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new 990ff15a Update hugegraph-api-0.4.4.md (#230)
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commit 990ff15a6bb6e971099b47ac4ea7280c72c93cf5
Author: imbajin <[email protected]>
AuthorDate: Wed May 17 04:30:21 2023 +0000
Update hugegraph-api-0.4.4.md (#230)
Completed translation to English. 3f0c792d60fad687c14df89437ec9dd17e949d1f
---
docs/_print/index.html | 2 +-
docs/index.xml | 125 ++++++++++-----------
docs/performance/_print/index.html | 2 +-
docs/performance/api-preformance/_print/index.html | 2 +-
.../api-preformance/hugegraph-api-0.4.4/index.html | 28 ++---
docs/performance/api-preformance/index.xml | 125 ++++++++++-----------
en/sitemap.xml | 2 +-
sitemap.xml | 2 +-
8 files changed, 138 insertions(+), 150 deletions(-)
diff --git a/docs/_print/index.html b/docs/_print/index.html
index f01f5afe..d1ad9954 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 61ea3a18..ffb57360 100644
--- a/docs/index.xml
+++ b/docs/index.xml
@@ -7456,16 +7456,16 @@ Implement a class inherited from
<code>Formatter</code> in the directory,
</span></span></code></pre></div><h6
id="8-graph-migration">8. Graph Migration</h6>
<div class="highlight"><pre tabindex="0"
style="background-color:#f8f8f8;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code
class="language-bash" data-lang="bash"><span
style="display:flex;"><span>./bin/hugegraph --url http://127.0.0.1:8080
--graph hugegraph migrate --target-url http://127.0.0.1:8090 --target-graph
hugegraph
</span></span></code></pre></div></description></item><item><title>Docs:
v0.4.4</title><link>/docs/performance/api-preformance/hugegraph-api-0.4.4/</link><pubDate>Mon,
01 Jan 0001 00:00:00
+0000</pubDate><guid>/docs/performance/api-preformance/hugegraph-api-0.4.4/</guid><description>
-<h3 id="1-测试环境">1 测试环境</h3>
-<p>被压机器信息</p>
+<h3 id="1-test-environment">1 Test environment</h3>
+<p>Target Machine Information</p>
<table>
<thead>
<tr>
<th>机器编号</th>
<th>CPU</th>
<th>Memory</th>
-<th>网卡</th>
-<th>磁盘</th>
+<th>NIC (Network Interface Card)</th>
+<th>Disk</th>
</tr>
</thead>
<tbody>
@@ -7486,100 +7486,99 @@ Implement a class inherited from
<code>Formatter</code> in the directory,
</tbody>
</table>
<ul>
-<li>起压力机器信息：与编号 1 机器同配置</li>
-<li>测试工具：apache-Jmeter-2.5.1</li>
+<li><strong>Pressure testing machine information:</strong> Configured
the same as machine number 1.</li>
+<li><strong>Testing tool:</strong> Apache JMeter 2.5.1.</li>
</ul>
-<p>注：起压机器和被压机器在同一机房</p>
-<h3 id="2-测试说明">2 测试说明</h3>
-<h4 id="21-名词定义时间的单位均为ms">2.1 名词定义（时间的单位均为ms）</h4>
+<p>Note: The pressure testing machine and the machine being tested are in
the same room.</p>
+<h3 id="2-test-description">2 Test Description</h3>
+<h4 id="21-definition-of-terms-the-unit-of-time-is-ms">2.1 Definition of
terms (the unit of time is ms)</h4>
<ul>
-<li>Samples &ndash; 本次场景中一共完成了多少个线程</li>
-<li>Average &ndash; 平均响应时间</li>
-<li>Median &ndash; 统计意义上面的响应时间的中值</li>
-<li>90% Line &ndash; 所有线程中90%的线程的响应时间都小于xx</li>
-<li>Min &ndash; 最小响应时间</li>
-<li>Max &ndash; 最大响应时间</li>
-<li>Error &ndash; 出错率</li>
-<li>Throughput &ndash; 吞吐量</li>
-<li>KB/sec &ndash; 以流量做衡量的吞吐量</li>
+<li>Samples &ndash; The total number of threads completed in this
scenario.</li>
+<li>Average &ndash; The average response time.</li>
+<li>Median &ndash; The median response time in terms of statistical
significance.</li>
+<li>90% Line &ndash; The response time of 90% of all threads is less
than xx.</li>
+<li>Min &ndash; The minimum response time.</li>
+<li>Max &ndash; The maximum response time.</li>
+<li>Error &ndash; The error rate.</li>
+<li>Throughput &ndash; The throughput.</li>
+<li>KB/sec &ndash; The throughput measured in terms of traffic.</li>
</ul>
-<h4 id="22-底层存储">2.2 底层存储</h4>
-<p>后端存储使用RocksDB，HugeGraph与RocksDB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。</p>
-<h3 id="3-性能结果总结">3 性能结果总结</h3>
+<h4 id="22-underlying-storage">2.2 Underlying storage</h4>
+<p>RocksDB is used for backend storage, HugeGraph and RocksDB are both
started on the same machine, and the configuration files related to the server
remain the default except for the modification of the host and port.</p>
+<h3 id="3-summary-of-performance-results">3 Summary of performance
results</h3>
<ol>
-<li>HugeGraph每秒能够处理的请求数目上限是7000</li>
-<li>批量插入速度远大于单条插入，在服务器上测试结果达到22w edges/s，37w vertices/s</li>
-<li>后端是RocksDB，增大CPU数目和内存大小可以增大批量插入的性能。CPU和内存扩大一倍，性能增加45%-60%</li>
-<li>批量插入场景，使用SSD替代HDD，性能提升较小，只有3%-5%</li>
+<li>The upper limit of the number of requests HugeGraph can handle per
second is 7000</li>
+<li>The speed of batch insertion is much higher than that of single
insertion, and the test results on the server reach 22w edges/s, 37w
vertices/s</li>
+<li>The backend is RocksDB, and increasing the number of CPUs and memory
size can improve the performance of batch inserts. Doubling the CPU and memory
size can increase performance by 45% to 60%.</li>
+<li>In the batch insertion scenario, using SSD instead of HDD, the
performance improvement is small, only 3%-5%</li>
</ol>
-<h3 id="4-测试结果及分析">4 测试结果及分析</h3>
-<h4 id="41-batch插入">4.1 batch插入</h4>
-<h5 id="411-压力上限测试">4.1.1 压力上限测试</h5>
-<h6 id="测试方法">测试方法</h6>
-<p>不断提升并发量，测试server仍能正常提供服务的压力上限</p>
-<h6 id="压力参数">压力参数</h6>
-<p>持续时间：5min</p>
-<h6
id="顶点和边的最大插入速度高性能服务器使用ssd存储rocksdb数据">顶点和边的最大插入速度（高性能服务器，使用SSD存储RocksDB数据）：</h6>
+<h3 id="4-test-results-and-analysis">4 Test results and analysis</h3>
+<h4 id="41-batch-insertion">4.1 Batch insertion</h4>
+<h5 id="411-maximum-pressure-test">4.1.1 Maximum Pressure Test</h5>
+<h6 id="test-methods">Test Methods</h6>
+<p>Continuously increase the concurrency level and test the upper limit of
the server&rsquo;s ability to provide services normally.</p>
+<h6 id="pressure-parameters">Pressure Parameters</h6>
+<p>Duration: 5 minutes</p>
+<h6
id="maximum-insertion-speed-of-vertices-and-edges-high-performance-server-with-ssd-storage-for-rocksdb-data">Maximum
Insertion Speed of Vertices and Edges (High-performance server with SSD
storage for RocksDB data):</h6>
<center>
<img src="/docs/images/API-perf/v0.4.4/best.png" alt="image">
</center>
-<h6 id="结论">结论：</h6>
+<h6 id="conclusion">Conclusion:</h6>
<ul>
-<li>并发1000，边的吞吐量是是451，每秒可处理的数据：451*500条=225500/s</li>
-<li>并发2000，顶点的吞吐量是1842.4，每秒可处理的数据：1842.4*200=368480/s</li>
+<li>With a concurrency of 1000, the edge throughput is 451, which can
process 225,500 data per second: 451 * 500 = 225,500/s.</li>
+<li>With a concurrency of 2000, the vertex throughput is 1842.4, which can
process 368,480 data per second: 1842.4 * 200 = 368,480/s.</li>
</ul>
-<p><strong>1.
CPU和内存对插入性能的影响（服务器都使用HDD存储RocksDB数据，批量插入）</strong></p>
+<p><strong>1. The Impact of CPU and Memory on Insertion Performance
(Servers Using HDD Storage for RocksDB Data, Batch Insertion)</strong></p>
<center>
<img src="/docs/images/API-perf/v0.4.4/cpu-memory.png" alt="image">
</center>
-<h6 id="结论-1">结论：</h6>
+<h6 id="conclusion-1">Conclusion:</h6>
<ul>
-<li>同样使用HDD硬盘，CPU和内存增加了1倍</li>
-<li>边：吞吐量从268提升至426，性能提升了约60%</li>
-<li>顶点：吞吐量从1263.8提升至1842.4，性能提升了约45%</li>
+<li>With the same HDD disk, doubling the CPU and memory size increases edge
throughput from 268 to 426, which improves performance by about 60%.</li>
+<li>With the same HDD disk, doubling the CPU and memory size increases
vertex throughput from 1263.8 to 1842.4, which improves performance by about
45%.</li>
</ul>
-<p><strong>2. SSD和HDD对插入性能的影响（高性能服务器，批量插入）</strong></p>
+<p><strong>2. The Impact of SSD and HDD on Insertion Performance
(High-performance Servers, Batch Insertion)</strong></p>
<center>
<img src="/docs/images/API-perf/v0.4.4/ssd.png" alt="image">
</center>
-<h6 id="结论-2">结论：</h6>
+<h6 id="conclusion-2">Conclusion:</h6>
<ul>
-<li>边：使用SSD吞吐量451.7,使用HDD吞吐量426.6，性能提升5%</li>
-<li>顶点：使用SSD吞吐量1842.4，使用HDD吞吐量1794，性能提升约3%</li>
+<li>For edge insertion, using SSD yields a throughput of 451.7, while using
HDD yields a throughput of 426.6, which results in a 5% performance
improvement.</li>
+<li>For vertex insertion, using SSD yields a throughput of 1842.4, while
using HDD yields a throughput of 1794, which results in a performance
improvement of about 3%.</li>
</ul>
-<p><strong>3. 不同并发线程数对插入性能的影响（普通服务器，使用HDD存储RocksDB数据）</strong></p>
+<p><strong>3. The Impact of Different Concurrent Thread Numbers on
Insertion Performance (Ordinary Servers, HDD Storage for RocksDB
Data)</strong></p>
<center>
<img src="/docs/images/API-perf/v0.4.4/threads-batch.png" alt="image">
</center>
-<h6 id="结论-3">结论：</h6>
+<h5 id="conclusion-3">Conclusion:</h5>
<ul>
-<li>顶点：1000并发，响应时间7ms和1500并发响应时间1028ms差距悬殊，且吞吐量一直保持在1300左右，因此拐点数据应该在1300
，且并发1300时，响应时间已达到22ms，在可控范围内，相比HugeGraph
0.2（1000并发：平均响应时间8959ms），处理能力出现质的飞跃；</li>
-<li>边：从1000并发到2000并发，处理时间过长，超过3s，且吞吐量几乎在270左右浮动，因此继续增大并发线程数吞吐量不会再大幅增长，270
是一个拐点，跟HugeGraph 0.2版本（1000并发：平均响应时间31849ms）相比较，处理能力提升非常明显；</li>
+<li>For vertices, at 1000 concurrency, the response time is 7ms and at 1500
concurrency, the response time is 1028ms. The throughput remained around 1300,
indicating that the inflection point data should be around 1300. At 1300
concurrency, the response time has reached 22ms, which is within a controllable
range. Compared to HugeGraph 0.2 (1000 concurrency: average response time
8959ms), the processing capacity has made a qualitative leap.</li>
+<li>For edges, the processing time is too long and exceeds 3 seconds from
1000 to 2000 concurrency, and the throughput almost fluctuates around 270.
Therefore, increasing the concurrency will not significantly increase the
throughput. 270 is an inflection point, and compared with HugeGraph 0.2 (1000
concurrency: average response time 31849ms), the processing capacity has
improved significantly.</li>
</ul>
-<h4 id="42-single插入">4.2 single插入</h4>
-<h5 id="421-压力上限测试">4.2.1 压力上限测试</h5>
-<h6 id="测试方法-1">测试方法</h6>
-<p>不断提升并发量，测试server仍能正常提供服务的压力上限</p>
-<h6 id="压力参数-1">压力参数</h6>
+<h4 id="42-single-insertion">4.2 single insertion</h4>
+<h5 id="421-upper-limit-test-under-pressure">4.2.1 Upper Limit Test under
Pressure</h5>
+<h6 id="test-method">Test Method</h6>
+<p>Continuously increase the concurrency level and test the upper limit of
the pressure at which the server can still provide normal services.</p>
+<h6 id="pressure-parameters-1">Pressure Parameters</h6>
<ul>
-<li>持续时间：5min</li>
-<li>服务异常标志：错误率大于0.00%</li>
+<li>Duration: 5 minutes</li>
+<li>Service exception criteria: Error rate greater than 0.00%.</li>
</ul>
<center>
<img src="/docs/images/API-perf/v0.4.4/threads-single.png" alt="image">
</center>
-<h6 id="结论-4">结论：</h6>
+<h4 id="conclusion-4">Conclusion:</h4>
<ul>
-<li>顶点：
+<li>Vertices:
<ul>
-<li>4000并发：正常，无错误率，平均耗时小于1ms， 6000并发无错误，平均耗时5ms，在可接受范围内；</li>
-<li>8000并发：存在0.01%的错误，已经无法处理，出现connection timeout错误，顶峰应该在7000左右</li>
+<li>At 4000 concurrent connections, there were no errors, with an average
response time of less than 1ms. At 6000 concurrent connections, there were no
errors, with an average response time of 5ms, which is acceptable.</li>
+<li>At 8000 concurrent connections, there were 0.01% errors and the system
could not handle it, resulting in connection timeout errors. The
system&rsquo;s peak performance should be around 7000 concurrent
connections.</li>
</ul>
</li>
-<li>边：
+<li>Edges:
<ul>
-<li>4000并发：响应时间1ms，6000并发无任何异常，平均响应时间8ms，主要差异在于 IO network
recv和send以及CPU）；</li>
-<li>8000并发：存在0.01%的错误率，平均耗15ms，拐点应该在7000左右，跟顶点结果匹配；</li>
+<li>At 4000 concurrent connections, the response time was 1ms. At 6000
concurrent connections, there were no abnormalities, with an average response
time of 8ms. The main differences were in IO network recv and send as well as
CPU usage.</li>
+<li>At 8000 concurrent connections, there was a 0.01% error rate, with an
average response time of 15ms. The turning point should be around 7000
concurrent connections, which matches the vertex results.</li>
</ul>
</li>
</ul></description></item><item><title>Docs: Validate Apache
Release</title><link>/docs/contribution-guidelines/validate-release/</link><pubDate>Mon,
01 Jan 0001 00:00:00
+0000</pubDate><guid>/docs/contribution-guidelines/validate-release/</guid><description>
diff --git a/docs/performance/_print/index.html
b/docs/performance/_print/index.html
index a17a3de8..53c3348d 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/[email protected]/dist/js/bootstrap.min.js
integrity="sha512-UR25UO94eTnCVwjbXozyeVd6ZqpaAE9naiEUBK/A+QDbfSTQFhPGj5lOR6d8tsgbBk84Ggb5A3EkjsOgPRPcKA=="
crossorigin=anonymous></script>
diff --git a/docs/performance/api-preformance/_print/index.html
b/docs/performance/api-preformance/_print/index.html
index c182078a..b0af02b0 100644
--- a/docs/performance/api-preformance/_print/index.html
+++ b/docs/performance/api-preformance/_print/index.html
@@ -2,7 +2,7 @@
Single …"><meta property="og:title" content="HugeGraph-API Performance"><meta
property="og:description" content="Apache HugeGraph site"><meta
property="og:type" content="website"><meta property="og:url"
content="/docs/performance/api-preformance/"><meta property="og:site_name"
content="HugeGraph"><meta itemprop=name content="HugeGraph-API
Performance"><meta itemprop=description content="Apache HugeGraph site"><meta
name=twitter:card content="summary"><meta name=twitter:title content="Hug [...]
<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
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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/api-preformance/>Return to the regular view of this
page</a>.</p></div><h1 class=title>HugeGraph-API Performance</h1><ul><li>1: <a
href=#pg-dbfafc29a5e930415f78f72c47ee67f2>v0.5.6
Stand-alone(RocksDB)</a></li><li>2: <a
href=#pg-fd5b165e28a07f1c35ab177b10e15dc8>v0.5.6
Cluster(Cassandra)</a></li><li>3: <a
href=#pg-0005aca20e30ef2795411939ccbf0fd9>v0.4.4</a></li><li>4: <a
href=#pg-d4233a3feb070643 [...]
+<a href=# onclick="return print(),!1">Click here to print</a>.</p><p><a
href=/docs/performance/api-preformance/>Return to the regular view of this
page</a>.</p></div><h1 class=title>HugeGraph-API Performance</h1><ul><li>1: <a
href=#pg-dbfafc29a5e930415f78f72c47ee67f2>v0.5.6
Stand-alone(RocksDB)</a></li><li>2: <a
href=#pg-fd5b165e28a07f1c35ab177b10e15dc8>v0.5.6
Cluster(Cassandra)</a></li><li>3: <a
href=#pg-0005aca20e30ef2795411939ccbf0fd9>v0.4.4</a></li><li>4: <a
href=#pg-d4233a3feb070643 [...]
</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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src=https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.min.js
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crossorigin=anonymous></script>
diff --git a/docs/performance/api-preformance/hugegraph-api-0.4.4/index.html
b/docs/performance/api-preformance/hugegraph-api-0.4.4/index.html
index 700276b7..ce66dac4 100644
--- a/docs/performance/api-preformance/hugegraph-api-0.4.4/index.html
+++ b/docs/performance/api-preformance/hugegraph-api-0.4.4/index.html
@@ -1,38 +1,28 @@
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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 [...]
-被压机器信息
+<!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 [...]
+Target Machine Information
机器编号
CPU
Memory
-网卡
-磁盘
+NIC (Network Interface Card)
+Disk
1
-24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz
-61G
-1000Mbps
-1.4T HDD
-
-
-2
-48 Intel(R) Xeon(R) CPU E5-2650 v4 …"><meta property="og:title"
content="v0.4.4"><meta property="og:description" content="1 测试环境 被压机器信息
-机器编号 CPU Memory 网卡 磁盘 1 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz 61G
1000Mbps 1.4T HDD 2 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz 128G 10000Mbps
750GB SSD,2.7T HDD 起压力机器信息：与编号 1 机器同配置 测试工具：apache-Jmeter-2.5.1 注：起压机器和被压机器在同一机房
-2 测试说明 2.1 名词定义（时间的单位均为ms） Samples – 本次场景中一共完成了多少个线程 Average –
平均响应时间 Median – 统计意义上面的响应时间的中值 90% Line – 所有线程中90%的线程的响应时间都小于xx Min
– 最小响应时间 Max – 最大响应时间 Error – 出错率 Throughput – 吞吐量
KB/sec – 以流量做衡量的吞吐量 2."><meta property="og:type" content="article"><meta
property="og:url"
content="/docs/performance/api-preformance/hugegraph-api-0.4.4/"><meta
property="article:section" content="docs"><meta
property="article:modified_time" content="2022 [...]
-机器编号 CPU Memory 网卡 磁盘 1 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz 61G
1000Mbps 1.4T HDD 2 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz 128G 10000Mbps
750GB SSD,2.7T HDD 起压力机器信息：与编号 1 机器同配置 测试工具：apache-Jmeter-2.5.1 注：起压机器和被压机器在同一机房
-2 测试说明 2.1 名词定义（时间的单位均为ms） Samples – 本次场景中一共完成了多少个线程 Average –
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– 最小响应时间 Max – 最大响应时间 Error – 出错率 Throughput – 吞吐量
KB/sec – 以流量做衡量的吞吐量 2."><meta itemprop=dateModified
content="2022-04-17T11:36:55+08:00"><meta itemprop=wordCount
content="138"><meta itemprop=keywords content><meta name=twitter:card
content="summary"><meta name=twitter:title content="v0.4.4"><meta name=tw [...]
-机器编号 CPU Memory 网卡 磁盘 1 24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz 61G
1000Mbps 1.4T HDD 2 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz 128G 10000Mbps
750GB SSD,2.7T HDD 起压力机器信息：与编号 1 机器同配置 测试工具：apache-Jmeter-2.5.1 注：起压机器和被压机器在同一机房
-2 测试说明 2.1 名词定义（时间的单位均为ms） Samples – 本次场景中一共完成了多少个线程 Average –
平均响应时间 Median – 统计意义上面的响应时间的中值 90% Line – 所有线程中90%的线程的响应时间都小于xx Min
– 最小响应时间 Max – 最大响应时间 Error – 出错率 Throughput – 吞吐量
KB/sec – 以流量做衡量的吞吐量 2."><link rel=preload
href=/scss/main.min.ad1b0560bef9c54394313a5bc50d3313d4e56ea590ddc5cfb84a077dfc6fec5e.css
as=style><link
href=/scss/main.min.ad1b0560bef9c54394313a5bc50d3313d4e56ea590ddc5cfb84a077dfc6fec5e.css
rel=stylesheet integr [...]
+24 Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz …"><meta property="og:title"
content="v0.4.4"><meta property="og:description" content="1 Test environment
Target Machine Information
+机器编号 CPU Memory NIC (Network Interface Card) Disk 1 24 Intel(R) Xeon(R) CPU
E5-2620 v2 @ 2.10GHz 61G 1000Mbps 1.4T HDD 2 48 Intel(R) Xeon(R) CPU E5-2650 v4
@ 2.20GHz 128G 10000Mbps 750GB SSD,2.7T HDD Pressure testing machine
information: Configured the same as machine number 1. Testing tool: Apache
JMeter 2.5.1. Note: The pressure testing machine and the machine being tested
are in the same room."><meta property="og:type" content="article"><meta
property="og:url" content="/docs/performan [...]
+机器编号 CPU Memory NIC (Network Interface Card) Disk 1 24 Intel(R) Xeon(R) CPU
E5-2620 v2 @ 2.10GHz 61G 1000Mbps 1.4T HDD 2 48 Intel(R) Xeon(R) CPU E5-2650 v4
@ 2.20GHz 128G 10000Mbps 750GB SSD,2.7T HDD Pressure testing machine
information: Configured the same as machine number 1. Testing tool: Apache
JMeter 2.5.1. Note: The pressure testing machine and the machine being tested
are in the same room."><meta itemprop=dateModified
content="2023-05-16T23:29:47-05:00"><meta itemprop=wordCount co [...]
+机器编号 CPU Memory NIC (Network Interface Card) Disk 1 24 Intel(R) Xeon(R) CPU
E5-2620 v2 @ 2.10GHz 61G 1000Mbps 1.4T HDD 2 48 Intel(R) Xeon(R) CPU E5-2650 v4
@ 2.20GHz 128G 10000Mbps 750GB SSD,2.7T HDD Pressure testing machine
information: Configured the same as machine number 1. Testing tool: Apache
JMeter 2.5.1. Note: The pressure testing machine and the machine being tested
are in the same room."><link rel=preload
href=/scss/main.min.ad1b0560bef9c54394313a5bc50d3313d4e56ea590ddc5cfb84a0 [...]
<link rel=stylesheet href=/css/prism.css><script
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-<a id=print href=/docs/performance/api-preformance/_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-测试说明>2 测试说明</a></li><li><a href=#3-性能结果总结>3 性能结果总结</a></li><li><a
href=#4-测试结果及分析>4 测试结果及分析</a></li></ul></li></ul></nav></div></aside><main
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+<a id=print href=/docs/performance/api-preformance/_print/><i class="fa
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id=TableOfContents><ul><li><ul><li><a href=#1-test-environment>1 Test
environment</a></li><li><a href=#2-test-description>2 Test
Description</a></li><li><a href=#3-summary-of-performance-results>3 Summary of
performance results</a></li><li><a href=#4-test-results-and-analysis>4 Test
results and analysis</a></li></ul></li></ul></nav></div></asid [...]
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diff --git a/docs/performance/api-preformance/index.xml
b/docs/performance/api-preformance/index.xml
index d7c36b6d..6529c81e 100644
--- a/docs/performance/api-preformance/index.xml
+++ b/docs/performance/api-preformance/index.xml
@@ -237,16 +237,16 @@
<ul>
<li>并发12000，吞吐量是10688，边的按id查询的并发能力为12000，平均延时为63ms</li>
</ul></description></item><item><title>Docs:
v0.4.4</title><link>/docs/performance/api-preformance/hugegraph-api-0.4.4/</link><pubDate>Mon,
01 Jan 0001 00:00:00
+0000</pubDate><guid>/docs/performance/api-preformance/hugegraph-api-0.4.4/</guid><description>
-<h3 id="1-测试环境">1 测试环境</h3>
-<p>被压机器信息</p>
+<h3 id="1-test-environment">1 Test environment</h3>
+<p>Target Machine Information</p>
<table>
<thead>
<tr>
<th>机器编号</th>
<th>CPU</th>
<th>Memory</th>
-<th>网卡</th>
-<th>磁盘</th>
+<th>NIC (Network Interface Card)</th>
+<th>Disk</th>
</tr>
</thead>
<tbody>
@@ -267,100 +267,99 @@
</tbody>
</table>
<ul>
-<li>起压力机器信息：与编号 1 机器同配置</li>
-<li>测试工具：apache-Jmeter-2.5.1</li>
+<li><strong>Pressure testing machine information:</strong> Configured
the same as machine number 1.</li>
+<li><strong>Testing tool:</strong> Apache JMeter 2.5.1.</li>
</ul>
-<p>注：起压机器和被压机器在同一机房</p>
-<h3 id="2-测试说明">2 测试说明</h3>
-<h4 id="21-名词定义时间的单位均为ms">2.1 名词定义（时间的单位均为ms）</h4>
+<p>Note: The pressure testing machine and the machine being tested are in
the same room.</p>
+<h3 id="2-test-description">2 Test Description</h3>
+<h4 id="21-definition-of-terms-the-unit-of-time-is-ms">2.1 Definition of
terms (the unit of time is ms)</h4>
<ul>
-<li>Samples &ndash; 本次场景中一共完成了多少个线程</li>
-<li>Average &ndash; 平均响应时间</li>
-<li>Median &ndash; 统计意义上面的响应时间的中值</li>
-<li>90% Line &ndash; 所有线程中90%的线程的响应时间都小于xx</li>
-<li>Min &ndash; 最小响应时间</li>
-<li>Max &ndash; 最大响应时间</li>
-<li>Error &ndash; 出错率</li>
-<li>Throughput &ndash; 吞吐量</li>
-<li>KB/sec &ndash; 以流量做衡量的吞吐量</li>
+<li>Samples &ndash; The total number of threads completed in this
scenario.</li>
+<li>Average &ndash; The average response time.</li>
+<li>Median &ndash; The median response time in terms of statistical
significance.</li>
+<li>90% Line &ndash; The response time of 90% of all threads is less
than xx.</li>
+<li>Min &ndash; The minimum response time.</li>
+<li>Max &ndash; The maximum response time.</li>
+<li>Error &ndash; The error rate.</li>
+<li>Throughput &ndash; The throughput.</li>
+<li>KB/sec &ndash; The throughput measured in terms of traffic.</li>
</ul>
-<h4 id="22-底层存储">2.2 底层存储</h4>
-<p>后端存储使用RocksDB，HugeGraph与RocksDB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。</p>
-<h3 id="3-性能结果总结">3 性能结果总结</h3>
+<h4 id="22-underlying-storage">2.2 Underlying storage</h4>
+<p>RocksDB is used for backend storage, HugeGraph and RocksDB are both
started on the same machine, and the configuration files related to the server
remain the default except for the modification of the host and port.</p>
+<h3 id="3-summary-of-performance-results">3 Summary of performance
results</h3>
<ol>
-<li>HugeGraph每秒能够处理的请求数目上限是7000</li>
-<li>批量插入速度远大于单条插入，在服务器上测试结果达到22w edges/s，37w vertices/s</li>
-<li>后端是RocksDB，增大CPU数目和内存大小可以增大批量插入的性能。CPU和内存扩大一倍，性能增加45%-60%</li>
-<li>批量插入场景，使用SSD替代HDD，性能提升较小，只有3%-5%</li>
+<li>The upper limit of the number of requests HugeGraph can handle per
second is 7000</li>
+<li>The speed of batch insertion is much higher than that of single
insertion, and the test results on the server reach 22w edges/s, 37w
vertices/s</li>
+<li>The backend is RocksDB, and increasing the number of CPUs and memory
size can improve the performance of batch inserts. Doubling the CPU and memory
size can increase performance by 45% to 60%.</li>
+<li>In the batch insertion scenario, using SSD instead of HDD, the
performance improvement is small, only 3%-5%</li>
</ol>
-<h3 id="4-测试结果及分析">4 测试结果及分析</h3>
-<h4 id="41-batch插入">4.1 batch插入</h4>
-<h5 id="411-压力上限测试">4.1.1 压力上限测试</h5>
-<h6 id="测试方法">测试方法</h6>
-<p>不断提升并发量，测试server仍能正常提供服务的压力上限</p>
-<h6 id="压力参数">压力参数</h6>
-<p>持续时间：5min</p>
-<h6
id="顶点和边的最大插入速度高性能服务器使用ssd存储rocksdb数据">顶点和边的最大插入速度（高性能服务器，使用SSD存储RocksDB数据）：</h6>
+<h3 id="4-test-results-and-analysis">4 Test results and analysis</h3>
+<h4 id="41-batch-insertion">4.1 Batch insertion</h4>
+<h5 id="411-maximum-pressure-test">4.1.1 Maximum Pressure Test</h5>
+<h6 id="test-methods">Test Methods</h6>
+<p>Continuously increase the concurrency level and test the upper limit of
the server&rsquo;s ability to provide services normally.</p>
+<h6 id="pressure-parameters">Pressure Parameters</h6>
+<p>Duration: 5 minutes</p>
+<h6
id="maximum-insertion-speed-of-vertices-and-edges-high-performance-server-with-ssd-storage-for-rocksdb-data">Maximum
Insertion Speed of Vertices and Edges (High-performance server with SSD
storage for RocksDB data):</h6>
<center>
<img src="/docs/images/API-perf/v0.4.4/best.png" alt="image">
</center>
-<h6 id="结论">结论：</h6>
+<h6 id="conclusion">Conclusion:</h6>
<ul>
-<li>并发1000，边的吞吐量是是451，每秒可处理的数据：451*500条=225500/s</li>
-<li>并发2000，顶点的吞吐量是1842.4，每秒可处理的数据：1842.4*200=368480/s</li>
+<li>With a concurrency of 1000, the edge throughput is 451, which can
process 225,500 data per second: 451 * 500 = 225,500/s.</li>
+<li>With a concurrency of 2000, the vertex throughput is 1842.4, which can
process 368,480 data per second: 1842.4 * 200 = 368,480/s.</li>
</ul>
-<p><strong>1.
CPU和内存对插入性能的影响（服务器都使用HDD存储RocksDB数据，批量插入）</strong></p>
+<p><strong>1. The Impact of CPU and Memory on Insertion Performance
(Servers Using HDD Storage for RocksDB Data, Batch Insertion)</strong></p>
<center>
<img src="/docs/images/API-perf/v0.4.4/cpu-memory.png" alt="image">
</center>
-<h6 id="结论-1">结论：</h6>
+<h6 id="conclusion-1">Conclusion:</h6>
<ul>
-<li>同样使用HDD硬盘，CPU和内存增加了1倍</li>
-<li>边：吞吐量从268提升至426，性能提升了约60%</li>
-<li>顶点：吞吐量从1263.8提升至1842.4，性能提升了约45%</li>
+<li>With the same HDD disk, doubling the CPU and memory size increases edge
throughput from 268 to 426, which improves performance by about 60%.</li>
+<li>With the same HDD disk, doubling the CPU and memory size increases
vertex throughput from 1263.8 to 1842.4, which improves performance by about
45%.</li>
</ul>
-<p><strong>2. SSD和HDD对插入性能的影响（高性能服务器，批量插入）</strong></p>
+<p><strong>2. The Impact of SSD and HDD on Insertion Performance
(High-performance Servers, Batch Insertion)</strong></p>
<center>
<img src="/docs/images/API-perf/v0.4.4/ssd.png" alt="image">
</center>
-<h6 id="结论-2">结论：</h6>
+<h6 id="conclusion-2">Conclusion:</h6>
<ul>
-<li>边：使用SSD吞吐量451.7,使用HDD吞吐量426.6，性能提升5%</li>
-<li>顶点：使用SSD吞吐量1842.4，使用HDD吞吐量1794，性能提升约3%</li>
+<li>For edge insertion, using SSD yields a throughput of 451.7, while using
HDD yields a throughput of 426.6, which results in a 5% performance
improvement.</li>
+<li>For vertex insertion, using SSD yields a throughput of 1842.4, while
using HDD yields a throughput of 1794, which results in a performance
improvement of about 3%.</li>
</ul>
-<p><strong>3. 不同并发线程数对插入性能的影响（普通服务器，使用HDD存储RocksDB数据）</strong></p>
+<p><strong>3. The Impact of Different Concurrent Thread Numbers on
Insertion Performance (Ordinary Servers, HDD Storage for RocksDB
Data)</strong></p>
<center>
<img src="/docs/images/API-perf/v0.4.4/threads-batch.png" alt="image">
</center>
-<h6 id="结论-3">结论：</h6>
+<h5 id="conclusion-3">Conclusion:</h5>
<ul>
-<li>顶点：1000并发，响应时间7ms和1500并发响应时间1028ms差距悬殊，且吞吐量一直保持在1300左右，因此拐点数据应该在1300
，且并发1300时，响应时间已达到22ms，在可控范围内，相比HugeGraph
0.2（1000并发：平均响应时间8959ms），处理能力出现质的飞跃；</li>
-<li>边：从1000并发到2000并发，处理时间过长，超过3s，且吞吐量几乎在270左右浮动，因此继续增大并发线程数吞吐量不会再大幅增长，270
是一个拐点，跟HugeGraph 0.2版本（1000并发：平均响应时间31849ms）相比较，处理能力提升非常明显；</li>
+<li>For vertices, at 1000 concurrency, the response time is 7ms and at 1500
concurrency, the response time is 1028ms. The throughput remained around 1300,
indicating that the inflection point data should be around 1300. At 1300
concurrency, the response time has reached 22ms, which is within a controllable
range. Compared to HugeGraph 0.2 (1000 concurrency: average response time
8959ms), the processing capacity has made a qualitative leap.</li>
+<li>For edges, the processing time is too long and exceeds 3 seconds from
1000 to 2000 concurrency, and the throughput almost fluctuates around 270.
Therefore, increasing the concurrency will not significantly increase the
throughput. 270 is an inflection point, and compared with HugeGraph 0.2 (1000
concurrency: average response time 31849ms), the processing capacity has
improved significantly.</li>
</ul>
-<h4 id="42-single插入">4.2 single插入</h4>
-<h5 id="421-压力上限测试">4.2.1 压力上限测试</h5>
-<h6 id="测试方法-1">测试方法</h6>
-<p>不断提升并发量，测试server仍能正常提供服务的压力上限</p>
-<h6 id="压力参数-1">压力参数</h6>
+<h4 id="42-single-insertion">4.2 single insertion</h4>
+<h5 id="421-upper-limit-test-under-pressure">4.2.1 Upper Limit Test under
Pressure</h5>
+<h6 id="test-method">Test Method</h6>
+<p>Continuously increase the concurrency level and test the upper limit of
the pressure at which the server can still provide normal services.</p>
+<h6 id="pressure-parameters-1">Pressure Parameters</h6>
<ul>
-<li>持续时间：5min</li>
-<li>服务异常标志：错误率大于0.00%</li>
+<li>Duration: 5 minutes</li>
+<li>Service exception criteria: Error rate greater than 0.00%.</li>
</ul>
<center>
<img src="/docs/images/API-perf/v0.4.4/threads-single.png" alt="image">
</center>
-<h6 id="结论-4">结论：</h6>
+<h4 id="conclusion-4">Conclusion:</h4>
<ul>
-<li>顶点：
+<li>Vertices:
<ul>
-<li>4000并发：正常，无错误率，平均耗时小于1ms， 6000并发无错误，平均耗时5ms，在可接受范围内；</li>
-<li>8000并发：存在0.01%的错误，已经无法处理，出现connection timeout错误，顶峰应该在7000左右</li>
+<li>At 4000 concurrent connections, there were no errors, with an average
response time of less than 1ms. At 6000 concurrent connections, there were no
errors, with an average response time of 5ms, which is acceptable.</li>
+<li>At 8000 concurrent connections, there were 0.01% errors and the system
could not handle it, resulting in connection timeout errors. The
system&rsquo;s peak performance should be around 7000 concurrent
connections.</li>
</ul>
</li>
-<li>边：
+<li>Edges:
<ul>
-<li>4000并发：响应时间1ms，6000并发无任何异常，平均响应时间8ms，主要差异在于 IO network
recv和send以及CPU）；</li>
-<li>8000并发：存在0.01%的错误率，平均耗15ms，拐点应该在7000左右，跟顶点结果匹配；</li>
+<li>At 4000 concurrent connections, the response time was 1ms. At 6000
concurrent connections, there were no abnormalities, with an average response
time of 8ms. The main differences were in IO network recv and send as well as
CPU usage.</li>
+<li>At 8000 concurrent connections, there was a 0.01% error rate, with an
average response time of 15ms. The turning point should be around 7000
concurrent connections, which matches the vertex results.</li>
</ul>
</li>
</ul></description></item><item><title>Docs:
v0.2</title><link>/docs/performance/api-preformance/hugegraph-api-0.2/</link><pubDate>Mon,
01 Jan 0001 00:00:00
+0000</pubDate><guid>/docs/performance/api-preformance/hugegraph-api-0.2/</guid><description>
diff --git a/en/sitemap.xml b/en/sitemap.xml
index f3418fe9..7f603f23 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 e7277da2..948153c9 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>2023-05-15T22:47:44-05:00</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>/en/sitemap.xml</loc><lastmod>2023-05-16T23:29:47-05:00</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
