Monitor DNS systems at Stripe

Ideas you will meet

Load Balancer

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

DNS

How names resolve, TTLs, and DNS-based traffic routing.

Observability

Metrics, logs, traces, SLOs and alerting.

What we already know about this

Our own lessons on these ideas. Read them here before you start, so it makes sense.

• Advanced Distributed Systems · 12 minLoad balancing: layer 4, layer 7 and gatewaysWhat a load balancer can see and do at each layer, the algorithms behind it, and how API gateways add routing, aggregation and policy.Read hereClose

  What a load balancer sees

  A load balancer spreads incoming work across servers, removes failed servers and makes scaling invisible to clients. What it can do depends on which layer it works at.

  	Layer 4 (transport)	Layer 7 (application)
  Sees	IP addresses, ports, TCP/UDP connections	HTTP method, URL, headers, cookies, body
  Decides by	Connection details	Request content
  TLS	Passes through, or terminates in some products	Usually terminates TLS
  Speed and cost	Very fast, low overhead	More work per request
  Typical features	Spread connections, health checks	Path routing, header rules, sticky sessions, rewrites, caching, WAF rules
  Examples	Network load balancers, LVS, HAProxy in TCP mode	NGINX, Envoy, HAProxy HTTP mode, cloud application balancers

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  Figure SDA-ADS-05-1 · Layers of balancing in front of an app · © forcsguy.com

  Many designs use both: an L4 tier for raw scale and an L7 tier for smart routing.

  Before: layer 4: ports and addresses

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  After: layer 7: reads the request

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  What changed

  • Layer 7 can route by path, header or cookie
  • Layer 7 can terminate TLS and retry safely
  • Layer 4 is simpler and faster, with less to configure

  Choosing a target: algorithms

  • Round robin / weighted round robin: simple rotation; weights favour bigger machines.
  • Least connections (and weighted): send to the server with fewest active requests; good when requests vary in length.
  • Least response time: combine active load with measured latency.
  • IP hash / consistent hashing: the same client or key goes to the same server, useful for caches and sticky state (see consistent hashing).
  • Resource-based: servers report load and the balancer follows.

  Health checks (active probes and passive error tracking) remove unhealthy servers; connection draining lets in-flight requests finish before a server is removed during a deploy.

  Global and DNS balancing

  Across regions, DNS-based or global server load balancing directs users to the nearest or healthiest region (see the DNS and CDN lessons). DNS answers are cached, so failover takes as long as the TTL; anycast and global balancers react faster.

  API gateway: policy at the edge

  An API gateway is an L7 component specialised for APIs. Beyond routing it commonly provides:

  • Authentication and authorisation (validate tokens, apply scopes),
  • Rate limiting and quotas (see the rate-limiter lesson),
  • Request and response transformation, versioning, and protocol translation,
  • Aggregation: one client call fans out to several services and merges results, saving round trips for mobile clients,
  • Caching, logging, metrics and tracing in one place.

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  Figure SDA-ADS-05-2 · Gateway routing and aggregation · © forcsguy.com

  Risks: the gateway is a single entry point (run several instances, keep it stateless) and can become a bottleneck or a place where business logic piles up. Keep it to cross-cutting concerns. A variation is a backend for frontend: one gateway per client type (web, mobile) tailored to its needs.

  Inside the cluster

  Between services, balancing is done by client-side libraries or sidecar proxies that know the healthy instances from service discovery, so no central balancer is needed for east-west traffic. See the microservices lessons for service mesh ideas.

  Failure habits

  • Run at least two of every balancer and gateway instance across zones.
  • Keep timeouts and retry budgets (see the resilience lesson) so the balancer does not amplify an outage.
  • Use slow start for new instances so they warm up before taking a full share.

  Real-world reading

  Related reading

  • Maglev: A Fast and Reliable Software Network Load Balancer (paper)Eisenbud et al. · GooglePapers
  • NGINX HTTP Load BalancingNGINX · F5 / NGINXDocumentation
  • Scaling Your API with Rate LimitersPaul Tarjan · StripeBlogs
  • Active-Active for Multi-Regional ResiliencyNetflix Engineering · NetflixCase Studies

  Show 6 moreShow fewer

  • ByteByteGo: System Design Video SeriesAlex Xu · ByteByteGoYouTube
  • Rate Limiting with NGINXNGINX · F5 / NGINXBlogs
  • Implementing health checksAmazon Builders' Library · Amazon Web ServicesBlogs
  • Google SRE Book: Load Balancing in the DatacenterGoogle SRE team · GoogleBlogs
  • Traffic Steering using Rum DNS at LinkedInLinkedIn engineers · LinkedInCase Studies
  • Google SRE BookBetsy Beyer et al. · GoogleCourses

  Open this lesson with its quiz and exercise

• System Design Foundations · 11 minNetworking, APIs and CDNsWhat happens between a click and your server: DNS, HTTP, API styles, and how a CDN moves content closer to users.Read hereClose

  The journey of a request

  Before your code runs, a lot happens. When a browser loads https://shop.example.com:

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  Figure SDA-FND-09-1 · Request journey: DNS, CDN, origin · © forcsguy.com

  1. DNS turns the name into an IP address. Answers are cached at several levels for a time given by the record's TTL, so changes spread gradually.
  2. A connection is opened (TCP) and secured (TLS). Each step costs round trips, so long distances hurt.
  3. HTTP carries the request and response.

  Latency is mostly physics: a round trip across an ocean costs over 100 ms no matter how fast your code is. The only fixes are fewer round trips and moving data closer to the user.

  Image: Syced · CC0 · Source · Wikimedia Commons: Fujikura optic fiber at Science Museum

  Name lookup is a short chain of questions, each answered by a more specific server:

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  Figure SDA-FND-09-2 · How a name becomes an address · © forcsguy.com

  Image: Howe's Adventures & Achievements of Americans · Public domain · Source · Wikimedia Commons: Transatlantic submarine cable map

  CDNs

  A content delivery network is a worldwide set of servers (edges) that keep copies of your content close to users.

  • Great for static files: images, video, scripts, stylesheets, and often whole pages that rarely change.
  • Cuts latency, absorbs traffic spikes and shields your origin from load.
  • Controlled by cache headers (Cache-Control: max-age=...) and purge APIs when content changes.
  • Use a versioned file name (app.3f9a.js) so you can cache for a year and still ship updates instantly.

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  Figure SDA-FND-09-3 · Content delivery in tiers · © forcsguy.com

  Before: everyone goes to the origin

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  After: a CDN serves copies near users

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  What changed

  • Users get files from a nearby location
  • The origin handles far fewer requests
  • Changed files need invalidation or versioned names

  Choosing an API style

  Style	Strength	Watch out for
  REST over HTTP/JSON	Simple, cacheable, universal	Over-fetching, many round trips for complex screens
  GraphQL	Client asks for exactly the fields it needs	Query cost control, caching is harder
  gRPC	Fast binary protocol, strong contracts	Needs tooling, awkward in browsers
  SOAP	XML messages with a formal contract (WSDL) and built-in standards for security and transactions, still common in banks and older enterprise systems	Verbose, heavier tooling, rarely chosen for new public APIs
  WebSockets / SSE	Server can push live updates	Long-lived connections need extra capacity and care

  A good default is REST for public APIs, gRPC between internal services if you need speed, and WebSockets only where live push is truly needed.

  Designing good APIs

  • Version them so old clients keep working.
  • Paginate list endpoints (cursor-based pagination stays stable as data changes).
  • Make writes idempotent with idempotency keys, so a retry does not double-charge.
  • Return clear errors with correct status codes.
  • Authenticate and rate limit at the edge, often in an API gateway that also handles routing, logging and TLS.

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  Figure SDA-FND-09-4 · API gateway in front of services · © forcsguy.com

  More on this site

  Related reading

  • High Performance Browser NetworkingIlya Grigorik · O'ReillyBooks
  • HTTP/3: the past, the present, and the futureCloudflareBlogs
  • MDN: HTTP cachingMDN contributors · MozillaDocumentation
  • Cloudflare BlogCloudflare engineers · CloudflareBlogs

  Show 6 moreShow fewer

  • The Road to QUICCloudflareBlogs
  • Flannel: An Application-Level Edge Cache to Make Slack ScaleSlack Engineering · SlackCase Studies
  • Cloudflare Learning Center: What is a CDN?CloudflareDocumentation
  • Cloudflare EngineeringCloudflare engineers · CloudflareBlogs
  • Justin.tv's Live Video Broadcasting Architecture - High Scalability -High Scalability engineers · High ScalabilityCase Studies
  • Service Workers at SlackSlack engineers · SlackCase Studies

  Open this lesson with its quiz and exercise

• System Design Foundations · 9 minObservability and running a systemMetrics, logs and traces, how to define 'healthy' with SLOs, and how to ship changes without breaking things.Read hereClose

  You cannot fix what you cannot see

  A system you cannot observe is a system you cannot trust. Observability is the ability to understand what is happening inside from the outside. Three kinds of signal work together.

  Signal	What it is	Best for
  Metrics	Numbers over time (requests per second, error rate, CPU)	Dashboards, alerts, trends
  Logs	Timestamped records of events	Details of what happened
  Traces	The path of one request through many services	Finding where time is spent

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  Figure SDA-FND-11-1 · From alert to root cause · © forcsguy.com

  What to measure

  A simple checklist, the four golden signals:

  1. Latency: how long requests take (use percentiles).
  2. Traffic: how much demand there is.
  3. Errors: how many requests fail.
  4. Saturation: how full the busiest resource is (CPU, memory, connections, disk, queue depth).

  Averages hide pain. If the average is 100 ms but p99 is 4 seconds, one user in a hundred is waiting four seconds, and with many requests per page that is most of your users sometimes.

  Defining healthy: SLIs, SLOs, error budgets

  • An SLI (indicator) is a measurement, like "fraction of requests answered successfully in under 300 ms".
  • An SLO (objective) is the target, like "99.9% over 30 days".
  • The error budget is what is left over: 0.1% of requests may fail. While there is budget, ship features; when it runs out, focus on reliability.

  Alert on symptoms users feel (error rate, latency) more than on causes (one machine's CPU). Page a human only for things that need action now.

  Ship changes safely

  Most outages come from changes. Reduce the risk:

  • Small, frequent releases are easier to understand and undo than big ones.
  • Canary release: send a few percent of traffic to the new version and watch the metrics before going wider.
  • Feature flags: deploy code turned off, then enable it gradually, and switch it off instantly if needed.
  • Blue-green: keep the old version running so you can switch back in seconds.
  • Backwards-compatible changes: database and API changes should work with both old and new code during rollout.
  • Fast rollback must be a rehearsed, boring button.

  When things go wrong

  Have an incident process: someone leads, someone communicates, mitigate first and investigate later. Afterwards write a blameless postmortem that asks "how did our system and process allow this?" rather than "who did this?". That is how reliability actually improves.

  A worked example: error budgets and burn rate

  Your service has an SLO of 99.9% of requests succeed over 30 days, and it serves 10 million requests a month.

  • Error budget: 0.1% of 10,000,000 = 10,000 failed requests may happen in the month before you break the promise.
  • Burn rate is how fast you spend it. A burn rate of 1 uses exactly the budget over 30 days. If 5% of requests are failing now, the error rate is 50 times the allowed 0.1%, a burn rate of 50: the whole month's budget is gone in 30 / 50 = 0.6 days, about 14 hours.
  • Alert on burn rate, not on a single bad minute. A common pattern watches two windows together: page someone if the burn rate over the last hour is above about 14 and the last 5 minutes agree (a fast burn: 2% of the monthly budget gone in an hour). Open a ticket for a slower burn, say rate above 6 over 6 hours. Two windows avoid paging for blips and avoid missing slow leaks.

  Why this beats "alert when error rate is above X": it ties alerts to what users were promised, it pages only when the budget is in danger, and it gives the team a rule for trade-offs: when the budget is healthy, ship faster; when it is nearly spent, slow down and fix reliability.

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  Figure SDA-FND-11-2 · Alerting on how fast the error budget burns · © forcsguy.com

  Finding the cause with all three signals

  A worked investigation shows why you want metrics, traces and logs together.

  1. A metric alerts: the checkout success rate dropped and p99 latency rose. Metrics tell you that something is wrong and how bad, cheaply and over time.
  2. Narrow it down by dimension: break the metric down by region, version and endpoint. Only the new version in one region is affected, a strong hint it is a deploy.
  3. A trace shows where the time goes: one slow checkout request shows 4.8 of its 5 seconds in a call to the payment provider's tokenisation endpoint.
  4. Logs give the detail: filter logs by the trace id from that request and read the exact error: a timeout talking to a new tokenisation URL.
  5. Fix and verify: roll back the version, and watch the metric recover.

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  Figure SDA-FND-11-3 · From an alert to a root cause · © forcsguy.com

  The glue is a correlation id (trace id) carried on every request and written into every log line, so you can jump from one signal to the others.

  Mistakes people make

  • Alerting on causes instead of symptoms. "CPU is 85%" may be fine; "users are seeing errors" never is. Page on symptoms users feel, and use cause metrics for diagnosis.
  • Averaging percentiles. The mean of two hosts' p99 is meaningless; merge distributions instead (see the t-digest lesson).
  • High-cardinality labels. A label such as user_id on a metric creates a new time series per user, which can multiply storage and cost by millions. Put such detail in logs or traces.
  • Logs without structure or ids. Free text is hard to query. Log JSON with a trace id, service and version.
  • Dashboards no one owns. A wall of graphs nobody reads is noise. Build a small dashboard per service around the golden signals: latency, traffic, errors and saturation.
  • No runbook. An alert that does not say what to check first wastes the first ten minutes of every incident.

  Try it yourself

  Run any test in the System Builder and read its charts as if they were production graphs: which signal moves first, and which part would you look at next? Build the search metrics lab to practise percentile maths, and read the t-digest lesson for how real systems summarise latency at scale.

  Real-world reading

  Related reading

  • The Site Reliability WorkbookGoogle SRE team · O'Reilly / GoogleBooks
  • Feature Toggles (aka Feature Flags)Pete Hodgson · Martin FowlerBlogs
  • InfluxData BlogInfluxData engineers · InfluxDataBlogs
  • MetricsDB: TimeSeries Database for storing metrics at TwitterTwitter engineers · TwitterCase Studies

  Show 6 moreShow fewer

  • The Architecture Twitter Uses to Deal with 150M Active Users, 300K QPS, a 22 MB/S Firehose, and Send Tweets in Under 5 SecondsHigh Scalability engineers · High ScalabilityCase Studies
  • Steady State: Always Put Logs on Separate DiskMicrosoft engineers · MicrosoftCase Studies
  • XCMetrics: All-in-One Tool for Tracking Xcode Build Metrics at SpotifySpotify engineers · SpotifyCase Studies
  • Honeycomb BlogHoneycomb engineers · HoneycombBlogs
  • BookKeeper: Distributed Log Storage at YahooYahoo engineers · YahooCase Studies
  • Analyzing V8 Garbage Collection Logs at AlibabaAlibaba engineers · AlibabaCase Studies

  Open this lesson with its quiz and exercise