency injection
  Adapter	Make one interface look like another	Wrap a third-party library
  Bridge	Separate an abstraction from its implementation	Several dimensions of variation
  Composite	Treat a tree of objects like a single object	File systems, UI trees
  Decorator	Add behaviour by wrapping an object	Logging, caching, compression layers
  Facade	A simple front for a complex subsystem	A clean API over messy internals
  Flyweight	Share common state to save memory	Many similar small objects
  Proxy	A stand-in that controls access	Lazy loading, access control, remote calls
  Strategy	Swap algorithms behind an interface	Pricing rules, sorting choices
  Observer	Notify dependants when something changes	Events, UI updates
  Command	Turn a request into an object	Undo, queues, macros
  State	Behaviour changes with internal state	Workflows, connections
  Template Method	A skeleton algorithm with customisable steps	Frameworks, hooks
  Iterator	Walk a collection without exposing its structure	Loops over any collection
  Chain of Responsibility	Pass a request along handlers until one handles it	Middleware, validation chains
  Mediator	One object coordinates others	Chat rooms, form logic
  Memento	Save and restore an object's state	Undo, snapshots
  Visitor	Add operations to a structure without changing it	Compilers, reports over trees
  Interpreter	Evaluate sentences of a small language	Rules, expressions

  The next lessons explain the most useful ones with examples.

  A practical way to learn

  1. Learn the problems first. Each pattern exists because of a pain: rigid code, duplicated code, tangled dependencies. Understand the pain, then the cure.
  2. Start with the ones you already use without naming them: Iterator (every for...of), Observer (event listeners), Decorator and Chain of Responsibility (middleware), Strategy (a function you pass in).
  3. Implement each once, small, in your language, and write what you gave up.
  4. Spot them in real code: frameworks and standard libraries are full of patterns.
  5. Refactor towards a pattern when the code hurts, rather than starting with one.

  Patterns look different in different languages

  In languages with first-class functions, several patterns shrink to a function: Strategy is a function argument, Command is a closure, Observer is a callback list, and Template Method is passing the varying step. In dynamic languages, Factory and Adapter are often trivial. Learn the intent, then use the lightest form your language allows.

  Anti-patterns to avoid

  • Pattern overload: five layers of abstraction for a feature that needs ten lines.
  • Singleton as global state: hides dependencies and makes testing hard.
  • Naming something by the pattern, not by what it does (ManagerFactoryProvider).
  • Applying a pattern "in case": wait until the second or third time you need the flexibility.

  Real-world reading

  Related reading

  • Google SRE Book: Managing Critical State (Distributed Consensus)Google SRE team · GoogleBlogs
  • Paxos Made Simple (paper)Leslie Lamport · Microsoft ResearchPapers
  • Design Patterns CatalogueRefactoring GuruDocumentation
  • The Byzantine Generals Problem (paper)Lamport, Shostak and Pease · Microsoft ResearchPapers

  Show 6 moreShow fewer

  • etcd Raft libraryetcd · CNCF / etcdGitHub
  • Domain-Driven Design ReferenceEric Evans · Domain LanguageBooks
  • Scaling Gradient Boosted Trees for Click-Through-Rate Prediction at YelpYelp engineers · YelpCase Studies
  • Natural Language Processing and Content Analysis (2 parts) at Condé NastCondé Nast engineers · Condé NastCase Studies
  • Building and Deploying MySQL Raft at MetaMeta engineers · MetaCase Studies
  • Lessons of Scale at Facebook - Bobby Johnson, Director of Engineering at FacebookFacebookYouTube

  Open this lesson with its quiz and exercise