What is Envoy

Ideas you will meet

Microservices

Decompose systems into independently deployable services.

Load Balancer

Spread traffic across servers with L4/L7 balancing, health checks and algorithms.

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

• System Design Foundations · 10 minFrom one server to many: scaling and load balancingVertical versus horizontal scaling, why stateless servers matter, and how a load balancer spreads traffic.Read hereClose

  Two ways to grow

  When one server is not enough you have two options.

  Image: Derrick Coetzee · CC0 · Source · Wikimedia Commons: Technician with laptop working on server rack at NERSC

  • Vertical scaling ("scale up"): use a bigger machine with more CPU, memory or disk. It is simple because nothing in the design changes.
  • Horizontal scaling ("scale out"): add more machines and share the work between them. It needs more design effort but has no hard ceiling.

  	Vertical	Horizontal
  Effort	Low	Higher
  Ceiling	Hardware limit	Practically none
  Failure	One machine is a single point of failure	Survives losing a machine
  Cost curve	Gets steep for the largest machines	Roughly linear with commodity machines

  Real systems use both: scale up until it gets expensive or risky, then scale out.

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  Figure SDA-FND-02-1 · Scale up versus scale out · © forcsguy.com

  What a load balancer does

  Once you have several servers, something must decide which one handles each request. That is the load balancer.

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  Figure SDA-FND-02-2 · Load balancer in front of a server pool · © forcsguy.com

  It does three jobs:

  1. Distributes requests so no server is overloaded.
  2. Checks health. It probes each server regularly and stops sending traffic to ones that fail.
  3. Hides the pool. Clients talk to one address, and servers can be added or removed without clients noticing.

  Common strategies

  • Round robin: take turns. Simple and fine when requests are similar.
  • Least connections: send to the server with the fewest active requests. Better when request cost varies.
  • Hashing (for example on user id or IP): the same key goes to the same server. Useful when a server keeps a local cache for that key.
  • Weighted: bigger machines get a bigger share.

  Load balancers work at different layers. A layer 4 balancer routes by IP and port and is very fast. A layer 7 balancer reads the HTTP request, so it can route by URL path or header and terminate TLS.

  A security benefit comes with this layout: only the balancer needs a public address. The servers behind it use private addresses, reachable from inside the network but not from the internet, so clients cannot bypass the balancer or attack a server directly. If one server goes offline, the balancer sends its traffic to the others and a replacement can join the pool.

  Before: one server

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  After: load balancer and several servers

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  What changed

  • Traffic is shared across servers
  • One server failing no longer takes the site down
  • You can add capacity without downtime

  Stateless servers make scaling easy

  A server is stateless when it keeps nothing about a user between requests. Everything it needs comes from the request or from a shared store.

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  Figure SDA-FND-02-3 · Stateful versus stateless servers · © forcsguy.com

  With stateless servers any request can go to any server, so you can add servers at will, replace a failed one without losing data, and run auto-scaling. The state does not disappear. It moves to a database, a cache or a session store, which you then have to scale and protect, which is what the next lessons are about.

  Putting it together

  A typical first scalable layout:

  1. Users reach a load balancer.
  2. It spreads requests over a pool of stateless application servers.
  3. Servers read and write a shared database (and later a cache).
  4. Static files such as images and scripts are served from a CDN so the servers do not carry that traffic (lesson 9).

  This design handles a surprisingly large amount of traffic. The first thing to give way is usually the database, which is where we go next.

  A worked example: how many servers do you need?

  Capacity planning is arithmetic you can do on a napkin. Say your product has 5 million daily users and each makes about 20 requests a day.

  1. Requests per day: 5,000,000 x 20 = 100 million.
  2. Per second on average: 100,000,000 / 86,400 = about 1,160 requests per second.
  3. Traffic is not flat. A common rule of thumb is that the busiest second is 2 to 3 times the average, so plan for about 3,500 per second.
  4. One server handles about 400 requests per second flat out, but you never want servers at 100%: queues build and latency explodes near full load. Plan for roughly 70% utilisation, so 280 requests per second each.
  5. 3,500 / 280 = 12.5, so 13 servers carry the peak.
  6. Add spare capacity so you survive failures: with 13 servers, losing 2 leaves 11, which carry only 3,080 requests per second. Plan for N+2: 15 servers.

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  Figure SDA-FND-02-4 · From users to a server count · © forcsguy.com

  The same method works for any tier. Only the "requests per server" number changes, and you should measure it with a load test rather than guess.

  Choosing a balancing method with real numbers

  Round robin is fair in the number of requests, not in the work. Imagine three servers and a stream where most requests take 10 ms but every fifth takes 1 second (a report export, say). Round robin gives each server the same count, but by chance one server collects several slow requests at once, its queue grows, and its users wait while the other two are nearly idle.

  Least connections notices: the server with the most open requests gets fewer new ones, so the slow requests spread out as they arrive. It costs the balancer a little bookkeeping and helps most when request costs vary a lot. Use plain round robin when requests are similar and servers identical; use least connections, or "power of two choices" (pick two servers at random and send to the less busy one), when they are not. For caches and stateful workloads, consistent hashing keeps the same key on the same server so its memory stays useful.

  Health checks and draining

  A balancer is only as good as its knowledge of which servers are alive.

  • Active health checks call a lightweight endpoint (for example /health) every few seconds. After two or three failures in a row, the server leaves rotation; after a few successes it returns. Make the check reflect real readiness: a server that is up but cannot reach its database should fail the check.
  • Passive checks watch real traffic: too many errors or timeouts from one server and it is ejected.
  • Connection draining matters at deploys. Before stopping a server, tell the balancer to send it no new requests, let in-flight requests finish (typically 30 to 60 seconds), then stop it. Without draining, every deploy drops a few user requests.

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  Figure SDA-FND-02-5 · Rolling out a new version without dropping requests · © forcsguy.com

  Mistakes people make

  • Treating the balancer as magic. It is a server too and can be a single point of failure. Run at least two, or use a managed one.
  • Adding servers behind a single database. App servers scale out easily; if the database is the bottleneck, more app servers only make it fail faster.
  • Keeping state on the server. Sessions, uploads and caches held in one server's memory break the moment a request lands elsewhere. Move them to a shared store.
  • Sizing for the average. The system fails at peak, which is when it matters.
  • Never testing failure. Kill a server during a load test. If users notice, you have found a gap before they did.

  Try it yourself

  Open the System Builder challenge "One server is not enough". Run the test on one server and watch it fail, add a load balancer without adding servers, then add servers and see where the numbers settle. Or skip the challenge and open a load-balanced system on the free canvas, then change the wiring yourself.

  More on this site

  Related reading

  • Maglev: A Fast and Reliable Software Network Load Balancer (paper)Eisenbud et al. · GooglePapers
  • NGINX HTTP Load BalancingNGINX · F5 / NGINXDocumentation
  • Google SRE Book: Load Balancing in the DatacenterGoogle SRE team · GoogleBlogs
  • Scaling Load Balancing Infra to Support 1.3 Billion Users at Facebook - Patrick Shuff, Production Engineer at FacebookFacebookYouTube

  Show 6 moreShow fewer

  • Load Balancing infrastructure to support more than 1.3 billion users at FacebookFacebook engineers · FacebookCase Studies
  • Implementing health checksAmazon Builders' Library · Amazon Web ServicesBlogs
  • Awesome System Design ResourcesAshish Pratap Singh · ashishps1GitHub
  • Load Balancing at GithubGithub engineers · GithubCase Studies
  • Google Cloud Architecture FrameworkGoogle · Google CloudDocumentation
  • HAProxyHAProxy Technologies · HAProxyDocumentation

  Open this lesson with its quiz and exercise