Resilience Engineering with Project Waterbear at LinkedIn

Ideas you will meet

Resilience

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

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 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

• 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 hereClose

  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.

  Image: 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

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  After: optimistic: check at the end

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  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

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

  Choosing

  	Optimistic	Pessimistic
  Conflicts	rare	frequent
  Lock duration	none	whole transaction
  Failure mode	retry or merge	waiting, deadlocks
  Good for	editing, profiles, most web apps	inventory, 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

  • ZooKeeper: Wait-free Coordination for Internet-scale Systems (paper)Hunt et al. · Yahoo! ResearchPapers
  • Chaos Monkey (source)Netflix · GitHubGitHub
  • Google SRE Book: Addressing Cascading FailuresGoogle SRE team · GoogleBlogs
  • Chaos MonkeyNetflixDocumentation

  Show 6 moreShow fewer

  • Static stability using Availability ZonesAmazon Builders' Library · Amazon Web ServicesBlogs
  • The Network is ReliableKyle Kingsbury · aphyr.comBlogs
  • Avoiding fallback in distributed systemsAmazon Builders' Library · Amazon Web ServicesBlogs
  • How to do distributed lockingMartin Kleppmann · martin.kleppmann.comBlogs
  • Distributed Locking at UberUberYouTube
  • Resilience Engineering with Project Waterbear at LinkedInLinkedIn engineers · LinkedInCase Studies

  Open this lesson with its quiz and exercise