Design an experiment assignment service
Design an Online Experimentation (A/B Testing) Assignment Service
Design a highly available online experiment assignment service that exposes an API. This API will take (userId, featureName) as input and return a variantIdentifier (e.g., "control" or "treatment"). The service must support both logged-in and logged-out users and operate globally across web and mobile applications.
Your design should clarify, specify, and propose solutions for the following critical aspects:
Core Assignment Logic
- Deterministic Sticky Assignment and Bucketing Strategy: How to ensure a user consistently receives the same variant for a given experiment, and how users are assigned to buckets.
- Eligibility Targeting and Audience Filters: Mechanisms to define which users are eligible for an experiment based on various attributes.
- Traffic Allocation and Ramps: Strategies for controlling the percentage of traffic exposed to an experiment and gradually increasing it.
- Mutual Exclusion and Experiment Layering: How to manage multiple concurrent experiments to prevent interference and define their hierarchical relationships.
Configuration and Control
- Configuration Storage for Experiments and Features: Where and how experiment definitions, feature flags, and their associated rules are stored and managed.
- Overrides (Holdouts, QA, Killswitch): Mechanisms for manual intervention, such as assigning specific users to variants, testing, or emergency deactivation.
- Experiment Lifecycle: The workflow for creating, starting, pausing, stopping, and analyzing experiments.
Data and Observability
- Exposure Logging and Idempotency: How to record when a user is exposed to a variant, ensuring that duplicate exposures are handled correctly.
- Metric Tagging and Data Pipeline: How to associate user actions and metrics with specific experiments and variants, and the pipeline for collecting and processing this data.
Operational and Compliance Considerations
- Latency/Availability Targets and Fallbacks: Defining performance expectations and strategies for graceful degradation or alternative behavior in case of service issues.
- Privacy and Compliance (e.g., GDPR/CCPA): How the service adheres to data privacy regulations, especially concerning user data used for targeting and logging.
System Architecture
- High-level Architecture and Key Components: An overview of the system's structure, its main building blocks, and their interactions.
You are expected to provide a high-level architecture, define key APIs, and justify critical trade-offs. Where relevant, include small examples and precise definitions (e.g., hashing and bucket math, namespace/layering).
AI interview prep
Core dilemma
Balancing the need for deterministic, globally consistent experiment assignments and configuration updates with extreme low-latency requirements for a high-volume, globally distributed service, all while ensuring robust data integrity for analytics and compliance.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design an experiment assignment service
Design an Online Experimentation (A/B Testing) Assignment Service
Design a highly available online experiment assignment service that exposes an API. This API will take (userId, featureName) as input and return a variantIdentifier (e.g., "control" or "treatment"). The service must support both logged-in and logged-out users and operate globally across web and mobile applications.
Your design should clarify, specify, and propose solutions for the following critical aspects:
Core Assignment Logic
- Deterministic Sticky Assignment and Bucketing Strategy: How to ensure a user consistently receives the same variant for a given experiment, and how users are assigned to buckets.
- Eligibility Targeting and Audience Filters: Mechanisms to define which users are eligible for an experiment based on various attributes.
- Traffic Allocation and Ramps: Strategies for controlling the percentage of traffic exposed to an experiment and gradually increasing it.
- Mutual Exclusion and Experiment Layering: How to manage multiple concurrent experiments to prevent interference and define their hierarchical relationships.
Configuration and Control
- Configuration Storage for Experiments and Features: Where and how experiment definitions, feature flags, and their associated rules are stored and managed.
- Overrides (Holdouts, QA, Killswitch): Mechanisms for manual intervention, such as assigning specific users to variants, testing, or emergency deactivation.
- Experiment Lifecycle: The workflow for creating, starting, pausing, stopping, and analyzing experiments.
Data and Observability
- Exposure Logging and Idempotency: How to record when a user is exposed to a variant, ensuring that duplicate exposures are handled correctly.
- Metric Tagging and Data Pipeline: How to associate user actions and metrics with specific experiments and variants, and the pipeline for collecting and processing this data.
Operational and Compliance Considerations
- Latency/Availability Targets and Fallbacks: Defining performance expectations and strategies for graceful degradation or alternative behavior in case of service issues.
- Privacy and Compliance (e.g., GDPR/CCPA): How the service adheres to data privacy regulations, especially concerning user data used for targeting and logging.
System Architecture
- High-level Architecture and Key Components: An overview of the system's structure, its main building blocks, and their interactions.
You are expected to provide a high-level architecture, define key APIs, and justify critical trade-offs. Where relevant, include small examples and precise definitions (e.g., hashing and bucket math, namespace/layering).