Container (8 parts) at Riot Games

Ideas you will meet

Microservices

Decompose systems into independently deployable services.

Docker

Package and ship applications as containers.

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 · 9 minUploading large files reliablyChunked, resumable, direct-to-storage uploads: how to move gigabytes over unreliable networks without tying up your servers.Read hereClose

  Why large uploads are hard

  A 5 GB file on a normal request has many ways to fail:

  • A single long connection dies on a flaky network, losing everything.
  • Timeouts in load balancers and proxies cut off long requests.
  • Your application servers are tied up carrying bytes, and memory or disk fills.
  • A failure at 99% means starting again.
  • Users want a progress bar and the ability to pause and resume.

  The design in one picture

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  Figure SDA-RWS-17-1 · Chunked, resumable upload flow · © forcsguy.com

  Before: upload through your server

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  After: upload straight to storage

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  What changed

  • Your servers no longer carry large files
  • A failed chunk is retried, not the whole file
  • Pre-signed URLs keep storage private

  Step by step

  1. Initiate. The client tells the API the file name, size and type. The API checks permissions and limits, and creates an upload session with a unique id.
  2. Direct to storage. The API returns pre-signed URLs: short-lived, scoped links that let the client upload straight to object storage. Your servers never carry the file.
  3. Chunk. The client splits the file into parts (for example 5 to 100 MB; object stores typically accept thousands of parts per object) and uploads them, in parallel.
  4. Verify. Each part carries a checksum (MD5, CRC32C or SHA-256). The storage service or your server checks it, and a bad part is simply re-sent.
  5. Resume. The session remembers which parts are done. After a drop the client asks which parts exist and sends only the missing ones. Open protocols such as tus standardise resumable uploads.
  6. Complete. The client sends the list of parts; storage assembles the object.
  7. Post-process asynchronously. An event triggers virus scanning, type verification, thumbnails or transcoding, and metadata updates. The user sees "processing" until it finishes.

  Checking each part

  When a part arrives, the store replies with a checksum tag. The client keeps the list of part numbers and tags, and sends it with the final "complete" request, so the store can verify and assemble exactly the parts you meant, in order. A failed part is re-sent alone; assembly of a very large object can take a few minutes, so treat "complete" as asynchronous from the user's point of view.

  Smart extras

  • Deduplication: hash the file (or chunks) on the client first. If the content already exists in storage, skip the upload entirely and just add a reference (as in cloud file storage services).
  • Parallelism and adaptive chunk size: larger parts on fast networks, smaller on poor ones.
  • Compression for compressible data before upload.
  • Upload acceleration: send to the nearest edge location, which forwards over the provider's backbone.
  • Progress and cancellation: the client tracks bytes per part; cancelling aborts the session.

  Safety and cost

  • Limit size, type and number of concurrent uploads per user, and expire pre-signed URLs quickly.
  • Validate on the server, not only in the client: check file type by content, scan for malware, and store uploads in a quarantine area until cleared.
  • Use lifecycle rules to delete incomplete multipart uploads after a few days, since unfinished parts cost money until removed.
  • Encrypt in transit and at rest; do not let user-provided names choose storage paths.
  • Rate limit session creation to prevent abuse.

  Failure handling checklist

  Failure	Handling
  Connection drops mid-part	Retry that part with backoff
  Client closes the app	Session persists; resume later by listing stored parts
  Corrupted part	Checksum mismatch, re-upload that part
  Duplicate completion request	Idempotent: completing twice returns the same result
  Abandoned upload	Lifecycle rule cleans up the leftovers
  Processing fails	Retry from the event queue, mark the file as failed, tell the user

  Real-world reading

  Related reading

  • Determinism (4 parts) in League of Legends ServerRiotgames engineers · RiotgamesCase Studies
  • Implementing health checksAmazon Builders' Library · Amazon Web ServicesBlogs
  • The Network is ReliableKyle Kingsbury · aphyr.comBlogs
  • Bulkheads: Partition and Tolerate Failure in One PartSkife engineers · SkifeCase Studies

  Show 6 moreShow fewer

  • Natural Language Processing and Content Analysis (2 parts) at Condé NastCondé Nast engineers · Condé NastCase Studies
  • Autoscaling Bigtable Clusters based on CPU Load at SpotifySpotify engineers · SpotifyCase Studies
  • Ambry: Distributed Immutable Object Store at LinkedInLinkedIn engineers · LinkedInCase Studies
  • Big Data Processing (2 parts) at SpotifySpotify engineers · SpotifyCase Studies
  • Container (8 parts) at Riot GamesRiot Games engineers · Riot GamesCase Studies
  • Architecture of Chat Service (3 parts) at Riot GamesRiot Games engineers · Riot GamesCase Studies

  Open this lesson with its quiz and exercise