通过idea开发mapreduce程序并直接run，提交到远程hadoop集群执行mapreduce。
简要流程：本地开发mapreduce程序–>设置yarn 模式 --> 直接本地run–>远程集群执行mapreduce程序；
完整的流程：本地开发mapreduce程序——> 设置yarn模式——>初次编译产生jar文件——>增加 job.setJar("mapreduce/build/libs/mapreduce-0.1.jar");——>直接在Idea中run——>远程集群执行mapreduce程序；
一图说明问题：
源码 build.gradle
代码语言：javascript复制plugins {
id 'java'
}
group 'com.ruizhiedu'
version '0.1'
sourceCompatibility = 1.8
repositories {
mavenCentral()
}
dependencies {
compile group: 'org.apache.hadoop', name: 'hadoop-common', version: '3.1.0'
compile group: 'org.apache.hadoop', name: 'hadoop-mapreduce-client-core', version: '3.1.0'
compile group: 'org.apache.hadoop', name: 'hadoop-mapreduce-client-jobclient', version: '3.1.0'
testCompile group: 'junit', name: 'junit', version: '4.12'
}java文件
输入、输出已经让我写死了，可以直接run。不需要再运行时候设置idea运行参数
wc.java
代码语言：javascript复制package com;
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.Counter;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.StringUtils;
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.net.URI;
import java.util.*;
/**
* @author wangxiaolei(王小雷)
* @since 2018/11/22
*/
public class wc {
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable> {
static enum CountersEnum { INPUT_WORDS }
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
private boolean caseSensitive;
private Set<String> patternsToSkip = new HashSet<String>();
private Configuration conf;
private BufferedReader fis;
@Override
public void setup(Context context) throws IOException,
InterruptedException {
conf = context.getConfiguration();
caseSensitive = conf.getBoolean("wordcount.case.sensitive", true);
if (conf.getBoolean("wordcount.skip.patterns", false)) {
URI[] patternsURIs = Job.getInstance(conf).getCacheFiles();
for (URI patternsURI : patternsURIs) {
Path patternsPath = new Path(patternsURI.getPath());
String patternsFileName = patternsPath.getName().toString();
parseSkipFile(patternsFileName);
}
}
}
private void parseSkipFile(String fileName) {
try {
fis = new BufferedReader(new FileReader(fileName));
String pattern = null;
while ((pattern = fis.readLine()) != null) {
patternsToSkip.add(pattern);
}
} catch (IOException ioe) {
System.err.println("Caught exception while parsing the cached file '"
StringUtils.stringifyException(ioe));
}
}
@Override
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
String line = (caseSensitive) ?
value.toString() : value.toString().toLowerCase();
for (String pattern : patternsToSkip) {
line = line.replaceAll(pattern, "");
}
StringTokenizer itr = new StringTokenizer(line);
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
Counter counter = context.getCounter(CountersEnum.class.getName(),
CountersEnum.INPUT_WORDS.toString());
counter.increment(1);
}
}
}
public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum = val.get();
}
result.set(sum);
context.write(key, result);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
conf.set("yarn.resourcemanager.address", "192.168.56.101:8050");
conf.set("mapreduce.framework.name", "yarn");
conf.set("fs.defaultFS", "hdfs://vbusuanzi:9000/");
// conf.set("mapred.jar", "mapreduce/build/libs/mapreduce-0.1.jar"); // 也可以在这里设置刚刚编译好的jar
conf.set("mapred.job.tracker", "vbusuanzi:9001");
// conf.set("mapreduce.app-submission.cross-platform", "true");// Windows开发者需要设置跨平台
args = new String[]{"/tmp/test/LICENSE.txt","/tmp/test/out30"};
GenericOptionsParser optionParser = new GenericOptionsParser(conf, args);
String[] remainingArgs = optionParser.getRemainingArgs();
if ((remainingArgs.length != 2) && (remainingArgs.length != 4)) {
System.err.println("Usage: wordcount <in> <out> [-skip skipPatternFile]");
System.exit(2);
}
Job job = Job.getInstance(conf,"test");
job.setJar("mapreduce/build/libs/mapreduce-0.1.jar");
job.setJarByClass(com.wc.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
List<String> otherArgs = new ArrayList<String>();
for (int i=0; i < remainingArgs.length; i) {
if ("-skip".equals(remainingArgs[i])) {
job.addCacheFile(new Path(remainingArgs[ i]).toUri());
job.getConfiguration().setBoolean("wordcount.skip.patterns", true);
} else {
otherArgs.add(remainingArgs[i]);
}
}
FileInputFormat.addInputPath(job, new Path(otherArgs.get(0)));
FileOutputFormat.setOutputPath(job, new Path(otherArgs.get(1)));
job.waitForCompletion(true);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}可以解决的问题：
Error: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class com.wc$TokenizerMapper not found
其实日志中有提示该问题出在哪： Not adding any jar to the list of resources.
2018-11-22 16:03:29,086 INFO [AsyncDispatcher event handler] org.apache.hadoop.mapreduce.v2.app.job.impl.TaskAttemptImpl: Job jar is not present. Not adding any jar to the list of resources.
所有增加
代码语言：javascript复制job.setJar("mapreduce/build/libs/mapreduce-0.1.jar");
