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Google SRE Book: Addressing Cascading Failures

How overload spreads between services and the defences: load shedding, deadlines, and graceful degradation.

AdvancedResilienceDistributed Systems

By Google SRE team · Google · Added 2026-10-12

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About 37 min left. Your work is saved as you go.

What you are about to study

Google wrote this to explain how they thought about a problem. It assumes you are comfortable with distributed systems.

Ideas you will meet

Resilience
Timeouts, retries, circuit breakers, bulkheads and load shedding.
Distributed Systems
Consensus, time, failure and the fundamentals of scale-out.

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 · 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 here

    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

    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.

    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

    Show 6 more

    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 here

    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.

    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.

    A vintage electrical circuit breaker, the physical idea behind the software circuit breakerImage: Alf van Beem · CC0 · Source · Wikimedia Commons: Circuit breaker, Single pole, Non-tripfree, 20Amp-30Volts, AN 3160 pic1

    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

    After: a breaker cuts the slow dependency off

    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).

    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.

    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

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    Open this lesson with its quiz and exercise

  • Core Concepts: How Things Actually Work · 6 minOptimistic and pessimistic lockingTwo strategies for concurrent updates: lock first, or detect conflicts at the end and retry.Read here

    The problem

    Two users load the same record, both change it, both save. Without protection the later save silently overwrites the earlier one (a lost update). Two families of solutions exist.

    A railway signal: only one train may hold a stretch of track at a time, just as a lock admits one writerImage: Svetlov Artem · CC0 · Source · Wikimedia Commons: RZD Selizarovo 2016-08 searchlight signal 2

    Pessimistic locking: assume conflict

    Lock the data before changing it, so others must wait.

    BEGIN;
SELECT * FROM seats WHERE id = 7 FOR UPDATE;  -- others block here
UPDATE seats SET taken = true WHERE id = 7;
COMMIT;
    • Safe and simple to reason about.
    • Cost: waiting, reduced throughput, risk of deadlock, and trouble if the lock is held while waiting for a user.

    Optimistic locking: assume no conflict, check at the end

    Read freely, and when saving check nothing changed.

    UPDATE pages SET body = :new, version = version + 1
WHERE id = 5 AND version = :versionIRead;
-- 0 rows updated means someone else saved first
    • No locks held while the user thinks.
    • Cost: on conflict the work is rejected and must be retried or merged. Under heavy contention many attempts fail.

    Before: pessimistic: lock first

    After: optimistic: check at the end

    What changed

    • Pessimistic makes others wait, optimistic lets them all try
    • Optimistic rejects a stale save instead of overwriting it
    • Optimistic wastes work only when a conflict really happens

    Optimistic locking in action

    Figure SDA-CORE-03-1 · Two writers with a version check · © forcsguy.com

    Choosing

    OptimisticPessimistic
    Conflictsrarefrequent
    Lock durationnonewhole transaction
    Failure moderetry or mergewaiting, deadlocks
    Good forediting, profiles, most web appsinventory, seat booking, money moves

    Beyond one database

    For resources not in a database (a file, an external job), use a distributed lock or a lease, covered in a later lesson. They need extra care because the lock holder can crash or stall.

    Real-world reading

    Related reading

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    Open this lesson with its quiz and exercise

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Go deeper on this

A path through videos, articles, other case studies and code on the same ideas as Google SRE Book: Addressing Cascading Failures.

  1. Warm upGet the vocabulary first.

  2. WatchHow others solve the same problem, on video.

    Videos belong to their creators and play from YouTube. More on YouTube (opens in a new tab)

  3. ReadEngineers explain it in their own words.

  4. See it in another companyThe same ideas, a different system.

  5. Read the codeSee how it looks when it is real.

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