Enterprise AI

    How AlloyDB ScaNN scales vector search to 10 billion vectors

    Google's new four-level tree indexing enables massive vector search at enterprise scale.

    To satisfy the demands of enterprise-grade agentic AI applications, underlying vector databases often struggle to scale effectively as modern use cases can scale to billions of vectors. As a fully managed PostgreSQL-compatible database service, AlloyDB is engineered to handle dem...

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    Google Cloud AI Blog

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    Google Cloud AI Blog
    1. Reduced compute intensity via hierarchical partitioning

    The four-level architecture drastically reduces compute intensity by using hierarchical partitioning to restrict the volume of vectors scanned during a query. Instead of traversing a flat or poorly segmented space, the multi-layered hierarchy narrows down the search path exponentially. Figure 2 illustrates the search spaces across different tree levels, demonstrating how structural layering optimizes traversal efficiency: Figure 2. Search space for two-, three- and four-level trees Two-level: Utilizes coarse partitioning to guide queries, resulting in a basic search complexity of O(N1/2). Three-level: Introduces an intermediate layer to further subdivide clusters, narrowing exploration to O(N1/3). Four-level: Implements refined, highly granular partitions that optimize traversal efficiency down to O(N1/4), sufficiently allowing for more than 10-billion vectors. By dynamically expanding hierarchical layers as the dataset expands, AlloyDB ScaNN maintains ultra-low query latency and avoids computational scale walls from impacting performance.

    1. Efficient memory usage

    Achieving a 10 billion vector scale requires high memory efficiency. AlloyDB ScaNN uses these strategies to maximize memory management performance: Balanced tree shape construction: The four-level tree utilizes a balanced configuration to circumvent memory limitations that restrict the size of training datasets. This balanced architecture effectively leverages reduced sampling sizes to construct high-fidelity tree partitions. Sampling optimization: When the system encounters memory limitations, it generates a condensed sampling set that considers performance and accuracy. Performance test results By leveraging the innovative four-level tree architecture in our internal tests, we are able to achieve the following performance results: AlloyDB can scale to over 10 billion vectors with its ScaNN index. AlloyDB can deliver Get started today Experience AlloyDB ScaNN's four-level tree (preview) architecture today. You can deploy ScaNN for AlloyDB by following our quickstart guide to set up an instance. For optimized, high-speed vector search, refer to the official ScaNN documentation. New users can also explore AlloyDB through our 30-day free trial program. We can’t wait to hear about what you build!

    AI Analysis

    Generated insight by OnAirToday AI

    Scaling vector databases to 10 billion items is critical for enterprise agents that require real-time access to massive, complex datasets. This architectural leap in AlloyDB ensures that high-fidelity retrieval remains performant as generative AI deployments grow in scale and complexity.

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