准备工作:
1.设置HADOOP_HOME,指向hadoop安装目录,否则报这个错:
2.在window下,需要把hadoop/bin那个目录替换下,在网上搜一个对应版本的
3.如果还报org.apache.hadoop.io.nativeio.NativeIO$Windows.access0错,把其中的hadoop.dll复制到c:\windows\system32目录
依赖的jar
1.common
hadoop-2.7.3\share\hadoop\common\hadoop-common-2.7.3.jar
hadoop-2.7.3\share\hadoop\common\lib下的所有
2.hdfs
hadoop-2.7.3\share\hadoop\hdfs\hadoop-hdfs-2.7.3.jar
hadoop-2.7.3\share\hadoop\hdfs\lib下的所有
3.mapreduce
hadoop-2.7.3\share\hadoop\mapreduce\hadoop-mapreduce-client-app-2.7.3.jar
hadoop-2.7.3\share\hadoop\mapreduce\hadoop-mapreduce-client-common-2.7.3.jar
hadoop-2.7.3\share\hadoop\mapreduce\hadoop-mapreduce-client-core-2.7.3.jar
hadoop-2.7.3\share\hadoop\mapreduce\hadoop-mapreduce-client-hs-2.7.3.jar
hadoop-2.7.3\share\hadoop\mapreduce\hadoop-mapreduce-client-hs-plugins-2.7.3.jar
hadoop-2.7.3\share\hadoop\mapreduce\hadoop-mapreduce-client-jobclient-2.7.3.jar
hadoop-2.7.3\share\hadoop\mapreduce\hadoop-mapreduce-client-jobclient-2.7.3-tests.jar
hadoop-2.7.3\share\hadoop\mapreduce\hadoop-mapreduce-client-shuffle-2.7.3.jar
hadoop-2.7.3\share\hadoop\mapreduce\lib下的所有
4.yarn
hadoop-2.7.3\share\hadoop\yarn\lib\hadoop-yarn-api-2.7.3.jar
hadoop-2.7.3\share\hadoop\yarn\lib\hadoop-yarn-applications-distributedshell-2.7.3.jar
hadoop-2.7.3\share\hadoop\yarn\lib\hadoop-yarn-applications-unmanaged-am-launcher-2.7.3.jar
hadoop-2.7.3\share\hadoop\yarn\lib\hadoop-yarn-client-2.7.3.jar
hadoop-2.7.3\share\hadoop\yarn\lib\hadoop-yarn-common-2.7.3.jar
hadoop-2.7.3\share\hadoop\yarn\lib\hadoop-yarn-registry-2.7.3.jar
hadoop-2.7.3\share\hadoop\yarn\lib\hadoop-yarn-server-applicationhistoryservice-2.7.3.jar
hadoop-2.7.3\share\hadoop\yarn\lib\hadoop-yarn-server-common-2.7.3.jar
hadoop-2.7.3\share\hadoop\yarn\lib\hadoop-yarn-server-nodemanager-2.7.3.jar
hadoop-2.7.3\share\hadoop\yarn\lib\hadoop-yarn-server-resourcemanager-2.7.3.jar
hadoop-2.7.3\share\hadoop\yarn\lib\hadoop-yarn-server-sharedcachemanager-2.7.3.jar
hadoop-2.7.3\share\hadoop\yarn\lib\hadoop-yarn-server-tests-2.7.3.jar
hadoop-2.7.3\share\hadoop\yarn\lib\hadoop-yarn-server-web-proxy-2.7.3.jar
hadoop-2.7.3\share\hadoop\yarn\lib下的所有
可以通过maven管理:
<?xml version="1.0" encoding="UTF-8"?> <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>xiaol</groupId> <artifactId>xiaol-hadoop</artifactId> <version>1.0-SNAPSHOT</version> <description>MapReduce</description> <properties> <project.build.sourceencoding>UTF-8</project.build.sourceencoding> <hadoop.version>2.7.3</hadoop.version> </properties> <dependencies> <dependency> <groupId>junit</groupId> <artifactId>junit</artifactId> <version>4.12</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-client</artifactId> <version>${hadoop.version}</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-common</artifactId> <version>${hadoop.version}</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-hdfs</artifactId> <version>${hadoop.version}</version> </dependency> </dependencies> </project>
编写Mapper:
package xiaol; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; import java.io.IOException; /** * 整个工作过程:input->split->map->shuffle->reduce->output * input: 每一行都是空格分割的单词 * hello java * hello python * split: 默认按行读取input,每一行作为一个KV对,交给下一步 * K就是行首地址,V就是行内容 * K:1 V:hello java * K:11 V:hello python * 当然这一步可以用户自己重写 * map: 必须由用户实现的步骤,进行业务逻辑处理 * 从split的结果中读取数据,统计单词,产生KEYOUT VALUEOUT交给shuffle * 这里交给shuffle的K是单词,V是单词出现的次数 * hello 1 * java 1 * shuffle map的结果是KV对的形式,会把相同的K移动到同一个Node上去进行reduce * 当传给reduce的时候会相同K的V组装成Iterable<VALUEOUT>类型 * hello 1,1 * 当然这一步可以用户自己重写 * reduce 必须由用户实现的步骤,进行业务逻辑处理,将shuffle过来的结果进行汇总 * 从shuffle的结果中读取数据,统计单词,产生KEYOUT VALUEOUT交给output * hello 2 */ /** * org.apache.hadoop.mapreduce.Mapper<KEYIN, VALUEIN, KEYOUT, VALUEOUT> * KEYIN split完成后交给map的key的类型 * VALUEIN split完成后交给map的value的类型 * KEYOUT map完成后交给shuffle的key的类型 * VALUEOUT map完成后交给shuffle的key的类型 * org.apache.hadoop.io.LongWritable hadoop自己的Long包装类 * org.apache.hadoop.io.Text hadoop自己的Text * org.apache.hadoop.io.IntWritable hadoop自己的Int包装类 */ public class WordMapper extends Mapper<LongWritable,Text,Text,IntWritable> { /** * 重写map方法 * protected void map(KEYIN key, VALUEIN value, Mapper<KEYIN, VALUEIN, KEYOUT, VALUEOUT>.Context context) throws IOException, InterruptedException * KEYIN split完成后交给map的key的类型,就是那一行的起始地址 * VALUEIN split完成后交给map的value的类型,就是那一行的内容 * Context 整个MapReduce的执行环境 */ @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String s = value.toString(); String[] words = s.split(" "); //由于每一行都是空格分割的单词,比如hello java这种的,要统计个数,就先拆分 for(String word: words){ /** * 在执行环境中写入KEYOUT和VALUEOUT作为下一步(shuffle)的输入 * * 这一步是要统计在当前处理这一行里每个单词出现的次数,这里直接给了个1 * 这里可能有的人会有疑问:如果在某一行里出现了两个相同的单词会怎么样? * 这个是不影响的,比如出现了两个hello,结果就是给shuffle的时候会有两个hello 1 * 然后shuffle的时候会把这两个hello 1交给reduce去处理 */ context.write(new Text(word), new IntWritable(1)); } } }
编写Reducer
package xiaol; 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.Reducer; /** * org.apache.hadoop.mapreduce.Reducer<KEYIN, VALUEIN, KEYOUT, VALUEOUT> */ public class WordReducer extends Reducer<Text, IntWritable, Text, LongWritable> { /** * 重写reduce方法 * protected void reduce(KEYIN key, Iterable<VALUEIN> values, Reducer<KEYIN, VALUEIN, KEYOUT, VALUEOUT>.Context context) throws IOException, InterruptedException * KEYIN shuffle完成后交给reduce的key的类型,其实就是map的KEYOUT * Iterable<VALUEIN> shuffle完成后交给reduce的value的类型的数组(shuffle那一步会把相同的K分发到同一个node上去进行reduce,所以这里是V数组),其实就是map的VALUEOUT数组 * Context 整个MapReduce的执行环境 */ @Override protected void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, LongWritable>.Context context) throws IOException, InterruptedException { long count = 0; for(IntWritable v : values) { count += v.get(); } context.write(key, new LongWritable(count)); } }
编写启动类:
package xiaol; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; 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; import java.util.Properties; /** * */ public class Test { public static void main(String[] args) throws Exception { //本地运行直接new一个Configuration,远程运行需要配集群相关的配置 Configuration conf = new Configuration(); Job job = Job.getInstance(conf); //设定mapper和reducer的class job.setMapperClass(WordMapper.class); job.setReducerClass(WordReducer.class); //设定mapper和outputKey和outputValue的class job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class); //设定reducer和outputKey和outputValue的class job.setOutputKeyClass(Text.class); job.setOutputValueClass(LongWritable.class); FileInputFormat.setInputPaths(job, "d:/test/test.txt"); FileOutputFormat.setOutputPath(job, new Path("d:/test/out/")); //等待结束,true代表打印中间日志 job.waitForCompletion(true); } }
原文地址:https://www.cnblogs.com/413xiaol/p/10054394.html