Deploying Brotli for Static Content at Dropbox

Ideas you will meet

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.

  100%

  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.

  100%

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

  Before: release to everyone at once

  100%

  After: canary: a few users first

  100%

  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

  100%

  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 } }
  

  100%

  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

• Core Concepts: How Things Actually Work · 6 minLatency, throughput and bandwidthThree numbers that are often confused, and why a fast network can still feel slow.Read hereClose

  Three different questions

  • Latency: how long does one request take from start to finish? Measured in milliseconds.
  • Bandwidth: how much could the pipe carry per second? A capacity, measured in bits per second.
  • Throughput: how much actually flows per second, measured in requests or bytes per second. It is at most the bandwidth, and often much less.

  A motorway analogy: bandwidth is the number of lanes, latency is the time one car needs to drive the length, and throughput is how many cars arrive per hour. Widening the road (more lanes) lets more cars through but does not make any single car arrive sooner.

  Why it matters in design

  Many speed problems are really latency problems, and more bandwidth will not fix them:

  Symptom	Likely cause	Lever
  Page makes 80 small requests and feels slow	Round-trip latency adds up	Fewer requests, bundling, HTTP/2, caching
  Users far away complain	Distance	CDN, regional servers
  Big downloads are slow	Throughput or bandwidth	Compression, parallel transfers, bigger pipe
  System is slow only at busy times	Throughput limit	Scale out, queue, shed load

  Image: Syced · CC0 · Source · Wikimedia Commons: Peabird Ethernet switch in 2003

  Where the time goes

  100%

  Figure SDA-CORE-01-1 · A latency budget for one page · © forcsguy.com

  Tail latency

  Averages hide the worst experiences. If 1% of requests take 3 seconds, a page that makes 50 calls will hit one of those slow calls most of the time. Track percentiles (p95, p99), and design to keep the slowest requests under control with timeouts and hedged requests.

  Latency budget

  Break a target (say 300 ms for a page) into a budget: network 80 ms, application 60 ms, database 50 ms, rendering 80 ms, slack 30 ms. When the total exceeds the target you know where to cut.

  Real-world reading

  Related reading

  • Optimizing Web Servers for High Throughput and Low Latency at DropboxDropbox engineers · DropboxCase Studies
  • Natural Language Processing and Content Analysis (2 parts) at Condé NastCondé Nast engineers · Condé NastCase Studies
  • Finding Kafka throughput limit in infrastructure at DropboxDropbox engineers · DropboxCase Studies
  • Scaling Gradient Boosted Trees for Click-Through-Rate Prediction at YelpYelp engineers · YelpCase Studies

  Show 6 moreShow fewer

  • Machine Learning (2 parts) at Condé NastCondé Nast engineers · Condé NastCase Studies
  • DataSift Architecture: Realtime Datamining at 120,000 Tweets Per Second - High Scalability -High Scalability engineers · High ScalabilityCase Studies
  • Deploying Brotli for Static Content at DropboxDropbox engineers · DropboxCase Studies
  • How Twitter Handles 3,000 Images Per Second - High Scalability -High Scalability engineers · High ScalabilityCase Studies
  • ESPN's Architecture at Scale - Operating at 100,000 Duh Nuh Nuhs Per Second - High Scalability -High Scalability engineers · High ScalabilityCase Studies
  • Architecture NotesArchitecture Notes · architecturenotes.coPodcasts & Newsletters

  Open this lesson with its quiz and exercise