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Posted to commits@hugegraph.apache.org by gi...@apache.org on 2023/05/15 03:32:11 UTC
[incubator-hugegraph-doc] branch asf-site updated: Update hugegraph-benchmark-0.5.6.md (#226)
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new 25095bbf Update hugegraph-benchmark-0.5.6.md (#226)
25095bbf is described below
commit 25095bbfc5378a0aa304c105845ea2eb21ce7812
Author: imbajin <im...@users.noreply.github.com>
AuthorDate: Mon May 15 03:32:07 2023 +0000
Update hugegraph-benchmark-0.5.6.md (#226)
Completed conversion to English. 2e5bf8c640d8f602c03795953c71df3164cbf4ef
---
docs/_print/index.html | 2 +-
docs/index.xml | 248 ++++++++++-----------
docs/performance/_print/index.html | 2 +-
.../hugegraph-benchmark-0.5.6/index.html | 48 +---
docs/performance/index.xml | 248 ++++++++++-----------
en/index.html | 2 +-
en/sitemap.xml | 2 +-
sitemap.xml | 2 +-
8 files changed, 246 insertions(+), 308 deletions(-)
diff --git a/docs/_print/index.html b/docs/_print/index.html
index f2da85b1..7bcf0d3e 100644
--- a/docs/_print/index.html
+++ b/docs/_print/index.html
@@ -6583,7 +6583,7 @@ Merging mode as needed, and when the Restore is completed, restore the graph mod
</span></span><span style=display:flex><span>
</span></span><span style=display:flex><span><span style=color:#8f5902;font-style:italic>// what is the name of the brother and the name of the place?
</span></span></span><span style=display:flex><span><span style=color:#8f5902;font-style:italic></span><span style=color:#000>g</span><span style=color:#ce5c00;font-weight:700>.</span><span style=color:#c4a000>V</span><span style=color:#ce5c00;font-weight:700>(</span><span style=color:#000>pluto</span><span style=color:#ce5c00;font-weight:700>).</span><span style=color:#c4a000>out</span><span style=color:#ce5c00;font-weight:700>(</span><span style=color:#4e9a06>'brother'</span><s [...]
-</span></span></code></pre></div><p>推荐使用<a href=/docs/quickstart/hugegraph-studio>HugeGraph-Studio</a> 通过可视化的方式来执行上述代码。另外也可以通过HugeGraph-Client、HugeApi、GremlinConsole和GremlinDriver等多种方式执行上述代码。</p><h4 id=32-总结>3.2 总结</h4><p>HugeGraph 目前支持 <code>Gremlin</code> 的语法，用户可以通过 <code>Gremlin / REST-API</code> 实现各种查询需求。</p></div><div class=td-content style=page-break-before:always><h1 id=pg-f0a22a813c843322c0d360d952e434ce>8 - PERFORMANCE</h1></div><div class=td-content><h1 id=pg-63f6d63db3ee3a5270 [...]
+</span></span></code></pre></div><p>推荐使用<a href=/docs/quickstart/hugegraph-studio>HugeGraph-Studio</a> 通过可视化的方式来执行上述代码。另外也可以通过HugeGraph-Client、HugeApi、GremlinConsole和GremlinDriver等多种方式执行上述代码。</p><h4 id=32-总结>3.2 总结</h4><p>HugeGraph 目前支持 <code>Gremlin</code> 的语法，用户可以通过 <code>Gremlin / REST-API</code> 实现各种查询需求。</p></div><div class=td-content style=page-break-before:always><h1 id=pg-f0a22a813c843322c0d360d952e434ce>8 - PERFORMANCE</h1></div><div class=td-content><h1 id=pg-63f6d63db3ee3a5270 [...]
</span></span><span style=display:flex><span> batch_size_fail_threshold_in_kb: 1000
</span></span></code></pre></div><ul><li>HugeGraphServer 与 HugeGremlinServer 与cassandra都在同一机器上启动，server 相关的配置文件除主机和端口有修改外，其余均保持默认。</li></ul><h4 id=13-名词解释>1.3 名词解释</h4><ul><li>Samples – 本次场景中一共完成了多少个线程</li><li>Average – 平均响应时间</li><li>Median – 统计意义上面的响应时间的中值</li><li>90% Line – 所有线程中90%的线程的响应时间都小于xx</li><li>Min – 最小响应时间</li><li>Max – 最大响应时间</li><li>Error – 出错率</li><li>Troughput – 吞吐量Â</li><li>KB/sec – 以流量做衡量的吞吐量</li></ul><p><em>注：时间的单位 [...]
</span></span><span style=display:flex><span>git clone https://github.com/<span style=color:#4e9a06>${</span><span style=color:#000>GITHUB_USER_NAME</span><span style=color:#4e9a06>}</span>/hugegraph
diff --git a/docs/index.xml b/docs/index.xml
index 3688c998..bc728bdb 100644
--- a/docs/index.xml
+++ b/docs/index.xml
@@ -851,8 +851,8 @@
<div class="highlight"><pre tabindex="0" style="background-color:#f8f8f8;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-shell" data-lang="shell"><span style="display:flex;"><span><span style="color:#8f5902;font-style:italic"># force push the local commit to fork repo</span>
</span></span><span style="display:flex;"><span>git push -f origin bugfix-branch:bugfix-branch
</span></span></code></pre></div><p>GitHub will automatically update the Pull Request after we push it, just wait for code review.</p></description></item><item><title>Docs: HugeGraph BenchMark Performance</title><link>/docs/performance/hugegraph-benchmark-0.5.6/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>/docs/performance/hugegraph-benchmark-0.5.6/</guid><description>
-<h3 id="1-测试环境">1 测试环境</h3>
-<h4 id="11-硬件信息">1.1 硬件信息</h4>
+<h3 id="1-test-environment">1 Test environment</h3>
+<h4 id="11-hardware-information">1.1 Hardware information</h4>
<table>
<thead>
<tr>
@@ -871,60 +871,45 @@
</tr>
</tbody>
</table>
-<h4 id="12-软件信息">1.2 软件信息</h4>
-<h5 id="121-测试用例">1.2.1 测试用例</h5>
-<p>测试使用<a href="https://github.com/socialsensor/graphdb-benchmarks">graphdb-benchmark</a>，一个图数据库测试集。该测试集主要包含4类测试：</p>
+<h4 id="12-software-information">1.2 Software information</h4>
+<h5 id="121-test-cases">1.2.1 Test cases</h5>
+<p>Testing is done using the <a href="https://github.com/socialsensor/graphdb-benchmarks">graphdb-benchmark</a>, a benchmark suite for graph databases. This benchmark suite mainly consists of four types of tests:</p>
<ul>
-<li>
-<p>Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交</p>
-</li>
-<li>
-<p>Single Insertion，单条插入，每个顶点或者每条边立即提交</p>
-</li>
-<li>
-<p>Query，主要是图数据库的基本查询操作：</p>
+<li>Massive Insertion, which involves batch insertion of vertices and edges, with a certain number of vertices or edges being submitted at once.</li>
+<li>Single Insertion, which involves the immediate insertion of each vertex or edge, one at a time.</li>
+<li>Query, which mainly includes the basic query operations of the graph database:
<ul>
-<li>Find Neighbors，查询所有顶点的邻居</li>
-<li>Find Adjacent Nodes，查询所有边的邻接顶点</li>
-<li>Find Shortest Path，查询第一个顶点到100个随机顶点的最短路径</li>
+<li>Find Neighbors, which queries the neighbors of all vertices.</li>
+<li>Find Adjacent Nodes, which queries the adjacent vertices of all edges.</li>
+<li>Find Shortest Path, which queries the shortest path from the first vertex to 100 random vertices.</li>
</ul>
</li>
-<li>
-<p>Clustering，基于Louvain Method的社区发现算法</p>
-</li>
+<li>Clustering, which is a community detection algorithm based on the Louvain Method.</li>
</ul>
-<h5 id="122-测试数据集">1.2.2 测试数据集</h5>
-<p>测试使用人造数据和真实数据</p>
+<h5 id="122-test-dataset">1.2.2 Test dataset</h5>
+<p>Tests are conducted using both synthetic and real data.</p>
<ul>
<li>
-<p>MIW、SIW和QW使用SNAP数据集</p>
+<p>MIW, SIW, and QW use SNAP datasets:</p>
<ul>
-<li>
-<p><a href="http://snap.stanford.edu/data/email-Enron.html">Enron Dataset</a></p>
-</li>
-<li>
-<p><a href="http://snap.stanford.edu/data/amazon0601.html">Amazon dataset</a></p>
-</li>
-<li>
-<p><a href="http://snap.stanford.edu/data/com-Youtube.html">Youtube dataset</a></p>
-</li>
-<li>
-<p><a href="http://snap.stanford.edu/data/com-LiveJournal.html">LiveJournal dataset</a></p>
-</li>
+<li><a href="http://snap.stanford.edu/data/email-Enron.html">Enron Dataset</a></li>
+<li><a href="http://snap.stanford.edu/data/amazon0601.html">Amazon dataset</a></li>
+<li><a href="http://snap.stanford.edu/data/com-Youtube.html">Youtube dataset</a></li>
+<li><a href="http://snap.stanford.edu/data/com-LiveJournal.html">LiveJournal dataset</a></li>
</ul>
</li>
<li>
-<p>CW使用<a href="https://sites.google.com/site/andrealancichinetti/files">LFR-Benchmark generator</a>生成的人造数据</p>
+<p>CW uses synthetic data generated by the <a href="https://sites.google.com/site/andrealancichinetti/files">LFR-Benchmark generator</a>.</p>
</li>
</ul>
-<h6 id="本测试用到的数据集规模">本测试用到的数据集规模</h6>
+<p>The size of the datasets used in this test are not mentioned.</p>
<table>
<thead>
<tr>
-<th>名称</th>
-<th>vertex数目</th>
-<th>edge数目</th>
-<th>文件大小</th>
+<th>Name</th>
+<th>Number of Vertices</th>
+<th>Number of Edges</th>
+<th>File Size</th>
</tr>
</thead>
<tbody>
@@ -954,29 +939,29 @@
</tr>
</tbody>
</table>
-<h4 id="13-服务配置">1.3 服务配置</h4>
+<h4 id="13-service-configuration">1.3 Service configuration</h4>
<ul>
<li>
-<p>HugeGraph版本：0.5.6，RestServer和Gremlin Server和backends都在同一台服务器上</p>
+<p>HugeGraph version: 0.5.6, RestServer and Gremlin Server and backends are on the same server</p>
<ul>
-<li>RocksDB版本：rocksdbjni-5.8.6</li>
+<li>RocksDB version: rocksdbjni-5.8.6</li>
</ul>
</li>
<li>
-<p>Titan版本：0.5.4, 使用thrift+Cassandra模式</p>
+<p>Titan version: 0.5.4, using thrift+Cassandra mode</p>
<ul>
-<li>Cassandra版本：cassandra-3.10，commit-log 和 data 共用SSD</li>
+<li>Cassandra version: cassandra-3.10, commit-log and data use SSD together</li>
</ul>
</li>
<li>
-<p>Neo4j版本：2.0.1</p>
+<p>Neo4j version: 2.0.1</p>
</li>
</ul>
<blockquote>
-<p>graphdb-benchmark适配的Titan版本为0.5.4</p>
+<p>The Titan version adapted by graphdb-benchmark is 0.5.4.</p>
</blockquote>
-<h3 id="2-测试结果">2 测试结果</h3>
-<h4 id="21-batch插入性能">2.1 Batch插入性能</h4>
+<h3 id="2-test-results">2 Test results</h3>
+<h4 id="21-batch-insertion-performance">2.1 Batch insertion performance</h4>
<table>
<thead>
<tr>
@@ -1011,23 +996,23 @@
</tr>
</tbody>
</table>
-<p><em>说明</em></p>
+<p><em>Instructions</em></p>
<ul>
-<li>表头&quot;（）&ldquo;中数据是数据规模，以边为单位</li>
-<li>表中数据是批量插入的时间，单位是s</li>
-<li>例如，HugeGraph使用RocksDB插入amazon0601数据集的300w条边，花费5.711s</li>
+<li>The data scale is in the table header in terms of edges</li>
+<li>The data in the table is the time for batch insertion, in seconds</li>
+<li>For example, HugeGraph(RocksDB) spent 5.711 seconds to insert 3 million edges of the amazon0601 dataset.</li>
</ul>
-<h5 id="结论">结论</h5>
+<h5 id="conclusion">Conclusion</h5>
<ul>
-<li>批量插入性能 HugeGraph(RocksDB) &gt; Neo4j &gt; Titan(thrift+Cassandra)</li>
+<li>The performance of batch insertion: HugeGraph(RocksDB) &gt; Neo4j &gt; Titan(thrift+Cassandra)</li>
</ul>
-<h4 id="22-遍历性能">2.2 遍历性能</h4>
-<h5 id="221-术语说明">2.2.1 术语说明</h5>
+<h4 id="22-traversal-performance">2.2 Traversal performance</h4>
+<h5 id="221-explanation-of-terms">2.2.1 Explanation of terms</h5>
<ul>
-<li>FN(Find Neighbor), 遍历所有vertex, 根据vertex查邻接edge, 通过edge和vertex查other vertex</li>
-<li>FA(Find Adjacent), 遍历所有edge，根据edge获得source vertex和target vertex</li>
+<li>FN(Find Neighbor): Traverse all vertices, find the adjacent edges based on each vertex, and use the edges and vertices to find the other vertices adjacent to the original vertex.</li>
+<li>FA(Find Adjacent): Traverse all edges, get the source vertex and target vertex based on each edge.</li>
</ul>
-<h5 id="222-fn性能">2.2.2 FN性能</h5>
+<h5 id="222-fn-performance">2.2.2 FN performance</h5>
<table>
<thead>
<tr>
@@ -1062,11 +1047,11 @@
</tr>
</tbody>
</table>
-<p><em>说明</em></p>
+<p><em>Instructions</em></p>
<ul>
-<li>表头&rdquo;（）&ldquo;中数据是数据规模，以顶点为单位</li>
-<li>表中数据是遍历顶点花费的时间，单位是s</li>
-<li>例如，HugeGraph使用RocksDB后端遍历amazon0601的所有顶点，并查找邻接边和另一顶点，总共耗时45.118s</li>
+<li>The data in the table header &ldquo;( )&rdquo; represents the data scale, in terms of vertices.</li>
+<li>The data in the table represents the time spent traversing vertices, in seconds.</li>
+<li>For example, HugeGraph uses the RocksDB backend to traverse all vertices in amazon0601, and search for adjacent edges and another vertex, which takes a total of 45.118 seconds.</li>
</ul>
<h5 id="223-fa性能">2.2.3 FA性能</h5>
<table>
@@ -1103,24 +1088,25 @@
</tr>
</tbody>
</table>
-<p><em>说明</em></p>
+<p><em>Explanation</em></p>
<ul>
-<li>表头&rdquo;（）&ldquo;中数据是数据规模，以边为单位</li>
-<li>表中数据是遍历边花费的时间，单位是s</li>
-<li>例如，HugeGraph使用RocksDB后端遍历amazon0601的所有边，并查询每条边的两个顶点，总共耗时10.764s</li>
+<li>The data size in the header &ldquo;( )&rdquo; is based on the number of vertices.</li>
+<li>The data in the table is the time it takes to traverse the vertices, in seconds.</li>
+<li>For example, HugeGraph with RocksDB backend traverses all vertices in the amazon0601 dataset, and looks up adjacent edges and other vertices, taking a total of 45.118 seconds.</li>
+<li></li>
</ul>
-<h6 id="结论-1">结论</h6>
+<h6 id="conclusion-1">Conclusion</h6>
<ul>
-<li>遍历性能 Neo4j &gt; HugeGraph(RocksDB) &gt; Titan(thrift+Cassandra)</li>
+<li>Traversal performance: Neo4j &gt; HugeGraph(RocksDB) &gt; Titan(thrift+Cassandra)</li>
</ul>
-<h4 id="23-hugegraph-图常用分析方法性能">2.3 HugeGraph-图常用分析方法性能</h4>
-<h5 id="术语说明">术语说明</h5>
+<h4 id="23-performance-of-common-graph-analysis-methods-in-hugegraph">2.3 Performance of Common Graph Analysis Methods in HugeGraph</h4>
+<h5 id="terminology-explanation">Terminology Explanation</h5>
<ul>
-<li>FS(Find Shortest Path), 寻找最短路径</li>
-<li>K-neighbor，从起始vertex出发，通过K跳边能够到达的所有顶点, 包括1, 2, 3&hellip;(K-1), K跳边可达vertex</li>
-<li>K-out, 从起始vertex出发，恰好经过K跳out边能够到达的顶点</li>
+<li>FS (Find Shortest Path): finding the shortest path between two vertices</li>
+<li>K-neighbor: all vertices that can be reached by traversing K hops (including 1, 2, 3&hellip;(K-1) hops) from the starting vertex</li>
+<li>K-out: all vertices that can be reached by traversing exactly K out-edges from the starting vertex.</li>
</ul>
-<h5 id="fs性能">FS性能</h5>
+<h5 id="fs-performance">FS performance</h5>
<table>
<thead>
<tr>
@@ -1155,35 +1141,35 @@
</tr>
</tbody>
</table>
-<p><em>说明</em></p>
+<p><em>Explanation</em></p>
<ul>
-<li>表头&rdquo;（）&ldquo;中数据是数据规模，以边为单位</li>
-<li>表中数据是找到<strong>从第一个顶点出发到达随机选择的100个顶点的最短路径</strong>的时间，单位是s</li>
-<li>例如，HugeGraph使用RocksDB后端在图amazon0601中查找第一个顶点到100个随机顶点的最短路径，总共耗时0.103s</li>
+<li>The data in the header &ldquo;()&rdquo; represents the data scale in terms of edges</li>
+<li>The data in the table is the time it takes to find the shortest path <strong>from the first vertex to 100 randomly selected vertices</strong> in seconds</li>
+<li>For example, HugeGraph using the RocksDB backend to find the shortest path from the first vertex to 100 randomly selected vertices in the amazon0601 graph took a total of 0.103s.</li>
</ul>
-<h6 id="结论-2">结论</h6>
+<h6 id="conclusion-2">Conclusion</h6>
<ul>
-<li>在数据规模小或者顶点关联关系少的场景下，HugeGraph性能优于Neo4j和Titan</li>
-<li>随着数据规模增大且顶点的关联度增高，HugeGraph与Neo4j性能趋近，都远高于Titan</li>
+<li>In scenarios with small data size or few vertex relationships, HugeGraph outperforms Neo4j and Titan.</li>
+<li>As the data size increases and the degree of vertex association increases, the performance of HugeGraph and Neo4j tends to be similar, both far exceeding Titan.</li>
</ul>
-<h5 id="k-neighbor性能">K-neighbor性能</h5>
+<h5 id="k-neighbor-performance">K-neighbor Performance</h5>
<table>
<thead>
<tr>
-<th>顶点</th>
-<th>深度</th>
-<th>一度</th>
-<th>二度</th>
-<th>三度</th>
-<th>四度</th>
-<th>五度</th>
-<th>六度</th>
+<th>Vertex</th>
+<th>Depth</th>
+<th>Degree 1</th>
+<th>Degree 2</th>
+<th>Degree 3</th>
+<th>Degree 4</th>
+<th>Degree 5</th>
+<th>Degree 6</th>
</tr>
</thead>
<tbody>
<tr>
<td>v1</td>
-<td>时间</td>
+<td>Time</td>
<td>0.031s</td>
<td>0.033s</td>
<td>0.048s</td>
@@ -1193,17 +1179,17 @@
</tr>
<tr>
<td>v111</td>
-<td>时间</td>
+<td>Time</td>
<td>0.027s</td>
<td>0.034s</td>
-<td>0.115</td>
+<td>0.115s</td>
<td>1.36s</td>
<td>OOM</td>
<td>&ndash;</td>
</tr>
<tr>
<td>v1111</td>
-<td>时间</td>
+<td>Time</td>
<td>0.039s</td>
<td>0.027s</td>
<td>0.052s</td>
@@ -1213,28 +1199,28 @@
</tr>
</tbody>
</table>
-<p><em>说明</em></p>
+<p><em>Explanation</em></p>
<ul>
-<li>HugeGraph-Server的JVM内存设置为32GB，数据量过大时会出现OOM</li>
+<li>HugeGraph-Server&rsquo;s JVM memory is set to 32GB and may experience OOM when the data is too large.</li>
</ul>
-<h5 id="k-out性能">K-out性能</h5>
+<h5 id="k-out-performance">K-out performance</h5>
<table>
<thead>
<tr>
-<th>顶点</th>
-<th>深度</th>
-<th>一度</th>
-<th>二度</th>
-<th>三度</th>
-<th>四度</th>
-<th>五度</th>
-<th>六度</th>
+<th>Vertex</th>
+<th>Depth</th>
+<th>1st Degree</th>
+<th>2nd Degree</th>
+<th>3rd Degree</th>
+<th>4th Degree</th>
+<th>5th Degree</th>
+<th>6th Degree</th>
</tr>
</thead>
<tbody>
<tr>
<td>v1</td>
-<td>时间</td>
+<td>Time</td>
<td>0.054s</td>
<td>0.057s</td>
<td>0.109s</td>
@@ -1243,7 +1229,7 @@
<td>OOM</td>
</tr>
<tr>
-<td>度</td>
+<td>Degree</td>
<td>10</td>
<td>133</td>
<td>2453</td>
@@ -1254,7 +1240,7 @@
</tr>
<tr>
<td>v111</td>
-<td>时间</td>
+<td>Time</td>
<td>0.032s</td>
<td>0.042s</td>
<td>0.136s</td>
@@ -1263,7 +1249,7 @@
<td>OOM</td>
</tr>
<tr>
-<td>度</td>
+<td>Degree</td>
<td>10</td>
<td>211</td>
<td>4944</td>
@@ -1274,7 +1260,7 @@
</tr>
<tr>
<td>v1111</td>
-<td>时间</td>
+<td>Time</td>
<td>0.039s</td>
<td>0.045s</td>
<td>0.053s</td>
@@ -1283,7 +1269,7 @@
<td>OOM</td>
</tr>
<tr>
-<td>度</td>
+<td>Degree</td>
<td>10</td>
<td>140</td>
<td>2555</td>
@@ -1294,24 +1280,24 @@
</tr>
</tbody>
</table>
-<p><em>说明</em></p>
+<p><em>Explanation</em></p>
<ul>
-<li>HugeGraph-Server的JVM内存设置为32GB，数据量过大时会出现OOM</li>
+<li>The JVM memory of HugeGraph-Server is set to 32GB, and OOM may occur when the data is too large.</li>
</ul>
-<h6 id="结论-3">结论</h6>
+<h6 id="conclusion-3">Conclusion</h6>
<ul>
-<li>FS场景，HugeGraph性能优于Neo4j和Titan</li>
-<li>K-neighbor和K-out场景，HugeGraph能够实现在5度范围内秒级返回结果</li>
+<li>In the FS scenario, HugeGraph outperforms Neo4j and Titan in terms of performance.</li>
+<li>In the K-neighbor and K-out scenarios, HugeGraph can achieve results returned within seconds within 5 degrees.</li>
</ul>
-<h4 id="24-图综合性能测试-cw">2.4 图综合性能测试-CW</h4>
+<h4 id="24-comprehensive-performance-test---cw">2.4 Comprehensive Performance Test - CW</h4>
<table>
<thead>
<tr>
-<th>数据库</th>
-<th>规模1000</th>
-<th>规模5000</th>
-<th>规模10000</th>
-<th>规模20000</th>
+<th>Database</th>
+<th>Size 1000</th>
+<th>Size 5000</th>
+<th>Size 10000</th>
+<th>Size 20000</th>
</tr>
</thead>
<tbody>
@@ -1338,16 +1324,16 @@
</tr>
</tbody>
</table>
-<p><em>说明</em></p>
+<p><em>Explanation</em></p>
<ul>
-<li>&ldquo;规模&quot;以顶点为单位</li>
-<li>表中数据是社区发现完成需要的时间，单位是s，例如HugeGraph使用RocksDB后端在规模10000的数据集，社区聚合不再变化，需要耗时744.780s</li>
-<li>CW测试是CRUD的综合评估</li>
-<li>该测试中HugeGraph跟Titan一样，没有通过client，直接对core操作</li>
+<li>The &ldquo;scale&rdquo; is based on the number of vertices.</li>
+<li>The data in the table is the time required to complete community discovery, in seconds. For example, if HugeGraph uses the RocksDB backend and operates on a dataset of 10,000 vertices, and the community aggregation is no longer changing, it takes 744.780 seconds.</li>
+<li>The CW test is a comprehensive evaluation of CRUD operations.</li>
+<li>In this test, HugeGraph, like Titan, did not use the client and directly operated on the core.</li>
</ul>
-<h5 id="结论-4">结论</h5>
+<h5 id="conclusion-4">Conclusion</h5>
<ul>
-<li>社区聚类算法性能 Neo4j &gt; HugeGraph &gt; Titan</li>
+<li>Performance of community detection algorithm: Neo4j &gt; HugeGraph &gt; Titan</li>
</ul></description></item><item><title>Docs: HugeGraph-Server Quick Start</title><link>/docs/quickstart/hugegraph-server/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>/docs/quickstart/hugegraph-server/</guid><description>
<h3 id="1-hugegraph-server-overview">1 HugeGraph-Server Overview</h3>
<p><code>HugeGraph-Server</code> is the core part of the HugeGraph Project, contains submodules such as Core、Backend、API.</p>
diff --git a/docs/performance/_print/index.html b/docs/performance/_print/index.html
index d6c18fd3..5a912e07 100644
--- a/docs/performance/_print/index.html
+++ b/docs/performance/_print/index.html
@@ -1,6 +1,6 @@
<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1,shrink-to-fit=no"><meta name=generator content="Hugo 0.102.3"><link rel=canonical type=text/html href=/docs/performance/><link rel=alternate type=application/rss+xml href=/docs/performance/index.xml><meta name=robots content="noindex, nofollow"><link rel="shortcut icon" href=/favicons/favicon.ico><link rel=apple-touch-icon href=/favicons/apple-touch-icon-180x [...]
<link rel=stylesheet href=/css/prism.css><script type=application/javascript>var doNotTrack=!1;doNotTrack||(window.ga=window.ga||function(){(ga.q=ga.q||[]).push(arguments)},ga.l=+new Date,ga("create","UA-00000000-0","auto"),ga("send","pageview"))</script><script async src=https://www.google-analytics.com/analytics.js></script></head><body class=td-section><header><nav class="js-navbar-scroll navbar navbar-expand navbar-dark flex-column flex-md-row td-navbar"><a class=navbar-brand href=/> [...]
-<a href=# onclick="return print(),!1">Click here to print</a>.</p><p><a href=/docs/performance/>Return to the regular view of this page</a>.</p></div><h1 class=title>PERFORMANCE</h1><ul><li>1: <a href=#pg-63f6d63db3ee3a5270fc1ca0a0c0e181>HugeGraph BenchMark Performance</a></li><li>2: <a href=#pg-699ebe5daed825049424b7539aad30f9>HugeGraph-API Performance</a></li><ul><li>2.1: <a href=#pg-dbfafc29a5e930415f78f72c47ee67f2>v0.5.6 Stand-alone(RocksDB)</a></li><li>2.2: <a href=#pg-fd5b165e28a07 [...]
+<a href=# onclick="return print(),!1">Click here to print</a>.</p><p><a href=/docs/performance/>Return to the regular view of this page</a>.</p></div><h1 class=title>PERFORMANCE</h1><ul><li>1: <a href=#pg-63f6d63db3ee3a5270fc1ca0a0c0e181>HugeGraph BenchMark Performance</a></li><li>2: <a href=#pg-699ebe5daed825049424b7539aad30f9>HugeGraph-API Performance</a></li><ul><li>2.1: <a href=#pg-dbfafc29a5e930415f78f72c47ee67f2>v0.5.6 Stand-alone(RocksDB)</a></li><li>2.2: <a href=#pg-fd5b165e28a07 [...]
</span></span><span style=display:flex><span> batch_size_fail_threshold_in_kb: 1000
</span></span></code></pre></div><ul><li>HugeGraphServer 与 HugeGremlinServer 与cassandra都在同一机器上启动，server 相关的配置文件除主机和端口有修改外，其余均保持默认。</li></ul><h4 id=13-名词解释>1.3 名词解释</h4><ul><li>Samples – 本次场景中一共完成了多少个线程</li><li>Average – 平均响应时间</li><li>Median – 统计意义上面的响应时间的中值</li><li>90% Line – 所有线程中90%的线程的响应时间都小于xx</li><li>Min – 最小响应时间</li><li>Max – 最大响应时间</li><li>Error – 出错率</li><li>Troughput – 吞吐量Â</li><li>KB/sec – 以流量做衡量的吞吐量</li></ul><p><em>注：时间的单位 [...]
<script src=https://cdn.jsdelivr.net/npm/bootstrap@4.6.1/dist/js/bootstrap.min.js integrity="sha512-UR25UO94eTnCVwjbXozyeVd6ZqpaAE9naiEUBK/A+QDbfSTQFhPGj5lOR6d8tsgbBk84Ggb5A3EkjsOgPRPcKA==" crossorigin=anonymous></script>
diff --git a/docs/performance/hugegraph-benchmark-0.5.6/index.html b/docs/performance/hugegraph-benchmark-0.5.6/index.html
index 1bef0b38..98575182 100644
--- a/docs/performance/hugegraph-benchmark-0.5.6/index.html
+++ b/docs/performance/hugegraph-benchmark-0.5.6/index.html
@@ -1,5 +1,5 @@
-<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1,shrink-to-fit=no"><meta name=generator content="Hugo 0.102.3"><meta name=robots content="index, follow"><link rel="shortcut icon" href=/favicons/favicon.ico><link rel=apple-touch-icon href=/favicons/apple-touch-icon-180x180.png sizes=180x180><link rel=icon type=image/png href=/favicons/favicon-16x16.png sizes=16x16><link rel=icon type=image/png href=/favicons [...]
-1.1 硬件信息
+<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1,shrink-to-fit=no"><meta name=generator content="Hugo 0.102.3"><meta name=robots content="index, follow"><link rel="shortcut icon" href=/favicons/favicon.ico><link rel=apple-touch-icon href=/favicons/apple-touch-icon-180x180.png sizes=180x180><link rel=icon type=image/png href=/favicons/favicon-16x16.png sizes=16x16><link rel=icon type=image/png href=/favicons [...]
+1.1 Hardware information
@@ -18,50 +18,16 @@ Memory
-1.2 软件信息
-1.2.1 …"><meta property="og:title" content="HugeGraph BenchMark Performance"><meta property="og:description" content="1 测试环境 1.1 硬件信息 CPU Memory 网卡 磁盘 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz 128G 10000Mbps 750GB SSD 1.2 软件信息 1.2.1 测试用例 测试使用graphdb-benchmark，一个图数据库测试集。该测试集主要包含4类测试：
-Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
-Single Insertion，单条插入，每个顶点或者每条边立即提交
-Query，主要是图数据库的基本查询操作：
-Find Neighbors，查询所有顶点的邻居 Find Adjacent Nodes，查询所有边的邻接顶点 Find Shortest Path，查询第一个顶点到100个随机顶点的最短路径 Clustering，基于Louvain Method的社区发现算法
-1.2.2 测试数据集 测试使用人造数据和真实数据
-MIW、SIW和QW使用SNAP数据集
-Enron Dataset
-Amazon dataset
-Youtube dataset
-LiveJournal dataset
-CW使用LFR-Benchmark generator生成的人造数据
-本测试用到的数据集规模 名称 vertex数目 edge数目 文件大小 email-enron.txt 36,691 367,661 4MB com-youtube.ungraph.txt 1,157,806 2,987,624 38.7MB amazon0601.txt 403,393 3,387,388 47."><meta property="og:type" content="article"><meta property="og:url" content="/docs/performance/hugegraph-benchmark-0.5.6/"><meta property="article:section" content="docs"><meta property="article:modified_time" content="2022-09-15T12:59:59+08:00"><meta property="og:site_name" content="HugeGraph"><meta itemprop=name content="HugeGrap [...]
-Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
-Single Insertion，单条插入，每个顶点或者每条边立即提交
-Query，主要是图数据库的基本查询操作：
-Find Neighbors，查询所有顶点的邻居 Find Adjacent Nodes，查询所有边的邻接顶点 Find Shortest Path，查询第一个顶点到100个随机顶点的最短路径 Clustering，基于Louvain Method的社区发现算法
-1.2.2 测试数据集 测试使用人造数据和真实数据
-MIW、SIW和QW使用SNAP数据集
-Enron Dataset
-Amazon dataset
-Youtube dataset
-LiveJournal dataset
-CW使用LFR-Benchmark generator生成的人造数据
-本测试用到的数据集规模 名称 vertex数目 edge数目 文件大小 email-enron.txt 36,691 367,661 4MB com-youtube.ungraph.txt 1,157,806 2,987,624 38.7MB amazon0601.txt 403,393 3,387,388 47."><meta itemprop=dateModified content="2022-09-15T12:59:59+08:00"><meta itemprop=wordCount content="364"><meta itemprop=keywords content><meta name=twitter:card content="summary"><meta name=twitter:title content="HugeGraph BenchMark Performance"><meta name=twitter:description content="1 测试环境 1.1 硬件信息 CPU Memory 网卡 磁盘 48 Intel(R) Xeo [...]
-Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交
-Single Insertion，单条插入，每个顶点或者每条边立即提交
-Query，主要是图数据库的基本查询操作：
-Find Neighbors，查询所有顶点的邻居 Find Adjacent Nodes，查询所有边的邻接顶点 Find Shortest Path，查询第一个顶点到100个随机顶点的最短路径 Clustering，基于Louvain Method的社区发现算法
-1.2.2 测试数据集 测试使用人造数据和真实数据
-MIW、SIW和QW使用SNAP数据集
-Enron Dataset
-Amazon dataset
-Youtube dataset
-LiveJournal dataset
-CW使用LFR-Benchmark generator生成的人造数据
-本测试用到的数据集规模 名称 vertex数目 edge数目 文件大小 email-enron.txt 36,691 367,661 4MB com-youtube.ungraph.txt 1,157,806 2,987,624 38.7MB amazon0601.txt 403,393 3,387,388 47."><link rel=preload href=/scss/main.min.ad1b0560bef9c54394313a5bc50d3313d4e56ea590ddc5cfb84a077dfc6fec5e.css as=style><link href=/scss/main.min.ad1b0560bef9c54394313a5bc50d3313d4e56ea590ddc5cfb84a077dfc6fec5e.css rel=stylesheet integrity><script src=https://code.jquery.com/jquery-3.5.1.min.js integrity="sha256-9/aliU8dGd2tb6OSsuzixe [...]
+1.2 …"><meta property="og:title" content="HugeGraph BenchMark Performance"><meta property="og:description" content="1 Test environment 1.1 Hardware information CPU Memory 网卡 磁盘 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz 128G 10000Mbps 750GB SSD 1.2 Software information 1.2.1 Test cases Testing is done using the graphdb-benchmark, a benchmark suite for graph databases. This benchmark suite mainly consists of four types of tests:
+Massive Insertion, which involves batch insertion of vertices and edges, with a certain number of vertices or edges being submitted at once."><meta property="og:type" content="article"><meta property="og:url" content="/docs/performance/hugegraph-benchmark-0.5.6/"><meta property="article:section" content="docs"><meta property="article:modified_time" content="2023-05-14T22:31:02-05:00"><meta property="og:site_name" content="HugeGraph"><meta itemprop=name content="HugeGraph BenchMark Perfor [...]
+Massive Insertion, which involves batch insertion of vertices and edges, with a certain number of vertices or edges being submitted at once."><meta itemprop=dateModified content="2023-05-14T22:31:02-05:00"><meta itemprop=wordCount content="1048"><meta itemprop=keywords content><meta name=twitter:card content="summary"><meta name=twitter:title content="HugeGraph BenchMark Performance"><meta name=twitter:description content="1 Test environment 1.1 Hardware information CPU Memory 网卡 磁盘 48 I [...]
+Massive Insertion, which involves batch insertion of vertices and edges, with a certain number of vertices or edges being submitted at once."><link rel=preload href=/scss/main.min.ad1b0560bef9c54394313a5bc50d3313d4e56ea590ddc5cfb84a077dfc6fec5e.css as=style><link href=/scss/main.min.ad1b0560bef9c54394313a5bc50d3313d4e56ea590ddc5cfb84a077dfc6fec5e.css rel=stylesheet integrity><script src=https://code.jquery.com/jquery-3.5.1.min.js integrity="sha256-9/aliU8dGd2tb6OSsuzixeV4y/faTqgFtohetphb [...]
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-<a id=print href=/docs/performance/_print/><i class="fa fa-print fa-fw"></i> Print entire section</a></div><div class=td-toc><nav id=TableOfContents><ul><li><ul><li><a href=#1-测试环境>1 测试环境</a></li><li><a href=#2-测试结果>2 测试结果</a></li></ul></li></ul></nav></div></aside><main class="col-12 col-md-9 col-xl-8 pl-md-5" role=main><nav aria-label=breadcrumb class=td-breadcrumbs><ol class=breadcrumb><li class=breadcrumb-item><a href=/docs/>Documentation</a></li><li class=breadcrumb-item><a href=/do [...]
+<a id=print href=/docs/performance/_print/><i class="fa fa-print fa-fw"></i> Print entire section</a></div><div class=td-toc><nav id=TableOfContents><ul><li><ul><li><a href=#1-test-environment>1 Test environment</a></li><li><a href=#2-test-results>2 Test results</a></li></ul></li></ul></nav></div></aside><main class="col-12 col-md-9 col-xl-8 pl-md-5" role=main><nav aria-label=breadcrumb class=td-breadcrumbs><ol class=breadcrumb><li class=breadcrumb-item><a href=/docs/>Documentation</a></ [...]
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diff --git a/docs/performance/index.xml b/docs/performance/index.xml
index 396863ef..4ba887f9 100644
--- a/docs/performance/index.xml
+++ b/docs/performance/index.xml
@@ -1,6 +1,6 @@
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>HugeGraph – PERFORMANCE</title><link>/docs/performance/</link><description>Recent content in PERFORMANCE on HugeGraph</description><generator>Hugo -- gohugo.io</generator><atom:link href="/docs/performance/index.xml" rel="self" type="application/rss+xml"/><item><title>Docs: HugeGraph BenchMark Performance</title><link>/docs/performance/hugegraph-benchmark-0.5.6/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pub [...]
-<h3 id="1-测试环境">1 测试环境</h3>
-<h4 id="11-硬件信息">1.1 硬件信息</h4>
+<h3 id="1-test-environment">1 Test environment</h3>
+<h4 id="11-hardware-information">1.1 Hardware information</h4>
<table>
<thead>
<tr>
@@ -19,60 +19,45 @@
</tr>
</tbody>
</table>
-<h4 id="12-软件信息">1.2 软件信息</h4>
-<h5 id="121-测试用例">1.2.1 测试用例</h5>
-<p>测试使用<a href="https://github.com/socialsensor/graphdb-benchmarks">graphdb-benchmark</a>，一个图数据库测试集。该测试集主要包含4类测试：</p>
+<h4 id="12-software-information">1.2 Software information</h4>
+<h5 id="121-test-cases">1.2.1 Test cases</h5>
+<p>Testing is done using the <a href="https://github.com/socialsensor/graphdb-benchmarks">graphdb-benchmark</a>, a benchmark suite for graph databases. This benchmark suite mainly consists of four types of tests:</p>
<ul>
-<li>
-<p>Massive Insertion，批量插入顶点和边，一定数量的顶点或边一次性提交</p>
-</li>
-<li>
-<p>Single Insertion，单条插入，每个顶点或者每条边立即提交</p>
-</li>
-<li>
-<p>Query，主要是图数据库的基本查询操作：</p>
+<li>Massive Insertion, which involves batch insertion of vertices and edges, with a certain number of vertices or edges being submitted at once.</li>
+<li>Single Insertion, which involves the immediate insertion of each vertex or edge, one at a time.</li>
+<li>Query, which mainly includes the basic query operations of the graph database:
<ul>
-<li>Find Neighbors，查询所有顶点的邻居</li>
-<li>Find Adjacent Nodes，查询所有边的邻接顶点</li>
-<li>Find Shortest Path，查询第一个顶点到100个随机顶点的最短路径</li>
+<li>Find Neighbors, which queries the neighbors of all vertices.</li>
+<li>Find Adjacent Nodes, which queries the adjacent vertices of all edges.</li>
+<li>Find Shortest Path, which queries the shortest path from the first vertex to 100 random vertices.</li>
</ul>
</li>
-<li>
-<p>Clustering，基于Louvain Method的社区发现算法</p>
-</li>
+<li>Clustering, which is a community detection algorithm based on the Louvain Method.</li>
</ul>
-<h5 id="122-测试数据集">1.2.2 测试数据集</h5>
-<p>测试使用人造数据和真实数据</p>
+<h5 id="122-test-dataset">1.2.2 Test dataset</h5>
+<p>Tests are conducted using both synthetic and real data.</p>
<ul>
<li>
-<p>MIW、SIW和QW使用SNAP数据集</p>
+<p>MIW, SIW, and QW use SNAP datasets:</p>
<ul>
-<li>
-<p><a href="http://snap.stanford.edu/data/email-Enron.html">Enron Dataset</a></p>
-</li>
-<li>
-<p><a href="http://snap.stanford.edu/data/amazon0601.html">Amazon dataset</a></p>
-</li>
-<li>
-<p><a href="http://snap.stanford.edu/data/com-Youtube.html">Youtube dataset</a></p>
-</li>
-<li>
-<p><a href="http://snap.stanford.edu/data/com-LiveJournal.html">LiveJournal dataset</a></p>
-</li>
+<li><a href="http://snap.stanford.edu/data/email-Enron.html">Enron Dataset</a></li>
+<li><a href="http://snap.stanford.edu/data/amazon0601.html">Amazon dataset</a></li>
+<li><a href="http://snap.stanford.edu/data/com-Youtube.html">Youtube dataset</a></li>
+<li><a href="http://snap.stanford.edu/data/com-LiveJournal.html">LiveJournal dataset</a></li>
</ul>
</li>
<li>
-<p>CW使用<a href="https://sites.google.com/site/andrealancichinetti/files">LFR-Benchmark generator</a>生成的人造数据</p>
+<p>CW uses synthetic data generated by the <a href="https://sites.google.com/site/andrealancichinetti/files">LFR-Benchmark generator</a>.</p>
</li>
</ul>
-<h6 id="本测试用到的数据集规模">本测试用到的数据集规模</h6>
+<p>The size of the datasets used in this test are not mentioned.</p>
<table>
<thead>
<tr>
-<th>名称</th>
-<th>vertex数目</th>
-<th>edge数目</th>
-<th>文件大小</th>
+<th>Name</th>
+<th>Number of Vertices</th>
+<th>Number of Edges</th>
+<th>File Size</th>
</tr>
</thead>
<tbody>
@@ -102,29 +87,29 @@
</tr>
</tbody>
</table>
-<h4 id="13-服务配置">1.3 服务配置</h4>
+<h4 id="13-service-configuration">1.3 Service configuration</h4>
<ul>
<li>
-<p>HugeGraph版本：0.5.6，RestServer和Gremlin Server和backends都在同一台服务器上</p>
+<p>HugeGraph version: 0.5.6, RestServer and Gremlin Server and backends are on the same server</p>
<ul>
-<li>RocksDB版本：rocksdbjni-5.8.6</li>
+<li>RocksDB version: rocksdbjni-5.8.6</li>
</ul>
</li>
<li>
-<p>Titan版本：0.5.4, 使用thrift+Cassandra模式</p>
+<p>Titan version: 0.5.4, using thrift+Cassandra mode</p>
<ul>
-<li>Cassandra版本：cassandra-3.10，commit-log 和 data 共用SSD</li>
+<li>Cassandra version: cassandra-3.10, commit-log and data use SSD together</li>
</ul>
</li>
<li>
-<p>Neo4j版本：2.0.1</p>
+<p>Neo4j version: 2.0.1</p>
</li>
</ul>
<blockquote>
-<p>graphdb-benchmark适配的Titan版本为0.5.4</p>
+<p>The Titan version adapted by graphdb-benchmark is 0.5.4.</p>
</blockquote>
-<h3 id="2-测试结果">2 测试结果</h3>
-<h4 id="21-batch插入性能">2.1 Batch插入性能</h4>
+<h3 id="2-test-results">2 Test results</h3>
+<h4 id="21-batch-insertion-performance">2.1 Batch insertion performance</h4>
<table>
<thead>
<tr>
@@ -159,23 +144,23 @@
</tr>
</tbody>
</table>
-<p><em>说明</em></p>
+<p><em>Instructions</em></p>
<ul>
-<li>表头&quot;（）&ldquo;中数据是数据规模，以边为单位</li>
-<li>表中数据是批量插入的时间，单位是s</li>
-<li>例如，HugeGraph使用RocksDB插入amazon0601数据集的300w条边，花费5.711s</li>
+<li>The data scale is in the table header in terms of edges</li>
+<li>The data in the table is the time for batch insertion, in seconds</li>
+<li>For example, HugeGraph(RocksDB) spent 5.711 seconds to insert 3 million edges of the amazon0601 dataset.</li>
</ul>
-<h5 id="结论">结论</h5>
+<h5 id="conclusion">Conclusion</h5>
<ul>
-<li>批量插入性能 HugeGraph(RocksDB) &gt; Neo4j &gt; Titan(thrift+Cassandra)</li>
+<li>The performance of batch insertion: HugeGraph(RocksDB) &gt; Neo4j &gt; Titan(thrift+Cassandra)</li>
</ul>
-<h4 id="22-遍历性能">2.2 遍历性能</h4>
-<h5 id="221-术语说明">2.2.1 术语说明</h5>
+<h4 id="22-traversal-performance">2.2 Traversal performance</h4>
+<h5 id="221-explanation-of-terms">2.2.1 Explanation of terms</h5>
<ul>
-<li>FN(Find Neighbor), 遍历所有vertex, 根据vertex查邻接edge, 通过edge和vertex查other vertex</li>
-<li>FA(Find Adjacent), 遍历所有edge，根据edge获得source vertex和target vertex</li>
+<li>FN(Find Neighbor): Traverse all vertices, find the adjacent edges based on each vertex, and use the edges and vertices to find the other vertices adjacent to the original vertex.</li>
+<li>FA(Find Adjacent): Traverse all edges, get the source vertex and target vertex based on each edge.</li>
</ul>
-<h5 id="222-fn性能">2.2.2 FN性能</h5>
+<h5 id="222-fn-performance">2.2.2 FN performance</h5>
<table>
<thead>
<tr>
@@ -210,11 +195,11 @@
</tr>
</tbody>
</table>
-<p><em>说明</em></p>
+<p><em>Instructions</em></p>
<ul>
-<li>表头&rdquo;（）&ldquo;中数据是数据规模，以顶点为单位</li>
-<li>表中数据是遍历顶点花费的时间，单位是s</li>
-<li>例如，HugeGraph使用RocksDB后端遍历amazon0601的所有顶点，并查找邻接边和另一顶点，总共耗时45.118s</li>
+<li>The data in the table header &ldquo;( )&rdquo; represents the data scale, in terms of vertices.</li>
+<li>The data in the table represents the time spent traversing vertices, in seconds.</li>
+<li>For example, HugeGraph uses the RocksDB backend to traverse all vertices in amazon0601, and search for adjacent edges and another vertex, which takes a total of 45.118 seconds.</li>
</ul>
<h5 id="223-fa性能">2.2.3 FA性能</h5>
<table>
@@ -251,24 +236,25 @@
</tr>
</tbody>
</table>
-<p><em>说明</em></p>
+<p><em>Explanation</em></p>
<ul>
-<li>表头&rdquo;（）&ldquo;中数据是数据规模，以边为单位</li>
-<li>表中数据是遍历边花费的时间，单位是s</li>
-<li>例如，HugeGraph使用RocksDB后端遍历amazon0601的所有边，并查询每条边的两个顶点，总共耗时10.764s</li>
+<li>The data size in the header &ldquo;( )&rdquo; is based on the number of vertices.</li>
+<li>The data in the table is the time it takes to traverse the vertices, in seconds.</li>
+<li>For example, HugeGraph with RocksDB backend traverses all vertices in the amazon0601 dataset, and looks up adjacent edges and other vertices, taking a total of 45.118 seconds.</li>
+<li></li>
</ul>
-<h6 id="结论-1">结论</h6>
+<h6 id="conclusion-1">Conclusion</h6>
<ul>
-<li>遍历性能 Neo4j &gt; HugeGraph(RocksDB) &gt; Titan(thrift+Cassandra)</li>
+<li>Traversal performance: Neo4j &gt; HugeGraph(RocksDB) &gt; Titan(thrift+Cassandra)</li>
</ul>
-<h4 id="23-hugegraph-图常用分析方法性能">2.3 HugeGraph-图常用分析方法性能</h4>
-<h5 id="术语说明">术语说明</h5>
+<h4 id="23-performance-of-common-graph-analysis-methods-in-hugegraph">2.3 Performance of Common Graph Analysis Methods in HugeGraph</h4>
+<h5 id="terminology-explanation">Terminology Explanation</h5>
<ul>
-<li>FS(Find Shortest Path), 寻找最短路径</li>
-<li>K-neighbor，从起始vertex出发，通过K跳边能够到达的所有顶点, 包括1, 2, 3&hellip;(K-1), K跳边可达vertex</li>
-<li>K-out, 从起始vertex出发，恰好经过K跳out边能够到达的顶点</li>
+<li>FS (Find Shortest Path): finding the shortest path between two vertices</li>
+<li>K-neighbor: all vertices that can be reached by traversing K hops (including 1, 2, 3&hellip;(K-1) hops) from the starting vertex</li>
+<li>K-out: all vertices that can be reached by traversing exactly K out-edges from the starting vertex.</li>
</ul>
-<h5 id="fs性能">FS性能</h5>
+<h5 id="fs-performance">FS performance</h5>
<table>
<thead>
<tr>
@@ -303,35 +289,35 @@
</tr>
</tbody>
</table>
-<p><em>说明</em></p>
+<p><em>Explanation</em></p>
<ul>
-<li>表头&rdquo;（）&ldquo;中数据是数据规模，以边为单位</li>
-<li>表中数据是找到<strong>从第一个顶点出发到达随机选择的100个顶点的最短路径</strong>的时间，单位是s</li>
-<li>例如，HugeGraph使用RocksDB后端在图amazon0601中查找第一个顶点到100个随机顶点的最短路径，总共耗时0.103s</li>
+<li>The data in the header &ldquo;()&rdquo; represents the data scale in terms of edges</li>
+<li>The data in the table is the time it takes to find the shortest path <strong>from the first vertex to 100 randomly selected vertices</strong> in seconds</li>
+<li>For example, HugeGraph using the RocksDB backend to find the shortest path from the first vertex to 100 randomly selected vertices in the amazon0601 graph took a total of 0.103s.</li>
</ul>
-<h6 id="结论-2">结论</h6>
+<h6 id="conclusion-2">Conclusion</h6>
<ul>
-<li>在数据规模小或者顶点关联关系少的场景下，HugeGraph性能优于Neo4j和Titan</li>
-<li>随着数据规模增大且顶点的关联度增高，HugeGraph与Neo4j性能趋近，都远高于Titan</li>
+<li>In scenarios with small data size or few vertex relationships, HugeGraph outperforms Neo4j and Titan.</li>
+<li>As the data size increases and the degree of vertex association increases, the performance of HugeGraph and Neo4j tends to be similar, both far exceeding Titan.</li>
</ul>
-<h5 id="k-neighbor性能">K-neighbor性能</h5>
+<h5 id="k-neighbor-performance">K-neighbor Performance</h5>
<table>
<thead>
<tr>
-<th>顶点</th>
-<th>深度</th>
-<th>一度</th>
-<th>二度</th>
-<th>三度</th>
-<th>四度</th>
-<th>五度</th>
-<th>六度</th>
+<th>Vertex</th>
+<th>Depth</th>
+<th>Degree 1</th>
+<th>Degree 2</th>
+<th>Degree 3</th>
+<th>Degree 4</th>
+<th>Degree 5</th>
+<th>Degree 6</th>
</tr>
</thead>
<tbody>
<tr>
<td>v1</td>
-<td>时间</td>
+<td>Time</td>
<td>0.031s</td>
<td>0.033s</td>
<td>0.048s</td>
@@ -341,17 +327,17 @@
</tr>
<tr>
<td>v111</td>
-<td>时间</td>
+<td>Time</td>
<td>0.027s</td>
<td>0.034s</td>
-<td>0.115</td>
+<td>0.115s</td>
<td>1.36s</td>
<td>OOM</td>
<td>&ndash;</td>
</tr>
<tr>
<td>v1111</td>
-<td>时间</td>
+<td>Time</td>
<td>0.039s</td>
<td>0.027s</td>
<td>0.052s</td>
@@ -361,28 +347,28 @@
</tr>
</tbody>
</table>
-<p><em>说明</em></p>
+<p><em>Explanation</em></p>
<ul>
-<li>HugeGraph-Server的JVM内存设置为32GB，数据量过大时会出现OOM</li>
+<li>HugeGraph-Server&rsquo;s JVM memory is set to 32GB and may experience OOM when the data is too large.</li>
</ul>
-<h5 id="k-out性能">K-out性能</h5>
+<h5 id="k-out-performance">K-out performance</h5>
<table>
<thead>
<tr>
-<th>顶点</th>
-<th>深度</th>
-<th>一度</th>
-<th>二度</th>
-<th>三度</th>
-<th>四度</th>
-<th>五度</th>
-<th>六度</th>
+<th>Vertex</th>
+<th>Depth</th>
+<th>1st Degree</th>
+<th>2nd Degree</th>
+<th>3rd Degree</th>
+<th>4th Degree</th>
+<th>5th Degree</th>
+<th>6th Degree</th>
</tr>
</thead>
<tbody>
<tr>
<td>v1</td>
-<td>时间</td>
+<td>Time</td>
<td>0.054s</td>
<td>0.057s</td>
<td>0.109s</td>
@@ -391,7 +377,7 @@
<td>OOM</td>
</tr>
<tr>
-<td>度</td>
+<td>Degree</td>
<td>10</td>
<td>133</td>
<td>2453</td>
@@ -402,7 +388,7 @@
</tr>
<tr>
<td>v111</td>
-<td>时间</td>
+<td>Time</td>
<td>0.032s</td>
<td>0.042s</td>
<td>0.136s</td>
@@ -411,7 +397,7 @@
<td>OOM</td>
</tr>
<tr>
-<td>度</td>
+<td>Degree</td>
<td>10</td>
<td>211</td>
<td>4944</td>
@@ -422,7 +408,7 @@
</tr>
<tr>
<td>v1111</td>
-<td>时间</td>
+<td>Time</td>
<td>0.039s</td>
<td>0.045s</td>
<td>0.053s</td>
@@ -431,7 +417,7 @@
<td>OOM</td>
</tr>
<tr>
-<td>度</td>
+<td>Degree</td>
<td>10</td>
<td>140</td>
<td>2555</td>
@@ -442,24 +428,24 @@
</tr>
</tbody>
</table>
-<p><em>说明</em></p>
+<p><em>Explanation</em></p>
<ul>
-<li>HugeGraph-Server的JVM内存设置为32GB，数据量过大时会出现OOM</li>
+<li>The JVM memory of HugeGraph-Server is set to 32GB, and OOM may occur when the data is too large.</li>
</ul>
-<h6 id="结论-3">结论</h6>
+<h6 id="conclusion-3">Conclusion</h6>
<ul>
-<li>FS场景，HugeGraph性能优于Neo4j和Titan</li>
-<li>K-neighbor和K-out场景，HugeGraph能够实现在5度范围内秒级返回结果</li>
+<li>In the FS scenario, HugeGraph outperforms Neo4j and Titan in terms of performance.</li>
+<li>In the K-neighbor and K-out scenarios, HugeGraph can achieve results returned within seconds within 5 degrees.</li>
</ul>
-<h4 id="24-图综合性能测试-cw">2.4 图综合性能测试-CW</h4>
+<h4 id="24-comprehensive-performance-test---cw">2.4 Comprehensive Performance Test - CW</h4>
<table>
<thead>
<tr>
-<th>数据库</th>
-<th>规模1000</th>
-<th>规模5000</th>
-<th>规模10000</th>
-<th>规模20000</th>
+<th>Database</th>
+<th>Size 1000</th>
+<th>Size 5000</th>
+<th>Size 10000</th>
+<th>Size 20000</th>
</tr>
</thead>
<tbody>
@@ -486,16 +472,16 @@
</tr>
</tbody>
</table>
-<p><em>说明</em></p>
+<p><em>Explanation</em></p>
<ul>
-<li>&ldquo;规模&quot;以顶点为单位</li>
-<li>表中数据是社区发现完成需要的时间，单位是s，例如HugeGraph使用RocksDB后端在规模10000的数据集，社区聚合不再变化，需要耗时744.780s</li>
-<li>CW测试是CRUD的综合评估</li>
-<li>该测试中HugeGraph跟Titan一样，没有通过client，直接对core操作</li>
+<li>The &ldquo;scale&rdquo; is based on the number of vertices.</li>
+<li>The data in the table is the time required to complete community discovery, in seconds. For example, if HugeGraph uses the RocksDB backend and operates on a dataset of 10,000 vertices, and the community aggregation is no longer changing, it takes 744.780 seconds.</li>
+<li>The CW test is a comprehensive evaluation of CRUD operations.</li>
+<li>In this test, HugeGraph, like Titan, did not use the client and directly operated on the core.</li>
</ul>
-<h5 id="结论-4">结论</h5>
+<h5 id="conclusion-4">Conclusion</h5>
<ul>
-<li>社区聚类算法性能 Neo4j &gt; HugeGraph &gt; Titan</li>
+<li>Performance of community detection algorithm: Neo4j &gt; HugeGraph &gt; Titan</li>
</ul></description></item><item><title>Docs: HugeGraph-API Performance</title><link>/docs/performance/api-preformance/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>/docs/performance/api-preformance/</guid><description>
<p>The HugeGraph API performance test mainly tests HugeGraph-Server&rsquo;s ability to concurrently process RESTful API requests, including:</p>
<ul>
diff --git a/en/index.html b/en/index.html
index 2b641798..017c4d50 100644
--- a/en/index.html
+++ b/en/index.html
@@ -1 +1 @@
-<!doctype html><html lang=en><head><title>/</title><link rel=canonical href=/><meta name=robots content="noindex"><meta charset=utf-8><meta http-equiv=refresh content="0; url=/"></head></html>
\ No newline at end of file
+<!doctype html><html lang=cn><head><title>/</title><link rel=canonical href=/><meta name=robots content="noindex"><meta charset=utf-8><meta http-equiv=refresh content="0; url=/"></head></html>
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diff --git a/en/sitemap.xml b/en/sitemap.xml
index 3ff705a5..8da9671a 100644
--- a/en/sitemap.xml
+++ b/en/sitemap.xml
@@ -1 +1 @@
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diff --git a/sitemap.xml b/sitemap.xml
index b5b50c11..340ffb4d 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -1 +1 @@
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\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><sitemapindex xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"><sitemap><loc>/cn/sitemap.xml</loc><lastmod>2023-05-14T22:39:27+08:00</lastmod></sitemap><sitemap><loc>/en/sitemap.xml</loc><lastmod>2023-05-14T22:31:02-05:00</lastmod></sitemap></sitemapindex>
\ No newline at end of file