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globalBreadcrumb_homeItem__9PTnF globalBreadcrumb_darkTheme__R4ILy" href="/"><svg width="13" height="12" viewBox="0 0 13 12" fill="none" xmlns="http://www.w3.org/2000/svg" color="#fff"><g clip-path="url(#clip0_19069_588)"><path d="M11.4526 5.66339L6.71842 1.17877C6.42915 0.90476 5.97612 0.90476 5.68685 1.17877L0.952637 5.66339" stroke="#fff" stroke-width="0.9" stroke-linecap="round" stroke-linejoin="round"></path><path d="M2.05817 7.37201V10.5598C2.05817 10.974 2.39395 11.3097 2.80817 11.3097H9.57781C9.99202 11.3097 10.3278 10.974 10.3278 10.5597V7.37201" stroke="#fff" stroke-width="0.9" stroke-linecap="round" stroke-linejoin="round"></path><path d="M7.50721 11.3097V8.12201C7.50721 7.7078 7.17142 7.37201 6.75721 7.37201H5.62885C5.21463 7.37201 4.87885 7.70779 4.87885 8.12201V11.3097" stroke="#fff" stroke-width="0.9" stroke-linecap="round" stroke-linejoin="round"></path></g><defs><clipPath id="clip0_19069_588"><rect width="12" height="12" fill="white" transform="translate(0.202637)"></rect></clipPath></defs></svg></a><svg width="8" height="14" viewBox="0 0 8 14" fill="none" xmlns="http://www.w3.org/2000/svg" color="#fff"><path d="M7.39502 1.20099L0.608318 12.7991" stroke="#fff"></path></svg></li><li class="globalBreadcrumb_listItem__4n_9F"><a class="globalBreadcrumb_breadcrumbItem__3Axv8 globalBreadcrumb_darkTheme__R4ILy" href="/customers">Customers</a><svg width="8" height="14" viewBox="0 0 8 14" fill="none" xmlns="http://www.w3.org/2000/svg" color="#fff"><path d="M7.39502 1.20099L0.608318 12.7991" stroke="#fff"></path></svg></li><li class="globalBreadcrumb_listItem__4n_9F"><p class="globalBreadcrumb_currentItem__2QgJK globalBreadcrumb_darkTheme__R4ILy">VIPSHOP</p></li></ul></div></h2><h1 class="globalPageHeader_title__wEJ6h globalPageHeader_longSentenceTitle__baBV5">VIPSHOP Builds a 10x Faster Personalized Recommender System Using Milvus</h1><p class="globalPageHeader_desc__zcPFG globalPageHeader_leftRightDesc__6SXEO"></p></div><div class="globalPageHeader_rightSection__3MIbW customerDetail_customRightPart__Utj0g"><img src="https://assets.zilliz.com/Group_4389_75e5e385db.png" alt="VIPSHOP"/></div></div></section><section class="customerDetail_highlighContainer__xlQJh"><ul class="page_container__IibMw customerDetail_highlighWrapper__15TxV"><li><p class="customerDetail_results__Wnijq">10x Faster Query Speed</p><p class="customerDetail_desc__IeyIi"> than the previous Elasticsearch solution </p></li><li><p class="customerDetail_results__Wnijq">&lt;30ms Response Time </p><p class="customerDetail_desc__IeyIi">for searching on millions of vectors </p></li><li><p class="customerDetail_results__Wnijq">Optimized User Experience </p><p class="customerDetail_desc__IeyIi">with more accurate recommendations based on users’ purchase behaviors</p></li></ul></section><section class="customerDetail_quoteContainer__dEmO_"><div class="customerDetail_quoteWrapper__1MCH0"><span class="customerDetail_leftQuotationMark__fg1q4">“</span><div><p class="customerDetail_quoteContent__LAShl">Milvus-powered vector search has been running steadily in our recommendation systems, providing high performance and allowing us more flexibility in selecting models and algorithms.</p><p class="customerDetail_quoteAuthor__uoxIX">VIPSHOP Search Service Team</p><p class="customerDetail_authorTitle__ti2jU">VIPSHOP</p></div></div></section><section class="page_container__IibMw customerDetail_contentContainer__x6d7S"><div class="docContainer customerDetail_docContainer__DUpVO"><h2 id="About-VIPSHOP" class="common-anchor-header">About VIPSHOP<button data-href="#About-VIPSHOP" class="anchor-icon" translate="no"> <svg aria-hidden="true" focusable="false" height="20" version="1.1" viewBox="0 0 16 16" width="16" > <path fill="#0092E4" fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z" ></path> </svg> </button></h2><p><a href="https://ir.vip.com/">VIPSHOP</a> is a renowned NYSE-listed online retailer headquartered in China, specializing in providing popular branded products to consumers with significant discounts. Its diverse product range includes fashion, apparel, accessories, beauty products, home goods, and electronics. Boasting a staggering customer base of over 52 million and facilitating nearly 270 million orders annually, VIPSHOP has earned its place as the 115th entry in Fortune's prestigious China 500 listing.</p> <p> <span class="img-wrapper"> <img src="https://assets.zilliz.com/VIPSHOP_image2_cropped_7a3f82899f.png" alt="" class="doc-image" id="" /> <span></span> </span> </p> <h2 id="Challenges-High-Latency-and-Spiking-Maintenance-Costs-Using-Elasticsearch" class="common-anchor-header">Challenges: High Latency and Spiking Maintenance Costs Using Elasticsearch<button data-href="#Challenges-High-Latency-and-Spiking-Maintenance-Costs-Using-Elasticsearch" class="anchor-icon" translate="no"> <svg aria-hidden="true" focusable="false" height="20" version="1.1" viewBox="0 0 16 16" width="16" > <path fill="#0092E4" fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z" ></path> </svg> </button></h2><p>With the rapid growth of its business, VIPSHOP faced a common dilemma: as its product portfolio expanded, so did the complexity of helping users discover what they were looking for. To address this issue, VIPSHOP created a personalized recommendation system based on user query keywords and users’ purchase bahaviors.</p> <p>Previously, the VIPSHOP team utilized the Cosine Similarity(7.x) capabilities of <a href="https://zilliz.com/comparison/milvus-vs-elastic">Elasticsearch</a> to power the recommendation system. However, this approach was inefficient for two reasons:</p> <ul> <li><p><strong>High latency in vector searching:</strong> Averaging around 300 ms for retrieving Top-K results from millions of vectors, resulting in seconds for the system’s overall response time.</p></li> <li><p><strong>High costs of maintaining Elasticsearch indexes:</strong> Vectors derived from products, consumers’ purchase behaviors, and all other data shared the same set of indexes, making the index construction, operation, and maintenance much more complicated.</p></li> </ul> <p>VIPSHOP attempted to enhance Elasticsearch's performance by developing a locality sensitive hashing plugin. However, it only improved throughput and failed to reduce the vector search time to below 100 ms. Therefore, the team was still in urgent need of a new vector searching stack to improve their system performance.</p> <h2 id="The-Milvus-Solution" class="common-anchor-header">The Milvus Solution<button data-href="#The-Milvus-Solution" class="anchor-icon" translate="no"> <svg aria-hidden="true" focusable="false" height="20" version="1.1" viewBox="0 0 16 16" width="16" > <path fill="#0092E4" fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z" ></path> </svg> </button></h2><p>After extensive research, the VIPSHOP team opted for <a href="https://zilliz.com/what-is-milvus">Milvus</a>, an open-source vector database capable of handling billions of vector embeddings and delivering lightning-fast responses. Milvus also offers rich features such as distributed deployment, multi-language SDKs, and read/write separation, making it a superior choice to Elasticsearch and many other vector search solutions like <a href="https://zilliz.com/blog/set-up-with-facebook-ai-similarity-search-faiss">FAISS</a>.</p> <h3 id="The-Architecture-of-VIPSHOP-Recommendation-System-Using-Milvus" class="common-anchor-header">The Architecture of VIPSHOP Recommendation System Using Milvus<button data-href="#The-Architecture-of-VIPSHOP-Recommendation-System-Using-Milvus" class="anchor-icon" translate="no"> <svg aria-hidden="true" focusable="false" height="20" version="1.1" viewBox="0 0 16 16" width="16" > <path fill="#0092E4" fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z" ></path> </svg> </button></h3><p> <span class="img-wrapper"> <img src="https://assets.zilliz.com/VIPSHOP_image1_f6072f0a0a.png" alt="" class="doc-image" id="" /> <span></span> </span> </p> <p>The diagram above demonstrates the architecture of VIPSHOP’s recommendation system with Milvus. It consists of two core parts:</p> <ul> <li><p><strong>Write process:</strong> the VIPSHOP team used a deep learning model to transform each product’s features into vector embeddings and then imported them into Milvus through MySQL and an ETL tool.</p></li> <li><p><strong>Read process:</strong> the team used the deep learning model to transform consumers’ queries and purchase behaviors into vectors and then retrieved similar results in Milvus. Milvus performed a similarity search and returned the Top-K most relevant results to consumers.</p></li> </ul> <h3 id="Milvus-Implementation-Details-Data-Update-and-Recall" class="common-anchor-header">Milvus Implementation Details: Data Update and Recall<button data-href="#Milvus-Implementation-Details-Data-Update-and-Recall" class="anchor-icon" translate="no"> <svg aria-hidden="true" focusable="false" height="20" version="1.1" viewBox="0 0 16 16" width="16" > <path fill="#0092E4" fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z" ></path> </svg> </button></h3><p>Data update and recall are the most essential processes for the Milvus-powered recommendation system.</p> <p>The data update procedure ensures data synchronization, encompassing tasks such as writing vector data, detecting vector data volumes, index construction, index pre-loading, and alias management. It begins with commodity data checks, ensuring the quantity in MySQL aligns with existing data. The entire data-building process follows, including alias initialization in Redis, creating new collections, writing vectors in batches, and pre-loading the index in Milvus. After verifying the new collection's data, the system seamlessly switches aliases between multiple data collections.</p> <p> <span class="img-wrapper"> <img src="https://assets.zilliz.com/VIPSHOP_image3_e69a309be5.png" alt="" class="doc-image" id="" /> <span></span> </span> </p> <p>The recall process, crucial for recommending products, involves acquiring vectors related to consumers’ queries and purchase behaviors, calculating their distance, and merging results. Utilizing Milvus, the system concurrently and asynchronously retrieves data in different Milvus partitions, calculates vector similarities, and ranks the top results based on similarity distance. Then, after multiple calls to Milvus partition data, it presents the final recommendation results to users. The overall workflow is as follows:</p> <p> <span class="img-wrapper"> <img src="https://assets.zilliz.com/VIPSHOP_image4_31585b40d6.png" alt="" class="doc-image" id="" /> <span></span> </span> </p> <p>The following table shows the performance of three primary Milvus services. As shown in the table, the average latency for recalling Top-K results is around 10ms.</p> <table> <thead> <tr><th>Service</th><th>Role</th><th>Input Parameters</th><th>Output parameters</th><th>Response latency</th></tr> </thead> <tbody> <tr><td>User vectors acquisition</td><td>Obtain user vector</td><td>user info + query</td><td>user vector</td><td>10 ms</td></tr> <tr><td>Milvus Search</td><td>Calculate the vector similarity and return Top-K results</td><td>user vector</td><td>item vector</td><td>10 ms</td></tr> <tr><td>Scheduling Logic</td><td>Concurrent result recalling and merging</td><td>Multi-channel recalled item vectors and the similarity score</td><td>Top-K items</td><td>10 ms</td></tr> </tbody> </table> <h2 id="Results-Better-System-Performance-and-Optimal-User-Experience" class="common-anchor-header">Results: Better System Performance and Optimal User Experience<button data-href="#Results-Better-System-Performance-and-Optimal-User-Experience" class="anchor-icon" translate="no"> <svg aria-hidden="true" focusable="false" height="20" version="1.1" viewBox="0 0 16 16" width="16" > <path fill="#0092E4" fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z" ></path> </svg> </button></h2><p>The adoption of Milvus in VIPSHOP's recommender system significantly improved the overall system performance, including:</p> <h3 id="10x-Faster-Query-Speed" class="common-anchor-header">10x Faster Query Speed<button data-href="#10x-Faster-Query-Speed" class="anchor-icon" translate="no"> <svg aria-hidden="true" focusable="false" height="20" version="1.1" viewBox="0 0 16 16" width="16" > <path fill="#0092E4" fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z" ></path> </svg> </button></h3><p>Using Milvus, the system query and response time has been reduced to below 30ms, 10x faster than the previous Elasticsearch solution.</p> <h3 id="Improved-System-Scalability" class="common-anchor-header">Improved System Scalability<button data-href="#Improved-System-Scalability" class="anchor-icon" translate="no"> <svg aria-hidden="true" focusable="false" height="20" version="1.1" viewBox="0 0 16 16" width="16" > <path fill="#0092E4" fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z" ></path> </svg> </button></h3><p>Milvus's distributed deployment and support for horizontal scaling enable the recommendation system to handle rapidly increasing data volumes and user queries effortlessly without compromising performance.</p> <h3 id="Enhanced-User-Experience" class="common-anchor-header">Enhanced User Experience<button data-href="#Enhanced-User-Experience" class="anchor-icon" translate="no"> <svg aria-hidden="true" focusable="false" height="20" version="1.1" viewBox="0 0 16 16" width="16" > <path fill="#0092E4" fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z" ></path> </svg> </button></h3><p>Milvus optimizes the recommendation process to provide tailored product suggestions based on user preferences and search intent, improving user satisfaction and engagement.</p> <h3 id="Reduced-Maintenance-Costs" class="common-anchor-header">Reduced Maintenance Costs<button data-href="#Reduced-Maintenance-Costs" class="anchor-icon" translate="no"> <svg aria-hidden="true" focusable="false" height="20" version="1.1" viewBox="0 0 16 16" width="16" > <path fill="#0092E4" fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z" ></path> </svg> </button></h3><p>Milvus efficiently handles vector data and streamlines querying mechanisms, reducing overall maintenance costs for the recommender system.</p> <h2 id="Lessons-Learned-and-Recommended-Practices" class="common-anchor-header">Lessons Learned and Recommended Practices<button data-href="#Lessons-Learned-and-Recommended-Practices" class="anchor-icon" translate="no"> <svg aria-hidden="true" focusable="false" height="20" version="1.1" viewBox="0 0 16 16" width="16" > <path fill="#0092E4" fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z" ></path> </svg> </button></h2><p>In their journey with Milvus, the VIPSHOP team learned some lessons and gained several crucial insights for optimal system performance and user experience:</p> <ul> <li><p>In situations where read operations take precedence, adopting a read-write separation deployment strategy can enhance the overall system performance.</p></li> <li><p>The Milvus Java client lacks a built-in reconnection mechanism due to its in-memory residency in the recall service. The VIPSHOP team built their own connection pool to ensure consistent connectivity between the Java client and the server through a heartbeat test.</p></li> <li><p>Slow queries occasionally occur in Milvus due to the insufficient warm-up of new collections. To address this issue, the VIPSHOP team simulated queries on the new collection.</p></li> <li><p>To strike the right balance between retrieval performance and accuracy, the VIPSHOP team recommends conducting rigorous pressure testing experiments tailored to your specific business scenario and setting a reasonable threshold value to optimize these parameters.</p></li> <li><p>In scenarios involving static data, it is more efficient to import all data into the collection first and build indexes later.</p></li> </ul> </div><div class="globalArticleCTASection_sectionContainer__z7vw5 customerDetail_rightAnchorSection__qoRbt"><ul class="globalArticleCTASection_listWrapper__l52HF"><h3 class="globalArticleCTASection_anchorTitle__iGSh_">Content</h3><li><a class="globalArticleCTASection_anchorLink__JXvsH" href="/customers/vipshop#About-VIPSHOP">About VIPSHOP</a></li><li><a class="globalArticleCTASection_anchorLink__JXvsH" href="/customers/vipshop#Challenges-High-Latency-and-Spiking-Maintenance-Costs-Using-Elasticsearch">Challenges: High Latency and Spiking Maintenance Costs Using Elasticsearch</a></li><li><a class="globalArticleCTASection_anchorLink__JXvsH" href="/customers/vipshop#The-Milvus-Solution">The Milvus Solution</a></li><li><a class="globalArticleCTASection_anchorLink__JXvsH" href="/customers/vipshop#Results-Better-System-Performance-and-Optimal-User-Experience">Results: Better System Performance and Optimal User Experience</a></li><li><a class="globalArticleCTASection_anchorLink__JXvsH" href="/customers/vipshop#Lessons-Learned-and-Recommended-Practices">Lessons Learned and Recommended Practices</a></li></ul><div class="customerDetail_usagesWrapper__VQc0i"><div><p class="usageSection_title__xPVsF">Use case</p><ul class=""><li><a class="usageSection_link__iVJ0S" href="https://zilliz.com/vector-database-use-cases/recommender-system">Recommender system</a></li></ul></div><div><p 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","cover_image_url":"https://assets.zilliz.com/Group_4389_75e5e385db.png","share_image_url":null,"start_quote":{"id":42,"content":"Milvus-powered vector search has been running steadily in our recommendation systems, providing high performance and allowing us more flexibility in selecting models and algorithms.","author_name":"VIPSHOP Search Service Team","job_title":"VIPSHOP","published_at":"2023-11-03T16:45:30.069Z","created_by":60,"updated_by":18,"created_at":"2023-10-24T08:53:46.664Z","updated_at":"2023-11-03T16:45:30.086Z","unique_mark":"VIPSHOP Search Service Team - VIPSHOP","marquee_item":true,"display_on_cloud":null,"cloud_link_label":null,"cloud_link_url":null,"display_on_what_is_milvus_page":null,"locale":"en","company_logo":{"id":2071,"name":"Frame 5323.png","alternativeText":"","caption":"","width":1280,"height":752,"formats":{"large":{"ext":".png","url":"https://assets.zilliz.com/large_Frame_5323_ad76030486.png","hash":"large_Frame_5323_ad76030486","mime":"image/png","name":"large_Frame 5323.png","path":null,"size":176.24,"width":1000,"height":588},"small":{"ext":".png","url":"https://assets.zilliz.com/small_Frame_5323_ad76030486.png","hash":"small_Frame_5323_ad76030486","mime":"image/png","name":"small_Frame 5323.png","path":null,"size":46.95,"width":500,"height":294},"medium":{"ext":".png","url":"https://assets.zilliz.com/medium_Frame_5323_ad76030486.png","hash":"medium_Frame_5323_ad76030486","mime":"image/png","name":"medium_Frame 5323.png","path":null,"size":96.08,"width":750,"height":441},"thumbnail":{"ext":".png","url":"https://assets.zilliz.com/thumbnail_Frame_5323_ad76030486.png","hash":"thumbnail_Frame_5323_ad76030486","mime":"image/png","name":"thumbnail_Frame 5323.png","path":null,"size":17.42,"width":245,"height":144}},"hash":"Frame_5323_ad76030486","ext":".png","mime":"image/png","size":159.28,"url":"https://assets.zilliz.com/Frame_5323_ad76030486.png","previewUrl":null,"provider":"s3","provider_metadata":null,"created_by":60,"updated_by":60,"created_at":"2023-10-24T08:53:42.185Z","updated_at":"2023-10-24T08:53:42.203Z"}},"end_quote":null,"content":"\u003ch2 id=\"About-VIPSHOP\" class=\"common-anchor-header\"\u003eAbout VIPSHOP\u003cbutton data-href=\"#About-VIPSHOP\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg\n aria-hidden=\"true\"\n focusable=\"false\"\n height=\"20\"\n version=\"1.1\"\n viewBox=\"0 0 16 16\"\n width=\"16\"\n \u003e\n \u003cpath\n fill=\"#0092E4\"\n fill-rule=\"evenodd\"\n d=\"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n \u003e\u003c/path\u003e\n \u003c/svg\u003e\n \u003c/button\u003e\u003c/h2\u003e\u003cp\u003e\u003ca href=\"https://ir.vip.com/\"\u003eVIPSHOP\u003c/a\u003e is a renowned NYSE-listed online retailer headquartered in China, specializing in providing popular branded products to consumers with significant discounts. Its diverse product range includes fashion, apparel, accessories, beauty products, home goods, and electronics. Boasting a staggering customer base of over 52 million and facilitating nearly 270 million orders annually, VIPSHOP has earned its place as the 115th entry in Fortune's prestigious China 500 listing.\u003c/p\u003e\n\u003cp\u003e\n \u003cspan class=\"img-wrapper\"\u003e\n \u003cimg src=\"https://assets.zilliz.com/VIPSHOP_image2_cropped_7a3f82899f.png\" alt=\"\" class=\"doc-image\" id=\"\" /\u003e\n \u003cspan\u003e\u003c/span\u003e\n \u003c/span\u003e\n\u003c/p\u003e\n\u003ch2 id=\"Challenges-High-Latency-and-Spiking-Maintenance-Costs-Using-Elasticsearch\" class=\"common-anchor-header\"\u003eChallenges: High Latency and Spiking Maintenance Costs Using Elasticsearch\u003cbutton data-href=\"#Challenges-High-Latency-and-Spiking-Maintenance-Costs-Using-Elasticsearch\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg\n aria-hidden=\"true\"\n focusable=\"false\"\n height=\"20\"\n version=\"1.1\"\n viewBox=\"0 0 16 16\"\n width=\"16\"\n \u003e\n \u003cpath\n fill=\"#0092E4\"\n fill-rule=\"evenodd\"\n d=\"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n \u003e\u003c/path\u003e\n \u003c/svg\u003e\n \u003c/button\u003e\u003c/h2\u003e\u003cp\u003eWith the rapid growth of its business, VIPSHOP faced a common dilemma: as its product portfolio expanded, so did the complexity of helping users discover what they were looking for. To address this issue, VIPSHOP created a personalized recommendation system based on user query keywords and users’ purchase bahaviors.\u003c/p\u003e\n\u003cp\u003ePreviously, the VIPSHOP team utilized the Cosine Similarity(7.x) capabilities of \u003ca href=\"https://zilliz.com/comparison/milvus-vs-elastic\"\u003eElasticsearch\u003c/a\u003e to power the recommendation system. However, this approach was inefficient for two reasons:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cp\u003e\u003cstrong\u003eHigh latency in vector searching:\u003c/strong\u003e Averaging around 300 ms for retrieving Top-K results from millions of vectors, resulting in seconds for the system’s overall response time.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cstrong\u003eHigh costs of maintaining Elasticsearch indexes:\u003c/strong\u003e Vectors derived from products, consumers’ purchase behaviors, and all other data shared the same set of indexes, making the index construction, operation, and maintenance much more complicated.\u003c/p\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eVIPSHOP attempted to enhance Elasticsearch's performance by developing a locality sensitive hashing plugin. However, it only improved throughput and failed to reduce the vector search time to below 100 ms. Therefore, the team was still in urgent need of a new vector searching stack to improve their system performance.\u003c/p\u003e\n\u003ch2 id=\"The-Milvus-Solution\" class=\"common-anchor-header\"\u003eThe Milvus Solution\u003cbutton data-href=\"#The-Milvus-Solution\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg\n aria-hidden=\"true\"\n focusable=\"false\"\n height=\"20\"\n version=\"1.1\"\n viewBox=\"0 0 16 16\"\n width=\"16\"\n \u003e\n \u003cpath\n fill=\"#0092E4\"\n fill-rule=\"evenodd\"\n d=\"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n \u003e\u003c/path\u003e\n \u003c/svg\u003e\n \u003c/button\u003e\u003c/h2\u003e\u003cp\u003eAfter extensive research, the VIPSHOP team opted for \u003ca href=\"https://zilliz.com/what-is-milvus\"\u003eMilvus\u003c/a\u003e, an open-source vector database capable of handling billions of vector embeddings and delivering lightning-fast responses. Milvus also offers rich features such as distributed deployment, multi-language SDKs, and read/write separation, making it a superior choice to Elasticsearch and many other vector search solutions like \u003ca href=\"https://zilliz.com/blog/set-up-with-facebook-ai-similarity-search-faiss\"\u003eFAISS\u003c/a\u003e.\u003c/p\u003e\n\u003ch3 id=\"The-Architecture-of-VIPSHOP-Recommendation-System-Using-Milvus\" class=\"common-anchor-header\"\u003eThe Architecture of VIPSHOP Recommendation System Using Milvus\u003cbutton data-href=\"#The-Architecture-of-VIPSHOP-Recommendation-System-Using-Milvus\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg\n aria-hidden=\"true\"\n focusable=\"false\"\n height=\"20\"\n version=\"1.1\"\n viewBox=\"0 0 16 16\"\n width=\"16\"\n \u003e\n \u003cpath\n fill=\"#0092E4\"\n fill-rule=\"evenodd\"\n d=\"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n \u003e\u003c/path\u003e\n \u003c/svg\u003e\n \u003c/button\u003e\u003c/h3\u003e\u003cp\u003e\n \u003cspan class=\"img-wrapper\"\u003e\n \u003cimg src=\"https://assets.zilliz.com/VIPSHOP_image1_f6072f0a0a.png\" alt=\"\" class=\"doc-image\" id=\"\" /\u003e\n \u003cspan\u003e\u003c/span\u003e\n \u003c/span\u003e\n\u003c/p\u003e\n\u003cp\u003eThe diagram above demonstrates the architecture of VIPSHOP’s recommendation system with Milvus. It consists of two core parts:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cp\u003e\u003cstrong\u003eWrite process:\u003c/strong\u003e the VIPSHOP team used a deep learning model to transform each product’s features into vector embeddings and then imported them into Milvus through MySQL and an ETL tool.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cstrong\u003eRead process:\u003c/strong\u003e the team used the deep learning model to transform consumers’ queries and purchase behaviors into vectors and then retrieved similar results in Milvus. Milvus performed a similarity search and returned the Top-K most relevant results to consumers.\u003c/p\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"Milvus-Implementation-Details-Data-Update-and-Recall\" class=\"common-anchor-header\"\u003eMilvus Implementation Details: Data Update and Recall\u003cbutton data-href=\"#Milvus-Implementation-Details-Data-Update-and-Recall\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg\n aria-hidden=\"true\"\n focusable=\"false\"\n height=\"20\"\n version=\"1.1\"\n viewBox=\"0 0 16 16\"\n width=\"16\"\n \u003e\n \u003cpath\n fill=\"#0092E4\"\n fill-rule=\"evenodd\"\n d=\"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n \u003e\u003c/path\u003e\n \u003c/svg\u003e\n \u003c/button\u003e\u003c/h3\u003e\u003cp\u003eData update and recall are the most essential processes for the Milvus-powered recommendation system.\u003c/p\u003e\n\u003cp\u003eThe data update procedure ensures data synchronization, encompassing tasks such as writing vector data, detecting vector data volumes, index construction, index pre-loading, and alias management. It begins with commodity data checks, ensuring the quantity in MySQL aligns with existing data. The entire data-building process follows, including alias initialization in Redis, creating new collections, writing vectors in batches, and pre-loading the index in Milvus. After verifying the new collection's data, the system seamlessly switches aliases between multiple data collections.\u003c/p\u003e\n\u003cp\u003e\n \u003cspan class=\"img-wrapper\"\u003e\n \u003cimg src=\"https://assets.zilliz.com/VIPSHOP_image3_e69a309be5.png\" alt=\"\" class=\"doc-image\" id=\"\" /\u003e\n \u003cspan\u003e\u003c/span\u003e\n \u003c/span\u003e\n\u003c/p\u003e\n\u003cp\u003eThe recall process, crucial for recommending products, involves acquiring vectors related to consumers’ queries and purchase behaviors, calculating their distance, and merging results. Utilizing Milvus, the system concurrently and asynchronously retrieves data in different Milvus partitions, calculates vector similarities, and ranks the top results based on similarity distance. Then, after multiple calls to Milvus partition data, it presents the final recommendation results to users. The overall workflow is as follows:\u003c/p\u003e\n\u003cp\u003e\n \u003cspan class=\"img-wrapper\"\u003e\n \u003cimg src=\"https://assets.zilliz.com/VIPSHOP_image4_31585b40d6.png\" alt=\"\" class=\"doc-image\" id=\"\" /\u003e\n \u003cspan\u003e\u003c/span\u003e\n \u003c/span\u003e\n\u003c/p\u003e\n\u003cp\u003eThe following table shows the performance of three primary Milvus services. As shown in the table, the average latency for recalling Top-K results is around 10ms.\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\u003cth\u003eService\u003c/th\u003e\u003cth\u003eRole\u003c/th\u003e\u003cth\u003eInput Parameters\u003c/th\u003e\u003cth\u003eOutput parameters\u003c/th\u003e\u003cth\u003eResponse latency\u003c/th\u003e\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\u003ctd\u003eUser vectors acquisition\u003c/td\u003e\u003ctd\u003eObtain user vector\u003c/td\u003e\u003ctd\u003euser info + query\u003c/td\u003e\u003ctd\u003euser vector\u003c/td\u003e\u003ctd\u003e10 ms\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\u003ctd\u003eMilvus Search\u003c/td\u003e\u003ctd\u003eCalculate the vector similarity and return Top-K results\u003c/td\u003e\u003ctd\u003euser vector\u003c/td\u003e\u003ctd\u003eitem vector\u003c/td\u003e\u003ctd\u003e10 ms\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\u003ctd\u003eScheduling Logic\u003c/td\u003e\u003ctd\u003eConcurrent result recalling and merging\u003c/td\u003e\u003ctd\u003eMulti-channel recalled item vectors and the similarity score\u003c/td\u003e\u003ctd\u003eTop-K items\u003c/td\u003e\u003ctd\u003e10 ms\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2 id=\"Results-Better-System-Performance-and-Optimal-User-Experience\" class=\"common-anchor-header\"\u003eResults: Better System Performance and Optimal User Experience\u003cbutton data-href=\"#Results-Better-System-Performance-and-Optimal-User-Experience\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg\n aria-hidden=\"true\"\n focusable=\"false\"\n height=\"20\"\n version=\"1.1\"\n viewBox=\"0 0 16 16\"\n width=\"16\"\n \u003e\n \u003cpath\n fill=\"#0092E4\"\n fill-rule=\"evenodd\"\n d=\"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n \u003e\u003c/path\u003e\n \u003c/svg\u003e\n \u003c/button\u003e\u003c/h2\u003e\u003cp\u003eThe adoption of Milvus in VIPSHOP's recommender system significantly improved the overall system performance, including:\u003c/p\u003e\n\u003ch3 id=\"10x-Faster-Query-Speed\" class=\"common-anchor-header\"\u003e10x Faster Query Speed\u003cbutton data-href=\"#10x-Faster-Query-Speed\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg\n aria-hidden=\"true\"\n focusable=\"false\"\n height=\"20\"\n version=\"1.1\"\n viewBox=\"0 0 16 16\"\n width=\"16\"\n \u003e\n \u003cpath\n fill=\"#0092E4\"\n fill-rule=\"evenodd\"\n d=\"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n \u003e\u003c/path\u003e\n \u003c/svg\u003e\n \u003c/button\u003e\u003c/h3\u003e\u003cp\u003eUsing Milvus, the system query and response time has been reduced to below 30ms, 10x faster than the previous Elasticsearch solution.\u003c/p\u003e\n\u003ch3 id=\"Improved-System-Scalability\" class=\"common-anchor-header\"\u003eImproved System Scalability\u003cbutton data-href=\"#Improved-System-Scalability\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg\n aria-hidden=\"true\"\n focusable=\"false\"\n height=\"20\"\n version=\"1.1\"\n viewBox=\"0 0 16 16\"\n width=\"16\"\n \u003e\n \u003cpath\n fill=\"#0092E4\"\n fill-rule=\"evenodd\"\n d=\"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n \u003e\u003c/path\u003e\n \u003c/svg\u003e\n \u003c/button\u003e\u003c/h3\u003e\u003cp\u003eMilvus's distributed deployment and support for horizontal scaling enable the recommendation system to handle rapidly increasing data volumes and user queries effortlessly without compromising performance.\u003c/p\u003e\n\u003ch3 id=\"Enhanced-User-Experience\" class=\"common-anchor-header\"\u003eEnhanced User Experience\u003cbutton data-href=\"#Enhanced-User-Experience\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg\n aria-hidden=\"true\"\n focusable=\"false\"\n height=\"20\"\n version=\"1.1\"\n viewBox=\"0 0 16 16\"\n width=\"16\"\n \u003e\n \u003cpath\n fill=\"#0092E4\"\n fill-rule=\"evenodd\"\n d=\"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n \u003e\u003c/path\u003e\n \u003c/svg\u003e\n \u003c/button\u003e\u003c/h3\u003e\u003cp\u003eMilvus optimizes the recommendation process to provide tailored product suggestions based on user preferences and search intent, improving user satisfaction and engagement.\u003c/p\u003e\n\u003ch3 id=\"Reduced-Maintenance-Costs\" class=\"common-anchor-header\"\u003eReduced Maintenance Costs\u003cbutton data-href=\"#Reduced-Maintenance-Costs\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg\n aria-hidden=\"true\"\n focusable=\"false\"\n height=\"20\"\n version=\"1.1\"\n viewBox=\"0 0 16 16\"\n width=\"16\"\n \u003e\n \u003cpath\n fill=\"#0092E4\"\n fill-rule=\"evenodd\"\n d=\"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n \u003e\u003c/path\u003e\n \u003c/svg\u003e\n \u003c/button\u003e\u003c/h3\u003e\u003cp\u003eMilvus efficiently handles vector data and streamlines querying mechanisms, reducing overall maintenance costs for the recommender system.\u003c/p\u003e\n\u003ch2 id=\"Lessons-Learned-and-Recommended-Practices\" class=\"common-anchor-header\"\u003eLessons Learned and Recommended Practices\u003cbutton data-href=\"#Lessons-Learned-and-Recommended-Practices\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg\n aria-hidden=\"true\"\n focusable=\"false\"\n height=\"20\"\n version=\"1.1\"\n viewBox=\"0 0 16 16\"\n width=\"16\"\n \u003e\n \u003cpath\n fill=\"#0092E4\"\n fill-rule=\"evenodd\"\n d=\"M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z\"\n \u003e\u003c/path\u003e\n \u003c/svg\u003e\n \u003c/button\u003e\u003c/h2\u003e\u003cp\u003eIn their journey with Milvus, the VIPSHOP team learned some lessons and gained several crucial insights for optimal system performance and user experience:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cp\u003eIn situations where read operations take precedence, adopting a read-write separation deployment strategy can enhance the overall system performance.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eThe Milvus Java client lacks a built-in reconnection mechanism due to its in-memory residency in the recall service. The VIPSHOP team built their own connection pool to ensure consistent connectivity between the Java client and the server through a heartbeat test.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eSlow queries occasionally occur in Milvus due to the insufficient warm-up of new collections. To address this issue, the VIPSHOP team simulated queries on the new collection.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eTo strike the right balance between retrieval performance and accuracy, the VIPSHOP team recommends conducting rigorous pressure testing experiments tailored to your specific business scenario and setting a reasonable threshold value to optimize these parameters.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eIn scenarios involving static data, it is more efficient to import all data into the collection first and build indexes later.\u003c/p\u003e\u003c/li\u003e\n\u003c/ul\u003e\n","industry":null,"published_at":"2023-11-03T17:12:21.442Z","created_by":{"id":60,"firstname":"Di","lastname":"Feng","username":null,"email":"fendy.feng@zilliz.com","password":"$2a$10$3n0EPwpsTTiNpylnqJ4MReO3yAuO3glfers.zS0Wo4pmwHzaFMbnm","resetPasswordToken":null,"registrationToken":null,"isActive":true,"blocked":null,"preferedLanguage":null},"updated_by":{"id":60,"firstname":"Di","lastname":"Feng","username":null,"email":"fendy.feng@zilliz.com","password":"$2a$10$3n0EPwpsTTiNpylnqJ4MReO3yAuO3glfers.zS0Wo4pmwHzaFMbnm","resetPasswordToken":null,"registrationToken":null,"isActive":true,"blocked":null,"preferedLanguage":null},"created_at":"2023-10-24T08:54:16.399Z","updated_at":"2024-07-19T06:48:38.720Z","home_page_position":"Bottom","customer_industry":{"id":6,"name":"E-commerce","link_url":null,"published_at":"2023-10-25T12:28:58.877Z","created_by":21,"updated_by":21,"created_at":"2023-10-25T12:28:57.724Z","updated_at":"2023-10-25T12:28:58.885Z","locale":"en"},"meta_keywords":null,"order":0,"locale":"en","highlights":[{"id":61,"result":"10x Faster Query Speed","figuresDesc":" than the previous Elasticsearch solution ","order":null,"mark":"Up"},{"id":62,"result":"\u003c30ms Response Time ","figuresDesc":"for searching on millions of vectors ","order":null,"mark":"Up"},{"id":63,"result":"Optimized User Experience ","figuresDesc":"with more accurate recommendations based on users’ purchase behaviors","order":null,"mark":"Up"}],"resources":[{"id":75,"tag_name":"blog","title":"Efficient Vector Similarity Search in Recommender Workflows Using Milvus with NVIDIA Merlin","link_label":null,"link_href":"https://zilliz.com/blog/efficient-vector-similarity-search-recommender-workflows-using-milvus-nvidia-merlin","order":1,"resource_link_label":"Learn More","resource_desc":null},{"id":76,"tag_name":"blog","title":"Building Personalized Recommender Systems with Milvus and PaddlePaddle","link_label":null,"link_href":"https://zilliz.com/blog/building-personalized-recommender-systems-milvus-paddlepaddle","order":2,"resource_link_label":"Learn More","resource_desc":null},{"id":77,"tag_name":"blog","title":"Building a Personalized Product Recommender System with Vipshop and Milvus","link_label":null,"link_href":"https://zilliz.com/blog/building-a-personalized-product-recommender-system-with-vipshop-and-milvus","order":3,"resource_link_label":"Learn More","resource_desc":null}],"home_page_logo":{"id":2072,"name":"Frame 5324.svg","alternativeText":"","caption":"","width":220,"height":50,"formats":null,"hash":"Frame_5324_39565a4311","ext":".svg","mime":"image/svg+xml","size":4.4,"url":"https://assets.zilliz.com/Frame_5324_39565a4311.svg","previewUrl":null,"provider":"s3","provider_metadata":null,"created_by":60,"updated_by":60,"created_at":"2023-10-24T08:54:13.298Z","updated_at":"2023-10-24T08:54:13.316Z"},"use_cases":[{"id":35,"name":"Recommender system","link_url":"https://zilliz.com/vector-database-use-cases/recommender-system","published_at":"2023-09-06T11:45:10.099Z","created_by":60,"updated_by":60,"created_at":"2023-09-06T11:45:07.819Z","updated_at":"2024-02-24T12:48:29.654Z","locale":"en"}],"technology_useds":[],"localizations":[]},"anchors":[{"label":"About VIPSHOP","href":"About-VIPSHOP","type":2,"isActive":false},{"label":"Challenges: High Latency and Spiking Maintenance Costs Using Elasticsearch","href":"Challenges-High-Latency-and-Spiking-Maintenance-Costs-Using-Elasticsearch","type":2,"isActive":false},{"label":"The Milvus Solution","href":"The-Milvus-Solution","type":2,"isActive":false},{"label":"Results: Better System Performance and Optimal User Experience","href":"Results-Better-System-Performance-and-Optimal-User-Experience","type":2,"isActive":false},{"label":"Lessons Learned and Recommended Practices","href":"Lessons-Learned-and-Recommended-Practices","type":2,"isActive":false}]},"__N_SSG":true},"page":"/customers/[id]","query":{"id":"vipshop"},"buildId":"jZQUVXSOCcaFSI1MpPnBS","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>