Site Resiliency at Expedia

Ideas you will meet

Resilience

Timeouts, retries, circuit breakers, bulkheads and load shedding.

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

• Advanced Distributed Systems · 13 minResilience: timeouts, retries, bulkheads, backpressure and circuit breakersHow to stop one slow or failing dependency from taking everything down: the patterns, how they fit together and where each one goes wrong.Read hereClose

  The core problem

  In a system of many services, slowness is more dangerous than failure. A dependency that fails fast is easy to handle. One that responds in 30 seconds makes callers hold threads, connections and memory until they too stop responding: a cascading failure. Resilience patterns limit how far a problem can spread.

  The toolbox

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  Figure SDA-ADS-06-1 · Resilience layers around a call · © forcsguy.com

  Timeouts

  Every network call needs a timeout, including connect, read and overall. Pick from measured latency (for example slightly above the 99th percentile), not a guess, and make the caller's budget smaller than its own caller's so work is abandoned in an orderly way. Propagate deadlines across calls.

  Retries

  Retry only transient failures and only idempotent operations (or use idempotency keys). Use exponential backoff with jitter (random spread) so clients do not retry in lockstep, cap the attempts, and apply a retry budget (for example no more than 10 percent extra traffic). Retry at one layer, not at every layer, or attempts multiply.

  Circuit breaker

  A breaker watches failures or slow calls. Closed: normal. After too many failures it opens: calls fail immediately (or use the fallback) so the dependency can recover and callers stop wasting resources. After a cool-down it goes half-open: a few trial calls decide whether to close again.

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  Figure SDA-ADS-06-2 · Circuit breaker states · © forcsguy.com

  Bulkheads

  Partition resources so one dependency cannot consume them all: separate thread pools or connection pools per dependency, separate queues per tenant or per priority, or separate instances for critical paths. A slow recommendation service then cannot starve checkout.

  Backpressure and load shedding

  If producers outpace consumers, work piles up. Without limits the buffer grows until memory is exhausted or latency becomes huge. Options:

  • Bounded queues that reject or block when full.
  • Signalling upstream to slow down (credit-based flow in streaming systems, TCP windows, reactive streams).
  • Load shedding: deliberately drop low-priority work early (return 429 or 503 quickly) to protect the rest.
  • Rate limiting at the edge (see the rate-limiter lesson).

  Failing fast and visibly under overload is better than accepting everything and failing slowly.

  Fallbacks and graceful degradation

  Return something useful when a dependency fails: cached data, a default, a simpler algorithm, or hide a non-essential section. Decide which features are essential before an incident, and test the degraded mode.

  How they interact

  • A timeout is the minimum; without it nothing else works.
  • Retries inside a breaker inside a bulkhead is the usual nesting.
  • Everything needs metrics: breaker state, rejected calls, queue depth, timeout counts. Alert on them.
  • Test it: inject latency and failures (chaos experiments) to confirm the patterns work before production does it for you.

  Common mistakes

  Retries with no backoff; timeouts longer than the caller's; unbounded queues; a shared thread pool for everything; breakers that never close; fallbacks that call the same failing service; and testing only the happy path.

  Real-world reading

  Related reading

  • Timeouts, retries, and backoff with jitterAmazon Builders' Library · Amazon Web ServicesBlogs
  • Using load shedding to avoid overloadAmazon Builders' Library · Amazon Web ServicesBlogs
  • Circuit BreakerMartin FowlerBlogs
  • Circuit Breaker patternMicrosoft · Microsoft AzureDocumentation

  Show 6 moreShow fewer

  • Google SRE Book: Addressing Cascading FailuresGoogle SRE team · GoogleBlogs
  • Chaos Monkey (source)Netflix · GitHubGitHub
  • Bulkheads: Partition and Tolerate Failure in One PartSkife engineers · SkifeCase Studies
  • Protector: Circuit Breaker for Time Series Databases at TrivagoTrivago engineers · TrivagoCase Studies
  • Autoscaling Jenkins at TrivagoTrivago engineers · TrivagoCase Studies
  • Troubleshooting a Connection Timeout Issue with tcp_tw_recycle Enabled at eBayeBay engineers · eBayCase Studies

  Open this lesson with its quiz and exercise

• 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