The Pragmatic Engineer

Ideas you will meet

Distributed Systems

Consensus, time, failure and the fundamentals of scale-out.

Deployment

Releases, feature flags, rollbacks and continuous delivery.

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

  Show 6 moreShow fewer

  • 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 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

  Show 6 moreShow fewer

  • 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

• Real-World Systems · 11 minShipping safely and handling a large outageHow strong teams build and operate software: small changes, automation, ownership, and a calm, practised response when things break.Read hereClose

  How strong teams ship software

  Big companies deploy thousands of times a day and still stay up. The habits are surprisingly consistent.

  • Small teams own services end to end: "you build it, you run it". The people who write the code carry the pager, so they care about reliability.
  • Small, frequent changes: each change is easier to review, test, understand and undo than a large release.
  • Automation: a CI/CD pipeline builds, runs tests, scans for vulnerabilities and deploys without manual steps.
  • Safe rollout: canary releases, feature flags, blue-green switches, automatic rollback on bad metrics (see the deployment lesson).
  • Observability: metrics, logs and traces, with alerts on user-visible symptoms.
  • Testing in layers: unit, integration and end-to-end tests, plus load and failure testing.
  • Standard platforms and templates ("golden paths") so teams do not rebuild logging, deployment or monitoring each time.

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  Figure SDA-RWS-15-1 · CI/CD with canary and rollback · © forcsguy.com

  When things break anyway

  Outages will happen. What separates good teams is how they respond.

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  Figure SDA-RWS-15-2 · Incident response lifecycle · © forcsguy.com

  1. Detect

  Good alerts fire on symptoms users feel (errors, latency), quickly, and point to a dashboard and a runbook.

  2. Triage and roles

  Declare an incident and a severity. Assign clear roles so people do not all do the same thing:

  • Incident commander: coordinates and decides; does not debug.
  • Communications lead: updates stakeholders and customers.
  • Operations or subject experts: investigate and apply fixes.
  • A scribe keeps a timeline.

  3. Mitigate first

  Stop the bleeding before finding the cause. Typical moves, roughly from safest to most disruptive:

  • Roll back the last change, or turn off a feature flag.
  • Fail over to a healthy region or replica.
  • Shed load or degrade non-essential features.
  • Scale up or add capacity.

  The question is "what is the fastest safe way to reduce user harm?", not "why did this happen?".

  4. Communicate

  Update a status page and internal channels early and at a promised cadence, even when the news is "still investigating". Silence breeds panic and duplicate effort. State the impact, what you know, and when the next update comes.

  5. Resolve and verify

  Confirm recovery with metrics, not hope. Watch for relapse and clean up temporary measures.

  6. Learn: the blameless postmortem

  Within a few days write a postmortem: timeline, impact, causes (usually several), what went well, what did not, and action items with owners and dates. Ask "how did our system and process allow this?" instead of "who did this?". People who fear blame hide details, and the same failure returns.

  Reducing the next outage

  • Eliminate single points of failure and practise failover.
  • Game days and chaos testing: break things on purpose in controlled ways.
  • Load shedding, timeouts and circuit breakers so a local problem stays local (see the reliability lesson).
  • Capacity planning and error budgets so reliability work is scheduled, not forgotten.
  • Keep runbooks current and rehearse them. An on-call engineer at 3 a.m. should not be discovering the system for the first time.

  Real-world reading

  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

  Show 6 moreShow fewer

  • Static stability using Availability ZonesAmazon Builders' Library · Amazon Web ServicesBlogs
  • The Pragmatic EngineerGergely Orosz · SubstackPodcasts & Newsletters
  • Software Engineering at GoogleTitus Winters, Tom Manshreck and Hyrum Wright · O'Reilly / GoogleBooks
  • Continuous Integration and Deployment with Bazel at DropboxDropbox engineers · DropboxCase Studies
  • Lightweight Distributed Architecture to Handle Thousands of Library Releases at eBayeBay engineers · eBayCase Studies
  • Data Checking at DropboxDropbox engineers · DropboxCase Studies

  Open this lesson with its quiz and exercise