Resilience: timeouts, retries, bulkheads, backpressure and circuit breakers

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