Software Engineering Radio

Ideas you will meet

Distributed Systems

Consensus, time, failure and the fundamentals of scale-out.

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

• Low-Level Design Practice · 11 minParking lotVehicles, spots, tickets and fees: a classic exercise in allocation, pricing rules and concurrent entry gates.Read hereClose

  The problem

  Design a parking lot with several levels. It accepts motorcycles, cars and buses; assigns the nearest suitable free spot on entry; tracks occupancy; and charges a fee on exit by duration and vehicle type. Several entry and exit gates work at once.

  Entities

  Class	Responsibility
  ParkingLot	Owns the levels and gates; entry point for park and unpark
  Level	A floor holding spots; answers "which free spot fits this vehicle"
  ParkingSpot	Has a type and occupied state; can be reserved and freed
  Vehicle	Base type with subclasses or a type field (motorcycle, car, bus)
  Ticket	Created on entry: vehicle, spot, entry time
  PricingStrategy	Turns a stay (duration, vehicle type) into a fee

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  Figure SDA-LLD-02-1 · Parking lot class structure · © forcsguy.com

  The entry and exit flow

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  Figure SDA-LLD-02-2 · Entry and exit sequence · © forcsguy.com

  Key code

  type SpotType = "MOTORCYCLE" | "COMPACT" | "LARGE";
  const fits: Record<string, SpotType[]> = {
    MOTORCYCLE: ["MOTORCYCLE", "COMPACT", "LARGE"],
    CAR: ["COMPACT", "LARGE"],
    BUS: ["LARGE"],
  };
  
  class ParkingSpot {
    occupied = false;
    constructor(readonly id: string, readonly type: SpotType) {}
    tryReserve(): boolean {        // atomic in a real system (lock or compare-and-set)
      if (this.occupied) return false;
      this.occupied = true; return true;
    }
    free() { this.occupied = false; }
  }
  
  class Level {
    constructor(readonly spots: ParkingSpot[]) {}
    findAndReserve(vehicleType: string): ParkingSpot | null {
      for (const s of this.spots)
        if (fits[vehicleType].includes(s.type) && s.tryReserve()) return s;
      return null;
    }
  }
  

  The important details are tryReserve (one winner per spot) and the fits table (compatibility in one place).

  Pricing as a strategy

  interface PricingStrategy { fee(vehicleType: string, minutes: number): number }
  const hourly: PricingStrategy = {
    fee: (t, m) => Math.ceil(m / 60) * ({ MOTORCYCLE: 1, CAR: 2, BUS: 5 } as any)[t],
  };
  

  Swap in a daily cap, a weekend rate or a subscriber discount without touching ParkingLot.

  Before: pricing hard-coded in the ticket

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  After: pricing as a strategy

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  What changed

  • A new fee rule is a new class, not an edit to old code
  • Each pricing rule can be tested alone
  • The ticket no longer knows how fees are calculated

  Edge cases and extensions

  • Lot full: park returns "no spot" cleanly rather than throwing.
  • Lost ticket: look the vehicle up by plate; charge the maximum rate.
  • Nearest spot: keep free spots in a sorted structure per level (a heap or a set per type) so allocation is not a linear scan at scale.
  • Display boards that show free counts are observers of spot changes.
  • Persistence: if the app restarts, rebuild occupancy from stored tickets.

  Real-world reading

  Related reading

  • Scaling Infrastructure at Twitter - Yao Yue, Staff Software Engineer at TwitterTwitterYouTube
  • Scaling Slack - Bing Wei, Software Engineer (Infrastructure) at SlackSlack engineers · SlackCase Studies
  • Scaling Gradient Boosted Trees for Click-Through-Rate Prediction at YelpYelp engineers · YelpCase Studies
  • Building Real Time Infrastructure at Facebook - Jeff Barber and Shie Erlich, Software Engineer at FacebookFacebook engineers · FacebookCase Studies

  Show 6 moreShow fewer

  • Software Engineering DailySoftware Engineering DailyPodcasts & Newsletters
  • Scaling Pinterest - Marty Weiner, Pinterest’s founding engineerYouTubeYouTube
  • Design Patterns CatalogueRefactoring GuruDocumentation
  • PricingNet: Pricing Modelling with Neural Networks at SkyscannerSkyscanner engineers · SkyscannerCase Studies
  • Software Engineering Advice from Building Large-Scale Distributed Systems - Jeff DeanGooglePapers
  • Software Engineering RadioIEEE Computer Society · SE RadioPodcasts & Newsletters

  Open this lesson with its quiz and exercise