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Optimizing Recommendation Systems with JDK’s Vector API

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Optimizing Recommendation Systems with JDK’s Vector API

netflixtechblog.comSaved March 5, 202611 minMarch 3, 2026

Engineering Blog Post

Summary

Netflix engineers optimized their Ranker service's serendipity scoring feature by replacing inefficient nested loops with batched matrix multiplication. This optimization significantly reduced CPU usage and cluster footprint by improving memory layout and leveraging the JDK Vector API.

Highlights

Identified serendipity scoring as a significant CPU hotspot, consuming 7.5% of total CPU per node.
Replaced O(M×N) nested loops for cosine similarity with more efficient batched matrix multiplication operations.
Improved cache locality and reduced garbage collection pressure by restructuring memory layout with flat buffers and ThreadLocal reuse.
Leveraged the JDK Vector API to optimize the computation of vector embeddings at scale.

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Netflix Technology Blog · via Netflix TechBlog

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Audience

Software Engineers, Machine Learning Engineers, and Backend Developers

DomainMachine Learning Engineering

Formatlong-form article

Accessfree online

Topics

Performance Optimization Vector Embeddings Recommendation Systems JDK Vector API Netflix Engineering

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Vector EmbeddingsCosine SimilarityJDK Vector APIMatrix MultiplicationPerformance OptimizationNetflix Engineering

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Optimizing Recommendation Systems with JDK’s Vector API

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