Ensuring rollback safety during deployments

Ideas you will meet

Deployment

Releases, feature flags, rollbacks and continuous delivery.

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.

• Core Concepts: How Things Actually Work · 8 minDeployment strategiesRolling, blue-green, canary and feature flags: how to release changes with less risk, including database changes.Read hereClose

  Why strategy matters

  Most outages follow a change. A good deployment strategy limits the blast radius and makes rollback fast.

  The main strategies

  Recreate (big bang)

  Stop the old version, start the new one. Simple, but there is downtime and no easy rollback.

  Rolling update

  Replace instances a few at a time. No downtime and little extra capacity, but for a while both versions serve traffic, so they must be compatible. Rollback means rolling back the same way.

  Blue-green

  Run two identical environments. Blue serves live traffic; deploy to green, test it, then switch the router to green. Rollback is flipping back. Cost: double the capacity during the switch, and shared state (databases) still needs care.

  Canary

  Send a small share (1%, 5%, 25%...) of traffic to the new version, watch the metrics, then widen. If errors rise, roll back automatically. Needs good monitoring and traffic splitting.

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  Figure SDA-CORE-12-1 · Canary · © forcsguy.com

  Feature flags

  Ship code switched off, and enable it for chosen users (or a percentage) at runtime. It separates deploying from releasing, and gives an instant kill switch. Remove old flags to avoid clutter.

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  Figure SDA-CORE-12-2 · Four ways to put a new version live · © forcsguy.com

  Before: release to everyone at once

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  After: canary: a few users first

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  What changed

  • A bad release reaches few users before it is caught
  • Rolling back means shifting traffic back, not redeploying
  • You compare error rates between old and new

  Databases are the hard part

  Code can roll back instantly; data cannot. Use expand and contract:

  1. Expand: add new columns or tables, compatible with the old code.
  2. Deploy code that works with both shapes and writes to both if needed.
  3. Migrate data (backfill in batches).
  4. Contract: remove the old structure in a later release.

  Never combine a destructive schema change with the code that depends on it in one step.

  Checklist for safer releases

  • Automated tests and a staging environment that resembles production.
  • Health checks and automatic rollback on failure.
  • Monitoring on error rate and latency, watched during rollout.
  • Small, frequent changes over large, rare ones.
  • A written, practised rollback plan.

  More on this site

  Related reading

  • Blue Green DeploymentMartin FowlerBlogs
  • Ensuring rollback safety during deploymentsAmazon Builders' Library · Amazon Web ServicesBlogs
  • Feature Toggles (aka Feature Flags)Pete Hodgson · Martin FowlerBlogs
  • Going faster with continuous deliveryAmazon Builders' Library · Amazon Web ServicesBlogs

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  • Online migrations at scaleJacqueline Xu · StripeCase Studies
  • Continuous Integration and Deployment with Bazel at DropboxDropbox engineers · DropboxCase Studies
  • HashiCorp BlogHashiCorp engineers · HashiCorpBlogs
  • Software Engineering at GoogleTitus Winters, Tom Manshreck and Hyrum Wright · O'Reilly / GoogleBooks
  • Scaling iOS CI with Anka at ShopifyShopify engineers · ShopifyCase Studies
  • Mozilla Release EngineeringMozilla Release Engineering engineers · Mozilla Release EngineeringBlogs

  Open this lesson with its quiz and exercise

• 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

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  • 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

• Advanced Distributed Systems · 13 minKubernetes essentialsPods, deployments, services, ingress and operators: the building blocks of the most common container platform and how a request reaches a pod.Read hereClose

  What Kubernetes does

  Kubernetes (K8s) runs containers across a cluster of machines. You describe the desired state (for example "three copies of this image, reachable at this address") and controllers continuously make reality match it: restarting failed containers, replacing lost nodes and rolling out new versions. (Containers themselves are introduced in the cloud models lesson.)

  The cluster

  • Control plane: the API server (everything talks to it), etcd (the consistent store of cluster state, built on Raft; see the consensus lesson), the scheduler (decides which node runs a pod) and controllers (reconcile desired and actual state).
  • Nodes: machines that run pods, each with a node agent (kubelet), a container runtime and a networking component.

  The core objects

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  Figure SDA-ADS-07-1 · From request to pod · © forcsguy.com

  Object	Purpose
  Pod	Smallest unit: one or more containers sharing network and volumes
  ReplicaSet / Deployment	Keep N pods running; roll out updates and roll back
  Service	Stable virtual IP and DNS name that load balances to matching pods
  Ingress	HTTP routing from outside to services (host, path, TLS), handled by an ingress controller
  ConfigMap / Secret	Configuration and sensitive values injected as environment variables or files
  StatefulSet	Pods with stable identity and storage, for databases and brokers
  DaemonSet	One pod per node (log shippers, agents)
  Job / CronJob	Run to completion, once or on a schedule
  Namespace	Logical separation of resources and quotas
  PersistentVolume(Claim)	Durable storage requested by pods

  Networking in one paragraph

  Every pod gets its own IP and can reach every other pod without NAT. Because pods are short-lived, clients use Services (found through cluster DNS such as orders.shop.svc). Network policies restrict which pods may talk to which. An Ingress (or the newer Gateway API) handles external HTTP(S) traffic.

  A deployment and a rolling update

  apiVersion: apps/v1
  kind: Deployment
  metadata: { name: web }
  spec:
    replicas: 3
    selector: { matchLabels: { app: web } }
    strategy: { type: RollingUpdate, rollingUpdate: { maxSurge: 1, maxUnavailable: 0 } }
    template:
      metadata: { labels: { app: web } }
      spec:
        containers:
          - name: web
            image: example/web:1.4.2
            resources: { requests: { cpu: 200m, memory: 256Mi }, limits: { memory: 512Mi } }
            readinessProbe: { httpGet: { path: /healthz, port: 8080 } }
  

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  Figure SDA-ADS-07-2 · Rolling update · © forcsguy.com

  Probes: readiness decides if a pod receives traffic; liveness decides if it should be restarted; startup gives slow starters time. Requests and limits tell the scheduler how much CPU and memory a pod needs and cap what it can use.

  Autoscaling

  A Horizontal Pod Autoscaler adds or removes pods by metrics such as CPU or request rate; a cluster autoscaler adds or removes nodes when pods cannot be placed. Scaling is useless without correct resource requests.

  Extending Kubernetes: operators and CRDs

  A Custom Resource Definition (CRD) adds a new object type (for example PostgresCluster). An operator is a controller that watches those objects and does the operational work (create replicas, take backups, fail over), encoding human runbooks as code. Many databases and brokers ship operators.

  Things that often go wrong

  Missing readiness probes (traffic to pods not ready), no resource requests (noisy neighbours, evictions), latest image tags (unrepeatable deploys), secrets in plain config, a single replica for a critical service, and running stateful systems without understanding storage and failure behaviour.

  Real-world reading

  Related reading

  • TechWorld with Nana: Docker & KubernetesNana Janashia · TechWorld with NanaYouTube
  • AWS Containers BlogAmazon Web Services engineers · Amazon Web ServicesBlogs
  • Awesome KubernetesRamit Surana · ramitsuranaGitHub
  • Kubernetes ConceptsKubernetes Authors · CNCF / KubernetesDocumentation

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  • Kubernetes: IngressKubernetes · CNCF / KubernetesDocumentation
  • Docker BlogDocker engineers · DockerBlogs
  • DevOps Roadmaproadmap.sh community · roadmap.shCourses
  • Kubernetes BlogKubernetes community · CNCF / KubernetesBlogs
  • From bare-metal to Kubernetes at BetabrandBetabrand engineers · BetabrandCase Studies
  • Large-scale cluster management at Google with Borg (paper)Verma et al. · GooglePapers

  Open this lesson with its quiz and exercise