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new 25095bbf Update hugegraph-benchmark-0.5.6.md (#226)
25095bbf is described below
commit 25095bbfc5378a0aa304c105845ea2eb21ce7812
Author: imbajin <[email protected]>
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 [...]
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type=application/javascript>var
doNotTrack=!1;doNotTrack||(window.ga=window.ga||function(){(ga.q=ga.q||[]).push(arguments)},ga.l=+new
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class=td-section><header><nav class="js-navbar-scroll navbar navbar-expand
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[...]
-<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 [...]
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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 @@
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name=viewport
content="width=device-width,initial-scale=1,shrink-to-fit=no"><meta
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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
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-Single Insertion，单条插入，每个顶点或者每条边立即提交
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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
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-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
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-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
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-MIW、SIW和QW使用SNAP数据集
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-Youtube dataset
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content="364"><meta itemprop=keywords content><meta name=twitter:card
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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：
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-Enron Dataset
-Amazon dataset
-Youtube dataset
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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
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+Massive Insertion, which involves batch insertion of vertices and edges, with
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a certain number of vertices or edges being submitted at once."><link
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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>
\ No newline at end of file
diff --git a/en/sitemap.xml b/en/sitemap.xml
index 3ff705a5..8da9671a 100644
--- a/en/sitemap.xml
+++ b/en/sitemap.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><urlset
xmlns="http://www.sitemaps.org/schemas/sitemap/0.9";
xmlns:xhtml="http://www.w3.org/1999/xhtml";><url><loc>/docs/guides/architectural/</loc><lastmod>2023-05-12T23:46:05-05:00</lastmod><xhtml:link
rel="alternate" hreflang="cn"
href="/cn/docs/guides/architectural/"/><xhtml:link rel="alternate"
hreflang="en"
href="/docs/guides/architectural/"/></url><url><loc>/docs/config/config-guide/</loc><lastmod>2023-05-10T12:08:15+08:00</last
[...]
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><urlset
xmlns="http://www.sitemaps.org/schemas/sitemap/0.9";
xmlns:xhtml="http://www.w3.org/1999/xhtml";><url><loc>/docs/guides/architectural/</loc><lastmod>2023-05-12T23:46:05-05:00</lastmod><xhtml:link
rel="alternate" hreflang="cn"
href="/cn/docs/guides/architectural/"/><xhtml:link rel="alternate"
hreflang="en"
href="/docs/guides/architectural/"/></url><url><loc>/docs/config/config-guide/</loc><lastmod>2023-05-10T12:08:15+08:00</last
[...]
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index b5b50c11..340ffb4d 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><sitemapindex
xmlns="http://www.sitemaps.org/schemas/sitemap/0.9";><sitemap><loc>/en/sitemap.xml</loc><lastmod>2023-05-14T22:30:51-05:00</lastmod></sitemap><sitemap><loc>/cn/sitemap.xml</loc><lastmod>2023-05-14T22:39:27+08:00</lastmod></sitemap></sitemapindex>
\ 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
