# OmniStream

**Repository Path**: openeuler/OmniStream

## Basic Information

- **Project Name**: OmniStream
- **Description**: 本项目已经迁移至 AtomGit  || This project has been migrated to AtomGit || Linked: https://atomgit.com/openeuler/OmniStream
- **Primary Language**: Unknown
- **License**: MulanPSL-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 42
- **Forks**: 50
- **Created**: 2025-08-05
- **Last Updated**: 2026-01-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: BigData

## README

# Notice: This project has been migrated to [AtomGit](https://atomgit.com/openeuler/OmniStream)
# 通知: 本项目已迁移至 [AtomGit](https://atomgit.com/openeuler/OmniStream)
# OmniStream

# 项目介绍

## 背景

大数据实时处理流计算引擎在互联网、金融、物流等各行各业应用广泛，承担不可或缺的重要角色，而随着业务发展，流计算引擎的性能逐渐成为瓶颈。以主流实时流计算引擎Flink为例，主要采用Java/Scala等高级编程语言实现，当前对Flink的优化主要是基于Java进行改进，但Java的本身的性能还是弱于Native Code，并且由于Java的语义的限制，对于整体的类SIMD指令支持较弱，无法完全发挥CPU的算力。此外，Flink采用行式数据结构计算流数据，无法充分利用芯片的向量化指令，执行效率有待提升。

## OmniStream介绍

OmniStream算子加速采用Native Code（C/C++）实现Flink SQL和DataStream算子。针对Flink SQL，OmniStream采用C++结合向量化指令实现算子，以列式内存数据格式OmniVec进行内存计算，充分利用向量化加速，提升SQL计算性能。针对Flink DataStream，OmniStream采用C++结合向量化指令实现算子，结合UDF翻译工具将UDF翻译成C++程序，充分发挥Native Code性能优势，提升DataStream场景性能。

关于OmniStream的详细介绍，请参考鲲鹏社区在线课程：<b>[《OmniStream开源项目介绍》](https://www.hikunpeng.com/learn/courses-list/detail/1983343850145337345)</b>。


# 版本说明

<a name="table237422676"></a>
<table><thead align="left"><tr id="row19375821672"><th class="cellrowborder" valign="top" width="50%" id="mcps1.1.3.1.1"><p id="p63757220711"><a name="p63757220711"></a><a name="p63757220711"></a>开源软件</p>
</th>
<th class="cellrowborder" valign="top" width="50%" id="mcps1.1.3.1.2"><p id="p1037519217711"><a name="p1037519217711"></a><a name="p1037519217711"></a>开源版本</p>
</th>
</tr>
</thead>
<tbody><tr id="row5375192475"><td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.1 "><p id="p937511219712"><a name="p937511219712"></a><a name="p937511219712"></a>Flink</p>
</td>
<td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.2 "><p id="p1375426711"><a name="p1375426711"></a><a name="p1375426711"></a>1.16.3</p>
</td>
</tr>
</tbody>
</table>


# 快速上手

## 源码编译

### 1、依赖组件

<a name="table12473143919118"></a>
<table><thead align="left"><tr id="row154733396114"><th class="cellrowborder" valign="top" width="50%" id="mcps1.1.3.1.1"><p id="p5473143971116"><a name="p5473143971116"></a><a name="p5473143971116"></a>软件</p>
</th>
<th class="cellrowborder" valign="top" width="50%" id="mcps1.1.3.1.2"><p id="p1947393921119"><a name="p1947393921119"></a><a name="p1947393921119"></a>版本</p>
</th>
</tr>
</thead>
<tbody><tr id="row547315397112"><td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.1 "><p id="p11473139161116"><a name="p11473139161116"></a><a name="p11473139161116"></a>GCC</p>
</td>
<td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.2 "><p id="p1447333916113"><a name="p1447333916113"></a><a name="p1447333916113"></a>10.3.1</p>
</td>
</tr>
<tr id="row19473939121120"><td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.1 "><p id="p847363915116"><a name="p847363915116"></a><a name="p847363915116"></a>CMAKE</p>
</td>
<td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.2 "><p id="p15473239131119"><a name="p15473239131119"></a><a name="p15473239131119"></a>3.22.0</p>
</td>
</tr>
<tr id="row9474193915114"><td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.1 "><p id="p1247463911110"><a name="p1247463911110"></a><a name="p1247463911110"></a>JDK</p>
</td>
<td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.2 "><p id="p1347433931113"><a name="p1347433931113"></a><a name="p1347433931113"></a>1.8.0_342</p>
</td>
</tr>
<tr id="row647473913118"><td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.1 "><p id="p3474153914116"><a name="p3474153914116"></a><a name="p3474153914116"></a>zlib</p>
</td>
<td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.2 "><p id="p2474143961111"><a name="p2474143961111"></a><a name="p2474143961111"></a>1.2.8</p>
</td>
</tr>
<tr id="row12474183911120"><td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.1 "><p id="p19474173917114"><a name="p19474173917114"></a><a name="p19474173917114"></a>LLVM</p>
</td>
<td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.2 "><p id="p9474143931119"><a name="p9474143931119"></a><a name="p9474143931119"></a>12.0.1</p>
</td>
</tr>
<tr id="row114741039161113"><td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.1 "><p id="p447410393119"><a name="p447410393119"></a><a name="p447410393119"></a>googletest</p>
</td>
<td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.2 "><p id="p447433981120"><a name="p447433981120"></a><a name="p447433981120"></a>1.10.0</p>
</td>
</tr>
<tr id="row17474173911111"><td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.1 "><p id="p104741239191112"><a name="p104741239191112"></a><a name="p104741239191112"></a>jemalloc</p>
</td>
<td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.2 "><p id="p18474183919116"><a name="p18474183919116"></a><a name="p18474183919116"></a>5.2.1</p>
</td>
</tr>
<tr id="row1474163919111"><td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.1 "><p id="p8474039101118"><a name="p8474039101118"></a><a name="p8474039101118"></a>nlomann json</p>
</td>
<td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.2 "><p id="p3474739121110"><a name="p3474739121110"></a><a name="p3474739121110"></a>3.11.3</p>
</td>
</tr>
<tr id="row4474639131117"><td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.1 "><p id="p647453931110"><a name="p647453931110"></a><a name="p647453931110"></a>libboundscheck</p>
</td>
<td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.2 "><p id="p147415395116"><a name="p147415395116"></a><a name="p147415395116"></a>V1.1.16</p>
</td>
</tr>
<tr id="row124741539151110"><td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.1 "><p id="p9474153919114"><a name="p9474153919114"></a><a name="p9474153919114"></a>OmniOperator</p>
</td>
<td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.2 "><p id="p18474113921117"><a name="p18474113921117"></a><a name="p18474113921117"></a>20250630</p>
</td>
</tr>
<tr id="row547413394112"><td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.1 "><p id="p16474203916115"><a name="p16474203916115"></a><a name="p16474203916115"></a>snappy</p>
</td>
<td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.2 "><p id="p1747443961114"><a name="p1747443961114"></a><a name="p1747443961114"></a>1.1.10</p>
</td>
</tr>
<tr id="row3474139111116"><td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.1 "><p id="p94749399115"><a name="p94749399115"></a><a name="p94749399115"></a>rocksdb</p>
</td>
<td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.2 "><p id="p174741339121113"><a name="p174741339121113"></a><a name="p174741339121113"></a>8.11.4</p>
</td>
</tr>
<tr id="row154740392119"><td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.1 "><p id="p104741439191118"><a name="p104741439191118"></a><a name="p104741439191118"></a>BoostKit-kaccjson</p>
</td>
<td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.2 "><p id="p1147423916119"><a name="p1147423916119"></a><a name="p1147423916119"></a>1.0.0</p>
</td>
</tr>
<tr id="row1147418391119"><td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.1 "><p id="p847513911117"><a name="p847513911117"></a><a name="p847513911117"></a>BoostKit-ksl</p>
</td>
<td class="cellrowborder" valign="top" width="50%" headers="mcps1.1.3.1.2 "><p id="p64751439141116"><a name="p64751439141116"></a><a name="p64751439141116"></a>2.5.1</p>
</td>
</tr>
</tbody>
</table>


### 2、编译命令

1.  编译OmniStream前需要编译OmniAdaptor代码，编译命令如下。

    ```
    cd omnistream/omniop-flink-extension/omni-flink-bundle
    mvn clean package -DskipTests
    ```

2.  OmniStream编译命令如下。

    ```
    cd OmniSteam/cpp
    mkdir build
    cd build
    cmake ..
    make install -j$PARALLELISM
    ```

    >**说明：** 
    >$PARALLELISM表示编译指定并行度

## 环境部署

部署请参考以下链接：

https://www.hikunpeng.com/document/detail/zh/kunpengbds/appAccelFeatures/sqlqueryaccelf/kunpengbds_omniruntime_20_09018.html

## 测试验证

进入Flink安装目录下的bin目录，并启动Flink。

```
cd $FLINK_HOME/bin/ && ./start-cluster.sh
```

调用sql-client后，进行测试。

```
./sql-client.sh
```

在命令行中输入。

```
SELECT 'Hello, Flink!';
```

可以正常输出结果即安装正常。

# 贡献指南

如果使用过程中有任何问题，或者需要反馈特性需求和bug报告，可以提交isssues联系我们，具体贡献方法可参考[这里](https://gitcode.com/boostkit/community/blob/master/docs/contributor/contributing.md)。

# 免责声明

此代码仓计划参与Flink软件开源，仅作Flink功能扩展/Flink性能提升，编码风格遵照原生开源软件，继承原生开源软件安全设计，不破坏原生开源软件设计及编码风格和方式，软件的任何漏洞与安全问题，均由相应的上游社区根据其漏洞和安全响应机制解决。请密切关注上游社区发布的通知和版本更新。鲲鹏计算社区对软件的漏洞及安全问题不承担任何责任。

# 参考文档

安装指南：

https://www.hikunpeng.com/document/detail/zh/kunpengbds/appAccelFeatures/sqlqueryaccelf/kunpengbds_omniruntime_20_09018.html