Azure Architecture Center

Ideas you will meet

Microservices

Decompose systems into independently deployable services.

CQRS

Separate read and write models to scale each independently.

Event Driven Architecture

Loosely coupled services reacting to events.

Caching

Cache-aside, write-through, eviction and invalidation strategies.

What we already know about this

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

• Messaging and Microservice Patterns · 14 minEvent-driven architecture: sagas, CQRS and event sourcingHow services coordinate without distributed transactions: events, sagas with compensation, separate read and write models, and storing changes instead of state.Read hereClose

  Events as the connective tissue

  In an event-driven design, services announce facts that happened ("OrderPlaced", "PaymentCaptured") and others react. This decouples services in time and knowledge: the publisher does not know who listens. The price is that the flow is spread over many services, so you need discipline about event design, ordering, retries (previous lesson) and observability.

  Three patterns come up constantly.

  Saga: transactions across services

  In a microservice system each service owns its database, so a single ACID transaction across them is not available (two-phase commit across services is slow and fragile). A saga is a sequence of local transactions, each followed by an event or command that triggers the next. If a step fails, the saga runs compensating actions for the steps already done.

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  Figure SDA-MSV-03-1 · Order saga with compensation · © forcsguy.com

  Two styles:

  • Choreography: services react to each other's events with no central controller. Simple for short flows; hard to follow and to change as it grows (nobody owns the whole flow).
  • Orchestration: a coordinator (saga orchestrator or a workflow engine) tells each service what to do and tracks state. Easier to understand, monitor and change; the coordinator is an extra component to build and keep available.

  Rules: every step and every compensation must be idempotent; saga state must be persisted so it survives crashes; and be explicit about isolation, since other requests can see intermediate states (use a "pending" status and semantic locks).

  CQRS: separate reads from writes

  Command Query Responsibility Segregation uses different models for changing data (commands) and for reading it (queries).

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  Figure SDA-MSV-03-2 · CQRS with a read model · © forcsguy.com

  Why: reads and writes often differ in shape (normalised writes, denormalised reads) and scale (reads vastly outnumber writes). Cost: extra moving parts and eventual consistency. Use it where the gap between read and write needs is real, not as a default. A light form (one database, separate query code and views) captures much of the benefit.

  Event sourcing: store the changes

  Instead of storing the current state, store the sequence of events that led to it, as an append-only log. Current state is derived by replaying events (with snapshots for speed).

  type Event = { type: "Deposited" | "Withdrawn"; amount: number };
  const apply = (balance: number, e: Event) => e.type === "Deposited" ? balance + e.amount : balance - e.amount;
  const balance = events.reduce(apply, 0);          // state = fold over history
  

  Benefits: a perfect audit trail, ability to rebuild any view (including new ones) from history, debugging by replay, and natural fit with CQRS projections.

  Costs: more complex to build and query, event schema evolution over years, handling of mistakes by compensating events (never edit history), eventual consistency of views, and privacy rules (erasing personal data in an immutable log needs design, such as encryption keys per person). Event sourcing suits domains where history is the point (finance, ledgers, workflows); it is overkill for plain CRUD. (A ledger in the payment lessons is the same idea.)

  Using them together, and not

  Situation	Reach for
  Business process spanning several services	Saga (orchestrated if non-trivial)
  Read load and shape differ greatly from write	CQRS
  Need full history and rebuildable views	Event sourcing
  Simple CRUD within one service	None of these

  These patterns add power and complexity. Start with the simplest design (a modular monolith or a few services with synchronous calls), and adopt them where a concrete problem demands, ideally one at a time.

  Event design tips

  • Name events in the past tense after business facts.
  • Include ids, timestamps and a version; avoid leaking internal tables.
  • Prefer event-carried state for what consumers need, to avoid chatty callbacks, but beware coupling.
  • Keep a schema registry and evolve compatibly.
  • Make the flow observable: carry a correlation id through every event (see the observability lesson).

  Real-world reading

  Related reading

  • Event SourcingMartin FowlerBlogs
  • CQRSMartin FowlerBlogs
  • CQRS patternMicrosoft · Microsoft AzureDocumentation
  • Microservices PatternsChris Richardson · ManningBooks

  Show 6 moreShow fewer

  • Azure Architecture CenterMicrosoft · Microsoft AzureDocumentation
  • Pattern: SagaChris Richardson · microservices.ioDocumentation
  • 10 Tips for Building Resilient Payment SystemsShopify Engineering · ShopifyCase Studies
  • Microservices Patterns CatalogChris Richardson · microservices.ioDocumentation
  • Zendesk BlogZendesk engineers · ZendeskBlogs
  • LogFeeder: Log Collection System at YelpYelp engineers · YelpCase Studies

  Open this lesson with its quiz and exercise

• 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

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

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

  Before: a chain of synchronous calls

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  After: events between services

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  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.

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  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.

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  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