Overview
The article discusses the implementation of streaming filters in the Manas search engine at Pinterest, which utilizes Hierarchical Navigable Small World graphs (HNSW) for Approximate Nearest Neighbor (ANN) search. It outlines the challenges of hybrid search queries and presents a new approach that integrates filtering during the HNSW graph traversal to enhance efficiency and scalability.
What You'll Learn
1
How to implement streaming filters in an ANN search system
2
Why modularity and backward compatibility are crucial in system design
3
When to apply time budgets in high filter rate scenarios
Prerequisites & Requirements
- Understanding of Approximate Nearest Neighbor (ANN) search algorithms
- Familiarity with filtering mechanisms in search systems(optional)
Key Questions Answered
What are the advantages of streaming filters in ANN search?
Streaming filters allow for more efficient processing by integrating filtering during the HNSW graph traversal, which reduces the need for overfetch tuning and improves scalability. This method also maintains backward compatibility with existing query structures, making it easier for clients to adopt without significant changes.
How does the stopping condition work in HNSW streaming?
The stopping condition in HNSW streaming is based on ensuring that all accumulated candidates are closer than the closest candidate in the candidate set. This approach helps to retrieve the best candidates with high probability, while also considering a time budget to avoid excessive latency in high filter rate scenarios.
What optimizations can be applied to improve filter performance?
Optimizations include dropping far candidates if enough results are already accumulated, initializing with a batch of candidates, and reordering filter tree nodes to evaluate stricter filters first. These strategies help to reduce latency and improve the efficiency of the filtering process.
Technologies & Tools
Algorithm
Hierarchical Navigable Small World Graphs
Used for Approximate Nearest Neighbor (ANN) search in the Manas search engine.
Key Actionable Insights
1Implement streaming filters to enhance the efficiency of your ANN search systems.By integrating filtering during the graph traversal, you can reduce the complexity of overfetch tuning and improve the overall performance of your search engine.
2Utilize time budgets to manage latency in high filter rate scenarios.Setting a time budget ensures that your search system meets client latency requirements while still providing relevant results, which is critical for maintaining user satisfaction.
3Focus on modular design to ensure future compatibility of your filtering mechanisms.A modular approach allows for easier updates and maintenance, ensuring that your system can adapt to new requirements without significant rewrites.
Common Pitfalls
1
Relying too heavily on overfetch tuning can lead to inefficiencies in search performance.
Overfetch tuning requires continuous adjustments and can vary significantly between requests, making it a less scalable solution compared to integrated filtering methods.
Related Concepts
Approximate Nearest Neighbor (ann) Search
Hierarchical Navigable Small World Graphs (hnsw)
Filtering Mechanisms In Search Systems