Monolith First

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

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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

• Real-World Systems · 13 minDesign a stock exchangeAn order book, a matching engine and a sequencer: how an exchange matches buyers and sellers fairly in microseconds.Read hereClose

  The problem

  An exchange lets buyers and sellers trade. Participants submit orders; the exchange matches them and reports the trades.

  Key properties:

  • Fairness: orders are handled in a strict, auditable order.
  • Very low latency: microseconds from order to acknowledgment.
  • High throughput: hundreds of thousands to millions of orders per second at peaks.
  • Correctness and durability: money is involved. No lost or duplicated trades.
  • Availability: an exchange outage halts a market.

  The order book

  For each symbol the order book holds resting orders:

  • Bids (buy orders), highest price first.
  • Asks (sell orders), lowest price first.
  • Within a price level, orders queue by arrival time.

  Common order types: limit (a worst price you accept; unmatched part rests in the book) and market (take the best available now).

  Matching: price-time priority

  An incoming buy matches the lowest-priced asks it can afford; among equal prices the earliest order matches first. Matching repeats until the order is filled or no longer matches, and any remainder rests in the book.

  Image: Thomas J. O'Halloran · Public domain · Source · Wikimedia Commons: NY stock exchange traders floor LC-U9-10548-6

  Architecture

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  Figure SDA-RWS-05-1 · Exchange: gateway, sequencer, matching engine · © forcsguy.com

  1. Gateways authenticate participants, validate orders, and run pre-trade risk checks (limits, available funds).
  2. The sequencer gives every event a sequence number: one agreed total order, the basis of fairness and recovery.
  3. A matching engine applies events in order to the in-memory order book and emits trades.
  4. Everything is written to a durable event log. Executions go to participants, and a market data stream tells everyone about prices and depth.
  5. Clearing and settlement later move money and securities between accounts.

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  Figure SDA-RWS-05-2 · An order book with a buy order sweeping the sell side · © forcsguy.com

  How it gets so fast

  • In-memory order books and no database in the critical path.
  • Single-threaded matching per symbol group: no locks, deterministic behaviour. Parallelism comes from partitioning symbols across engines.
  • Event sourcing: state is the result of applying an ordered log. Persist the event first (to a memory-mapped file or replicated log), then process.
  • Pre-allocated memory, ring buffers, no garbage-collection pauses, and careful cache-friendly data structures.
  • Hardware and network: servers co-located in the exchange's data centre, fast network cards and kernel-bypass networking, precise time sources.

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  Figure SDA-RWS-05-3 · A fast engine on one machine: shared log and busy loops · © forcsguy.com

  Reliability

  • A hot standby applies the same event log and can take over within moments. Use sequence numbers to detect gaps and avoid duplicate processing.
  • Replicate the log across machines or sites before acknowledging, trading a little latency for durability.
  • Idempotent order identifiers prevent a resubmitted order executing twice.
  • Circuit breakers halt trading when prices move too fast.

  Market data fairness

  Everyone should see the same information at about the same time. Exchanges publish through multicast feeds, give all participants equal-length cables inside the data centre, and timestamp events at the matching engine. A separate, slower snapshot and retransmission service helps receivers that miss packets.

  What is out of scope here

  Clearing houses, settlement cycles, regulation and surveillance are large systems of their own. The core design is the sequencer plus deterministic matching engine plus durable log.

  Real-world reading

  Related reading

  • Event SourcingMartin FowlerBlogs
  • Monolith FirstMartin FowlerBlogs
  • Rewriting the heart of our sync engineDropboxCase Studies
  • Cape: Event Stream Processing Framework at DropboxDropbox engineers · DropboxCase Studies

  Show 6 moreShow fewer

  • Improving Search Engine Efficiency by over 25% at eBayeBay engineers · eBayCase Studies
  • Finding Kafka throughput limit in infrastructure at DropboxDropbox engineers · DropboxCase Studies
  • Bullet: Forward-Looking Query Engine for Streaming Data at YahooYahoo engineers · YahooCase Studies
  • MicroservicesJames Lewis and Martin Fowler · Martin FowlerBlogs
  • The LMAX ArchitectureMartin FowlerCase Studies
  • Microservices and OrchestrationMartinfowler engineers · MartinfowlerCase Studies

  Open this lesson with its quiz and exercise