Running Apache Storm on Windows

本文介绍了如何在Windows环境下安装并运行Apache Storm,包括安装Java、Python、Zookeeper及配置环境变量等步骤,并通过部署“WordCount”示例展示了整个过程。

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In the past, running Storm on Windows has been a challenge. While possible, it often involved hacking Storm’s source, hunting down (or building from source) native dependencies, and mucking around with various ways to trick Windows into thinking it’s like UNIX/POSIX.

That alienated a large number of potential adopters who stand to gain from integrating Storm into their big data strategy.

Thanks in large part to contributions from Storm committer David Lao, as well as contributions from Yahoo!, the next release of Storm (0.9.1-incubating) will make life much easier for users who want or need to deploy Storm in an environment where Windows is necessary.

Below I’ve listed the steps necessary to get storm up and running with a sample topology on Windows. It walks through the process of creating a single-node cluster (pseudo-cluster) and deploying a sample “Word Count” topology.

Install Java

Download and install a JDK (Storm works with both Oracle and OpenJDK 6/7). For this setup I used JDK 7 from Oracle.

I installed Java in:

C:\Java\jdk1.7.0_45\

Install Python

To test the installation, we’ll be deploying the “word count” sample from the storm-starter project which uses a multi-lang bolt written in python. I used python 2.7.6 which can be downloaded here.

I installed python in:

C:\Python27\

Install and Run Zookeeper

Download Apache Zookeeper 3.3.6 and extract it. Configure and run Zookeeper with the following commands:

> cd zookeeper-3.3.6
> copy conf\zoo_sample.cfg conf\zoo.cfg
> .\bin\zkServer.cmd

Install Storm

The changes that allow Storm to run seamlessly on Windows have not been officially released yet, but you can download a build with those changes incorporated here.

(Source branch for that build can be found here).

Extract that file to the location of your choice. I chose C:\.

Configure Environment Variables

On Windows Storm requires the STORM_HOME and JAVA_HOME environment variables to be set, as well as some additions to the PATH variable:

JAVA_HOME

C:\Java\jdk1.7.0_45\

STORM_HOME

C:\storm-0.9.1-incubating-SNAPSHOT-12182013\

PATH Add:

%STORM_HOME%\bin;%JAVA_HOME%\bin;C:\Python27;C:\Python27\Lib\site-packages\;C:\Python27\Scripts\;

PATHEXT Add:

.PY

Start Nimbus, Supervisor, and Storm UI Daemons

For each deamon, open a separate command prompt.

Nimbus

> cd %STORM_HOME%
> storm nimbus

Supervisor

> cd %STORM_HOME%
> storm supervisor

Storm UI

> cd %STORM_HOME%
> storm ui

Verify that Storm is running by opening http://localhost:8080/ in a browser.

Deploy the “Word Count” Topology

Either build the storm-starter project from source, or download a pre-built jar

Deploy the Word Count topology to your local cluster with the storm jar command:

> storm jar storm-starter-0.0.1-SNAPSHOT-jar-with-dependencies.jar storm.starter.WordCountTopology WordCount -c nimbus.host=localhost

If you reload the Storm UI page, you should now see the “WordCount” topology listed and can click on the link to verify that the topology is processing data.

 Dec 18th, 2013  big datastormwindows

http://ptgoetz.github.io/blog/2013/12/18/running-apache-storm-on-windows/

内容概要:该论文探讨了一种基于粒子群优化(PSO)的STAR-RIS辅助NOMA无线通信网络优化方法。STAR-RIS作为一种新型可重构智能表面,能同时反射和传输信号,与传统仅能反射的RIS不同。结合NOMA技术,STAR-RIS可以提升覆盖范围、用户容量和频谱效率。针对STAR-RIS元素众多导致获取完整信道状态信息(CSI)开销大的问题,作者提出一种在不依赖完整CSI的情况下,联合优化功率分配、基站波束成形以及STAR-RIS的传输和反射波束成形向量的方法,以最大化总可实现速率并确保每个用户的最低速率要求。仿真结果显示,该方案优于STAR-RIS辅助的OMA系统。 适合人群:具备一定无线通信理论基础、对智能反射面技术和非正交多址接入技术感兴趣的科研人员和工程师。 使用场景及目标:①适用于希望深入了解STAR-RIS与NOMA结合的研究者;②为解决无线通信中频谱资源紧张、提高系统性能提供新的思路和技术手段;③帮助理解PSO算法在无线通信优化问题中的应用。 其他说明:文中提供了详细的Python代码实现,涵盖系统参数设置、信道建模、速率计算、目标函数定义、约束条件设定、主优化函数设计及结果可视化等环节,便于读者理解和复现实验结果。此外,文章还对比了PSO与其他优化算法(如DDPG)的区别,强调了PSO在不需要显式CSI估计方面的优势。
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