hadoop开发MapReduce程序
准备工作：
1.设置HADOOP_HOME，指向hadoop安装目录
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>
配置Log4J，放到src/main/resources目录下
log4j.rootCategory=INFO, stdout log4j.appender.stdout=org.apache.log4j.ConsoleAppender log4j.appender.stdout.layout=org.apache.log4j.PatternLayout log4j.appender.stdout.layout.ConversionPattern=[QC] %p [%t] %C.%M(%L) | %m%n
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：
本地运行（本地数据源，本地计算）：
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); } }
拉取远程数据到本地运行
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, "hdfs://192.168.0.104:9000/input/input.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); } }
在远程运行：
准备工作：把本地的工程打成一个jar包（程序里要用）
程序里需要告诉hadoop通过这个jar去做计算，不用手动传到yarn框架里，只要告诉程序就好了
我这个例子里，直接放在项目根目录下
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 { Properties properties = System.getProperties(); properties.setProperty("HADOOP_USER_NAME", "root"); Configuration conf = new Configuration(); //配置hdfs地址 conf.set("fs.defaultFS", "hdfs://192.168.0.104:9000/"); //配置运行的是那个jar conf.set("mapreduce.job.jar", "xiaolhadoop.jar"); //配置计算框架 conf.set("mapreduce.framework.name", "yarn"); //配置yarn的ResourceManage地址 conf.set("yarn.resourcemanager.hostname", "192.168.0.104"); //告诉hadoop这是从window上提交的任务（好像这步也并没有做什么） conf.set("mapreduce.app-submission.cross-platform", "true"); 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, "/input/input.txt"); FileOutputFormat.setOutputPath(job, new Path("/out/")); //等待结束，true代表打印中间日志 job.waitForCompletion(true); } }
