diff --git a/cn/docs/_print/index.html b/cn/docs/_print/index.html
index 80e7d1cf7..16e655222 100644
--- a/cn/docs/_print/index.html
+++ b/cn/docs/_print/index.html
@@ -1343,14 +1343,14 @@
hugeClient . close ();
}
}
-4.4 运行Example 运行Example之前需要启动Server, 启动过程见HugeGraph-Server Quick Start
4.5 Example示例说明 示例说明见HugeGraph-Client基本API介绍
3.6 - HugeGraph-Computer Quick Start 1 HugeGraph-Computer 概述 HugeGraph-ComputerPregel 的一个实现. 它可以运行在 Kubernetes 上。
特性 支持分布式MPP图计算，集成HugeGraph作为图输入输出存储。 算法基于BSP(Bulk Synchronous Parallel)模型，通过多次并行迭代进行计算，每一次迭代都是一次超步。 自动内存管理。该框架永远不会出现 OOM（内存不足），因为如果它没有足够的内存来容纳所有数据，它会将一些数据拆分到磁盘。 边的部分或超级节点的消息可以在内存中，所以你永远不会丢失它。 您可以从 HDFS 或 HugeGraph 或任何其他系统加载数据。 您可以将结果输出到 HDFS 或 HugeGraph，或任何其他系统。 易于开发新算法。您只需要像在单个服务器中一样专注于仅顶点处理，而不必担心消息传输和内存存储管理。 2 开始 要使用 HugeGraph-Computer 运行算法，您需要安装 64 位 Java 11 或更高版本。
还需要首先部署 HugeGraph-Server 和 Etcd .
有两种方式可以获取 HugeGraph-Computer：
2.1 Download the compiled archive 下载最新版本的 HugeGraph-Computer release 包：
wget https://github.com/apache/hugegraph-computer/releases/download/v${ version } /hugegraph-loader-${ version } .tar.gz
- tar zxvf hugegraph-computer-${ version } .tar.gz
+ 4.4 运行Example 运行Example之前需要启动Server, 启动过程见HugeGraph-Server Quick Start
4.5 Example示例说明 示例说明见HugeGraph-Client基本API介绍
3.6 - HugeGraph-Computer Quick Start 1 HugeGraph-Computer 概述 HugeGraph-ComputerPregel 的一个实现. 它可以运行在 Kubernetes 上。
特性 支持分布式MPP图计算，集成HugeGraph作为图输入输出存储。 算法基于BSP(Bulk Synchronous Parallel)模型，通过多次并行迭代进行计算，每一次迭代都是一次超步。 自动内存管理。该框架永远不会出现 OOM（内存不足），因为如果它没有足够的内存来容纳所有数据，它会将一些数据拆分到磁盘。 边的部分或超级节点的消息可以在内存中，所以你永远不会丢失它。 您可以从 HDFS 或 HugeGraph 或任何其他系统加载数据。 您可以将结果输出到 HDFS 或 HugeGraph，或任何其他系统。 易于开发新算法。您只需要像在单个服务器中一样专注于仅顶点处理，而不必担心消息传输和内存存储管理。 2 开始 要使用 HugeGraph-Computer 运行算法，您需要安装 64 位 Java 11 或更高版本。
还需要首先部署 HugeGraph-Server 和 Etcd .
有两种方式可以获取 HugeGraph-Computer：
2.1 Download the compiled archive 下载最新版本的 HugeGraph-Computer release 包：
wget https://downloads.apache.org/incubator/hugegraph/${ version } /apache-hugegraph-computer-incubating-${ version } .tar.gz
+ tar zxvf apache-hugegraph-computer-incubating-${ version } .tar.gz -C hugegraph-computer
2.2 Clone source code to compile and package 克隆最新版本的 HugeGraph-Computer 源码包：
$ git clone https://github.com/apache/hugegraph-computer.git
编译生成tar包:
cd hugegraph-computer
mvn clean package -DskipTests
- 2.3 启动 master 节点 您可以使用 -c 参数指定配置文件, 更多computer 配置请看: Computer Config Options
cd hugegraph-computer-${ version }
+2.3 启动 master 节点 您可以使用 -c 参数指定配置文件, 更多computer 配置请看: Computer Config Options
cd hugegraph-computer
bin/start-computer.sh -d local -r master
- 2.4 启动 worker 节点 bin/start-computer.sh -d local -r worker
+ 2.4 启动 worker 节点 bin/start-computer.sh -d local -r worker
2.5 查询算法结果 2.5.1 为 server 启用 OLAP 索引查询
如果没有启用OLAP索引，则需要启用, 更多参考: modify-graphs-read-mode
PUT http://localhost:8080/graphs/hugegraph/graph_read_mode
"ALL"
diff --git a/cn/docs/index.xml b/cn/docs/index.xml
index 103cc9037..8f103674f 100644
--- a/cn/docs/index.xml
+++ b/cn/docs/index.xml
@@ -10700,8 +10700,8 @@ HugeGraph Toolchain 版本: toolchain-1.0.0</p>
</ul>
<h4 id="21-download-the-compiled-archive">2.1 Download the compiled archive</h4>
<p>下载最新版本的 HugeGraph-Computer release 包：</p>
-<div class="highlight"><pre tabindex="0" style="background-color:#f8f8f8;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-bash" data-lang="bash"><span style="display:flex;"><span>wget https://github.com/apache/hugegraph-computer/releases/download/v<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>/hugegraph-loader-<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>.tar.gz
-</span></span><span style="display:flex;"><span>tar zxvf hugegraph-computer-<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>.tar.gz
+<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>wget https://downloads.apache.org/incubator/hugegraph/<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>/apache-hugegraph-computer-incubating-<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>.tar.gz
+</span></span><span style="display:flex;"><span>tar zxvf apache-hugegraph-computer-incubating-<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>.tar.gz -C hugegraph-computer
</span></span></code></pre></div><h4 id="22-clone-source-code-to-compile-and-package">2.2 Clone source code to compile and package</h4>
<p>克隆最新版本的 HugeGraph-Computer 源码包：</p>
<div class="highlight"><pre tabindex="0" style="background-color:#f8f8f8;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-bash" data-lang="bash"><span style="display:flex;"><span>$ git clone https://github.com/apache/hugegraph-computer.git
@@ -10712,10 +10712,10 @@ HugeGraph Toolchain 版本: toolchain-1.0.0</p>
<blockquote>
<p>您可以使用 <code>-c</code> 参数指定配置文件, 更多computer 配置请看: <a href="/docs/config/config-computer#computer-config-options">Computer Config Options</a></p>
</blockquote>
-<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><span style="color:#204a87">cd</span> hugegraph-computer-<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>
+<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><span style="color:#204a87">cd</span> hugegraph-computer
</span></span><span style="display:flex;"><span>bin/start-computer.sh -d <span style="color:#204a87">local</span> -r master
</span></span></code></pre></div><h4 id="24-启动-worker-节点">2.4 启动 worker 节点</h4>
-<div class="highlight"><pre tabindex="0" style="background-color:#f8f8f8;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-fallback" data-lang="fallback"><span style="display:flex;"><span>bin/start-computer.sh -d local -r worker
+<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/start-computer.sh -d <span style="color:#204a87">local</span> -r worker
</span></span></code></pre></div><h4 id="25-查询算法结果">2.5 查询算法结果</h4>
<p>2.5.1 为 server 启用 <code>OLAP</code> 索引查询</p>
<p>如果没有启用OLAP索引，则需要启用, 更多参考: <a href="/docs/clients/restful-api/graphs/#634-modify-graphs-read-mode-this-operation-requires-administrator-privileges">modify-graphs-read-mode</a></p>
diff --git a/cn/docs/quickstart/_print/index.html b/cn/docs/quickstart/_print/index.html
index 803e9f2e6..7b905d397 100644
--- a/cn/docs/quickstart/_print/index.html
+++ b/cn/docs/quickstart/_print/index.html
@@ -1337,14 +1337,14 @@
hugeClient . close ();
}
}
-4.4 运行Example 运行Example之前需要启动Server, 启动过程见HugeGraph-Server Quick Start
4.5 Example示例说明 示例说明见HugeGraph-Client基本API介绍
6 - HugeGraph-Computer Quick Start 1 HugeGraph-Computer 概述 HugeGraph-ComputerPregel 的一个实现. 它可以运行在 Kubernetes 上。
特性 支持分布式MPP图计算，集成HugeGraph作为图输入输出存储。 算法基于BSP(Bulk Synchronous Parallel)模型，通过多次并行迭代进行计算，每一次迭代都是一次超步。 自动内存管理。该框架永远不会出现 OOM（内存不足），因为如果它没有足够的内存来容纳所有数据，它会将一些数据拆分到磁盘。 边的部分或超级节点的消息可以在内存中，所以你永远不会丢失它。 您可以从 HDFS 或 HugeGraph 或任何其他系统加载数据。 您可以将结果输出到 HDFS 或 HugeGraph，或任何其他系统。 易于开发新算法。您只需要像在单个服务器中一样专注于仅顶点处理，而不必担心消息传输和内存存储管理。 2 开始 要使用 HugeGraph-Computer 运行算法，您需要安装 64 位 Java 11 或更高版本。
还需要首先部署 HugeGraph-Server 和 Etcd .
有两种方式可以获取 HugeGraph-Computer：
2.1 Download the compiled archive 下载最新版本的 HugeGraph-Computer release 包：
wget https://github.com/apache/hugegraph-computer/releases/download/v${ version } /hugegraph-loader-${ version } .tar.gz
- tar zxvf hugegraph-computer-${ version } .tar.gz
+ 4.4 运行Example 运行Example之前需要启动Server, 启动过程见HugeGraph-Server Quick Start
4.5 Example示例说明 示例说明见HugeGraph-Client基本API介绍
6 - HugeGraph-Computer Quick Start 1 HugeGraph-Computer 概述 HugeGraph-ComputerPregel 的一个实现. 它可以运行在 Kubernetes 上。
特性 支持分布式MPP图计算，集成HugeGraph作为图输入输出存储。 算法基于BSP(Bulk Synchronous Parallel)模型，通过多次并行迭代进行计算，每一次迭代都是一次超步。 自动内存管理。该框架永远不会出现 OOM（内存不足），因为如果它没有足够的内存来容纳所有数据，它会将一些数据拆分到磁盘。 边的部分或超级节点的消息可以在内存中，所以你永远不会丢失它。 您可以从 HDFS 或 HugeGraph 或任何其他系统加载数据。 您可以将结果输出到 HDFS 或 HugeGraph，或任何其他系统。 易于开发新算法。您只需要像在单个服务器中一样专注于仅顶点处理，而不必担心消息传输和内存存储管理。 2 开始 要使用 HugeGraph-Computer 运行算法，您需要安装 64 位 Java 11 或更高版本。
还需要首先部署 HugeGraph-Server 和 Etcd .
有两种方式可以获取 HugeGraph-Computer：
2.1 Download the compiled archive 下载最新版本的 HugeGraph-Computer release 包：
wget https://downloads.apache.org/incubator/hugegraph/${ version } /apache-hugegraph-computer-incubating-${ version } .tar.gz
+ tar zxvf apache-hugegraph-computer-incubating-${ version } .tar.gz -C hugegraph-computer
2.2 Clone source code to compile and package 克隆最新版本的 HugeGraph-Computer 源码包：
$ git clone https://github.com/apache/hugegraph-computer.git
编译生成tar包:
cd hugegraph-computer
mvn clean package -DskipTests
- 2.3 启动 master 节点 您可以使用 -c 参数指定配置文件, 更多computer 配置请看: Computer Config Options
cd hugegraph-computer-${ version }
+2.3 启动 master 节点 您可以使用 -c 参数指定配置文件, 更多computer 配置请看: Computer Config Options
cd hugegraph-computer
bin/start-computer.sh -d local -r master
- 2.4 启动 worker 节点 bin/start-computer.sh -d local -r worker
+ 2.4 启动 worker 节点 bin/start-computer.sh -d local -r worker
2.5 查询算法结果 2.5.1 为 server 启用 OLAP 索引查询
如果没有启用OLAP索引，则需要启用, 更多参考: modify-graphs-read-mode
PUT http://localhost:8080/graphs/hugegraph/graph_read_mode
"ALL"
diff --git a/cn/docs/quickstart/hugegraph-computer/index.html b/cn/docs/quickstart/hugegraph-computer/index.html
index 15fe1936f..359b51ddf 100644
--- a/cn/docs/quickstart/hugegraph-computer/index.html
+++ b/cn/docs/quickstart/hugegraph-computer/index.html
@@ -8,30 +8,30 @@
还需要首先部署 HugeGraph-Server 和 Etcd.
有两种方式可以获取 HugeGraph-Computer：
下载已编译的压缩包 克隆源码编译打包 2.1 Download the compiled archive 下载最新版本的 HugeGraph-Computer release 包：
-wget https://github.com/apache/hugegraph-computer/releases/download/v${version}/hugegraph-loader-${version}.tar.gz tar zxvf hugegraph-computer-${version}.tar.gz 2.2 Clone source code to compile and package 克隆最新版本的 HugeGraph-Computer 源码包：">Documentation Quick Start Analysis with HugeGraph-Computer HugeGraph-Computer Quick Start 1 HugeGraph-Computer 概述 HugeGraph-ComputerPregel 的一个实现. 它可以运行在 Kubernetes 上。
特性 支持分布式MPP图计算，集成HugeGraph作为图输入输出存储。 算法基于BSP(Bulk Synchronous Parallel)模型，通过多次并行迭代进行计算，每一次迭代都是一次超步。 自动内存管理。该框架永远不会出现 OOM（内存不足），因为如果它没有足够的内存来容纳所有数据，它会将一些数据拆分到磁盘。 边的部分或超级节点的消息可以在内存中，所以你永远不会丢失它。 您可以从 HDFS 或 HugeGraph 或任何其他系统加载数据。 您可以将结果输出到 HDFS 或 HugeGraph，或任何其他系统。 易于开发新算法。您只需要像在单个服务器中一样专注于仅顶点处理，而不必担心消息传输和内存存储管理。 2 开始 要使用 HugeGraph-Computer 运行算法，您需要安装 64 位 Java 11 或更高版本。
还需要首先部署 HugeGraph-Server 和 Etcd .
有两种方式可以获取 HugeGraph-Computer：
2.1 Download the compiled archive 下载最新版本的 HugeGraph-Computer release 包：
wget https://github.com/apache/hugegraph-computer/releases/download/v${ version } /hugegraph-loader-${ version } .tar.gz
- tar zxvf hugegraph-computer-${ version } .tar.gz
+ Documentation Quick Start Analysis with HugeGraph-Computer HugeGraph-Computer Quick Start 1 HugeGraph-Computer 概述 HugeGraph-ComputerPregel 的一个实现. 它可以运行在 Kubernetes 上。
特性 支持分布式MPP图计算，集成HugeGraph作为图输入输出存储。 算法基于BSP(Bulk Synchronous Parallel)模型，通过多次并行迭代进行计算，每一次迭代都是一次超步。 自动内存管理。该框架永远不会出现 OOM（内存不足），因为如果它没有足够的内存来容纳所有数据，它会将一些数据拆分到磁盘。 边的部分或超级节点的消息可以在内存中，所以你永远不会丢失它。 您可以从 HDFS 或 HugeGraph 或任何其他系统加载数据。 您可以将结果输出到 HDFS 或 HugeGraph，或任何其他系统。 易于开发新算法。您只需要像在单个服务器中一样专注于仅顶点处理，而不必担心消息传输和内存存储管理。 2 开始 要使用 HugeGraph-Computer 运行算法，您需要安装 64 位 Java 11 或更高版本。
还需要首先部署 HugeGraph-Server 和 Etcd .
有两种方式可以获取 HugeGraph-Computer：
2.1 Download the compiled archive 下载最新版本的 HugeGraph-Computer release 包：
wget https://downloads.apache.org/incubator/hugegraph/${ version } /apache-hugegraph-computer-incubating-${ version } .tar.gz
+ tar zxvf apache-hugegraph-computer-incubating-${ version } .tar.gz -C hugegraph-computer
2.2 Clone source code to compile and package 克隆最新版本的 HugeGraph-Computer 源码包：
$ git clone https://github.com/apache/hugegraph-computer.git
编译生成tar包:
cd hugegraph-computer
mvn clean package -DskipTests
- 2.3 启动 master 节点 您可以使用 -c 参数指定配置文件, 更多computer 配置请看: Computer Config Options
cd hugegraph-computer-${ version }
+2.3 启动 master 节点 您可以使用 -c 参数指定配置文件, 更多computer 配置请看: Computer Config Options
cd hugegraph-computer
bin/start-computer.sh -d local -r master
- 2.4 启动 worker 节点 bin/start-computer.sh -d local -r worker
+ 2.4 启动 worker 节点 bin/start-computer.sh -d local -r worker
2.5 查询算法结果 2.5.1 为 server 启用 OLAP 索引查询
如果没有启用OLAP索引，则需要启用, 更多参考: modify-graphs-read-mode
PUT http://localhost:8080/graphs/hugegraph/graph_read_mode
"ALL"
@@ -86,7 +86,7 @@
# 注意: 诊断日志仅在作业失败时存在，并且只会保存一小时。
kubectl get event --field-selector reason = ComputerJobFailed --field-selector involvedObject.name= pagerank-sample -n hugegraph-computer-system
2.2.8 显示作业的成功事件 NOTE: it will only be saved for one hour
kubectl get event --field-selector reason = ComputerJobSucceed --field-selector involvedObject.name= pagerank-sample -n hugegraph-computer-system
- 2.2.9 查询算法结果 如果输出到 Hugegraph-Server 则与 Locally 模式一致，如果输出到 HDFS ，请检查 hugegraph-computerresults{jobId}目录下的结果文件。
3 内置算法文档 3.1 支持的算法列表: 中心性算法: PageRank BetweennessCentrality ClosenessCentrality DegreeCentrality 社区算法: ClusteringCoefficient Kcore Lpa TriangleCount Wcc 路径算法: RingsDetection RingsDetectionWithFilter 更多算法请看: Built-In algorithms
3.2 算法描述 TODO
4 算法开发指南 TODO
5 注意事项 如果computer-k8s模块下面的某些类不存在，你需要运行mvn compile来提前生成对应的类。
+
2.2.9 查询算法结果 如果输出到 Hugegraph-Server 则与 Locally 模式一致，如果输出到 HDFS ，请检查 hugegraph-computerresults{jobId}目录下的结果文件。
3 内置算法文档 3.1 支持的算法列表: 中心性算法: PageRank BetweennessCentrality ClosenessCentrality DegreeCentrality 社区算法: ClusteringCoefficient Kcore Lpa TriangleCount Wcc 路径算法: RingsDetection RingsDetectionWithFilter 更多算法请看: Built-In algorithms
3.2 算法描述 TODO
4 算法开发指南 TODO
5 注意事项 如果computer-k8s模块下面的某些类不存在，你需要运行mvn compile来提前生成对应的类。 /cn/docs/guides/architectural/ 2023-06-25T21:06:07+08:00 /cn/docs/config/config-guide/ 2023-06-21T14:48:04+08:00 /cn/docs/language/hugegraph-gremlin/ 2023-01-01T16:16:43+08:00 /cn/docs/performance/hugegraph-benchmark-0.5.6/ 2022-09-15T15:16:23+08:00 /cn/docs/quickstart/hugegraph-server/ 2023-06-25T21:06:07+08:00 /cn/docs/introduction/readme/ 2023-06-18T14:57:33+08:00 /cn/docs/changelog/hugegraph-1.0.0-release-notes/ 2023-01-09T07:41:46+08:00 /cn/docs/clients/restful-api/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/schema/ 2023-05-14T19:35:13+08:00 /cn/docs/performance/api-preformance/hugegraph-api-0.5.6-rocksdb/ 2023-01-01T16:16:43+08:00 /cn/docs/contribution-guidelines/contribute/ 2023-01-04T22:59:07+08:00 /cn/docs/config/config-option/ 2023-02-08T20:56:09+08:00 /cn/docs/guides/desgin-concept/ 2022-04-17T11:36:55+08:00 /cn/docs/download/download/ 2023-06-17T14:43:04+08:00 /cn/docs/language/hugegraph-example/ 2023-02-02T01:21:10+08:00 /cn/docs/clients/hugegraph-client/ 2022-09-15T15:16:23+08:00 /cn/docs/performance/api-preformance/ 2023-06-17T14:43:04+08:00 /cn/docs/quickstart/hugegraph-loader/ 2023-05-17T23:12:35+08:00 /cn/docs/clients/restful-api/propertykey/ 2023-05-19T05:15:56-05:00 /cn/docs/changelog/hugegraph-0.11.2-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.12.0-release-notes/ 2023-01-01T16:16:43+08:00 /cn/docs/performance/api-preformance/hugegraph-api-0.5.6-cassandra/ 2023-01-01T16:16:43+08:00 /cn/docs/contribution-guidelines/subscribe/ 2023-06-17T14:43:04+08:00 /cn/docs/config/config-authentication/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/gremlin-console/ 2023-06-12T23:52:07+08:00 /cn/docs/guides/custom-plugin/ 2022-09-15T15:16:23+08:00 /cn/docs/performance/hugegraph-loader-performance/ 2022-04-17T11:36:55+08:00 /cn/docs/quickstart/hugegraph-tools/ 2023-05-09T21:27:34+08:00 /cn/docs/quickstart/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.10.4-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/vertexlabel/ 2022-04-17T11:36:55+08:00 /cn/docs/contribution-guidelines/validate-release/ 2023-02-15T16:14:21+08:00 /cn/docs/guides/backup-restore/ 2022-04-17T11:36:55+08:00 /cn/docs/config/ 2022-04-17T11:36:55+08:00 /cn/docs/config/config-https/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/edgelabel/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.9.2-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/quickstart/hugegraph-hubble/ 2023-01-01T16:16:43+08:00 /cn/docs/contribution-guidelines/hugegraph-server-idea-setup/ 2023-06-25T21:06:07+08:00 /cn/docs/clients/ 2022-04-17T11:36:55+08:00 /cn/docs/config/config-computer/ 2023-01-01T16:16:43+08:00 /cn/docs/guides/faq/ 2023-01-04T22:59:07+08:00 /cn/docs/clients/restful-api/indexlabel/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.8.0-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/quickstart/hugegraph-client/ 2023-05-18T11:09:55+08:00 /cn/docs/guides/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/rebuild/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.7.4-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/quickstart/hugegraph-computer/ 2023-06-08T01:07:55+08:00 /cn/docs/language/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.6.1-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/vertex/ 2023-06-04T23:04:47+08:00 /cn/docs/clients/restful-api/edge/ 2022-09-15T15:16:23+08:00 /cn/docs/performance/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.5.6-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/ 2022-04-17T11:36:55+08:00 /cn/docs/contribution-guidelines/ 2022-12-30T19:57:48+08:00 /cn/docs/changelog/hugegraph-0.4.4-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/traverser/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/rank/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.3.3-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.2-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/variable/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/graphs/ 2022-05-27T09:27:37+08:00 /cn/docs/changelog/hugegraph-0.2.4-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/task/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/gremlin/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/auth/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/other/ 2022-04-17T11:36:55+08:00 /cn/docs/ 2022-12-30T19:57:48+08:00 /cn/blog/news/ 2022-04-17T11:36:55+08:00 /cn/blog/releases/ 2022-04-17T11:36:55+08:00 /cn/blog/2018/10/06/easy-documentation-with-docsy/ 2022-04-17T11:36:55+08:00 /cn/blog/2018/10/06/the-second-blog-post/ 2022-04-17T11:36:55+08:00 /cn/blog/2018/01/04/another-great-release/ 2022-04-17T11:36:55+08:00 /cn/docs/cla/ 2022-04-17T11:36:55+08:00 /cn/docs/performance/hugegraph-benchmark-0.4.4/ 2022-09-15T15:16:23+08:00 /cn/docs/summary/ 2022-11-27T21:05:55+08:00 /cn/blog/ 2022-04-17T11:36:55+08:00 /cn/categories/ /cn/community/ 2022-04-17T11:36:55+08:00 /cn/ 2023-01-04T22:59:07+08:00 /cn/search/ 2022-04-17T11:36:55+08:00 /cn/tags/ /cn/docs/guides/architectural/ 2023-06-25T21:06:07+08:00 /cn/docs/config/config-guide/ 2023-06-21T14:48:04+08:00 /cn/docs/language/hugegraph-gremlin/ 2023-01-01T16:16:43+08:00 /cn/docs/performance/hugegraph-benchmark-0.5.6/ 2022-09-15T15:16:23+08:00 /cn/docs/quickstart/hugegraph-server/ 2023-06-25T21:06:07+08:00 /cn/docs/introduction/readme/ 2023-06-18T14:57:33+08:00 /cn/docs/changelog/hugegraph-1.0.0-release-notes/ 2023-01-09T07:41:46+08:00 /cn/docs/clients/restful-api/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/schema/ 2023-05-14T19:35:13+08:00 /cn/docs/performance/api-preformance/hugegraph-api-0.5.6-rocksdb/ 2023-01-01T16:16:43+08:00 /cn/docs/contribution-guidelines/contribute/ 2023-01-04T22:59:07+08:00 /cn/docs/config/config-option/ 2023-02-08T20:56:09+08:00 /cn/docs/guides/desgin-concept/ 2022-04-17T11:36:55+08:00 /cn/docs/download/download/ 2023-06-17T14:43:04+08:00 /cn/docs/language/hugegraph-example/ 2023-02-02T01:21:10+08:00 /cn/docs/clients/hugegraph-client/ 2022-09-15T15:16:23+08:00 /cn/docs/performance/api-preformance/ 2023-06-17T14:43:04+08:00 /cn/docs/quickstart/hugegraph-loader/ 2023-05-17T23:12:35+08:00 /cn/docs/clients/restful-api/propertykey/ 2023-05-19T05:15:56-05:00 /cn/docs/changelog/hugegraph-0.11.2-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.12.0-release-notes/ 2023-01-01T16:16:43+08:00 /cn/docs/performance/api-preformance/hugegraph-api-0.5.6-cassandra/ 2023-01-01T16:16:43+08:00 /cn/docs/contribution-guidelines/subscribe/ 2023-06-17T14:43:04+08:00 /cn/docs/config/config-authentication/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/gremlin-console/ 2023-06-12T23:52:07+08:00 /cn/docs/guides/custom-plugin/ 2022-09-15T15:16:23+08:00 /cn/docs/performance/hugegraph-loader-performance/ 2022-04-17T11:36:55+08:00 /cn/docs/quickstart/hugegraph-tools/ 2023-05-09T21:27:34+08:00 /cn/docs/quickstart/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.10.4-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/vertexlabel/ 2022-04-17T11:36:55+08:00 /cn/docs/contribution-guidelines/validate-release/ 2023-02-15T16:14:21+08:00 /cn/docs/guides/backup-restore/ 2022-04-17T11:36:55+08:00 /cn/docs/config/ 2022-04-17T11:36:55+08:00 /cn/docs/config/config-https/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/edgelabel/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.9.2-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/quickstart/hugegraph-hubble/ 2023-01-01T16:16:43+08:00 /cn/docs/contribution-guidelines/hugegraph-server-idea-setup/ 2023-06-25T21:06:07+08:00 /cn/docs/clients/ 2022-04-17T11:36:55+08:00 /cn/docs/config/config-computer/ 2023-01-01T16:16:43+08:00 /cn/docs/guides/faq/ 2023-01-04T22:59:07+08:00 /cn/docs/clients/restful-api/indexlabel/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.8.0-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/quickstart/hugegraph-client/ 2023-05-18T11:09:55+08:00 /cn/docs/guides/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/rebuild/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.7.4-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/quickstart/hugegraph-computer/ 2023-06-25T21:06:46+08:00 /cn/docs/language/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.6.1-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/vertex/ 2023-06-04T23:04:47+08:00 /cn/docs/clients/restful-api/edge/ 2022-09-15T15:16:23+08:00 /cn/docs/performance/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.5.6-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/ 2022-04-17T11:36:55+08:00 /cn/docs/contribution-guidelines/ 2022-12-30T19:57:48+08:00 /cn/docs/changelog/hugegraph-0.4.4-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/traverser/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/rank/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.3.3-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/changelog/hugegraph-0.2-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/variable/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/graphs/ 2022-05-27T09:27:37+08:00 /cn/docs/changelog/hugegraph-0.2.4-release-notes/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/task/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/gremlin/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/auth/ 2022-04-17T11:36:55+08:00 /cn/docs/clients/restful-api/other/ 2022-04-17T11:36:55+08:00 /cn/docs/ 2022-12-30T19:57:48+08:00 /cn/blog/news/ 2022-04-17T11:36:55+08:00 /cn/blog/releases/ 2022-04-17T11:36:55+08:00 /cn/blog/2018/10/06/easy-documentation-with-docsy/ 2022-04-17T11:36:55+08:00 /cn/blog/2018/10/06/the-second-blog-post/ 2022-04-17T11:36:55+08:00 /cn/blog/2018/01/04/another-great-release/ 2022-04-17T11:36:55+08:00 /cn/docs/cla/ 2022-04-17T11:36:55+08:00 /cn/docs/performance/hugegraph-benchmark-0.4.4/ 2022-09-15T15:16:23+08:00 /cn/docs/summary/ 2022-11-27T21:05:55+08:00 /cn/blog/ 2022-04-17T11:36:55+08:00 /cn/categories/ /cn/community/ 2022-04-17T11:36:55+08:00 /cn/ 2023-01-04T22:59:07+08:00 /cn/search/ 2022-04-17T11:36:55+08:00 /cn/tags/ hugeClient . close ();
}
}
-4.4 Run The Example Before running Example, you need to start the Server. For the startup process, seeHugeGraph-Server Quick Start .
SeeIntroduce basic API of HugeGraph-Client .
3.6 - HugeGraph-Computer Quick Start 1 HugeGraph-Computer Overview The HugeGraph-ComputerPregel . It runs on Kubernetes framework.
Features Support distributed MPP graph computing, and integrates with HugeGraph as graph input/output storage. Based on BSP(Bulk Synchronous Parallel) model, an algorithm performs computing through multiple parallel iterations, every iteration is a superstep. Auto memory management. The framework will never be OOM(Out of Memory) since it will split some data to disk if it doesn’t have enough memory to hold all the data. The part of edges or the messages of super node can be in memory, so you will never lose it. You can load the data from HDFS or HugeGraph, or any other system. You can output the results to HDFS or HugeGraph, or any other system. Easy to develop a new algorithm. You just need to focus on a vertex only processing just like as in a single server, without worrying about message transfer and memory/storage management. 2 Get Started To run algorithm with HugeGraph-Computer, you need to install 64-bit Java 11 or later versions.
You also need to deploy HugeGraph-Server and Etcd .
There are two ways to get HugeGraph-Computer:
Download the compiled tarball Clone source code then compile and package 2.1 Download the compiled archive Download the latest version of the HugeGraph-Computer release package:
wget https://github.com/apache/hugegraph-computer/releases/download/v${ version } /hugegraph-loader-${ version } .tar.gz
- tar zxvf hugegraph-computer-${ version } .tar.gz
+ 4.4 Run The Example Before running Example, you need to start the Server. For the startup process, seeHugeGraph-Server Quick Start .
SeeIntroduce basic API of HugeGraph-Client .
3.6 - HugeGraph-Computer Quick Start 1 HugeGraph-Computer Overview The HugeGraph-ComputerPregel . It runs on Kubernetes framework.
Features Support distributed MPP graph computing, and integrates with HugeGraph as graph input/output storage. Based on BSP(Bulk Synchronous Parallel) model, an algorithm performs computing through multiple parallel iterations, every iteration is a superstep. Auto memory management. The framework will never be OOM(Out of Memory) since it will split some data to disk if it doesn’t have enough memory to hold all the data. The part of edges or the messages of super node can be in memory, so you will never lose it. You can load the data from HDFS or HugeGraph, or any other system. You can output the results to HDFS or HugeGraph, or any other system. Easy to develop a new algorithm. You just need to focus on a vertex only processing just like as in a single server, without worrying about message transfer and memory/storage management. 2 Get Started To run algorithm with HugeGraph-Computer, you need to install 64-bit Java 11 or later versions.
You also need to deploy HugeGraph-Server and Etcd .
There are two ways to get HugeGraph-Computer:
Download the compiled tarball Clone source code then compile and package 2.1 Download the compiled archive Download the latest version of the HugeGraph-Computer release package:
wget https://downloads.apache.org/incubator/hugegraph/${ version } /apache-hugegraph-computer-incubating-${ version } .tar.gz
+ tar zxvf apache-hugegraph-computer-incubating-${ version } .tar.gz -C hugegraph-computer
2.2 Clone source code to compile and package Clone the latest version of HugeGraph-Computer source package:
$ git clone https://github.com/apache/hugegraph-computer.git
Compile and generate tar package:
cd hugegraph-computer
mvn clean package -DskipTests
- 2.3 Start master node You can use -c parameter specify the configuration file, more computer config please see:Computer Config Options
cd hugegraph-computer-${ version }
+2.3 Start master node You can use -c parameter specify the configuration file, more computer config please see:Computer Config Options
cd hugegraph-computer
bin/start-computer.sh -d local -r master
- 2.4 Start worker node bin/start-computer.sh -d local -r worker
+ 2.4 Start worker node bin/start-computer.sh -d local -r worker
2.5 Query algorithm results 2.5.1 Enable OLAP index query for server
If OLAP index is not enabled, it needs to enable, more reference: modify-graphs-read-mode
PUT http://localhost:8080/graphs/hugegraph/graph_read_mode
"ALL"
diff --git a/docs/index.xml b/docs/index.xml
index 0a2c7b7c1..064af07d0 100644
--- a/docs/index.xml
+++ b/docs/index.xml
@@ -10113,8 +10113,8 @@ Merging mode as needed, and when the Restore is completed, restore the graph mod
</ul>
<h4 id="21-download-the-compiled-archive">2.1 Download the compiled archive</h4>
<p>Download the latest version of the HugeGraph-Computer release package:</p>
-<div class="highlight"><pre tabindex="0" style="background-color:#f8f8f8;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-bash" data-lang="bash"><span style="display:flex;"><span>wget https://github.com/apache/hugegraph-computer/releases/download/v<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>/hugegraph-loader-<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>.tar.gz
-</span></span><span style="display:flex;"><span>tar zxvf hugegraph-computer-<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>.tar.gz
+<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>wget https://downloads.apache.org/incubator/hugegraph/<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>/apache-hugegraph-computer-incubating-<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>.tar.gz
+</span></span><span style="display:flex;"><span>tar zxvf apache-hugegraph-computer-incubating-<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>.tar.gz -C hugegraph-computer
</span></span></code></pre></div><h4 id="22-clone-source-code-to-compile-and-package">2.2 Clone source code to compile and package</h4>
<p>Clone the latest version of HugeGraph-Computer source package:</p>
<div class="highlight"><pre tabindex="0" style="background-color:#f8f8f8;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-bash" data-lang="bash"><span style="display:flex;"><span>$ git clone https://github.com/apache/hugegraph-computer.git
@@ -10125,10 +10125,10 @@ Merging mode as needed, and when the Restore is completed, restore the graph mod
<blockquote>
<p>You can use <code>-c</code> parameter specify the configuration file, more computer config please see:<a href="/docs/config/config-computer#computer-config-options">Computer Config Options</a></p>
</blockquote>
-<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><span style="color:#204a87">cd</span> hugegraph-computer-<span style="color:#4e9a06">${</span><span style="color:#000">version</span><span style="color:#4e9a06">}</span>
+<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><span style="color:#204a87">cd</span> hugegraph-computer
</span></span><span style="display:flex;"><span>bin/start-computer.sh -d <span style="color:#204a87">local</span> -r master
</span></span></code></pre></div><h4 id="24-start-worker-node">2.4 Start worker node</h4>
-<div class="highlight"><pre tabindex="0" style="background-color:#f8f8f8;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-fallback" data-lang="fallback"><span style="display:flex;"><span>bin/start-computer.sh -d local -r worker
+<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/start-computer.sh -d <span style="color:#204a87">local</span> -r worker
</span></span></code></pre></div><h4 id="25-query-algorithm-results">2.5 Query algorithm results</h4>
<p>2.5.1 Enable <code>OLAP</code> index query for server</p>
<p>If OLAP index is not enabled, it needs to enable, more reference: <a href="/docs/clients/restful-api/graphs/#634-modify-graphs-read-mode-this-operation-requires-administrator-privileges">modify-graphs-read-mode</a></p>
diff --git a/docs/quickstart/_print/index.html b/docs/quickstart/_print/index.html
index 5121534af..fdd6c3b48 100644
--- a/docs/quickstart/_print/index.html
+++ b/docs/quickstart/_print/index.html
@@ -1347,14 +1347,14 @@
hugeClient . close ();
}
}
-4.4 Run The Example Before running Example, you need to start the Server. For the startup process, seeHugeGraph-Server Quick Start .
SeeIntroduce basic API of HugeGraph-Client .
6 - HugeGraph-Computer Quick Start 1 HugeGraph-Computer Overview The HugeGraph-ComputerPregel . It runs on Kubernetes framework.
Features Support distributed MPP graph computing, and integrates with HugeGraph as graph input/output storage. Based on BSP(Bulk Synchronous Parallel) model, an algorithm performs computing through multiple parallel iterations, every iteration is a superstep. Auto memory management. The framework will never be OOM(Out of Memory) since it will split some data to disk if it doesn’t have enough memory to hold all the data. The part of edges or the messages of super node can be in memory, so you will never lose it. You can load the data from HDFS or HugeGraph, or any other system. You can output the results to HDFS or HugeGraph, or any other system. Easy to develop a new algorithm. You just need to focus on a vertex only processing just like as in a single server, without worrying about message transfer and memory/storage management. 2 Get Started To run algorithm with HugeGraph-Computer, you need to install 64-bit Java 11 or later versions.
You also need to deploy HugeGraph-Server and Etcd .
There are two ways to get HugeGraph-Computer:
Download the compiled tarball Clone source code then compile and package 2.1 Download the compiled archive Download the latest version of the HugeGraph-Computer release package:
wget https://github.com/apache/hugegraph-computer/releases/download/v${ version } /hugegraph-loader-${ version } .tar.gz
- tar zxvf hugegraph-computer-${ version } .tar.gz
+ 4.4 Run The Example Before running Example, you need to start the Server. For the startup process, seeHugeGraph-Server Quick Start .
SeeIntroduce basic API of HugeGraph-Client .
6 - HugeGraph-Computer Quick Start 1 HugeGraph-Computer Overview The HugeGraph-ComputerPregel . It runs on Kubernetes framework.
Features Support distributed MPP graph computing, and integrates with HugeGraph as graph input/output storage. Based on BSP(Bulk Synchronous Parallel) model, an algorithm performs computing through multiple parallel iterations, every iteration is a superstep. Auto memory management. The framework will never be OOM(Out of Memory) since it will split some data to disk if it doesn’t have enough memory to hold all the data. The part of edges or the messages of super node can be in memory, so you will never lose it. You can load the data from HDFS or HugeGraph, or any other system. You can output the results to HDFS or HugeGraph, or any other system. Easy to develop a new algorithm. You just need to focus on a vertex only processing just like as in a single server, without worrying about message transfer and memory/storage management. 2 Get Started To run algorithm with HugeGraph-Computer, you need to install 64-bit Java 11 or later versions.
You also need to deploy HugeGraph-Server and Etcd .
There are two ways to get HugeGraph-Computer:
Download the compiled tarball Clone source code then compile and package 2.1 Download the compiled archive Download the latest version of the HugeGraph-Computer release package:
wget https://downloads.apache.org/incubator/hugegraph/${ version } /apache-hugegraph-computer-incubating-${ version } .tar.gz
+ tar zxvf apache-hugegraph-computer-incubating-${ version } .tar.gz -C hugegraph-computer
2.2 Clone source code to compile and package Clone the latest version of HugeGraph-Computer source package:
$ git clone https://github.com/apache/hugegraph-computer.git
Compile and generate tar package:
cd hugegraph-computer
mvn clean package -DskipTests
- 2.3 Start master node You can use -c parameter specify the configuration file, more computer config please see:Computer Config Options
cd hugegraph-computer-${ version }
+2.3 Start master node You can use -c parameter specify the configuration file, more computer config please see:Computer Config Options
cd hugegraph-computer
bin/start-computer.sh -d local -r master
- 2.4 Start worker node bin/start-computer.sh -d local -r worker
+ 2.4 Start worker node bin/start-computer.sh -d local -r worker
2.5 Query algorithm results 2.5.1 Enable OLAP index query for server
If OLAP index is not enabled, it needs to enable, more reference: modify-graphs-read-mode
PUT http://localhost:8080/graphs/hugegraph/graph_read_mode
"ALL"
diff --git a/docs/quickstart/hugegraph-computer/index.html b/docs/quickstart/hugegraph-computer/index.html
index b8e4643c0..d64696eff 100644
--- a/docs/quickstart/hugegraph-computer/index.html
+++ b/docs/quickstart/hugegraph-computer/index.html
@@ -1,21 +1,21 @@
HugeGraph-Computer Quick Start | HugeGraph Documentation Quick Start Analysis with HugeGraph-Computer HugeGraph-Computer Quick Start 1 HugeGraph-Computer Overview The HugeGraph-ComputerPregel . It runs on Kubernetes framework.
Features Support distributed MPP graph computing, and integrates with HugeGraph as graph input/output storage. Based on BSP(Bulk Synchronous Parallel) model, an algorithm performs computing through multiple parallel iterations, every iteration is a superstep. Auto memory management. The framework will never be OOM(Out of Memory) since it will split some data to disk if it doesn’t have enough memory to hold all the data. The part of edges or the messages of super node can be in memory, so you will never lose it. You can load the data from HDFS or HugeGraph, or any other system. You can output the results to HDFS or HugeGraph, or any other system. Easy to develop a new algorithm. You just need to focus on a vertex only processing just like as in a single server, without worrying about message transfer and memory/storage management. 2 Get Started To run algorithm with HugeGraph-Computer, you need to install 64-bit Java 11 or later versions.
You also need to deploy HugeGraph-Server and Etcd .
There are two ways to get HugeGraph-Computer:
Download the compiled tarball Clone source code then compile and package 2.1 Download the compiled archive Download the latest version of the HugeGraph-Computer release package:
wget https://github.com/apache/hugegraph-computer/releases/download/v${ version } /hugegraph-loader-${ version } .tar.gz
- tar zxvf hugegraph-computer-${ version } .tar.gz
+ Documentation Quick Start Analysis with HugeGraph-Computer HugeGraph-Computer Quick Start 1 HugeGraph-Computer Overview The HugeGraph-ComputerPregel . It runs on Kubernetes framework.
Features Support distributed MPP graph computing, and integrates with HugeGraph as graph input/output storage. Based on BSP(Bulk Synchronous Parallel) model, an algorithm performs computing through multiple parallel iterations, every iteration is a superstep. Auto memory management. The framework will never be OOM(Out of Memory) since it will split some data to disk if it doesn’t have enough memory to hold all the data. The part of edges or the messages of super node can be in memory, so you will never lose it. You can load the data from HDFS or HugeGraph, or any other system. You can output the results to HDFS or HugeGraph, or any other system. Easy to develop a new algorithm. You just need to focus on a vertex only processing just like as in a single server, without worrying about message transfer and memory/storage management. 2 Get Started To run algorithm with HugeGraph-Computer, you need to install 64-bit Java 11 or later versions.
You also need to deploy HugeGraph-Server and Etcd .
There are two ways to get HugeGraph-Computer:
Download the compiled tarball Clone source code then compile and package 2.1 Download the compiled archive Download the latest version of the HugeGraph-Computer release package:
wget https://downloads.apache.org/incubator/hugegraph/${ version } /apache-hugegraph-computer-incubating-${ version } .tar.gz
+ tar zxvf apache-hugegraph-computer-incubating-${ version } .tar.gz -C hugegraph-computer
2.2 Clone source code to compile and package Clone the latest version of HugeGraph-Computer source package:
$ git clone https://github.com/apache/hugegraph-computer.git
Compile and generate tar package:
cd hugegraph-computer
mvn clean package -DskipTests
- 2.3 Start master node You can use -c parameter specify the configuration file, more computer config please see:Computer Config Options
cd hugegraph-computer-${ version }
+2.3 Start master node You can use -c parameter specify the configuration file, more computer config please see:Computer Config Options
cd hugegraph-computer
bin/start-computer.sh -d local -r master
- 2.4 Start worker node bin/start-computer.sh -d local -r worker
+ 2.4 Start worker node bin/start-computer.sh -d local -r worker
2.5 Query algorithm results 2.5.1 Enable OLAP index query for server
If OLAP index is not enabled, it needs to enable, more reference: modify-graphs-read-mode
PUT http://localhost:8080/graphs/hugegraph/graph_read_mode
"ALL"
@@ -70,7 +70,7 @@
# NOTE: diagnostic log exist only when the job fails, and it will only be saved for one hour.
kubectl get event --field-selector reason = ComputerJobFailed --field-selector involvedObject.name= pagerank-sample -n hugegraph-computer-system
2.2.8 Show success event of a job NOTE: it will only be saved for one hour
kubectl get event --field-selector reason = ComputerJobSucceed --field-selector involvedObject.name= pagerank-sample -n hugegraph-computer-system
- 2.2.9 Query algorithm results If the output to Hugegraph-Server is consistent with Locally, if output to HDFS, please check the result file in the directory of /hugegraph-computer/results/{jobId} directory.
3 Built-In algorithms document 3.1 Supported algorithms list: Centrality Algorithm: PageRank BetweennessCentrality ClosenessCentrality DegreeCentrality ClusteringCoefficient Kcore Lpa TriangleCount Wcc Path Algorithm: RingsDetection RingsDetectionWithFilter More algorithms please see: Built-In algorithms
3.2 Algorithm describe TODO
4 Algorithm development guide TODO
5 Note If some classes under computer-k8s cannot be found, you need to execute mvn compile in advance to generate corresponding classes.
+
2.2.9 Query algorithm results If the output to Hugegraph-Server is consistent with Locally, if output to HDFS, please check the result file in the directory of /hugegraph-computer/results/{jobId} directory.
3 Built-In algorithms document 3.1 Supported algorithms list: Centrality Algorithm: PageRank BetweennessCentrality ClosenessCentrality DegreeCentrality ClusteringCoefficient Kcore Lpa TriangleCount Wcc Path Algorithm: RingsDetection RingsDetectionWithFilter More algorithms please see: Built-In algorithms
3.2 Algorithm describe TODO
4 Algorithm development guide TODO
5 Note If some classes under computer-k8s cannot be found, you need to execute mvn compile in advance to generate corresponding classes. /docs/guides/architectural/ 2023-06-25T21:06:07+08:00 /docs/config/config-guide/ 2023-06-21T14:48:04+08:00 /docs/language/hugegraph-gremlin/ 2023-05-14T07:29:41-05:00 /docs/contribution-guidelines/contribute/ 2023-01-04T22:59:07+08:00 /docs/performance/hugegraph-benchmark-0.5.6/ 2023-05-14T22:31:02-05:00 /docs/quickstart/hugegraph-server/ 2023-06-25T21:06:07+08:00 /docs/introduction/readme/ 2023-06-18T14:57:33+08:00 /docs/changelog/hugegraph-1.0.0-release-notes/ 2023-01-09T07:41:46+08:00 /docs/clients/restful-api/ 2023-05-22T00:00:33-05:00 /docs/clients/restful-api/schema/ 2023-05-14T19:35:13+08:00 /docs/performance/api-preformance/hugegraph-api-0.5.6-rocksdb/ 2023-05-15T22:47:44-05:00 /docs/config/config-option/ 2023-02-08T20:56:09+08:00 /docs/guides/desgin-concept/ 2023-05-14T07:20:21-05:00 /docs/download/download/ 2023-06-17T14:43:04+08:00 /docs/language/hugegraph-example/ 2023-02-02T01:21:10+08:00 /docs/clients/hugegraph-client/ 2023-01-01T16:16:43+08:00 /docs/performance/api-preformance/ 2023-06-17T14:43:04+08:00 /docs/quickstart/hugegraph-loader/ 2023-05-17T23:12:35+08:00 /docs/clients/restful-api/propertykey/ 2023-05-19T05:15:56-05:00 /docs/changelog/hugegraph-0.12.0-release-notes/ 2023-05-18T06:11:19-05:00 /docs/contribution-guidelines/subscribe/ 2023-06-17T14:43:04+08:00 /docs/performance/api-preformance/hugegraph-api-0.5.6-cassandra/ 2023-05-16T23:30:00-05:00 /docs/config/config-authentication/ 2023-05-19T05:12:35-05:00 /docs/clients/gremlin-console/ 2023-06-12T23:52:07+08:00 /docs/guides/custom-plugin/ 2023-05-14T07:22:46-05:00 /docs/performance/hugegraph-loader-performance/ 2023-05-18T00:34:48-05:00 /docs/quickstart/hugegraph-tools/ 2023-05-09T21:27:34+08:00 /docs/quickstart/ 2022-04-17T11:36:55+08:00 /docs/contribution-guidelines/validate-release/ 2023-02-15T16:14:21+08:00 /docs/clients/restful-api/vertexlabel/ 2023-05-19T04:03:23-05:00 /docs/guides/backup-restore/ 2023-05-14T07:26:12-05:00 /docs/config/ 2022-04-17T11:36:55+08:00 /docs/config/config-https/ 2023-05-19T05:04:16-05:00 /docs/clients/restful-api/edgelabel/ 2023-05-19T05:17:26-05:00 /docs/contribution-guidelines/hugegraph-server-idea-setup/ 2023-06-25T21:06:07+08:00 /docs/quickstart/hugegraph-hubble/ 2023-01-04T22:59:07+08:00 /docs/clients/ 2022-04-17T11:36:55+08:00 /docs/config/config-computer/ 2023-01-01T16:16:43+08:00 /docs/guides/faq/ 2023-05-14T07:28:41-05:00 /docs/clients/restful-api/indexlabel/ 2023-05-19T05:18:17-05:00 /docs/quickstart/hugegraph-client/ 2023-05-14T22:39:27+08:00 /docs/guides/ 2022-04-17T11:36:55+08:00 /docs/clients/restful-api/rebuild/ 2022-05-09T18:43:53+08:00 /docs/quickstart/hugegraph-computer/ 2023-06-08T01:07:55+08:00 /docs/language/ 2022-04-17T11:36:55+08:00 /docs/clients/restful-api/vertex/ 2023-06-04T23:04:47+08:00 /docs/clients/restful-api/edge/ 2023-05-19T22:22:11-05:00 /docs/performance/ 2022-04-17T11:36:55+08:00 /docs/contribution-guidelines/ 2022-12-30T19:36:31+08:00 /docs/clients/restful-api/traverser/ 2023-05-20T06:12:55-05:00 /docs/changelog/ 2022-04-28T21:26:41+08:00 /docs/clients/restful-api/rank/ 2022-09-15T12:59:59+08:00 /docs/clients/restful-api/variable/ 2023-05-21T04:38:57-05:00 /docs/clients/restful-api/graphs/ 2022-05-27T09:27:37+08:00 /docs/clients/restful-api/task/ 2022-09-15T12:59:59+08:00 /docs/clients/restful-api/gremlin/ 2023-05-21T04:39:11-05:00 /docs/clients/restful-api/auth/ 2023-05-21T04:40:00-05:00 /docs/clients/restful-api/other/ 2023-05-21T04:39:25-05:00 /docs/ 2022-12-30T19:57:48+08:00 /blog/news/ 2022-03-21T18:55:33+08:00 /blog/releases/ 2022-03-21T18:55:33+08:00 /blog/2018/10/06/easy-documentation-with-docsy/ 2022-03-21T18:55:33+08:00 /blog/2018/10/06/the-second-blog-post/ 2022-03-21T18:55:33+08:00 /blog/2018/01/04/another-great-release/ 2022-03-21T18:55:33+08:00 /docs/cla/ 2022-03-21T19:51:14+08:00 /docs/performance/hugegraph-benchmark-0.4.4/ 2022-09-15T12:59:59+08:00 /docs/summary/ 2022-11-27T21:05:55+08:00 /blog/ 2022-03-21T18:55:33+08:00 /categories/ /community/ 2022-03-21T18:55:33+08:00 / 2023-01-15T13:44:01+00:00 /search/ 2022-03-21T18:55:33+08:00 /tags/ /docs/guides/architectural/ 2023-06-25T21:06:07+08:00 /docs/config/config-guide/ 2023-06-21T14:48:04+08:00 /docs/language/hugegraph-gremlin/ 2023-05-14T07:29:41-05:00 /docs/contribution-guidelines/contribute/ 2023-01-04T22:59:07+08:00 /docs/performance/hugegraph-benchmark-0.5.6/ 2023-05-14T22:31:02-05:00 /docs/quickstart/hugegraph-server/ 2023-06-25T21:06:07+08:00 /docs/introduction/readme/ 2023-06-18T14:57:33+08:00 /docs/changelog/hugegraph-1.0.0-release-notes/ 2023-01-09T07:41:46+08:00 /docs/clients/restful-api/ 2023-05-22T00:00:33-05:00 /docs/clients/restful-api/schema/ 2023-05-14T19:35:13+08:00 /docs/performance/api-preformance/hugegraph-api-0.5.6-rocksdb/ 2023-05-15T22:47:44-05:00 /docs/config/config-option/ 2023-02-08T20:56:09+08:00 /docs/guides/desgin-concept/ 2023-05-14T07:20:21-05:00 /docs/download/download/ 2023-06-17T14:43:04+08:00 /docs/language/hugegraph-example/ 2023-02-02T01:21:10+08:00 /docs/clients/hugegraph-client/ 2023-01-01T16:16:43+08:00 /docs/performance/api-preformance/ 2023-06-17T14:43:04+08:00 /docs/quickstart/hugegraph-loader/ 2023-05-17T23:12:35+08:00 /docs/clients/restful-api/propertykey/ 2023-05-19T05:15:56-05:00 /docs/changelog/hugegraph-0.12.0-release-notes/ 2023-05-18T06:11:19-05:00 /docs/contribution-guidelines/subscribe/ 2023-06-17T14:43:04+08:00 /docs/performance/api-preformance/hugegraph-api-0.5.6-cassandra/ 2023-05-16T23:30:00-05:00 /docs/config/config-authentication/ 2023-05-19T05:12:35-05:00 /docs/clients/gremlin-console/ 2023-06-12T23:52:07+08:00 /docs/guides/custom-plugin/ 2023-05-14T07:22:46-05:00 /docs/performance/hugegraph-loader-performance/ 2023-05-18T00:34:48-05:00 /docs/quickstart/hugegraph-tools/ 2023-05-09T21:27:34+08:00 /docs/quickstart/ 2022-04-17T11:36:55+08:00 /docs/contribution-guidelines/validate-release/ 2023-02-15T16:14:21+08:00 /docs/clients/restful-api/vertexlabel/ 2023-05-19T04:03:23-05:00 /docs/guides/backup-restore/ 2023-05-14T07:26:12-05:00 /docs/config/ 2022-04-17T11:36:55+08:00 /docs/config/config-https/ 2023-05-19T05:04:16-05:00 /docs/clients/restful-api/edgelabel/ 2023-05-19T05:17:26-05:00 /docs/contribution-guidelines/hugegraph-server-idea-setup/ 2023-06-25T21:06:07+08:00 /docs/quickstart/hugegraph-hubble/ 2023-01-04T22:59:07+08:00 /docs/clients/ 2022-04-17T11:36:55+08:00 /docs/config/config-computer/ 2023-01-01T16:16:43+08:00 /docs/guides/faq/ 2023-05-14T07:28:41-05:00 /docs/clients/restful-api/indexlabel/ 2023-05-19T05:18:17-05:00 /docs/quickstart/hugegraph-client/ 2023-05-14T22:39:27+08:00 /docs/guides/ 2022-04-17T11:36:55+08:00 /docs/clients/restful-api/rebuild/ 2022-05-09T18:43:53+08:00 /docs/quickstart/hugegraph-computer/ 2023-06-25T21:06:46+08:00 /docs/language/ 2022-04-17T11:36:55+08:00 /docs/clients/restful-api/vertex/ 2023-06-04T23:04:47+08:00 /docs/clients/restful-api/edge/ 2023-05-19T22:22:11-05:00 /docs/performance/ 2022-04-17T11:36:55+08:00 /docs/contribution-guidelines/ 2022-12-30T19:36:31+08:00 /docs/clients/restful-api/traverser/ 2023-05-20T06:12:55-05:00 /docs/changelog/ 2022-04-28T21:26:41+08:00 /docs/clients/restful-api/rank/ 2022-09-15T12:59:59+08:00 /docs/clients/restful-api/variable/ 2023-05-21T04:38:57-05:00 /docs/clients/restful-api/graphs/ 2022-05-27T09:27:37+08:00 /docs/clients/restful-api/task/ 2022-09-15T12:59:59+08:00 /docs/clients/restful-api/gremlin/ 2023-05-21T04:39:11-05:00 /docs/clients/restful-api/auth/ 2023-05-21T04:40:00-05:00 /docs/clients/restful-api/other/ 2023-05-21T04:39:25-05:00 /docs/ 2022-12-30T19:57:48+08:00 /blog/news/ 2022-03-21T18:55:33+08:00 /blog/releases/ 2022-03-21T18:55:33+08:00 /blog/2018/10/06/easy-documentation-with-docsy/ 2022-03-21T18:55:33+08:00 /blog/2018/10/06/the-second-blog-post/ 2022-03-21T18:55:33+08:00 /blog/2018/01/04/another-great-release/ 2022-03-21T18:55:33+08:00 /docs/cla/ 2022-03-21T19:51:14+08:00 /docs/performance/hugegraph-benchmark-0.4.4/ 2022-09-15T12:59:59+08:00 /docs/summary/ 2022-11-27T21:05:55+08:00 /blog/ 2022-03-21T18:55:33+08:00 /categories/ /community/ 2022-03-21T18:55:33+08:00 / 2023-01-15T13:44:01+00:00 /search/ 2022-03-21T18:55:33+08:00 /tags/ /en/sitemap.xml 2023-06-25T21:06:07+08:00 /cn/sitemap.xml 2023-06-25T21:06:07+08:00 /en/sitemap.xml 2023-06-25T21:06:46+08:00 /cn/sitemap.xml 2023-06-25T21:06:46+08:00