The Evolution of Global Traffic Routing and Failover

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

• Messaging and Microservice Patterns · 12 minService discovery, service mesh and sidecarsHow services find each other as instances come and go, and how sidecar proxies and a mesh move retries, security and telemetry out of application code.Read hereClose

  The problem: moving targets

  In a microservice system, instances are created and destroyed constantly by autoscaling, deployments and failures. A caller cannot rely on a fixed address. It needs an answer to "where are the healthy instances of the orders service right now?"

  Service discovery

  Instances register themselves (or are registered by the platform) in a registry; callers look up the current list.

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  Figure SDA-MSV-04-1 · Service registry and lookup · © forcsguy.com

  • Client-side discovery: the caller queries the registry and balances itself (smart client library).
  • Server-side discovery: the caller talks to a load balancer or the platform's service address, which knows the instances (Kubernetes Services and cluster DNS work this way; see the Kubernetes lesson).
  • DNS-based discovery is simple but constrained by caching.
  • Registries must be highly available (often built on consensus; see the consensus lesson), and the system should keep working with the last known list if the registry is briefly unreachable.

  Sidecars

  A sidecar is a helper process or container deployed alongside each service instance (in Kubernetes, in the same pod). All the service's network traffic goes through it. It can take over cross-cutting jobs so application code does not have to implement them in every language:

  • retries, timeouts and circuit breaking (see the resilience lesson),
  • mutual TLS (encrypting and authenticating service-to-service traffic),
  • load balancing and service discovery,
  • metrics, logs and tracing headers,
  • access policy.

  Other sidecar uses: log shippers, config reloaders, secret agents.

  Service mesh

  A service mesh is a dedicated infrastructure layer made of two parts:

  • Data plane: the fleet of sidecar proxies (often Envoy) that carry the traffic.
  • Control plane: configures and secures the proxies, distributes certificates and policy (for example Istio, Linkerd, Consul).

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  Figure SDA-MSV-04-2 · Control plane and data plane · © forcsguy.com

  What a mesh enables:

  • Traffic management: weighted routing for canary releases, mirroring, fault injection, timeouts and retries by route.
  • Security: automatic mutual TLS and service-level authorisation, giving each service a cryptographic identity.
  • Observability: uniform metrics, traces and access logs for every call, without code changes.

  Costs and alternatives

  • Resources and latency: a proxy per instance adds CPU, memory and a small latency per hop.
  • Operational complexity: another system to upgrade and debug; misconfiguration can break everything.
  • Learning curve and sometimes opaque behaviour.
  • Newer designs reduce per-pod cost (node-level or "ambient" proxies).

  Alternatives: a shared library for resilience and telemetry (language-bound, needs upgrades everywhere), a managed platform's built-in features, or an API gateway for north-south traffic only (see the load balancing lesson).

  When a mesh makes sense

  Many services, several languages, strong needs for mutual TLS or consistent policy, and a platform team able to run it. For a handful of services, platform defaults and a good library are usually enough. As ever, adopt the pattern for a problem you have.

  Real-world reading

  Related reading

  • CNCF BlogCNCF community · CNCFBlogs
  • CNCF Cloud Native LandscapeCNCFDocumentation
  • Kubernetes: IngressKubernetes · CNCF / KubernetesDocumentation
  • Kubernetes BlogKubernetes community · CNCF / KubernetesBlogs

  Show 6 moreShow fewer

  • Envoy ProxyEnvoy · CNCF / EnvoyGitHub
  • CNCFCloud Native Computing Foundation · CNCFYouTube
  • DevOps Roadmaproadmap.sh community · roadmap.shCourses
  • Operate Kubernetes Reliably at StripeStripe engineers · StripeCase Studies
  • Cloud Design PatternsMicrosoft · Microsoft AzureDocumentation
  • Kubernetes overviewKubernetes · CNCF / KubernetesDocumentation

  Open this lesson with its quiz and exercise