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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

• Search, Analytics and Geo · 11 minSharding a search index: scatter, gather and mergeHow a search index is split across machines, why queries fan out to every shard, how top-k results are merged, and what goes wrong with deep paging and uneven shards.Read hereClose

  Why shard

  One machine cannot hold or search a very large index within the latency budget. Sharding splits the index into pieces, each on its own machine, so storage and work are spread out. Replicas copy each shard for availability and extra read capacity.

  Before: one big index

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  After: sharded, scatter and gather

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  What changed

  • Storage and work are spread over machines
  • Each shard returns its own top results to be merged
  • The slowest shard decides how fast the query is

  Two ways to partition

  • Document-partitioned (local index): each shard indexes a subset of the documents and holds a complete inverted index for them. Writes touch one shard. Every query must visit every shard. This is what Elasticsearch and Solr do.
  • Term-partitioned (global index): each shard holds the postings for a subset of terms. A query touches only the shards for its terms, but a write touches many shards and long posting lists must be combined across machines. It is rare in practice.

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  Figure SDA-SAG-05-1 · Scatter, gather and merge · © forcsguy.com

  Decisions that matter

  • Shard size and count. Too many tiny shards waste memory and add fan-out; too few huge shards recover slowly and cannot spread load. Many teams aim for tens of gigabytes per shard. The primary shard count is hard to change later, so plan for growth or use time-based indexes.
  • Routing. By default a document goes to a shard by hashing its id, which spreads data evenly. Routing by tenant or user keeps one customer's data together so queries can hit a single shard, at the risk of uneven shards.
  • Tail latency. A query is as slow as its slowest shard. Use replicas, timeouts, and hedged requests (send to a second replica if the first is slow).
  • Deep pagination. Page N needs the first N x pageSize results from every shard. Use cursors (search-after) instead of large offsets.
  • Scoring across shards. Each shard computes statistics such as IDF from its own documents. Even, large shards make the difference small; otherwise gather global statistics first.
  • Rebalancing and reindexing. Plan how to add capacity without downtime, usually by indexing into a new index and switching an alias.

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  Figure SDA-SAG-05-2 · Time-based indexes · © forcsguy.com

  Keeping the index fresh

  Documents usually live in a primary database and are copied to the search index. Use a change stream or an outbox so updates are not lost, accept that search is eventually consistent, and keep a way to rebuild the whole index from the source of truth.

  Real-world reading

  Related reading

  • Elasticsearch: scalability and resilienceElasticDocumentation
  • SolrCloud: shards and indexing dataApache Solr · Apache Software FoundationDocumentation
  • Elasticsearch GuideElasticDocumentation
  • Scaling Elasticsearch Clusters at UberUber engineers · UberCase Studies

  Show 2 moreShow fewer

  • Elasticsearch Performance Tuning Practice at eBayeBay engineers · eBayCase Studies
  • Solr: Improving Performance for Batch Indexing at BoxBox engineers · BoxCase Studies

  Open this lesson with its quiz and exercise