Real-Time Search Evaluation Infrastructure for Signal Testing
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Solution Overview
Problem
Conventional systems face challenges in efficiently introducing new signals for real-time search services, with inflexible setups and failure to reuse code, and struggle to account for complex product features in evaluating search quality, leading to long turnaround times and interpretable results.
Innovation Solution
A search quality infrastructure system with a unified signal ingestion subsystem and evaluation and monitoring subsystem, which includes a sandbox environment for stable and reproducible testing, and a machine-learning pipeline for optimizing feature weights, along with a real-time signal ingester for dynamic signal processing and flexible schema updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems use inflexible setups for introducing new signals, then code reuse is poor and adaptability is low, but system complexity increases and turnaround time lengthens
Solution Approach 1:
The system is divided into distinct components: a signal ingestion subsystem that handles new signal introduction, an evaluation subsystem that tests changes, and a production environment. This segmentation allows each component to be independently modified and reused, improving adaptability while managing complexity through modular architecture.
Solution Approach 2:
The evaluation infrastructure is designed to be universal, capable of testing any change to search functionality including new signals, ranking algorithms, and query processing. The sandbox environment and evaluation subsystem can handle diverse test cases through a unified interface, reducing the need for separate testing systems for different features.
2Reliability
If conventional systems lack a sandbox environment, then testing in real-time environment is noisy and results are not reproducible, but separating signal from noise requires complex infrastructure
Solution Approach 1:
A sandbox environment is introduced as an intermediary layer between the evaluation subsystem and the production environment. This sandbox freezes the state of the world at a given point in time, providing a stable, reproducible testing environment that isolates tests from the noise and changes of the live system while maintaining realistic search conditions.
Solution Approach 2:
The system performs preliminary testing in the sandbox environment before deploying changes to production. By freezing and preserving historical search data and system state in the sandbox, the system can repeatedly test changes against identical conditions, ensuring reproducibility without requiring complex real-time test infrastructure.
3Productivity
If conventional systems deploy changes directly to production, then turnaround time is fast, but search quality may deteriorate due to unvalidated changes
Solution Approach 1:
The evaluation subsystem performs preliminary validation of changes in the sandbox environment before production deployment. This includes running test queries, evaluating search results quality, and assessing performance impacts. Only changes that pass evaluation criteria are deployed to production, maintaining quality while enabling rapid iteration through automated testing.
Solution Approach 2:
The system implements feedback loops where search quality metrics are continuously monitored both during evaluation and in production. The evaluation subsystem provides feedback on test results, and production monitoring provides feedback on real-world performance. This feedback drives iterative improvement while preventing poor-quality changes from reaching users.
4Measurement precision
If conventional systems lack evaluation tooling, then implementing A/B testing and analysis is difficult, but gaining insight into search quality requires sophisticated measurement capabilities
Solution Approach 1:
The evaluation subsystem combines multiple measurement capabilities into a unified infrastructure: A/B testing framework, result difference analysis, crowd sourcing evaluation, and human rater feedback. These diverse evaluation methods are integrated through common data collection and analysis mechanisms, providing comprehensive search quality measurement without requiring separate complex systems for each evaluation type.
Data Source
AI summary
Systems and methods provide an experimentation system, or testing engine, for a real-time search infrastructure. An example method includes generating a snapshot of a production search environment and performing testing of a signal or index change in the snapshot. The change can be specified as parameters passed to the system. The method may include estimating an impact of the change based on the testing and determining, based on the estimate, whether the impact is positive. Responsive to determining the impact is positive the method may include generating a holdback environment of the production search environment, the holdback environment being a portion of the production search environment selected not to receive the change, pushing the change to the production search environment, and monitoring the change by running partial production traffic through the holdback environment.