Study design

As explained in Google Anthos in Action

A/B testing is an experimentation technique that compares multiple variants of an application or service by exposing them to different user groups or requests.

A/B testing compares service versions by directing different portions or categories of production traffic to each variant. In Anthos Service Mesh, traffic management supports gradual releases without disrupting end users. section 4.5.3

Use traffic management to:

• Send a small percentage of traffic to a new service version, verify its behavior, and increase its share progressively. section 4.5.3
• Route requests by predefined rules, such as user-agent, while keeping the established version active for other users. section 4.5.3
• Use the approach for bug fixes, new features, canary deployments, and progressive rollouts without requiring application code changes. section 4.5.3 section 4.8

Canary release

Redirect 5% of traffic to a new release of Service A. If the release performs acceptably, increase its traffic share until it receives all traffic; otherwise, retain the established version. section 4.5.3

Figure 4.7 Istio canary deployment feature based on traffic

Visualizes an Istio canary deployment that splits traffic between service versions. section 4.5.3

Figure 4.8 Istio canary deployment feature based on user-agent

Visualizes an Istio canary deployment that targets iPhone traffic using user-agent routing. section 4.5.3

How Google Anthos in Action covers it

How deep it goes

Explains percentage- and rule-based traffic splitting with rollout rationale, but stops short of configuration commands or implementation syntax.

Examples

Uses a 5% Service A canary and user-agent routing for iPhone traffic as concrete deployment examples.

What you can do after

Operate/configure progressive service releases by splitting traffic between versions through Anthos Service Mesh.

How it connects

Connects A/B testing with traffic routing, canary deployments, progressive rollouts, service meshes, and Envoy sidecars.

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Also explained in

• 

  liveBookmanning.com
  Experimentation for EngineersThe planning of an experiment's configurations, measurements, allocation, and duration to obtain useful results efficiently.
• 

  liveBookmanning.com
  Effective Data AnalysisThe planned structure of a study specifying how questions, data, methods, and comparisons will support valid conclusions.
• 

  liveBookmanning.com
  Starting Data Analytics with Generative AI and PythonThe planning of an experiment or analytical investigation, including its procedures, possible outcomes, and interpretation of those outcomes.
• 

  liveBookmanning.com
  Design for DevelopersA/B testing is an experimental technique that compares different versions of an interface or design by exposing them to separate user groups and measuring their outcomes.
• 

  liveBookmanning.com
  Delivering Machine Learning Projects: From design to deploymentA/B testing is a controlled experiment that assigns users or observations to alternative versions or conditions to compare their outcomes.
• 

  liveBookmanning.com
  Causal Inference for Data ScienceThe plan specifying how an experiment assigns conditions, measures outcomes, and controls sources of bias.

Compare how 7 books explain study design →

Related concepts

• regretproduces · Regret is the difference between the reward or utility obtained from a choice and that obtainable from the best alternative under the same circumstances, often accumulated across decisions.
• randomizationuses · Randomization is the deliberate use of chance to select, assign, arrange, or vary elements of a process.
• experimentationkind of · Experimentation is the deliberate testing of an idea, intervention, or alternative by observing outcomes to gather evidence or compare effects.
• metricmeasures · A metric is a quantitative measure used to characterize or assess a property, condition, behavior, performance, or outcome.
• canary deploymentalternative to · A canary deployment is a release strategy that exposes a change to a limited portion of users, traffic, or infrastructure before expanding it more broadly.
• contextual banditmeasures · A contextual bandit is a sequential decision model in which an agent observes context, selects an action, and receives feedback only about that action.

This explainer was generated by Manning's knowledge graph from the text of Google Anthos in Action. The section links point to the parts of the book it draws on.