Rate Limiting

Ideas you will meet

Rate Limiting

Token bucket, leaky bucket and sliding-window limiters.

What we already know about this

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

• System Design Foundations · 12 minDesigning for failure: reliabilityTimeouts, retries, circuit breakers, rate limiting and redundancy: how systems stay up when parts of them break.Read hereClose

  Failure is normal

  At scale something is always broken: a disk, a network link, a deployment, a dependency having a bad day. Reliable systems do not avoid failure, they expect it and contain it.

  Two words to keep apart:

  • Availability: the fraction of time the system works (99.9% allows about 8.8 hours of downtime a year; 99.99% about 53 minutes).
  • Durability: the chance stored data is not lost.

  Remove single points of failure

  A single point of failure (SPOF) is any component whose failure takes everything down. Hunt for them:

  • Run several application servers behind a load balancer.
  • Replicate databases and plan failover (lesson 5).
  • Spread across availability zones, and for the most critical services, across regions.
  • Do not forget the "invisible" ones: the load balancer, DNS, a certificate that expires, a single queue.

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  Figure SDA-FND-10-1 · Removing single points of failure tier by tier · © forcsguy.com

  Protect calls between services

  Most outages spread through calls between components. Four basic defences:

  1. Timeouts

  Every network call needs a timeout. Without it a slow dependency holds your threads hostage. Set timeouts based on what a healthy call looks like (for example a little above the 99th percentile), and make inner timeouts shorter than outer ones.

  2. Retries, carefully

  Transient errors often succeed on a second try. But retries multiply load on a struggling service, so:

  • Retry only idempotent operations or those with an idempotency key (lesson 8).
  • Use exponential backoff with jitter.
  • Cap the number of attempts, and ideally cap the share of traffic that can be retries.

  3. Circuit breakers

  If a dependency keeps failing, stop calling it for a short time and fail fast.

  Image: Alf van Beem · CC0 · Source · Wikimedia Commons: Circuit breaker, Single pole, Non-tripfree, 20Amp-30Volts, AN 3160 pic1

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  Figure SDA-FND-10-2 · Circuit breaker states · © forcsguy.com

  • Closed: normal operation, failures are counted.
  • Open: calls fail immediately, saving resources.
  • Half-open: a few trial calls check whether the dependency has recovered.

  4. Bulkheads

  Give each dependency its own pool of threads or connections, like watertight compartments in a ship, so one slow dependency cannot use up everything.

  Before: a slow dependency drags everyone down

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  After: a breaker cuts the slow dependency off

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  What changed

  • Callers stop waiting on a failing service
  • Threads and connections are not used up
  • A fallback answer is returned while the service recovers

  Protect yourself from too much traffic

  • Rate limiting caps how many requests a client may make (common algorithms: token bucket, sliding window). It protects against abuse and accidental floods.
  • Load shedding: when overloaded, deliberately reject low-priority work early so important requests still succeed. It is better to serve 80% of requests well than 100% badly.
  • Back pressure: tell producers to slow down instead of letting queues grow without bound.

  Degrade gracefully

  When a non-critical part fails, keep the rest working: show the page without recommendations, serve cached prices, queue the email. Decide in advance which features are essential.

  Practise failure

  • Health checks so traffic leaves broken instances.
  • Chaos testing: deliberately kill instances in a controlled way to prove the system copes.
  • Runbooks and game days so people have rehearsed the response.

  A worked example: the arithmetic of availability

  Downtime in a month. 99% allows about 7.3 hours; 99.9% about 43 minutes; 99.99% about 4.4 minutes; 99.999% about 26 seconds. Each extra nine is ten times harder and usually much more expensive, so choose the target your users need, not the biggest number.

  Dependencies in series multiply. If a request needs three services, each 99.9% available, the request is available only 0.999 x 0.999 x 0.999 = 99.7%, about 2 hours 11 minutes of downtime a month. The more things in the critical path, the worse it gets, which is why teams shorten the critical path and make non-essential calls optional.

  Redundancy in parallel helps. Two independent replicas, each 99% available, are both down only 1% x 1% = 0.01% of the time: 99.99% together. The word "independent" carries all the weight: replicas that share a rack, a deploy, a bug or a dependency fail together, and the formula no longer applies.

  Retries multiply load. If every one of 1,000 requests per second fails and each caller retries 3 times, the failing service sees up to 4,000 requests per second, making recovery harder. And if three layers each retry 3 times, one user request can become 64 attempts at the bottom. Retries need limits, backoff with jitter, and a retry budget (for example, retries may be at most 10% of normal traffic).

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  Figure SDA-FND-10-3 · How availability combines · © forcsguy.com

  Anatomy of a cascading failure

  The classic outage is rarely one failure; it is one failure that spreads.

  1. A database gets slow (a bad query, a noisy neighbour). Responses that took 10 ms now take 5 seconds.
  2. App servers wait on it. Each request holds a thread for 5 seconds, so threads and connections fill up.
  3. The app can no longer accept new requests, even ones that do not need the database. The load balancer sees timeouts and marks servers unhealthy, so the same traffic lands on fewer servers.
  4. Callers time out and retry, which adds load to the already struggling system.
  5. Everything is down, and fixing the original slow query no longer brings it back, because the retry storm keeps it overloaded.

  The defences are cheap compared with the outage:

  • Timeouts on every call, shorter than the caller's own timeout.
  • Circuit breakers that stop calling a failing dependency for a while and fail fast.
  • Bulkheads: separate thread or connection pools per dependency, so one slow one cannot use all of them.
  • Load shedding: when overloaded, reject some requests early to keep serving the rest.
  • Backoff with jitter and retry budgets.

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  Figure SDA-FND-10-4 · A circuit breaker protects both sides · © forcsguy.com

  Mistakes people make

  • No timeouts, or timeouts longer than the caller's. The default in many libraries is "wait forever" or minutes.
  • Retrying everything everywhere. Retry only safe, idempotent operations, at one layer, with backoff, jitter and a budget.
  • Health checks that lie. A server that answers /health but cannot reach its database should fail the check.
  • Untested failover. Failover that has never run is a guess. Practise it on purpose, in business hours.
  • Shared fate. Two "redundant" systems in one zone, on one deploy pipeline, or on one dependency are one system.
  • No graceful degradation. Decide in advance which features can be switched off (recommendations, images, analytics) to protect the critical path.

  Try it yourself

  In the System Builder, lose three servers at peak and find how much spare capacity you need. Read the resilience patterns lesson for circuit breakers and bulkheads in more depth, and build the lease leader election lab to see failover without split brain.

  More on this site

  Related reading

  • Timeouts, retries, and backoff with jitterAmazon Builders' Library · Amazon Web ServicesBlogs
  • Using load shedding to avoid overloadAmazon Builders' Library · Amazon Web ServicesBlogs
  • Maglev: A Fast and Reliable Software Network Load Balancer (paper)Eisenbud et al. · GooglePapers
  • Designing robust and predictable APIs with idempotencyBrandur Leach · StripeBlogs

  Show 6 moreShow fewer

  • Circuit BreakerMartin FowlerBlogs
  • Circuit Breaker patternMicrosoft · Microsoft AzureDocumentation
  • Google SRE Book: Addressing Cascading FailuresGoogle SRE team · GoogleBlogs
  • Google SRE Book: Handling OverloadGoogle SRE team · GoogleBlogs
  • Testing for Disaster Recovery Failover TestingUSENIX engineers · USENIXCase Studies
  • Improved Production Stability with Circuit Breakers at HerokuHeroku engineers · HerokuCase Studies

  Open this lesson with its quiz and exercise

• Real-World Systems · 10 minSecuring a web APIAuthentication, authorisation, input handling and abuse protection: the layers that keep an API safe.Read hereClose

  Think in layers

  No single control secures an API. Defence comes from several layers, each assuming the others may fail.

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  Figure SDA-RWS-14-1 · API security layers · © forcsguy.com

  1. Transport

  Use HTTPS everywhere, with modern TLS and HSTS. Never accept credentials over plain HTTP.

  2. Authentication: who is calling?

  • Users: OAuth 2.0 / OpenID Connect access tokens, or secure session cookies for browsers (see the earlier lessons).
  • Machines: API keys or client credentials, stored as hashes, scoped, and rotatable.
  • Send credentials in the Authorization header, never in URLs.
  • Keep token lifetimes short, support revocation, and protect refresh tokens carefully.
  • Add multi-factor authentication for sensitive accounts, and protect login endpoints with rate limits.

  3. Authorisation: what may they do?

  This is where most real API breaches happen.

  • Check on every request, on the server, both the action (role or scope) and the specific object. "Is this user logged in?" is not enough; ask "may this user read order 123?" (broken object-level authorisation is the top API risk).
  • Apply least privilege: tokens carry only the scopes they need.
  • Never rely on the client hiding buttons.
  • Do not expose sequential ids as a security measure. Use authorisation checks (random ids only help a little).

  4. Input and output

  • Validate every input against a schema: types, lengths, ranges, allowed values. Reject unknown fields (prevents mass assignment, where a caller sets fields like isAdmin).
  • Use parameterised queries to prevent SQL injection, and encode output to prevent injection into other systems.
  • Limit request size and file types, and scan uploads.
  • Return only the fields needed (avoid excessive data exposure), and paginate lists.
  • Return generic error messages to clients and keep detail in logs, so errors do not leak internals.

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  Figure SDA-RWS-14-2 · Two ways a caller proves who it is · © forcsguy.com

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  Figure SDA-RWS-14-3 · Signing each request with a shared secret (HMAC) · © forcsguy.com

  Image: Spanish Coches · CC BY 2.0 · Source · Wikimedia Commons: Peaje - Toll - Péage (5020183265)

  5. Abuse protection

  • Rate limiting per user, key and IP, with stricter limits on expensive or sensitive endpoints.
  • Quotas and timeouts; protect against huge queries (depth limits in GraphQL, page size caps).
  • Bot and credential-stuffing defences: lockouts, delays, CAPTCHAs, breached-password checks.

  6. Webhooks and service-to-service calls

  • Sign outgoing webhooks with an HMAC over the body and a timestamp. Receivers verify the signature in constant time, reject old timestamps and handle events idempotently.
  • Between services use mutual TLS or signed tokens, and network policies, instead of trusting "internal" traffic.

  7. Browsers: CORS and cookies

  • Configure CORS with an explicit allow-list of origins. Never reflect any origin with credentials enabled.
  • Cookies: HttpOnly, Secure, SameSite. Protect state-changing requests from CSRF.

  An example worth knowing: in 2021 a flaw in a popular Java logging library (Log4Shell) let attackers run their own code simply by getting a crafted string written to a log.

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  Figure SDA-RWS-14-4 · How untrusted text became remote code execution · © forcsguy.com

  8. Secrets, data and visibility

  • Keep secrets in a secrets manager, not in code or images, and rotate them.
  • Encrypt sensitive data at rest, and minimise what you store.
  • Log authentication events and authorisation failures (never secrets), and alert on anomalies. Keep an inventory of your APIs so old versions and forgotten endpoints do not stay exposed.
  • Keep dependencies patched, and test: security tests, dependency scanning, and periodic penetration testing.

  More on this site

  Related reading

  • MDN: Cross-Origin Resource Sharing (CORS)MDN contributors · MozillaDocumentation
  • Introduction to JSON Web TokensAuth0 · jwt.ioDocumentation
  • RFC 6749: The OAuth 2.0 Authorization FrameworkIETFDocumentation
  • AWS Security BlogAmazon Web Services engineers · Amazon Web ServicesBlogs

  Show 6 moreShow fewer

  • OWASP Cheat Sheet SeriesOWASP · OWASP FoundationDocumentation
  • Google Security BlogGoogle engineers · GoogleBlogs
  • How we built rate limiting capable of scaling to millions of domainsCloudflare engineering · CloudflareCase Studies
  • OWASP Top 10OWASP · OWASP FoundationDocumentation
  • Rate LimitingKeycdn engineers · KeycdnCase Studies
  • Scalability and Authentication at TwitchTwitch engineers · TwitchCase Studies

  Open this lesson with its quiz and exercise