[Spark、hadoop]DStream的窗口操作、输出操作、实例——实现网站热词排序

深碍√TFBOYSˉ_ 2024-04-03 11:41 48阅读 0赞

**目录**

DStream窗口操作

DStream输出操作

DStream实例——实现网站热词排序

--------------------

**DStream的概述  
Dstream(Discretized Stream)是Spark Streaming数据的基本传输单位。它表示一个连续的数据流,这个数据流可以是原始的数据输入流,也可以是将原始的数据输入流通过转换生成已处理的数据输入流**

**特点**

**1、Spark Streaming对流数据按秒/分等时间间隔进行微批划分,每个微批就是一个RDD  
2、DStream由一系列连续的RDD组成,每个RDD都包含来自特定间隔的数据  
3、DStream本质上就是一系列时间上连续的RDD(DStream = Seq\[RDD\])**

#### **DStream窗口操作** ####

![5b2a52c4cecd41f2abd3cd465bdff3b5.png][]

**一、window（）**

**1编写WindowTest.scala**

注意红框内容！！！

![c062879584644e9191de87ab69b1dfba.png][]

**2 运行程序并在master 9999 端口不断输入单词，观察到控制台输出内容**

$ nc -lk 9999

![6dbfa126defe46a0909e912839146847.png][]

**二、ReduceByKeyAndWindow()**

**1编写ReduceByKeyAndWindowTest.scala**

注意红框内容！！！

![614d9d2e03d9468a9e82ee4152ef025e.png][]

**2 运行程序并在master 9999 端口不断输入单词，观察到控制台输出内容**

$ nc -lk 9999

![1d46ce4e3070419bb4ae39f2c04f80ee.png][]

#### **DStream输出操作** ####

**1编写SaveAsTestFilesTest.scala**

注意红框内容！！！

![c60840187efd41679219e447692dd5a1.png][]

**2 运行程序**

![c4033d5074d5478db0d23959a649acbf.png][]

**3 启动Hadoop集群**

$ start-all.sh

![653819f7331944a28989be6bbfabb8ca.png][]

**4 访问192.168.196.101（master）:50070 点击【utilities】à【browse the file system】**

![e8c3138631944995bc1a500056a030dd.png][]

**5 点击【data】à【SaveAsTestFiles】**

![dbf14482ca9b45aea1f0d28b4171c2cd.png][]

![76bd1906eda44494a055ba4177c99615.png][]

**6 可以看到均是以satf为前缀，txt为后缀**

![6609081b0a284526b5b5de1ed7a27c76.png][]

#### **DStream实例——实现网站热词排序** ####

**1.进入数据库**

**$** **mysql -uroot –p**

![476ec7d227fb40a585f4ab59fb324ac8.png][]

**2.创建spark数据库，使用该数据库**

**$****create database spark;**

**$** **use spark;**

![72d6b3f77afa45d690e364c678bcaf2c.png][]

**3.创建表**

**$** **create table searchKeyWord(insert\_time date,keyword varchar(30),search\_count integer);**

![eb2d928dedce459b91cc5bffa77a8ece.png][]

**4.编写HotWordBySort.scala**

**注意红框内容！！！**

![356807709a044bc2b28cc780a8270a96.png][]

![6653e31401764f40a561fe81fc94ac94.png][]

**5.运行程序**

![d5d92d194e11441087293a058091fc61.png][]

重新复制一个master窗口，在master 9999端口输入数据

nc –lk 9999

Hadoop,111

Spark,222

Hadoop,222

Hive,222

![934f1e3d1dce4812ad95faf7bfc4ab7b.png][]

可以看到MySQL被写入数据

select \* from searchKeyWord;

![5775b1785bef463da9f08669c4b072f0.png][]

![1449d30da4cf4cd4b03c4d824eae0d9b.png][]

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