1MapReduce概述
1.1 MapReduce
1.2MapReduce优缺点
1.2.1 优点
1.2.2 缺点
1.3apReduce核心
MapReduce
1）分布式的运算程序往往需要分成至少2个阶段。
2）第一个阶段的MapTask并发实例，完全并行运行，互不相干。
3）第二个阶段的ReduceTask并发实例互不相干，但是他们的数据依赖于上一个阶段的所有MapTask并发实例的输出。
4）MapReduce编程模型只能包含一个Map阶段和一个Reduce阶段，如果用户的业务逻辑非常复杂，那就只能多个MapReduce
总结分析WordCount数据流走向深入MapReduce
1.4apReduce进程
1.5WordCount源码
采用WordCountMap类、Reduce类和且Hadoop类型
1.6 常用数据
Hadoop
| Java | Hadoop Writable |
| boolean | BooleanWritable |
| byte | ByteWritable |
| int | IntWritable |
| float | FloatWritable |
| long | LongWritable |
| double | DoubleWritable |
| String | Text |
| map | MapWritable |
| array | ArrayWritable |
1.7MapReduce编程规范
MapperReducerDriver
1.8WordCount
1
在给定的文本文件中统计输出每一个单词出现的总次数
1
atguigu atguigu ss ss cls cls jiao banzhang xue hadoop
2
atguigu 2 banzhang 1 cls 2 hadoop 1 jiao 1 ss 2 xue 1
2．需求
按照MapReduceMapperReducerDriver
3．环境
1maven
2pom.xml文件添加如下依赖
<dependencies> <dependency> <groupId>junit</groupId> <artifactId>junit</artifactId> <version>RELEASE</version> </dependency> <dependency> <groupId>org.apache.logging.log4j</groupId> <artifactId>log4j-core</artifactId> <version>2.8.2</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-common</artifactId> <version>2.7.2</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-client</artifactId> <version>2.7.2</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-hdfs</artifactId> <version>2.7.2</version> </dependency> </dependencies>
2src/main/resourceslog4j.properties
log4j.rootLogger=INFO, stdout log4j.appender.stdout=org.apache.log4j.ConsoleAppender log4j.appender.stdout.layout=org.apache.log4j.PatternLayout log4j.appender.stdout.layout.ConversionPattern=%d %p [%c] - %m%n log4j.appender.logfile=org.apache.log4j.FileAppender log4j.appender.logfile.File=target/spring.log log4j.appender.logfile.layout=org.apache.log4j.PatternLayout log4j.appender.logfile.layout.ConversionPattern=%d %p [%c] - %m%n
4．编写程序
1Mapper
package com.atguigu.mapreduce; import java.io.IOException; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; public class WordcountMapper extends Mapper<LongWritable, Text, Text, IntWritable>{ Text k = new Text(); IntWritable v = new IntWritable(1); @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { // 1 获取一行 String line = value.toString(); // 2 切割 String[] words = line.split(" "); // 3 输出 for (String word : words) { k.set(word); context.write(k, v); } } } 2Reducer
package com.atguigu.mapreduce.wordcount; import java.io.IOException; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; public class WordcountReducer extends Reducer<Text, IntWritable, Text, IntWritable>{ int sum; IntWritable v = new IntWritable(); @Override protected void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException { // 1 累加求和 sum = 0; for (IntWritable count : values) { sum += count.get(); } // 2 输出 v.set(sum); context.write(key,v); } } 3Driver驱动类
package com.atguigu.mapreduce.wordcount; import java.io.IOException; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class WordcountDriver { public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { // 1 获取配置信息以及封装任务 Configuration configuration = new Configuration(); Job job = Job.getInstance(configuration); // 2 设置jar加载路径 job.setJarByClass(WordcountDriver.class); // 3 设置map和reduce类 job.setMapperClass(WordcountMapper.class); job.setReducerClass(WordcountReducer.class); // 4 设置map输出 job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class); // 5 设置最终输出kv类型 job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); // 6 设置输入和输出路径 FileInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); // 7 提交 boolean result = job.waitForCompletion(true); System.exit(result ? 0 : 1); } } 5
1win7的将win7的hadoop jar解压在WindowsHADOOP_HOME环境。是电脑win10操作系统，win10的hadoop jar，HADOOP_HOME环境。
注意win8电脑win10家庭
2Eclipse/Idea程序
6
0maven打jar，依赖
注意标记为自己
<build> <plugins> <plugin> <artifactId>maven-compiler-plugin</artifactId> <version>2.3.2</version> <configuration> <source>1.8</source> <target>1.8</target> </configuration> </plugin> <plugin> <artifactId>maven-assembly-plugin </artifactId> <configuration> <descriptorRefs> <descriptorRef>jar-with-dependencies</descriptorRef> </descriptorRefs> <archive> <manifest> <mainClass>com.atguigu.mr.WordcountDriver</mainClass> </manifest> </archive> </configuration> <executions> <execution> <id>make-assembly</id> <phase>package</phase> <goals> <goal>single</goal> </goals> </execution> </executions> </plugin> </plugins> </build>
注意显示在->maven->update project
1jarHadoop
步骤详情->Run as->maven install。会在项目targetjar。看-Refresh修改不jar为wc.jar，并拷贝该jarHadoop
2Hadoop
3WordCount
[atguigu@hadoop102 software]$ hadoop jar wc.jar com.atguigu.wordcount.WordcountDriver /user/atguigu/input /user/atguigu/output