This is an automated email from the ASF dual-hosted git repository.
jin pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-hugegraph-doc.git
The following commit(s) were added to refs/heads/master by this push:
new 329c566b refact: add 3 SEC issues & enhance the intro/perf doc (#358)
329c566b is described below
commit 329c566b725776efd46c02ff61fc6fac65ff6091
Author: imbajin <[email protected]>
AuthorDate: Tue Apr 23 23:55:16 2024 +0800
refact: add 3 SEC issues & enhance the intro/perf doc (#358)
* refact: add 3 SEC issues & enhance the intro/perf doc
also add the .editorconfig for devs
* Remove extra # in h6
* Update hugegraph-benchmark-0.5.6.md
---
.editorconfig | 34 +++++++++
README.md | 66 ++++++++---------
content/cn/docs/guides/security.md | 24 ++++--
content/cn/docs/introduction/README.md | 5 +-
.../hugegraph-api-0.5.6-Cassandra.md | 59 ++++++++-------
.../api-preformance/hugegraph-api-0.5.6-RocksDB.md | 59 ++++++++-------
.../docs/performance/hugegraph-benchmark-0.5.6.md | 85 ++++++++++++----------
.../performance/hugegraph-loader-performance.md | 18 +++--
content/cn/docs/quickstart/hugegraph-ai.md | 27 +++----
content/en/docs/guides/security.md | 16 +++-
content/en/docs/introduction/README.md | 5 +-
.../hugegraph-api-0.5.6-Cassandra.md | 17 +++--
.../api-preformance/hugegraph-api-0.5.6-RocksDB.md | 27 ++++---
.../docs/performance/hugegraph-benchmark-0.5.6.md | 54 ++++++++------
.../performance/hugegraph-loader-performance.md | 17 ++++-
content/en/docs/quickstart/hugegraph-ai.md | 9 ++-
16 files changed, 313 insertions(+), 209 deletions(-)
diff --git a/.editorconfig b/.editorconfig
new file mode 100644
index 00000000..fa6c64db
--- /dev/null
+++ b/.editorconfig
@@ -0,0 +1,34 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+root = true
+
+[*]
+charset = utf-8
+end_of_line = lf
+insert_final_newline = true
+
+[*.{java,xml,py}]
+indent_style = space
+indent_size = 4
+
+[*.{java,xml}]
+# Ignore the IDEA unsupported warning & it works well (indeed)
+continuation_indent_size = 8
+
+[*.md]
+max_line_length = off
diff --git a/README.md b/README.md
index 47120790..cda86ac9 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@ Please visit the [contribution doc](./contribution.md) to get
start, include the
### Summary
-HugeGraph is an easy-to-use, efficient, general-purpose open source graph
database system(Graph Database, [GitHub project
address](https://github.com/apache/hugegraph)),
+HugeGraph is an easy-to-use, efficient, general-purpose open source graph
database system (Graph Database, [GitHub project
address](https://github.com/apache/hugegraph)),
implemented the [Apache TinkerPop3](https://tinkerpop.apache.org) framework
and is fully compatible with the
[Gremlin](https://tinkerpop.apache.org/gremlin.html) query language.
With complete toolchain components, it helps users to easily build
applications and products based on graph databases. HugeGraph supports fast
import of more than 10 billion vertices and edges, and provides
millisecond-level relational query capability (OLTP).
It supports large-scale distributed graph processing (OLAP).
@@ -29,55 +29,47 @@ The functions of this system include but are not limited to:
- Supports batch import of data from multiple data sources (including local
files, HDFS files, MySQL databases and other data sources), and supports import
of multiple file formats (including TXT, CSV, JSON and other formats)
- With a visual operation interface, it can be used for operation, analysis
and display diagrams, reducing the threshold for users to use
- Optimized graph interface: shortest path (Shortest Path), K-step connected
subgraph (K-neighbor), K-step to reach the adjacent point (K-out), personalized
recommendation algorithm PersonalRank, etc.
-- Implemented based on Apache TinkerPop3 framework, supports Gremlin graph
query language
+- Implemented based on the Apache-TinkerPop3 framework, supports Gremlin graph
query language
- Support attribute graph, attributes can be added to vertices and edges, and
support rich attribute types
- Has independent schema metadata information, has powerful graph modeling
capabilities, and facilitates third-party system integration
- Support multi-vertex ID strategy: support primary key ID, support automatic
ID generation, support user-defined string ID, support user-defined digital ID
- The attributes of edges and vertices can be indexed to support precise
query, range query, and full-text search
- The storage system adopts plug-in mode, supporting RocksDB, Cassandra,
ScyllaDB, HBase, MySQL, PostgreSQL, Palo, and InMemory, etc.
- Integrate with big data systems such as Hadoop and Spark GraphX, and support
Bulk Load operations
-- Support high availability(HA), multiple copies of data, backup recovery,
monitoring, etc.
+- Support high availability (HA), multiple copies of data, backup recovery,
monitoring, etc.
### Modules
--
[HugeGraph-Server](https://hugegraph.apache.org/docs/quickstart/hugegraph-server):
HugeGraph-Server is the core part of the HugeGraph project, including
submodules such as Core, Backend, and API;
- - Core: Graph engine implementation, connecting the Backend module downward
and supporting the API module upward;
- - Backend: Realize the storage of graph data to the backend. The supported
backends include: Memory, Cassandra, ScyllaDB, RocksDB, HBase, MySQL and
PostgreSQL. Users can choose one according to the actual situation;
- - API: Built-in REST Server, provides RESTful API to users, and is fully
compatible with Gremlin query.
--
[HugeGraph-Client](https://hugegraph.apache.org/docs/quickstart/hugegraph-client):
- HugeGraph-Client provides a RESTful API client for connecting to
HugeGraph-Server. Currently, only Java version is implemented. Users of other
languages can implement it by themselves;
--
[HugeGraph-Loader](https://hugegraph.apache.org/docs/quickstart/hugegraph-loader):
HugeGraph-Loader is a data import tool based on HugeGraph-Client, which
converts ordinary text data into graph vertices and edges and inserts them into
graph database;
--
[HugeGraph-Computer](https://hugegraph.apache.org/docs/quickstart/hugegraph-computer):
HugeGraph-Computer is a distributed graph processing system for HugeGraph
(OLAP). It is an implementation of
[Pregel](https://kowshik.github.io/JPregel/pregel_paper.pdf). It runs on
Kubernetes framework;
--
[HugeGraph-Hubble](https://hugegraph.apache.org/docs/quickstart/hugegraph-hubble):
HugeGraph-Hubble is HugeGraph's web visualization management platform, a
one-stop visual analysis platform. The platform covers the whole process from
data modeling, to rapid data import, to online and offline analysis of data,
and unified management of graphs;
--
[HugeGraph-Tools](https://hugegraph.apache.org/docs/quickstart/hugegraph-tools):
HugeGraph-Tools is HugeGraph's deployment and management tools, including
functions such as managing graphs, backup/restore, Gremlin execution, etc.
-- [HugeGraph-Ai
(Beta)](https://hugegraph.apache.org/docs/quickstart/hugegraph-ai):
HugeGraph-Ai is a tool that integrates HugeGraph and artificial intelligence
(AI), including applications combined with large models, integration with graph
machine learning components, etc., to provide comprehensive support for
developers to use HugeGraph's AI capabilities in projects.
-
-### Subscribe the mailing list
-
-HugeGraph offers an email list for development and user discussions.
-- hugegraph-dev: [[email protected]](mailto:[email protected])
for both development and users discussions.
-
-Subscribe to the mailing list by following steps:
-- Email
[[email protected]](mailto:[email protected])
through your email account, and then you will receive a confirmation email.
-- Reply to the confirmation email to confirm your subscription. Then, you will
receive another confirmation email.
-- Now you are a subscriber of the mailing list. If you have more questions,
just email the mailing list and someone will reply to you soon.
-- If you want to unsubscribe from the mailing list, just email
[[email protected]](mailto:[email protected])
and follow the steps in the confirmation email.
-
-You can subscribe to the mailing list anytime you want. Additionally, you can
check [historical emails / all
emails](https://lists.apache.org/[email protected]) easily
(even if you are not subscribing to the list).
-
-Some notes:
-- If you don't receive the confirmation email, please send it after 24 hours
later.
-- Don't email to **dev** until you subscribe to the mailing list successfully
(otherwise the mail will be banned).
-
-More information on mailing subscribe can be found at:
-- http://apache.org/foundation/mailinglists.html#subscribing
+- [HugeGraph-Server](/docs/quickstart/hugegraph-server): HugeGraph-Server is
the core part of the HugeGraph project, containing Core, Backend, API and other
submodules;
+ - Core: Implements the graph engine, connects to the Backend module
downwards, and supports the API module upwards;
+ - Backend: Implements the storage of graph data to the backend, supports
backends including Memory, Cassandra, ScyllaDB, RocksDB, HBase, MySQL and
PostgreSQL, users can choose one according to the actual situation;
+ - API: Built-in REST Server, provides RESTful API to users, and is fully
compatible with Gremlin queries. (Supports distributed storage and computation
pushdown)
+- [HugeGraph-Toolchain](https://github.com/apache/hugegraph-toolchain):
(Toolchain)
+ - [HugeGraph-Client](/docs/quickstart/hugegraph-client): HugeGraph-Client
provides a RESTful API client for connecting to HugeGraph-Server, currently
only the Java version is implemented, users of other languages can implement it
themselves;
+ - [HugeGraph-Loader](/docs/quickstart/hugegraph-loader): HugeGraph-Loader is
a data import tool based on HugeGraph-Client, which transforms ordinary text
data into vertices and edges of the graph and inserts them into the graph
database;
+ - [HugeGraph-Hubble](/docs/quickstart/hugegraph-hubble): HugeGraph-Hubble is
HugeGraph's Web
+visualization management platform, a one-stop visualization analysis platform,
the platform covers the whole process from data modeling, to fast data import,
to online and offline analysis of data, and unified management of the graph;
+ - [HugeGraph-Tools](/docs/quickstart/hugegraph-tools): HugeGraph-Tools is
HugeGraph's deployment and management tool, including graph management,
backup/recovery, Gremlin execution and other functions.
+- [HugeGraph-Computer](/docs/quickstart/hugegraph-computer):
HugeGraph-Computer is a distributed graph processing system (OLAP).
+ It is an implementation of
[Pregel](https://kowshik.github.io/JPregel/pregel_paper.pdf). It can run on
clusters such as Kubernetes/Yarn, and supports large-scale graph computing.
+- [HugeGraph-AI](/docs/quickstart/hugegraph-ai): HugeGraph-AI is HugeGraph's
independent AI
+ component, providing training and inference functions of graph neural
networks, LLM/Graph RAG combination/Python-Client and other related components,
continuously updating.
+
+## Contributing
+
+- Welcome to contribute to HugeGraph, please see [`How to
Contribute`](CONTRIBUTING.md) &
[Guidelines](https://hugegraph.apache.org/docs/contribution-guidelines/) for
more information.
+- Note: It's recommended to use [GitHub Desktop](https://desktop.github.com/)
to greatly simplify the PR and commit process.
+- Thank you to all the people who already contributed to HugeGraph!
+
+[](https://github.com/apache/incubator-hugegraph-doc/graphs/contributors)
### Contact Us
---
- - [GitHub Issues](https://github.com/apache/incubator-hugegraph-doc/issues):
Feedback on usage issues and functional requirements (quick response)
- - Feedback Email:
[[email protected]](mailto:[email protected])
([subscriber](https://hugegraph.apache.org/docs/contribution-guidelines/subscribe/)
only)
- - WeChat public account: Apache HugeGraph, welcome to scan this QR code to
follow us.
+- [GitHub Issues](https://github.com/apache/incubator-hugegraph-doc/issues):
Feedback on usage issues and functional requirements (quick response)
+- Feedback Email:
[[email protected]](mailto:[email protected])
([subscriber](https://hugegraph.apache.org/docs/contribution-guidelines/subscribe/)
only)
+- Security Email:
[[email protected]](mailto:[email protected]) (Report
SEC problems)
+- WeChat public account: Apache HugeGraph, welcome to scan this QR code to
follow us.
<img src="./assets/images/wechat.png" alt="QR png" width="350"/>
diff --git a/content/cn/docs/guides/security.md
b/content/cn/docs/guides/security.md
index d83cf7de..0f71ff16 100644
--- a/content/cn/docs/guides/security.md
+++ b/content/cn/docs/guides/security.md
@@ -4,20 +4,32 @@ linkTitle: "安全公告"
weight: 6
---
-## 报告Apache HugeGraph的安全问题
+## 报告 Apache HugeGraph 的安全问题
-遵循 ASF 的规范, HugeGraph 社区对**解决修复**项目中的安全问题保持非常积极和开放的态度。
+遵循 ASF 的规范，HugeGraph 社区对**解决修复**项目中的安全问题保持非常积极和开放的态度。
我们强烈建议用户首先向我们的独立安全邮件列表报告此类问题，相关详细的流程规范请参考 [ASF
SEC](https://www.apache.org/security/committers.html) 守则。
-请注意，安全邮件组适用于报告**未公开**的安全漏洞并跟进漏洞处理的过程。常规的软件 `Bug/Error` 报告应该使用 `Github
Issue/Discussion` 或是 `HugeGraph-Dev` 邮箱组。发送到安全邮件组但与安全问题无关的邮件将被忽略。
+请注意，安全邮件组适用于报告**未公开**的安全漏洞并跟进漏洞处理的过程。常规的软件 `Bug/Error` 报告应该使用 `Github
Issue/Discussion`
+或是 `HugeGraph-Dev` 邮箱组。发送到安全邮件组但与安全问题无关的邮件将被忽略。
-独立的安全邮件(组)地址为: `[email protected]`
+独立的安全邮件 (组) 地址为： `[email protected]`
安全漏洞处理大体流程如下：
- 报告人私下向 Apache HugeGraph SEC 邮件组报告漏洞 (尽可能包括复现的版本/相关说明/复现方式/影响范围等)
- HugeGraph 项目安全团队与报告人私下合作/商讨漏洞解决方案 (初步确认后可申请 `CVE` 编号予以登记)
- 项目创建一个新版本的受漏洞影响的软件包，以提供修复程序
-- 合适的时间可公开漏洞的大体问题 & 描述如何应用修复程序 (遵循 ASF 规范, 公告中不应携带复现细节等敏感信息)
-- 正式的 CVE 发布及相关流程同 ASF-SEC 页面
\ No newline at end of file
+- 合适的时间可公开漏洞的大体问题 & 描述如何应用修复程序 (遵循 ASF 规范，公告中不应携带复现细节等敏感信息)
+- 正式的 CVE 发布及相关流程同 ASF-SEC 页面
+
+## 已发现的安全漏洞 (CVEs)
+
+### [HugeGraph](https://github.com/apache/hugegraph) 主仓库 (Server/PD/Store)
+
+- [CVE-2024-27348](https://www.cve.org/CVERecord?id=CVE-2024-27348):
HugeGraph-Server - Command execution in gremlin
+- [CVE-2024-27349](https://www.cve.org/CVERecord?id=CVE-2024-27349):
HugeGraph-Server - Bypass whitelist in Auth mode
+
+### [HugeGraph-Toolchain](https://github.com/apache/hugegraph-toolchain) 仓库
(Hubble/Loader/Client/Tools/..)
+
+- [CVE-2024-27347](https://www.cve.org/CVERecord?id=CVE-2024-27347):
HugeGraph-Hubble - SSRF in Hubble connection page
diff --git a/content/cn/docs/introduction/README.md
b/content/cn/docs/introduction/README.md
index 5a89a1b5..57902342 100644
--- a/content/cn/docs/introduction/README.md
+++ b/content/cn/docs/introduction/README.md
@@ -58,13 +58,14 @@ HugeGraph 支持多用户并行操作，用户可输入 Gremlin 查询语句，
-
[HugeGraph-Computer](/cn/docs/quickstart/hugegraph-computer)：HugeGraph-Computer
是分布式图处理系统 (OLAP).
它是 [Pregel](https://kowshik.github.io/JPregel/pregel_paper.pdf) 的一个实现。它可以运行在
Kubernetes/Yarn
等集群上，支持超大规模图计算。
-- [HugeGraph-AI(Beta)](/cn/docs/quickstart/hugegraph-ai)：HugeGraph-AI 是
HugeGraph 独立的 AI
+- [HugeGraph-AI](/cn/docs/quickstart/hugegraph-ai)：HugeGraph-AI 是 HugeGraph
独立的 AI
组件，提供了图神经网络的训练和推理功能，LLM/Graph RAG 结合/Python-Client 等相关组件，持续更新 ing。
### Contact Us
- [GitHub Issues](https://github.com/apache/incubator-hugegraph/issues):
使用途中出现问题或提供功能性建议，可通过此反馈 (推荐)
- 邮件反馈：[[email protected]](mailto:[email protected])
([邮箱订阅方式](https://hugegraph.apache.org/docs/contribution-guidelines/subscribe/))
+- SEC 反馈：
[[email protected]](mailto:[email protected]) (报告安全相关问题)
- 微信公众号：Apache HugeGraph, 欢迎扫描下方二维码加入我们！
- <img
src="https://github.com/apache/incubator-hugegraph-doc/blob/master/assets/images/wechat.png?raw=true";
alt="QR png" width="300"/>
+ <img
src="https://github.com/apache/hugegraph-doc/blob/master/assets/images/wechat.png?raw=true";
alt="QR png" width="300"/>
diff --git
a/content/cn/docs/performance/api-preformance/hugegraph-api-0.5.6-Cassandra.md
b/content/cn/docs/performance/api-preformance/hugegraph-api-0.5.6-Cassandra.md
index 1a847102..c75ba100 100644
---
a/content/cn/docs/performance/api-preformance/hugegraph-api-0.5.6-Cassandra.md
+++
b/content/cn/docs/performance/api-preformance/hugegraph-api-0.5.6-Cassandra.md
@@ -4,6 +4,11 @@ linkTitle: "v0.5.6 Cluster(Cassandra)"
weight: 2
---
+> **Note:**
+>
+> 当前的性能指标测试基于很早期的版本。**最新版本**在性能和功能上都有显著的改进。我们鼓励您参考最新的发布版本，
+> 该版本具有**自主分布式存储**和**增强的计算推下能力**。或者，您可以等待社区更新相关测试数据 (也欢迎反馈共建)。
+
### 1 测试环境
被压机器信息
@@ -19,12 +24,12 @@ weight: 2
### 2 测试说明
-#### 2.1 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）
- 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
+- 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
@@ -33,23 +38,23 @@ weight: 2
#### 2.2 底层存储
-后端存储使用15节点Cassandra集群，HugeGraph与Cassandra集群位于不同的服务器，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。
+后端存储使用 15 节点 Cassandra 集群，HugeGraph 与 Cassandra 集群位于不同的服务器，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。
### 3 性能结果总结
-1. HugeGraph单条插入顶点和边的速度分别为9000和4500
+1. HugeGraph 单条插入顶点和边的速度分别为 9000 和 4500
2. 顶点和边的批量插入速度分别为5w/s和15w/s，远大于单条插入速度
-3. 按id查询顶点和边的并发度可达到12000以上，且请求的平均延时小于70ms
+3. 按 id 查询顶点和边的并发度可达到 12000 以上，且请求的平均延时小于 70ms
### 4 测试结果及分析
-#### 4.1 batch插入
+#### 4.1 batch 插入
##### 4.1.1 压力上限测试
###### 测试方法
-不断提升并发量，测试server仍能正常提供服务的压力上限
+不断提升并发量，测试 server 仍能正常提供服务的压力上限
###### 压力参数
@@ -62,9 +67,9 @@ weight: 2
</div>
-####### 结论：
+###### 结论：
-- 并发3500，顶点的吞吐量是261，每秒可处理的数据：261*200=52200/s
+- 并发 3500，顶点的吞吐量是 261，每秒可处理的数据：261*200=52200/s
###### 边的最大插入速度
@@ -73,22 +78,22 @@ weight: 2
</div>
-####### 结论：
+###### 结论：
-- 并发1000，边的吞吐量是323，每秒可处理的数据：323*500=161500/s
+- 并发 1000，边的吞吐量是 323，每秒可处理的数据：323*500=161500/s
-#### 4.2 single插入
+#### 4.2 single 插入
##### 4.2.1 压力上限测试
###### 测试方法
-不断提升并发量，测试server仍能正常提供服务的压力上限
+不断提升并发量，测试 server 仍能正常提供服务的压力上限
###### 压力参数
- 持续时间：5min
-- 服务异常标志：错误率大于0.00%
+- 服务异常标志：错误率大于 0.00%
###### 顶点的单条插入
@@ -97,9 +102,9 @@ weight: 2
</div>
-####### 结论：
+###### 结论：
-- 并发9000，吞吐量为8400，顶点的单条插入并发能力为9000
+- 并发 9000，吞吐量为 8400，顶点的单条插入并发能力为 9000
###### 边的单条插入
@@ -108,40 +113,40 @@ weight: 2
</div>
-####### 结论：
+###### 结论：
-- 并发4500，吞吐量是4160，边的单条插入并发能力为4500
+- 并发 4500，吞吐量是 4160，边的单条插入并发能力为 4500
-#### 4.3 按id查询
+#### 4.3 按 id 查询
##### 4.3.1 压力上限测试
###### 测试方法
-不断提升并发量，测试server仍能正常提供服务的压力上限
+不断提升并发量，测试 server 仍能正常提供服务的压力上限
###### 压力参数
- 持续时间：5min
-- 服务异常标志：错误率大于0.00%
+- 服务异常标志：错误率大于 0.00%
-###### 顶点的按id查询
+###### 顶点的按 id 查询
<div style="text-align: center;">
<img src="/docs/images/API-perf/v0.5.6/cassandra/vertex_id_query.png"
alt="image">
</div>
-####### 结论：
+###### 结论：
-- 并发14500，吞吐量是13576，顶点的按id查询的并发能力为14500，平均延时为11ms
+- 并发 14500，吞吐量是 13576，顶点的按 id 查询的并发能力为 14500，平均延时为 11ms
-###### 边的按id查询
+###### 边的按 id 查询
<div style="text-align: center;">
<img src="/docs/images/API-perf/v0.5.6/cassandra/edge_id_query.png"
alt="image">
</div>
-####### 结论：
+###### 结论：
-- 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
+- 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
diff --git
a/content/cn/docs/performance/api-preformance/hugegraph-api-0.5.6-RocksDB.md
b/content/cn/docs/performance/api-preformance/hugegraph-api-0.5.6-RocksDB.md
index 0e88cb89..c921074c 100644
--- a/content/cn/docs/performance/api-preformance/hugegraph-api-0.5.6-RocksDB.md
+++ b/content/cn/docs/performance/api-preformance/hugegraph-api-0.5.6-RocksDB.md
@@ -4,6 +4,11 @@ linkTitle: "v0.5.6 Stand-alone(RocksDB)"
weight: 1
---
+> **Note:**
+>
+> 当前的性能指标测试基于很早期的版本。**最新版本**在性能和功能上都有显著的改进。我们鼓励您参考最新的发布版本，
+> 该版本具有**自主分布式存储**和**增强的计算推下能力**。或者，您可以等待社区更新相关测试数据 (也欢迎反馈共建)。
+
### 1 测试环境
被压机器信息
@@ -19,12 +24,12 @@ weight: 1
### 2 测试说明
-#### 2.1 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）
- 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
+- 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
@@ -33,23 +38,23 @@ weight: 1
#### 2.2 底层存储
-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。
+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。
### 3 性能结果总结
-1. HugeGraph单条插入顶点和边的速度在每秒1w左右
+1. HugeGraph 单条插入顶点和边的速度在每秒 1w 左右
2. 顶点和边的批量插入速度远大于单条插入速度
-3. 按id查询顶点和边的并发度可达到13000以上，且请求的平均延时小于50ms
+3. 按 id 查询顶点和边的并发度可达到 13000 以上，且请求的平均延时小于 50ms
### 4 测试结果及分析
-#### 4.1 batch插入
+#### 4.1 batch 插入
##### 4.1.1 压力上限测试
###### 测试方法
-不断提升并发量，测试server仍能正常提供服务的压力上限
+不断提升并发量，测试 server 仍能正常提供服务的压力上限
###### 压力参数
@@ -62,9 +67,9 @@ weight: 1
</div>
-####### 结论：
+###### 结论：
-- 并发2200，顶点的吞吐量是2026.8，每秒可处理的数据：2026.8*200=405360/s
+- 并发 2200，顶点的吞吐量是 2026.8，每秒可处理的数据：2026.8*200=405360/s
###### 边的最大插入速度
@@ -72,22 +77,22 @@ weight: 1
<img src="/docs/images/API-perf/v0.5.6/rocksdb/edge_batch.png" alt="image">
</div>
-####### 结论：
+###### 结论：
-- 并发900，边的吞吐量是776.9，每秒可处理的数据：776.9*500=388450/s
+- 并发 900，边的吞吐量是 776.9，每秒可处理的数据：776.9*500=388450/s
-#### 4.2 single插入
+#### 4.2 single 插入
##### 4.2.1 压力上限测试
###### 测试方法
-不断提升并发量，测试server仍能正常提供服务的压力上限
+不断提升并发量，测试 server 仍能正常提供服务的压力上限
###### 压力参数
- 持续时间：5min
-- 服务异常标志：错误率大于0.00%
+- 服务异常标志：错误率大于 0.00%
###### 顶点的单条插入
@@ -96,9 +101,9 @@ weight: 1
</div>
-####### 结论：
+###### 结论：
-- 并发11500，吞吐量为10730，顶点的单条插入并发能力为11500
+- 并发 11500，吞吐量为 10730，顶点的单条插入并发能力为 11500
###### 边的单条插入
@@ -107,40 +112,40 @@ weight: 1
</center>
-####### 结论：
+###### 结论：
-- 并发9000，吞吐量是8418，边的单条插入并发能力为9000
+- 并发 9000，吞吐量是 8418，边的单条插入并发能力为 9000
-#### 4.3 按id查询
+#### 4.3 按 id 查询
##### 4.3.1 压力上限测试
###### 测试方法
-不断提升并发量，测试server仍能正常提供服务的压力上限
+不断提升并发量，测试 server 仍能正常提供服务的压力上限
###### 压力参数
- 持续时间：5min
-- 服务异常标志：错误率大于0.00%
+- 服务异常标志：错误率大于 0.00%
-###### 顶点的按id查询
+###### 顶点的按 id 查询
<center>
<img src="/docs/images/API-perf/v0.5.6/rocksdb/vertex_id_query.png"
alt="image">
</center>
-####### 结论：
+###### 结论：
-- 并发14000，吞吐量是12663，顶点的按id查询的并发能力为14000，平均延时为44ms
+- 并发 14000，吞吐量是 12663，顶点的按 id 查询的并发能力为 14000，平均延时为 44ms
-###### 边的按id查询
+###### 边的按 id 查询
<center>
<img src="/docs/images/API-perf/v0.5.6/rocksdb/edge_id_query.png"
alt="image">
</center>
-####### 结论：
+###### 结论：
-- 并发13000，吞吐量是12225，边的按id查询的并发能力为13000，平均延时为12ms
+- 并发 13000，吞吐量是 12225，边的按 id 查询的并发能力为 13000，平均延时为 12ms
diff --git a/content/cn/docs/performance/hugegraph-benchmark-0.5.6.md
b/content/cn/docs/performance/hugegraph-benchmark-0.5.6.md
index b096c49f..f06dd8b4 100644
--- a/content/cn/docs/performance/hugegraph-benchmark-0.5.6.md
+++ b/content/cn/docs/performance/hugegraph-benchmark-0.5.6.md
@@ -4,6 +4,11 @@ linkTitle: "HugeGraph 基准测试"
weight: 1
---
+> **Note:**
+>
+> 当前的性能指标测试基于很早期的版本。**最新版本**在性能和功能上都有显著的改进。我们鼓励您参考最新的发布版本，
+> 该版本具有**自主分布式存储**和**增强的计算推下能力**。或者，您可以等待社区更新相关测试数据 (也欢迎反馈共建)。
+
### 1 测试环境
#### 1.1 硬件信息
@@ -16,7 +21,7 @@ weight: 1
##### 1.2.1 测试用例
-测试使用[graphdb-benchmark](https://github.com/socialsensor/graphdb-benchmarks)，一个图数据库测试集。该测试集主要包含4类测试：
+测试使用[graphdb-benchmark](https://github.com/socialsensor/graphdb-benchmarks)，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
@@ -24,15 +29,15 @@ weight: 1
- 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
+ - 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
+- 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
+- MIW、SIW 和 QW 使用 SNAP 数据集
- [Enron Dataset](http://snap.stanford.edu/data/email-Enron.html)
@@ -42,11 +47,11 @@ weight: 1
- [LiveJournal dataset](http://snap.stanford.edu/data/com-LiveJournal.html)
-- CW使用[LFR-Benchmark
generator](https://sites.google.com/site/andrealancichinetti/files)生成的人造数据
+- CW 使用[LFR-Benchmark
generator](https://sites.google.com/site/andrealancichinetti/files)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 |
+| 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 |
@@ -55,21 +60,21 @@ weight: 1
#### 1.3 服务配置
-- HugeGraph版本：0.5.6，RestServer和Gremlin Server和backends都在同一台服务器上
+- HugeGraph 版本：0.5.6，RestServer 和 Gremlin Server 和 backends 都在同一台服务器上
- - RocksDB版本：rocksdbjni-5.8.6
+ - RocksDB 版本：rocksdbjni-5.8.6
-- Titan版本：0.5.4, 使用thrift+Cassandra模式
+- Titan 版本：0.5.4, 使用 thrift+Cassandra 模式
- - Cassandra版本：cassandra-3.10，commit-log 和 data 共用SSD
+ - Cassandra 版本：cassandra-3.10，commit-log 和 data 共用 SSD
-- Neo4j版本：2.0.1
+- Neo4j 版本：2.0.1
-> graphdb-benchmark适配的Titan版本为0.5.4
+> graphdb-benchmark 适配的 Titan 版本为 0.5.4
### 2 测试结果
-#### 2.1 Batch插入性能
+#### 2.1 Batch 插入性能
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w)
| com-lj.ungraph(3000w) |
|-----------|------------------|------------------|---------------------------|-----------------------|
@@ -80,8 +85,8 @@ weight: 1
_说明_
- 表头"（）"中数据是数据规模，以边为单位
-- 表中数据是批量插入的时间，单位是s
-- 例如，HugeGraph使用RocksDB插入amazon0601数据集的300w条边，花费5.711s
+- 表中数据是批量插入的时间，单位是 s
+- 例如，HugeGraph 使用 RocksDB 插入 amazon0601 数据集的 300w 条边，花费 5.711s
##### 结论
@@ -91,10 +96,10 @@ _说明_
##### 2.2.1 术语说明
-- FN(Find Neighbor), 遍历所有vertex, 根据vertex查邻接edge, 通过edge和vertex查other vertex
-- FA(Find Adjacent), 遍历所有edge，根据edge获得source vertex和target vertex
+- FN(Find Neighbor), 遍历所有 vertex, 根据 vertex 查邻接 edge, 通过 edge 和 vertex 查 other
vertex
+- FA(Find Adjacent), 遍历所有 edge，根据 edge 获得 source vertex 和 target vertex
-##### 2.2.2 FN性能
+##### 2.2.2 FN 性能
| Backend | email-enron(3.6w) | amazon0601(40w) | com-youtube.ungraph(120w)
| com-lj.ungraph(400w) |
|-----------|-------------------|-----------------|---------------------------|----------------------|
@@ -105,10 +110,10 @@ _说明_
_说明_
- 表头"（）"中数据是数据规模，以顶点为单位
-- 表中数据是遍历顶点花费的时间，单位是s
-- 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
+- 表中数据是遍历顶点花费的时间，单位是 s
+- 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
-##### 2.2.3 FA性能
+##### 2.2.3 FA 性能
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w)
| com-lj.ungraph(3000w) |
|-----------|------------------|------------------|---------------------------|-----------------------|
@@ -119,8 +124,8 @@ _说明_
_说明_
- 表头"（）"中数据是数据规模，以边为单位
-- 表中数据是遍历边花费的时间，单位是s
-- 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
+- 表中数据是遍历边花费的时间，单位是 s
+- 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
###### 结论
@@ -131,10 +136,10 @@ _说明_
##### 术语说明
- FS(Find Shortest Path), 寻找最短路径
-- 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...(K-1), K跳边可达vertex
-- K-out, 从起始vertex出发，恰好经过K跳out边能够到达的顶点
+- 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...(K-1), K 跳边可达 vertex
+- K-out, 从起始 vertex 出发，恰好经过 K 跳 out 边能够到达的顶点
-##### FS性能
+##### FS 性能
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w)
| com-lj.ungraph(3000w) |
|-----------|------------------|------------------|---------------------------|-----------------------|
@@ -145,15 +150,15 @@ _说明_
_说明_
- 表头"（）"中数据是数据规模，以边为单位
-- 表中数据是找到**从第一个顶点出发到达随机选择的100个顶点的最短路径**的时间，单位是s
-- 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
+- 表中数据是找到**从第一个顶点出发到达随机选择的 100 个顶点的最短路径**的时间，单位是 s
+- 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
###### 结论
-- 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
-- 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
+- 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
+- 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
-##### K-neighbor性能
+##### K-neighbor 性能
顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度
----- | -- | ------ | ------ | ------ | ------ | ------ | ---
@@ -163,9 +168,9 @@ v1111 | 时间 | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s |
OOM
_说明_
-- HugeGraph-Server的JVM内存设置为32GB，数据量过大时会出现OOM
+- HugeGraph-Server 的 JVM 内存设置为 32GB，数据量过大时会出现 OOM
-##### K-out性能
+##### K-out 性能
顶点 | 深度 | 一度 | 二度 | 三度 | 四度 | 五度 | 六度
----- | -- | ------ | ------ | ------ | ------ | --------- | ---
@@ -178,16 +183,16 @@ v1111 | 时间 | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s
| OOM
_说明_
-- HugeGraph-Server的JVM内存设置为32GB，数据量过大时会出现OOM
+- HugeGraph-Server 的 JVM 内存设置为 32GB，数据量过大时会出现 OOM
###### 结论
-- FS场景，HugeGraph性能优于Neo4j和Titan
-- K-neighbor和K-out场景，HugeGraph能够实现在5度范围内秒级返回结果
+- FS 场景，HugeGraph 性能优于 Neo4j 和 Titan
+- K-neighbor 和 K-out 场景，HugeGraph 能够实现在 5 度范围内秒级返回结果
#### 2.4 图综合性能测试-CW
-| 数据库 | 规模1000 | 规模5000 | 规模10000 | 规模20000 |
+| 数据库 | 规模 1000 | 规模 5000 | 规模 10000 | 规模 20000 |
|-----------------|--------|---------|----------|----------|
| HugeGraph(core) | 20.804 | 242.099 | 744.780 | 1700.547 |
| Titan | 45.790 | 820.633 | 2652.235 | 9568.623 |
@@ -196,9 +201,9 @@ _说明_
_说明_
- "规模"以顶点为单位
--
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
-- CW测试是CRUD的综合评估
-- 该测试中HugeGraph跟Titan一样，没有通过client，直接对core操作
+- 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
+- CW 测试是 CRUD 的综合评估
+- 该测试中 HugeGraph 跟 Titan 一样，没有通过 client，直接对 core 操作
##### 结论
diff --git a/content/cn/docs/performance/hugegraph-loader-performance.md
b/content/cn/docs/performance/hugegraph-loader-performance.md
index ef755d50..b541cc29 100644
--- a/content/cn/docs/performance/hugegraph-loader-performance.md
+++ b/content/cn/docs/performance/hugegraph-loader-performance.md
@@ -4,19 +4,25 @@ linkTitle: "HugeGraph-Loader 性能"
weight: 3
---
+> **Note:**
+>
+> 当前的性能指标测试基于很早期的版本。**最新版本**在性能和功能上都有显著的改进。我们鼓励您参考最新的发布版本，
+> 该版本具有**自主分布式存储**和**增强的计算推下能力**。或者，您可以等待社区更新相关测试数据 (也欢迎反馈共建)。
+
## 使用场景
-当要批量插入的图数据（包括顶点和边）条数为billion级别及以下，或者总数据量小于TB时，可以采用[HugeGraph-Loader](/docs/quickstart/hugegraph-loader)工具持续、高速导入图数据
+当要批量插入的图数据（包括顶点和边）条数为 billion 级别及以下，或者总数据量小于 TB 时，
+可以采用 [HugeGraph-Loader](/docs/quickstart/hugegraph-loader) 工具持续、高速导入图数据
## 性能
> 测试均采用网址数据的边数据
-### RocksDB单机性能
+### RocksDB 单机性能
-- 关闭label index，22.8w edges/s
-- 开启label index，15.3w edges/s
+- 关闭 label index，22.8w edges/s
+- 开启 label index，15.3w edges/s
-### Cassandra集群性能
+### Cassandra 集群性能
-- 默认开启label index，6.3w edges/s
\ No newline at end of file
+- 默认开启 label index，6.3w edges/s
diff --git a/content/cn/docs/quickstart/hugegraph-ai.md
b/content/cn/docs/quickstart/hugegraph-ai.md
index 1e0f6d56..705b4b14 100644
--- a/content/cn/docs/quickstart/hugegraph-ai.md
+++ b/content/cn/docs/quickstart/hugegraph-ai.md
@@ -1,11 +1,12 @@
---
-title: "HugeGraph-Ai Quick Start (Beta)"
-linkTitle: "使用 HugeGraph-Ai (Beta)"
+title: "HugeGraph-AI Quick Start"
+linkTitle: "使用 HugeGraph-AI"
weight: 4
---
-### 1 HugeGraph-Ai 概述
-hugegraph-ai 旨在探索 HugeGraph 与人工智能（AI）的融合，包括与大模型结合的应用，与图机器学习组件的集成等，为开发者在项目中利用
HugeGraph 的 AI 能力提供全面支持。
+### 1 HugeGraph-AI 概述
+hugegraph-ai 旨在探索 HugeGraph 与人工智能（AI）的融合，包括与大模型结合的应用，与图机器学习组件的集成等，为开发者在项目中利用
HugeGraph
+的 AI 能力提供全面支持。
### 2 环境要求
- python 3.8+
@@ -21,8 +22,8 @@ python3 ./hugegraph-llm/src/hugegraph_llm/utils/gradio_demo.py
```
- 配置 HugeGraph 数据库连接信息和 LLM 模型信息，可以通过两种方式配置：
1. 配置 `./hugegraph-llm/src/config/config.ini` 文件
- 2. 在 gradio 中，分别完成 LLM 和 HugeGraph 的配置后，点击 `Initialize
configs`，将返回初始化后的完整配置文件。如图所示:
- 
+ 2. 在 gradio 中，分别完成 LLM 和 HugeGraph 的配置后，点击 `Initialize
configs`，将返回初始化后的完整配置文件。如图所示：
+ 
- 离线下载 NLTK stopwords
```bash
python3 ./hugegraph_llm/operators/common_op/nltk_helper.py
@@ -40,7 +41,7 @@ python3 ./hugegraph_llm/operators/common_op/nltk_helper.py
- Disambiguate word sense：是否进行词义消除歧义。
- Commit to hugegraph：是否将构建的知识图谱提交到 HugeGraph 服务器
-
+
##### 4.1.2 通过代码构建知识图谱
- 完整代码
@@ -60,16 +61,16 @@ builder = KgBuilder(llm)
)
```
- 时序图
-
+
-
-1. 初始化: 初始化 LLMs 实例，获取 LLM，然后创建图谱构建的任务实例 `KgBuilder`，KgBuilder 中定义了多个
operator，用户可以根据需求自由组合达到目的 。（tip: `print_result()` 可以在控制台打印每一步输出的结果，不影响整体执行逻辑）
+1. 初始化：初始化 LLMs 实例，获取 LLM，然后创建图谱构建的任务实例 `KgBuilder`，KgBuilder 中定义了多个
operator，用户可以根据需求自由组合达到目的。（tip: `print_result()` 可以在控制台打印每一步输出的结果，不影响整体执行逻辑）
```python
llm = LLMs().get_llm()
builder = KgBuilder(llm)
```
-2. 导入 Schema：使用 `import_schema` 方法导入, 支持三种模式：
+
+2. 导入 Schema：使用 `import_schema` 方法导入，支持三种模式：
- 从 HugeGraph 实例导入，指定 HugeGraph 图实例的名称，它将自动提取图的模式。
- 从用户定义的模式导入，接受用户定义的 JSON 格式模式。
- 从提取结果导入（即将发布）
@@ -106,9 +107,9 @@ builder.run()
#### 4.2 基于 HugeGraph 的检索增强生成（RAG）
##### 4.1.1 通过 gradio 交互问答
1. 首先点击 `Initialize HugeGraph test data` 按钮，初始化 HugeGraph 数据。
- 
+ 
2. 然后点击 `Retrieval augmented generation` 按钮，生成问题的答案。
- 
+ 
##### 4.1.2 通过代码构建 Graph RAG
- 完整代码
diff --git a/content/en/docs/guides/security.md
b/content/en/docs/guides/security.md
index c76f1687..b24b3ef3 100644
--- a/content/en/docs/guides/security.md
+++ b/content/en/docs/guides/security.md
@@ -10,7 +10,8 @@ Adhering to the specifications of ASF, the HugeGraph
community maintains a highl
We strongly recommend that users first report such issues to our dedicated
security email list, with detailed procedures specified in the [ASF
SEC](https://www.apache.org/security/committers.html) code of conduct.
-Please note that the security email group is reserved for reporting
**undisclosed** security vulnerabilities and following up on the vulnerability
resolution process. Regular software `Bug/Error` reports should be directed to
`Github Issue/Discussion` or the `HugeGraph-Dev` email group. Emails sent to
the security list that are unrelated to security issues will be ignored.
+Please note that the security email group is reserved for reporting
**undisclosed** security vulnerabilities and following up on the vulnerability
resolution process.
+Regular software `Bug/Error` reports should be directed to `Github
Issue/Discussion` or the `HugeGraph-Dev` email group. Emails sent to the
security list that are unrelated to security issues will be ignored.
The independent security email (group) address is:
`[email protected]`
@@ -20,4 +21,15 @@ The general process for handling security vulnerabilities is
as follows:
- The HugeGraph project security team collaborates privately with the reporter
to discuss the vulnerability resolution (after preliminary confirmation, a
`CVE` number can be requested for registration)
- The project creates a new version of the software package affected by the
vulnerability to provide a fix
- At an appropriate time, a general description of the vulnerability and how
to apply the fix will be publicly disclosed (in compliance with ASF standards,
the announcement should not disclose sensitive information such as reproduction
details)
-- Official CVE release and related procedures follow the ASF-SEC page
\ No newline at end of file
+- Official CVE release and related procedures follow the ASF-SEC page
+
+## Known Security Vulnerabilities (CVEs)
+
+### HugeGraph main project (Server/PD/Store)
+
+- [CVE-2024-27348](https://www.cve.org/CVERecord?id=CVE-2024-27348):
HugeGraph-Server - Command execution in gremlin
+- [CVE-2024-27349](https://www.cve.org/CVERecord?id=CVE-2024-27349):
HugeGraph-Server - Bypass whitelist in Auth mode
+
+### HugeGraph-Toolchain project (Hubble/Loader/Client/Tools/..)
+
+- [CVE-2024-27347](https://www.cve.org/CVERecord?id=CVE-2024-27347):
HugeGraph-Hubble - SSRF in Hubble connection page
diff --git a/content/en/docs/introduction/README.md
b/content/en/docs/introduction/README.md
index c8aabaed..d9d71b23 100644
--- a/content/en/docs/introduction/README.md
+++ b/content/en/docs/introduction/README.md
@@ -54,13 +54,14 @@ visualization management platform, a one-stop visualization
analysis platform, t
- [HugeGraph-Tools](/docs/quickstart/hugegraph-tools): HugeGraph-Tools is
HugeGraph's deployment and management tool, including graph management,
backup/recovery, Gremlin execution and other functions.
- [HugeGraph-Computer](/docs/quickstart/hugegraph-computer):
HugeGraph-Computer is a distributed graph processing system (OLAP).
It is an implementation of
[Pregel](https://kowshik.github.io/JPregel/pregel_paper.pdf). It can run on
clusters such as Kubernetes/Yarn, and supports large-scale graph computing.
-- [HugeGraph-AI(Beta)](/docs/quickstart/hugegraph-ai): HugeGraph-AI is
HugeGraph's independent AI
+- [HugeGraph-AI](/docs/quickstart/hugegraph-ai): HugeGraph-AI is HugeGraph's
independent AI
component, providing training and inference functions of graph neural
networks, LLM/Graph RAG combination/Python-Client and other related components,
continuously updating.
### Contact Us
- [GitHub Issues](https://github.com/apache/incubator-hugegraph/issues):
Feedback on usage issues and functional requirements (quick response)
- Feedback Email:
[[email protected]](mailto:[email protected])
([subscriber](https://hugegraph.apache.org/docs/contribution-guidelines/subscribe/)
only)
+- Security Email:
[[email protected]](mailto:[email protected]) (Report
SEC problems)
- WeChat public account: Apache HugeGraph, welcome to scan this QR code to
follow us.
- <img
src="https://github.com/apache/incubator-hugegraph-doc/blob/master/assets/images/wechat.png?raw=true";
alt="QR png" width="300"/>
+ <img
src="https://github.com/apache/hugegraph-doc/blob/master/assets/images/wechat.png?raw=true";
alt="QR png" width="300"/>
diff --git
a/content/en/docs/performance/api-preformance/hugegraph-api-0.5.6-Cassandra.md
b/content/en/docs/performance/api-preformance/hugegraph-api-0.5.6-Cassandra.md
index d7666b90..a4f1e305 100644
---
a/content/en/docs/performance/api-preformance/hugegraph-api-0.5.6-Cassandra.md
+++
b/content/en/docs/performance/api-preformance/hugegraph-api-0.5.6-Cassandra.md
@@ -4,15 +4,22 @@ linkTitle: "v0.5.6 Cluster(Cassandra)"
weight: 2
---
+> **Note:**
+>
+> The current performance metrics are based on an earlier version. The
**latest version** has significant
+> improvements in both performance and functionality. We encourage you to
refer to the most recent release featuring
+> **autonomous distributed storage** and **enhanced computational push down
capabilities**. Alternatively,
+> you may wait for the community to update the data with these enhancements.
+
### 1 Test environment
Compressed machine information
-CPU | Memory | 网卡 | 磁盘
--------------------------------------------- | ------ | --------- |
------------------
-48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB
SSD,2.7T HDD
+| CPU | Memory | 网卡 | 磁盘
|
+|----------------------------------------------|--------|-----------|--------------------|
+| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB
SSD,2.7T HDD |
-- Starting Pressure Machine Information: Configured the same as the compressed
machine.
+- Starting Pressure Machine Information: Configure the same as the compressed
machine.
- Testing tool: Apache JMeter 2.5.1.
Note: The machine used to initiate the load and the machine being tested are
located in the same data center (or server room)
@@ -37,7 +44,7 @@ A 15-node Cassandra cluster is used for backend storage.
HugeGraph and the Cassa
### 3 Summary of Performance Results
-1. The speed of single vertex and edge insertion in HugeGraph is 9000 and 4500
per second, respectively.
+1. The speed of a single vertex and edge insertion in HugeGraph is 9000 and
4500 per second, respectively.
2. The speed of bulk vertex and edge insertion is 50,000 and 150,000 per
second, respectively, which is much higher than the single insertion speed.
3. The concurrency for querying vertices and edges by ID can reach more than
12,000, and the average request delay is less than 70ms.
diff --git
a/content/en/docs/performance/api-preformance/hugegraph-api-0.5.6-RocksDB.md
b/content/en/docs/performance/api-preformance/hugegraph-api-0.5.6-RocksDB.md
index 99e742b1..4251f784 100644
--- a/content/en/docs/performance/api-preformance/hugegraph-api-0.5.6-RocksDB.md
+++ b/content/en/docs/performance/api-preformance/hugegraph-api-0.5.6-RocksDB.md
@@ -4,15 +4,22 @@ linkTitle: "v0.5.6 Stand-alone(RocksDB)"
weight: 1
---
+> **Note:**
+>
+> The current performance metrics are based on an earlier version. The
**latest version** has significant
+> improvements in both performance and functionality. We encourage you to
refer to the most recent release featuring
+> **autonomous distributed storage** and **enhanced computational push down
capabilities**. Alternatively,
+> you may wait for the community to update the data with these enhancements.
+
### 1 Test environment
Compressed machine information:
-CPU | Memory | 网卡 | 磁盘
--------------------------------------------- | ------ | --------- |
------------------
-48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB
SSD,2.7T HDD
+| CPU | Memory | 网卡 | 磁盘
|
+|----------------------------------------------|--------|-----------|--------------------|
+| 48 Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz | 128G | 10000Mbps | 750GB
SSD,2.7T HDD |
-- Information about the machine used to generate load: configured the same as
the machine that is being tested under load.
+- Information about the machine used to generate loads: configured the same as
the machine that is being tested under load.
- Testing tool: Apache JMeter 2.5.1
Note: The load-generating machine and the machine under test are located in
the same local network.
@@ -62,7 +69,7 @@ Duration: 5 minutes
</center>
-####### in conclusion:
+###### in conclusion:
- With a concurrency of 2200, the throughput for vertices is 2026.8. This
means that the system can process data at a rate of 405360 per second (2026.8 *
200).
@@ -74,7 +81,7 @@ Duration: 5 minutes
<img src="/docs/images/API-perf/v0.5.6/rocksdb/edge_batch.png" alt="image">
</center>
-####### Conclusion:
+###### Conclusion:
- With a concurrency of 900, the throughput for edges is 776.9. This means
that the system can process data at a rate of 388450 per second (776.9 * 500).
@@ -98,7 +105,7 @@ Stress limit testing is a process of continuously increasing
the concurrency lev
</center>
-####### Conclusion:
+###### Conclusion:
- With a concurrency of 11500, the throughput is 10730. This means that the
system can handle a single concurrent insertion of vertices at a concurrency
level of 11500.
@@ -109,7 +116,7 @@ Stress limit testing is a process of continuously
increasing the concurrency lev
</center>
-####### Conclusion:
+###### Conclusion:
- With a concurrency of 9000, the throughput is 8418. This means that the
system can handle a single concurrent insertion of edges at a concurrency level
of 9000.
@@ -132,7 +139,7 @@ Continuously increasing the concurrency level to test the
upper limit of the ser
<img src="/docs/images/API-perf/v0.5.6/rocksdb/vertex_id_query.png"
alt="image">
</center>
-####### Conclusion：
+###### Conclusion：
- Concurrency is 14,000, throughput is 12,663. The concurrency capacity for
querying vertices by ID is 14,000, with an average delay of 44ms.
@@ -143,6 +150,6 @@ Continuously increasing the concurrency level to test the
upper limit of the ser
</center>
-####### Conclusion：
+###### Conclusion：
- Concurrency is 13,000, throughput is 12,225. The concurrency capacity for
querying edges by ID is 13,000, with an average delay of 12ms.
diff --git a/content/en/docs/performance/hugegraph-benchmark-0.5.6.md
b/content/en/docs/performance/hugegraph-benchmark-0.5.6.md
index 4df9a9e7..d8f39ea3 100644
--- a/content/en/docs/performance/hugegraph-benchmark-0.5.6.md
+++ b/content/en/docs/performance/hugegraph-benchmark-0.5.6.md
@@ -4,6 +4,13 @@ linkTitle: "HugeGraph BenchMark Performance"
weight: 1
---
+> **Note:**
+>
+> The current performance metrics are based on an earlier version. The
**latest version** has significant
+> improvements in both performance and functionality. We encourage you to
refer to the most recent release featuring
+> **autonomous distributed storage** and **enhanced computational push down
capabilities**. Alternatively,
+> you may wait for the community to update the data with these enhancements.
+
### 1 Test environment
#### 1.1 Hardware information
@@ -23,7 +30,7 @@ Testing is done using the
[graphdb-benchmark](https://github.com/socialsensor/gr
- Query, which mainly includes the basic query operations of the graph
database:
- Find Neighbors, which queries the neighbors of all vertices.
- Find Adjacent Nodes, which queries the adjacent vertices of all edges.
- - Find Shortest Path, which queries the shortest path from the first vertex
to 100 random vertices.
+ - Find the Shortest Path, which queries the shortest path from the first
vertex to 100 random vertices.
- Clustering, which is a community detection algorithm based on the Louvain
Method.
##### 1.2.2 Test dataset
@@ -39,7 +46,7 @@ Tests are conducted using both synthetic and real data.
- CW uses synthetic data generated by the [LFR-Benchmark
generator](https://sites.google.com/site/andrealancichinetti/files).
-The size of the datasets used in this test are not mentioned.
+The size of the datasets used in this test is not mentioned.
| Name | Number of Vertices | Number of Edges | File Size |
|-------------------------|-----------|-----------|--------|
@@ -99,11 +106,11 @@ _Instructions_
_Instructions_
-- The data in the table header "( )" represents the data scale, in terms of
vertices.
-- The data in the table represents the time spent traversing vertices, in
seconds.
+- The data in the table header "()" represents the data scale, in terms of
vertices.
+- The data in the table represents the time spent traversing vertices in
seconds.
- 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.
-##### 2.2.3 FA性能
+##### 2.2.3 FA performance
| Backend | email-enron(30w) | amazon0601(300w) | com-youtube.ungraph(300w)
| com-lj.ungraph(3000w) |
|-----------|------------------|------------------|---------------------------|-----------------------|
@@ -113,10 +120,10 @@ _Instructions_
_Explanation_
-- The data size in the header "( )" is based on the number of vertices.
-- The data in the table is the time it takes to traverse the vertices, in
seconds.
-- 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.
--
+- The data size in the header "()" is based on the number of vertices.
+- The data in the table is the time it takes to traverse the vertices in
seconds.
+- For example, HugeGraph with RocksDB backend traverses all vertices in the
amazon0601 dataset, and it looks up adjacent edges and other vertices, taking a
total of 45.118 seconds.
+
###### Conclusion
- Traversal performance: Neo4j > HugeGraph(RocksDB) > Titan(thrift+Cassandra)
@@ -150,11 +157,11 @@ _Explanation_
##### K-neighbor Performance
-Vertex | Depth | Degree 1 | Degree 2 | Degree 3 | Degree 4 | Degree 5 | Degree
6
------ | ----- | -------- | -------- | -------- | -------- | -------- | --------
-v1 | Time | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM
-v111 | Time | 0.027s | 0.034s | 0.115s | 1.36s | OOM | --
-v1111 | Time | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM
+| Vertex | Depth | Degree 1 | Degree 2 | Degree 3 | Degree 4 | Degree 5 |
Degree 6 |
+|--------|-------|----------|----------|----------|----------|----------|----------|
+| v1 | Time | 0.031s | 0.033s | 0.048s | 0.500s | 11.27s | OOM
|
+| v111 | Time | 0.027s | 0.034s | 0.115s | 1.36s | OOM | --
|
+| v1111 | Time | 0.039s | 0.027s | 0.052s | 0.511s | 10.96s | OOM
|
_Explanation_
@@ -162,15 +169,14 @@ _Explanation_
##### K-out performance
-Vertex | Depth | 1st Degree | 2nd Degree | 3rd Degree | 4th Degree | 5th
Degree | 6th Degree
------ | -- | ------ | ------ | ------ | ------ | --------- | ---
-v1 | Time | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s | OOM
- | Degree | 10 | 133 | 2453 | 50,830 | 1,128,688 |
-v111 | Time | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s | OOM
- | Degree | 10 | 211 | 4944 | 113150 | 2,629,970 |
-v1111 | Time | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s | OOM
- | Degree | 10 | 140 | 2555 | 50825 | 1,070,230 |
-
+| Vertex | Depth | 1st Degree | 2nd Degree | 3rd Degree | 4th Degree | 5th
Degree | 6th Degree |
+|--------|-------|------------|------------|------------|------------|------------|------------|
+| v1 | Time | 0.054s | 0.057s | 0.109s | 0.526s | 3.77s
| OOM |
+| Degree | 10 | 133 | 2453 | 50,830 | 1,128,688 |
| |
+| v111 | Time | 0.032s | 0.042s | 0.136s | 1.25s | 20.62s
| OOM |
+| Degree | 10 | 211 | 4944 | 113150 | 2,629,970 |
| |
+| v1111 | Time | 0.039s | 0.045s | 0.053s | 1.10s | 2.92s
| OOM |
+| Degree | 10 | 140 | 2555 | 50825 | 1,070,230 |
| |
_Explanation_
@@ -192,7 +198,7 @@ _Explanation_
_Explanation_
- The "scale" is based on the number of vertices.
-- 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.
+- 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.
- The CW test is a comprehensive evaluation of CRUD operations.
- In this test, HugeGraph, like Titan, did not use the client and directly
operated on the core.
diff --git a/content/en/docs/performance/hugegraph-loader-performance.md
b/content/en/docs/performance/hugegraph-loader-performance.md
index 2512ced1..77b55f3f 100644
--- a/content/en/docs/performance/hugegraph-loader-performance.md
+++ b/content/en/docs/performance/hugegraph-loader-performance.md
@@ -4,18 +4,27 @@ linkTitle: "HugeGraph-Loader Performance"
weight: 3
---
+> **Note:**
+>
+> The current performance metrics are based on an earlier version. The
**latest version** has significant
+> improvements in both performance and functionality. We encourage you to
refer to the most recent release featuring
+> **autonomous distributed storage** and **enhanced computational push down
capabilities**. Alternatively,
+> you may wait for the community to update the data with these enhancements.
+
## Use Cases
-When the number of graph data to be batch inserted (including vertices and
edges) is at the billion level or below, or the total data size is less than
TB, the [HugeGraph-Loader](/docs/quickstart/hugegraph-loader) tool can be used
to continuously and quickly import graph data.
+When the number of graph data to be batch inserted (including vertices and
edges) is at the billion level or below,
+or the total data size is less than TB, the
[HugeGraph-Loader](/docs/quickstart/hugegraph-loader) tool can be used to
continuously and quickly import
+graph data.
## Performance
> The test uses the edge data of website.
-### RocksDB single-machine performance
+### RocksDB single-machine performance (Update: multi-raft + rocksdb cluster
is supported now)
-- When label index is turned off, 228k edges/s.
-- When label index is turned on, 153k edges/s.
+- When the label index is turned off, 228k edges/s.
+- When the label index is turned on, 153k edges/s.
### Cassandra cluster performance
diff --git a/content/en/docs/quickstart/hugegraph-ai.md
b/content/en/docs/quickstart/hugegraph-ai.md
index 39bacdac..d0751b29 100644
--- a/content/en/docs/quickstart/hugegraph-ai.md
+++ b/content/en/docs/quickstart/hugegraph-ai.md
@@ -1,11 +1,12 @@
---
-title: "HugeGraph-Ai Quick Start (Beta)"
-linkTitle: "Explore with HugeGraph-Ai (Beta)"
+title: "HugeGraph-AI Quick Start"
+linkTitle: "Explore with HugeGraph-AI"
weight: 4
---
-### 1 HugeGraph-Ai Overview
-hugegraph-ai aims to explore the integration of HugeGraph and artificial
intelligence (AI), including applications combined with large models,
integration with graph machine learning components, etc., to provide
comprehensive support for developers to use HugeGraph's AI capabilities in
projects.
+### 1 HugeGraph-AI Overview
+hugegraph-ai aims to explore the integration of HugeGraph and artificial
intelligence (AI), including applications combined
+with large models, integration with graph machine learning components, etc.,
to provide comprehensive support for developers to use HugeGraph's AI
capabilities in projects.
### 2 Environment Requirements
- python 3.8+
![https://github.com/apache/incubator-hugegraph-doc/blob/master/assets/images/wechat.png?raw=true"](https://github.com/apache/incubator-hugegraph-doc/blob/master/assets/images/wechat.png?raw=true"){kind=link}
![https://github.com/apache/hugegraph-doc/blob/master/assets/images/wechat.png?raw=true"](https://github.com/apache/hugegraph-doc/blob/master/assets/images/wechat.png?raw=true"){kind=link}