{"product_id":"engineering-vector-databases-at-scale-designing-high-dimensional-indexing-and-distributed-retrieval-systems-for-ai-applications-9798258778017","title":"Engineering Vector Databases at Scale: Designing High-Dimensional Indexing and Distributed Retrieval Systems for AI Applications","description":"\u003cp\u003e • Author(s): Roland Sayer\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Distributed Systems - Cloud Computing\u003c\/p\u003e\u003cp\u003e\u003cb\u003eBuild vector search systems that hold up in production.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eThis book gives engineers, architects, and technical leaders a practical blueprint for designing, building, and operating vector databases at scale. It moves from core retrieval concepts to distributed execution, showing how to turn embeddings into fast, reliable, and measurable search services for modern applications.\u003c\/p\u003e\u003cp\u003eThrough a full systems view, it covers the decisions that matter most, what to store, how to index it, how to shard it, how to merge results, and how to keep latency, recall, and cost under control. The chapters connect theory with implementation, so readers can understand not only \u003ci\u003ewhat\u003c\/i\u003e works, but \u003ci\u003ewhy\u003c\/i\u003e it works and \u003ci\u003ewhen\u003c\/i\u003e to choose one approach over another.\u003c\/p\u003e\u003cb\u003eInside, you will find guidance on: \u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eVector representations, similarity metrics, and exact search baselines\u003c\/li\u003e\n\u003cli\u003eEmbedding pipelines, normalization, updates, deletes, and schema design\u003c\/li\u003e\n\u003cli\u003eApproximate nearest neighbor methods, including graphs, trees, and quantization\u003c\/li\u003e\n\u003cli\u003eCompression techniques that reduce memory use while preserving retrieval quality\u003c\/li\u003e\n\u003cli\u003eIndex build workflows, compaction, refresh, and integrity checks\u003c\/li\u003e\n\u003cli\u003eSharding, replication, routing, and distributed top-k result merging\u003c\/li\u003e\n\u003cli\u003eHybrid retrieval with metadata filters, candidate generation, and reranking\u003c\/li\u003e\n\u003cli\u003ePerformance tuning for CPU, GPU, throughput, caching, and capacity planning\u003c\/li\u003e\n\u003cli\u003eReliability, security, governance, audit logging, and deletion workflows\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThe later chapters focus on operational reality, scaling ingestion, handling skewed data, maintaining consistent rankings, and supporting updates without sacrificing service quality. You also get reference implementations and end-to-end examples that tie the concepts together into working systems.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eWhy this book stands out: \u003c\/b\u003e it treats vector search as an engineering discipline, not just a model feature. That means clear tradeoffs, measurable outcomes, and a strong emphasis on production readiness. If you are building retrieval infrastructure for search, RAG, recommendation, or related AI products, this guide offers a grounded path from design to deployment.\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47883473191063,"sku":"9798258778017","price":1596.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798258778017.webp?v=1781102065","url":"https:\/\/atlanticbooks.com\/products\/engineering-vector-databases-at-scale-designing-high-dimensional-indexing-and-distributed-retrieval-systems-for-ai-applications-9798258778017","provider":"Atlantic Books","version":"1.0","type":"link"}