Traffic Steering using Rum DNS at LinkedIn

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.

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 · 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