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class="doc-style doc-post-content"><h1 id="Milvus-22-Benchmark-Test-Report" class="common-anchor-header">Milvus 2.2 Benchmark Test Report<button data-href="#Milvus-22-Benchmark-Test-Report" class="anchor-icon" translate="no"> <svg translate="no" 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></h1><p>This report shows the major test results of Milvus 2.2.0. It aims to provide a picture of Milvus 2.2.0 search performance, especially in the capability to scale up and scale out.</p> <div class="alert note"> <div style="display: flex;"> <div style="flex:0.3;"> <img translate="no" src="https://zilliz.com/images/whitepaper/performance.png" alt="Milvus Performance Evaluation 2023" /> </div> </div> <div style="flex:1;padding: 10px;"> <p>We have recently run a benchmark against Milvus 2.2.3 and have the following key findings:</p> <ul> <li>A 2.5x reduction in search latency</li> <li>A 4.5x increase in QPS</li> <li>Billion-scale similarity search with little performance degradation</li> <li>Linear scalability when using multiple replicas</li> </ul> <p>For details, welcome referring to <a href="https://zilliz.com/resources/whitepaper/milvus-performance-benchmark">this whitepaper</a> and <a href="https://github.com/zilliztech/VectorDBBench">related benchmark test code</a>. </p> </div> </div> <h2 id="Summary" class="common-anchor-header">Summary<button data-href="#Summary" class="anchor-icon" translate="no"> <svg translate="no" 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><ul> <li>Comparing with Milvus 2.1, the QPS of Milvus 2.2.0 increases over 48% in cluster mode and over 75% in standalone mode.</li> <li>Milvus 2.2.0 has an impressive capability to scale up and scale out: <ul> <li>QPS increases linearly when expanding CPU cores from 8 to 32.</li> <li>QPS increases linearly when expanding Querynode replicas from 1 to 8.</li> </ul></li> </ul> <h2 id="Terminology" class="common-anchor-header">Terminology<button data-href="#Terminology" class="anchor-icon" translate="no"> <svg translate="no" 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><details> <summary>Click to see the details of the terms used in the test</summary> <table class="terminology"> <thead> <tr> <th>Term</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>nq</td> <td>Number of vectors to be searched in one search request</td> </tr> <tr> <td>topk</td> <td>Number of the nearest vectors to be retrieved for each vector (in nq) in a search request</td> </tr> <tr> <td>ef</td> <td>A search parameter specific to <a href="https://milvus.io/docs/v2.2.x/index.md">HNSW index</a></td> </tr> <tr> <td>RT</td> <td>Response time from sending the request to receiving the response</td> </tr> <tr> <td>QPS</td> <td>Number of search requests that are successfully processed per second</td> </tr> </tbody> </table> </details></p> <h2 id="Test-environment" class="common-anchor-header">Test environment<button data-href="#Test-environment" class="anchor-icon" translate="no"> <svg translate="no" 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>All tests are performed under the following environments.</p> <h3 id="Hardware-environment" class="common-anchor-header">Hardware environment</h3><table> <thead> <tr><th>Hardware</th><th>Specification</th></tr> </thead> <tbody> <tr><td>CPU</td><td>Intel® Xeon® Gold 6226R CPU @ 2.90GHz</td></tr> <tr><td>Memory</td><td>16*\32 GB RDIMM, 3200 MT/s</td></tr> <tr><td>SSD</td><td>SATA 6 Gbps</td></tr> </tbody> </table> <h3 id="Software-environment" class="common-anchor-header">Software environment</h3><table> <thead> <tr><th>Software</th><th>Version</th></tr> </thead> <tbody> <tr><td>Milvus</td><td>v2.2.0</td></tr> <tr><td>Milvus GO SDK</td><td>v2.2.0</td></tr> </tbody> </table> <h3 id="Deployment-scheme" class="common-anchor-header">Deployment scheme</h3><ul> <li>Milvus instances (standalone or cluster) are deployed via <a href="https://milvus.io/docs/install_standalone-helm.md">Helm</a> on a Kubernetes cluster based on physical or virtual machines.</li> <li>Different tests merely vary in the number of CPU cores, the size of memory, and the number of replicas (worker nodes), which only applies to Milvus clusters.</li> <li>Unspecified configurations are identical to <a href="https://github.com/milvus-io/milvus-helm/blob/master/charts/milvus/values.yaml">default configurations</a>.</li> <li>Milvus dependencies (MinIO, Pulsar and Etcd) store data on the local SSD in each node.</li> <li>Search requests are sent to the Milvus instances via <a href="https://github.com/milvus-io/milvus-sdk-go/tree/master/tests">Milvus GO SDK</a>.</li> </ul> <h3 id="Data-sets" class="common-anchor-header">Data sets</h3><p>The test uses the open-source dataset SIFT (128 dimensions) from <a href="https://github.com/erikbern/ann-benchmarks/#data-sets">ANN-Benchmarks</a>.</p> <h2 id="Test-pipeline" class="common-anchor-header">Test pipeline<button data-href="#Test-pipeline" class="anchor-icon" translate="no"> <svg translate="no" 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><ol> <li>Start a Milvus instance by Helm with respective server configurations as listed in each test.</li> <li>Connect to the Milvus instance via Milvus GO SDK and get the corresponding test results.</li> <li>Create a collection.</li> <li>Insert 1 million SIFT vectors. Build an HNSW index and configure the index parameters by setting <code translate="no">M</code> to <code translate="no">8</code> and <code translate="no">efConstruction</code> to <code translate="no">200</code>.</li> <li>Load the collection.</li> <li>Search with different concurrent numbers with search parameters <code translate="no">nq=1, topk=1, ef=64</code>, the duration of each concurrency is at least 1 hour.</li> </ol> <h2 id="Test-results" class="common-anchor-header">Test results<button data-href="#Test-results" class="anchor-icon" translate="no"> <svg translate="no" 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><h3 id="Milvus-220-vs-Milvus-210" class="common-anchor-header">Milvus 2.2.0 v.s. Milvus 2.1.0</h3><h4 id="Cluster" class="common-anchor-header">Cluster</h4><p><details> <summary><b>Server configurations (cluster)</b></summary> <code translate="no">yaml queryNode: replicas: 1 resources: limits: cpu: "12.0" memory: 8Gi requests: cpu: "12.0" memory: 8Gi</code> </details></p> <p><strong>Search performance</strong></p> <table> <thead> <tr><th>Milvus</th><th>QPS</th><th>RT(TP99) / ms</th><th>RT(TP50) / ms</th><th>fail/s</th></tr> </thead> <tbody> <tr><td>2.1.0</td><td>6904</td><td>59</td><td>28</td><td>0</td></tr> <tr><td>2.2.0</td><td>10248</td><td>63</td><td>24</td><td>0</td></tr> </tbody> </table> <p> <span class="img-wrapper"> <img translate="no" src="/docs/v2.5.x/assets/cluster_search_performance_210_vs_220.png" alt="Cluster search performance" class="doc-image" id="cluster-search-performance" /> <span>Cluster search performance</span> </span> </p> <h4 id="Standalone" class="common-anchor-header">Standalone</h4><p><details> <summary><b>Server configurations (standalone)</b></summary> <code translate="no">yaml standalone: replicas: 1 resources: limits: cpu: "12.0" memory: 16Gi requests: cpu: "12.0" memory: 16Gi</code> </details></p> <p><strong>Search performance</strong></p> <table> <thead> <tr><th>Milvus</th><th>QPS</th><th>RT(TP99) / ms</th><th>RT(TP50) / ms</th><th>fail/s</th></tr> </thead> <tbody> <tr><td>2.1.0</td><td>4287</td><td>104</td><td>76</td><td>0</td></tr> <tr><td>2.2.0</td><td>7522</td><td>127</td><td>79</td><td>0</td></tr> </tbody> </table> <p> <span class="img-wrapper"> <img translate="no" src="/docs/v2.5.x/assets/standalone_search_performance_210_vs_220.png" alt="Standalone search performance" class="doc-image" id="standalone-search-performance" /> <span>Standalone search performance</span> </span> </p> <h3 id="Milvus-220-Scale-up" class="common-anchor-header">Milvus 2.2.0 Scale-up</h3><p>Expand the CPU cores in one Querynode to check the capability to scale up.</p> <p><details> <summary><b>Server configurations (cluster)</b></summary> <code translate="no">yaml queryNode: replicas: 1 resources: limits: cpu: "8.0" /"12.0" /"16.0" /"32.0" memory: 8Gi requests: cpu: "8.0" /"12.0" /"16.0" /"32.0" memory: 8Gi</code> </details></p> <p><strong>Search Performance</strong></p> <table> <thead> <tr><th>CPU cores</th><th>Concurrent Number</th><th>QPS</th><th>RT(TP99) / ms</th><th>RT(TP50) / ms</th><th>fail/s</th></tr> </thead> <tbody> <tr><td>8</td><td>500</td><td>7153</td><td>127</td><td>83</td><td>0</td></tr> <tr><td>12</td><td>300</td><td>10248</td><td>63</td><td>24</td><td>0</td></tr> <tr><td>16</td><td>600</td><td>14135</td><td>85</td><td>42</td><td>0</td></tr> <tr><td>32</td><td>600</td><td>20281</td><td>63</td><td>28</td><td>0</td></tr> </tbody> </table> <p> <span class="img-wrapper"> <img translate="no" src="/docs/v2.5.x/assets/search_performance_by_querynode_cpu_cores.png" alt="Search performance by Querynode CPU cores" class="doc-image" id="search-performance-by-querynode-cpu-cores" /> <span>Search performance by Querynode CPU cores</span> </span> </p> <h3 id="Milvus-220-Scale-out" class="common-anchor-header">Milvus 2.2.0 Scale-out</h3><p>Expand more replicas with more Querynodes to check the capability to scale out.</p> <div class="alert note"> <p>Note: the number of Querynodes equals the <code translate="no">replica_number</code> when loading the collection.</p> </div> <p><details> <summary><b>Server configurations (cluster)</b></summary> <code translate="no">yaml queryNode: replicas: 1 / 2 / 4 / 8 resources: limits: cpu: "8.0" memory: 8Gi requests: cpu: "8.0" memory: 8Gi</code> </details></p> <table> <thead> <tr><th>Replicas</th><th>Concurrent Number</th><th>QPS</th><th>RT(TP99) / ms</th><th>RT(TP50) / ms</th><th>fail/s</th></tr> </thead> <tbody> <tr><td>1</td><td>500</td><td>7153</td><td>127</td><td>83</td><td>0</td></tr> <tr><td>2</td><td>500</td><td>15903</td><td>105</td><td>27</td><td>0</td></tr> <tr><td>4</td><td>800</td><td>19281</td><td>109</td><td>40</td><td>0</td></tr> <tr><td>8</td><td>1200</td><td>30655</td><td>93</td><td>38</td><td>0</td></tr> </tbody> </table> <p> <span class="img-wrapper"> <img translate="no" src="/docs/v2.5.x/assets/search_performance_by_querynode_replicas.png" alt="Search performance by Querynode replicas" class="doc-image" id="search-performance-by-querynode-replicas" /> <span>Search performance by Querynode replicas</span> </span> </p> <h2 id="Whats-next" class="common-anchor-header">What’s next<button data-href="#Whats-next" class="anchor-icon" translate="no"> <svg translate="no" 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><ul> <li>Try performing Milvus 2.2.0 benchmark tests on your own by referring to <a href="https://milvus.io/blog/2022-08-16-A-Quick-Guide-to-Benchmarking-Milvus-2-1.md">this guide</a>, except that you should instead use Milvus 2.2 and Pymilvus 2.2 in this guide.</li> </ul> </div></div></div><div class="docDetail_asideSection__54VmZ"><div class="index_rightNavWrapper__Sp7NU docDetail_rightAnchorTreeWrapper__1UvlH"><div class="index_anchorsWrapper__6iXSV"><h5 class="index_tocWrapper__xhR3x"><svg width="18" height="18" viewBox="0 0 18 18" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0 13H12" stroke="black" stroke-width="1.5"></path><path d="M0 9H6" stroke="black" stroke-width="1.5"></path><path d="M0 5H12" stroke="black" stroke-width="1.5"></path></svg><span class="index_tocTitle__aNxxi">Table of contents</span></h5><ul 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stroke-linejoin="round"></path></svg></a></li></ul></div></div></div></div></footer></div></div></main></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"homeData":{"tree":"\u003ch1 id=\"Milvus-22-Benchmark-Test-Report\" class=\"common-anchor-header\"\u003eMilvus 2.2 Benchmark Test Report\u003cbutton data-href=\"#Milvus-22-Benchmark-Test-Report\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg translate=\"no\"\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/h1\u003e\u003cp\u003eThis report shows the major test results of Milvus 2.2.0. It aims to provide a picture of Milvus 2.2.0 search performance, especially in the capability to scale up and scale out.\u003c/p\u003e\n\u003cdiv class=\"alert note\"\u003e\n \u003cdiv style=\"display: flex;\"\u003e\n \u003cdiv style=\"flex:0.3;\"\u003e\n \u003cimg translate=\"no\" src=\"https://zilliz.com/images/whitepaper/performance.png\" alt=\"Milvus Performance Evaluation 2023\" /\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv style=\"flex:1;padding: 10px;\"\u003e\n \u003cp\u003eWe have recently run a benchmark against Milvus 2.2.3 and have the following key findings:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eA 2.5x reduction in search latency\u003c/li\u003e\n \u003cli\u003eA 4.5x increase in QPS\u003c/li\u003e\n \u003cli\u003eBillion-scale similarity search with little performance degradation\u003c/li\u003e\n \u003cli\u003eLinear scalability when using multiple replicas\u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eFor details, welcome referring to \u003ca href=\"https://zilliz.com/resources/whitepaper/milvus-performance-benchmark\"\u003ethis whitepaper\u003c/a\u003e and \u003ca href=\"https://github.com/zilliztech/VectorDBBench\"\u003erelated benchmark test code\u003c/a\u003e. \u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch2 id=\"Summary\" class=\"common-anchor-header\"\u003eSummary\u003cbutton data-href=\"#Summary\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg translate=\"no\"\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\u003cul\u003e\n\u003cli\u003eComparing with Milvus 2.1, the QPS of Milvus 2.2.0 increases over 48% in cluster mode and over 75% in standalone mode.\u003c/li\u003e\n\u003cli\u003eMilvus 2.2.0 has an impressive capability to scale up and scale out:\n\u003cul\u003e\n\u003cli\u003eQPS increases linearly when expanding CPU cores from 8 to 32.\u003c/li\u003e\n\u003cli\u003eQPS increases linearly when expanding Querynode replicas from 1 to 8.\u003c/li\u003e\n\u003c/ul\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"Terminology\" class=\"common-anchor-header\"\u003eTerminology\u003cbutton data-href=\"#Terminology\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg translate=\"no\"\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\u003cdetails\u003e\n\u003csummary\u003eClick to see the details of the terms used in the test\u003c/summary\u003e\n\u003ctable class=\"terminology\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eTerm\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003enq\u003c/td\u003e\n\u003ctd\u003eNumber of vectors to be searched in one search request\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003etopk\u003c/td\u003e\n\u003ctd\u003eNumber of the nearest vectors to be retrieved for each vector (in nq) in a search request\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eef\u003c/td\u003e\n\u003ctd\u003eA search parameter specific to \u003ca href=\"https://milvus.io/docs/v2.2.x/index.md\"\u003eHNSW index\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT\u003c/td\u003e\n\u003ctd\u003eResponse time from sending the request to receiving the response\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQPS\u003c/td\u003e\n\u003ctd\u003eNumber of search requests that are successfully processed per second\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/details\u003e\u003c/p\u003e\n\u003ch2 id=\"Test-environment\" class=\"common-anchor-header\"\u003eTest environment\u003cbutton data-href=\"#Test-environment\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg translate=\"no\"\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\u003eAll tests are performed under the following environments.\u003c/p\u003e\n\u003ch3 id=\"Hardware-environment\" class=\"common-anchor-header\"\u003eHardware environment\u003c/h3\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\u003cth\u003eHardware\u003c/th\u003e\u003cth\u003eSpecification\u003c/th\u003e\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\u003ctd\u003eCPU\u003c/td\u003e\u003ctd\u003eIntel® Xeon® Gold 6226R CPU @ 2.90GHz\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\u003ctd\u003eMemory\u003c/td\u003e\u003ctd\u003e16*\\32 GB RDIMM, 3200 MT/s\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\u003ctd\u003eSSD\u003c/td\u003e\u003ctd\u003eSATA 6 Gbps\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3 id=\"Software-environment\" class=\"common-anchor-header\"\u003eSoftware environment\u003c/h3\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\u003cth\u003eSoftware\u003c/th\u003e\u003cth\u003eVersion\u003c/th\u003e\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\u003ctd\u003eMilvus\u003c/td\u003e\u003ctd\u003ev2.2.0\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\u003ctd\u003eMilvus GO SDK\u003c/td\u003e\u003ctd\u003ev2.2.0\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3 id=\"Deployment-scheme\" class=\"common-anchor-header\"\u003eDeployment scheme\u003c/h3\u003e\u003cul\u003e\n\u003cli\u003eMilvus instances (standalone or cluster) are deployed via \u003ca href=\"https://milvus.io/docs/install_standalone-helm.md\"\u003eHelm\u003c/a\u003e on a Kubernetes cluster based on physical or virtual machines.\u003c/li\u003e\n\u003cli\u003eDifferent tests merely vary in the number of CPU cores, the size of memory, and the number of replicas (worker nodes), which only applies to Milvus clusters.\u003c/li\u003e\n\u003cli\u003eUnspecified configurations are identical to \u003ca href=\"https://github.com/milvus-io/milvus-helm/blob/master/charts/milvus/values.yaml\"\u003edefault configurations\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eMilvus dependencies (MinIO, Pulsar and Etcd) store data on the local SSD in each node.\u003c/li\u003e\n\u003cli\u003eSearch requests are sent to the Milvus instances via \u003ca href=\"https://github.com/milvus-io/milvus-sdk-go/tree/master/tests\"\u003eMilvus GO SDK\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"Data-sets\" class=\"common-anchor-header\"\u003eData sets\u003c/h3\u003e\u003cp\u003eThe test uses the open-source dataset SIFT (128 dimensions) from \u003ca href=\"https://github.com/erikbern/ann-benchmarks/#data-sets\"\u003eANN-Benchmarks\u003c/a\u003e.\u003c/p\u003e\n\u003ch2 id=\"Test-pipeline\" class=\"common-anchor-header\"\u003eTest pipeline\u003cbutton data-href=\"#Test-pipeline\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg translate=\"no\"\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\u003col\u003e\n\u003cli\u003eStart a Milvus instance by Helm with respective server configurations as listed in each test.\u003c/li\u003e\n\u003cli\u003eConnect to the Milvus instance via Milvus GO SDK and get the corresponding test results.\u003c/li\u003e\n\u003cli\u003eCreate a collection.\u003c/li\u003e\n\u003cli\u003eInsert 1 million SIFT vectors. Build an HNSW index and configure the index parameters by setting \u003ccode translate=\"no\"\u003eM\u003c/code\u003e to \u003ccode translate=\"no\"\u003e8\u003c/code\u003e and \u003ccode translate=\"no\"\u003eefConstruction\u003c/code\u003e to \u003ccode translate=\"no\"\u003e200\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eLoad the collection.\u003c/li\u003e\n\u003cli\u003eSearch with different concurrent numbers with search parameters \u003ccode translate=\"no\"\u003enq=1, topk=1, ef=64\u003c/code\u003e, the duration of each concurrency is at least 1 hour.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2 id=\"Test-results\" class=\"common-anchor-header\"\u003eTest results\u003cbutton data-href=\"#Test-results\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg translate=\"no\"\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\u003ch3 id=\"Milvus-220-vs-Milvus-210\" class=\"common-anchor-header\"\u003eMilvus 2.2.0 v.s. Milvus 2.1.0\u003c/h3\u003e\u003ch4 id=\"Cluster\" class=\"common-anchor-header\"\u003eCluster\u003c/h4\u003e\u003cp\u003e\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eServer configurations (cluster)\u003c/b\u003e\u003c/summary\u003e\n\u003ccode translate=\"no\"\u003eyaml queryNode: replicas: 1 resources: limits: cpu: \u0026quot;12.0\u0026quot; memory: 8Gi requests: cpu: \u0026quot;12.0\u0026quot; memory: 8Gi\u003c/code\u003e\n\u003c/details\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSearch performance\u003c/strong\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\u003cth\u003eMilvus\u003c/th\u003e\u003cth\u003eQPS\u003c/th\u003e\u003cth\u003eRT(TP99) / ms\u003c/th\u003e\u003cth\u003eRT(TP50) / ms\u003c/th\u003e\u003cth\u003efail/s\u003c/th\u003e\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\u003ctd\u003e2.1.0\u003c/td\u003e\u003ctd\u003e6904\u003c/td\u003e\u003ctd\u003e59\u003c/td\u003e\u003ctd\u003e28\u003c/td\u003e\u003ctd\u003e0\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\u003ctd\u003e2.2.0\u003c/td\u003e\u003ctd\u003e10248\u003c/td\u003e\u003ctd\u003e63\u003c/td\u003e\u003ctd\u003e24\u003c/td\u003e\u003ctd\u003e0\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\n \u003cspan class=\"img-wrapper\"\u003e\n \u003cimg translate=\"no\" src=\"/docs/v2.5.x/assets/cluster_search_performance_210_vs_220.png\" alt=\"Cluster search performance\" class=\"doc-image\" id=\"cluster-search-performance\" /\u003e\n \u003cspan\u003eCluster search performance\u003c/span\u003e\n \u003c/span\u003e\n\u003c/p\u003e\n\u003ch4 id=\"Standalone\" class=\"common-anchor-header\"\u003eStandalone\u003c/h4\u003e\u003cp\u003e\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eServer configurations (standalone)\u003c/b\u003e\u003c/summary\u003e\n\u003ccode translate=\"no\"\u003eyaml standalone: replicas: 1 resources: limits: cpu: \u0026quot;12.0\u0026quot; memory: 16Gi requests: cpu: \u0026quot;12.0\u0026quot; memory: 16Gi\u003c/code\u003e\n\u003c/details\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSearch performance\u003c/strong\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\u003cth\u003eMilvus\u003c/th\u003e\u003cth\u003eQPS\u003c/th\u003e\u003cth\u003eRT(TP99) / ms\u003c/th\u003e\u003cth\u003eRT(TP50) / ms\u003c/th\u003e\u003cth\u003efail/s\u003c/th\u003e\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\u003ctd\u003e2.1.0\u003c/td\u003e\u003ctd\u003e4287\u003c/td\u003e\u003ctd\u003e104\u003c/td\u003e\u003ctd\u003e76\u003c/td\u003e\u003ctd\u003e0\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\u003ctd\u003e2.2.0\u003c/td\u003e\u003ctd\u003e7522\u003c/td\u003e\u003ctd\u003e127\u003c/td\u003e\u003ctd\u003e79\u003c/td\u003e\u003ctd\u003e0\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\n \u003cspan class=\"img-wrapper\"\u003e\n \u003cimg translate=\"no\" src=\"/docs/v2.5.x/assets/standalone_search_performance_210_vs_220.png\" alt=\"Standalone search performance\" class=\"doc-image\" id=\"standalone-search-performance\" /\u003e\n \u003cspan\u003eStandalone search performance\u003c/span\u003e\n \u003c/span\u003e\n\u003c/p\u003e\n\u003ch3 id=\"Milvus-220-Scale-up\" class=\"common-anchor-header\"\u003eMilvus 2.2.0 Scale-up\u003c/h3\u003e\u003cp\u003eExpand the CPU cores in one Querynode to check the capability to scale up.\u003c/p\u003e\n\u003cp\u003e\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eServer configurations (cluster)\u003c/b\u003e\u003c/summary\u003e\n\u003ccode translate=\"no\"\u003eyaml queryNode: replicas: 1 resources: limits: cpu: \u0026quot;8.0\u0026quot; /\u0026quot;12.0\u0026quot; /\u0026quot;16.0\u0026quot; /\u0026quot;32.0\u0026quot; memory: 8Gi requests: cpu: \u0026quot;8.0\u0026quot; /\u0026quot;12.0\u0026quot; /\u0026quot;16.0\u0026quot; /\u0026quot;32.0\u0026quot; memory: 8Gi\u003c/code\u003e\n\u003c/details\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSearch Performance\u003c/strong\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\u003cth\u003eCPU cores\u003c/th\u003e\u003cth\u003eConcurrent Number\u003c/th\u003e\u003cth\u003eQPS\u003c/th\u003e\u003cth\u003eRT(TP99) / ms\u003c/th\u003e\u003cth\u003eRT(TP50) / ms\u003c/th\u003e\u003cth\u003efail/s\u003c/th\u003e\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\u003ctd\u003e8\u003c/td\u003e\u003ctd\u003e500\u003c/td\u003e\u003ctd\u003e7153\u003c/td\u003e\u003ctd\u003e127\u003c/td\u003e\u003ctd\u003e83\u003c/td\u003e\u003ctd\u003e0\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\u003ctd\u003e12\u003c/td\u003e\u003ctd\u003e300\u003c/td\u003e\u003ctd\u003e10248\u003c/td\u003e\u003ctd\u003e63\u003c/td\u003e\u003ctd\u003e24\u003c/td\u003e\u003ctd\u003e0\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\u003ctd\u003e16\u003c/td\u003e\u003ctd\u003e600\u003c/td\u003e\u003ctd\u003e14135\u003c/td\u003e\u003ctd\u003e85\u003c/td\u003e\u003ctd\u003e42\u003c/td\u003e\u003ctd\u003e0\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\u003ctd\u003e32\u003c/td\u003e\u003ctd\u003e600\u003c/td\u003e\u003ctd\u003e20281\u003c/td\u003e\u003ctd\u003e63\u003c/td\u003e\u003ctd\u003e28\u003c/td\u003e\u003ctd\u003e0\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\n \u003cspan class=\"img-wrapper\"\u003e\n \u003cimg translate=\"no\" src=\"/docs/v2.5.x/assets/search_performance_by_querynode_cpu_cores.png\" alt=\"Search performance by Querynode CPU cores\" class=\"doc-image\" id=\"search-performance-by-querynode-cpu-cores\" /\u003e\n \u003cspan\u003eSearch performance by Querynode CPU cores\u003c/span\u003e\n \u003c/span\u003e\n\u003c/p\u003e\n\u003ch3 id=\"Milvus-220-Scale-out\" class=\"common-anchor-header\"\u003eMilvus 2.2.0 Scale-out\u003c/h3\u003e\u003cp\u003eExpand more replicas with more Querynodes to check the capability to scale out.\u003c/p\u003e\n\u003cdiv class=\"alert note\"\u003e\n\u003cp\u003eNote: the number of Querynodes equals the \u003ccode translate=\"no\"\u003ereplica_number\u003c/code\u003e when loading the collection.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eServer configurations (cluster)\u003c/b\u003e\u003c/summary\u003e\n\u003ccode translate=\"no\"\u003eyaml queryNode: replicas: 1 / 2 / 4 / 8 resources: limits: cpu: \u0026quot;8.0\u0026quot; memory: 8Gi requests: cpu: \u0026quot;8.0\u0026quot; memory: 8Gi\u003c/code\u003e\n\u003c/details\u003e\u003c/p\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\u003cth\u003eReplicas\u003c/th\u003e\u003cth\u003eConcurrent Number\u003c/th\u003e\u003cth\u003eQPS\u003c/th\u003e\u003cth\u003eRT(TP99) / ms\u003c/th\u003e\u003cth\u003eRT(TP50) / 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\u003cspan class=\"img-wrapper\"\u003e\n \u003cimg translate=\"no\" src=\"/docs/v2.5.x/assets/search_performance_by_querynode_replicas.png\" alt=\"Search performance by Querynode replicas\" class=\"doc-image\" id=\"search-performance-by-querynode-replicas\" /\u003e\n \u003cspan\u003eSearch performance by Querynode replicas\u003c/span\u003e\n \u003c/span\u003e\n\u003c/p\u003e\n\u003ch2 id=\"Whats-next\" class=\"common-anchor-header\"\u003eWhat’s next\u003cbutton data-href=\"#Whats-next\" class=\"anchor-icon\" translate=\"no\"\u003e\n \u003csvg translate=\"no\"\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 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