Hadoop，往map/reduce中传值的问题解决方法实例

Hadoop，往map/reduce中传值的问题解决方法实例

最近在看一些map/reduce的程序，其中遇到一个问题：就是在类中定义的属性无法被mapreduce程序直接获取。

具体代码如下

public class KeyJob {

public static class myMap extends Mapper<LongWritable, Text, Text, IntWritable
4000
> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
private final static List<String> target_words = new ArrayList<String>();

@Override
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String[] items = value.toString().split(",");
for (String item : items) {
if (target_words.contains(item)) {
word.set(item);
context.write(word, one);
}
}
}

public static void add(String word) {
target_words.add(word);
for (String s : target_words) {
System.out.println(s);
}
System.out.println("------");
}
}

public static class myReduce extends Reducer<Text, IntWritable, Text, IntWritable> {

@Override
public void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
context.write(key, new IntWritable(sum));
}
}

public static void main(String[] args) throws Exception {
String job_name="keywordcount";
Configuration conf = new Configuration();
conf.set("fs.default.name","hdfs://bigdata:9000");
conf.set ("mapred.job.tracker", "bigdata:9001");

Scanner scanner=new Scanner(System.in);
String str=scanner.next();
//  Add to target
String[] target_words = str.split(",");
for (String word : target_words) {
myMap.add(word.toLowerCase());
}

Job job = new Job(conf, job_name);
job.setJarByClass(KeyJob.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);

job.setMapperClass(myMap.class);
job.setReducerClass(myReduce.class);
FileInputFormat.addInputPath(job, new Path("/user/hadoop/input/"+job_name+"/"));
FileOutputFormat.setOutputPath(job, new Path("/user/hadoop/out/"+job_name));

job.waitForCompletion(true);
}

}

如果说按java代码的逻辑去理解是没有问题的

然而一个奇怪的坑就出现了



这里的target_words是没有值的，导致程序不会进入if语句里面，最后我们想查找的单词输出为空。而我们明明是赋过值的





查阅资料发现，原来map reduce是无法直接读取到target_words里面的值的。因为执行的main函数是在作业发布的客户端的JVM进程里对target_words赋的值，而你要取的target_words值则是在另外一个JVM里，即TASK运行的MAP这JVM进程里，所以这个值无法传递过去。

解决办法

通过 Configuration 来传递参数

在main函数中将要传递的值通过conf 来 set进去



然后在执行函数里面将set的值读取出来



问题解决

源码

以下是完整源码，笔者无偿共享，笔者被这个问题坑了一整天，希望大家多多交流，不要犯类似的问题。

package com.zlf.job;

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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

import com.zlf.util.HdUtil;

import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.Scanner;

public class Run {
private static List<String> target_words = new ArrayList<String>();

public static class WordMap extends
Mapper<LongWritable, Text, Text, IntWritable> {
private final IntWritable one = new IntWritable(1);
private Text word = new Text();

protected void setup(
Context context)
throws IOException, InterruptedException {

/** 通过conf把传入的值取出来*/
Configuration conf=context.getConfiguration();
String keywords=conf.get("keywords");
String[] key_words=keywords.split(",");
for ( String word : key_words) {
target_words.add(word);
System.out.println(word);
}
};

protected void map(LongWritable key, Text value, Context context)
throws java.io.IOException, InterruptedException {
String[] items = value.toString().split(",");

/** 手动添加list内容 */
// target_words.add("what");
// target_words.add("do");
// target_words.add("to");

for (String item : items) {

/** 测试单词统计功能 */
// word.set(target_words.get(1));//不手动赋值发现get第一个元素
// 会出现数组越界--说明target_words里面是没有值的
// context.write(word,one );

/** 测试contains关键词统计功能 */
if (target_words.contains(item)) {
System.out.println(item);
word.set(item);
context.write(word, one);
}

}
}

}

public static class Myreduce extends
Reducer<Text, IntWritable, Text, IntWritable> {

protected void reduce(Text key, Iterable<IntWritable> values,
Context context) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
context.write(key, new IntWritable(sum));
}
}

public static void main(String[] args) throws Exception {
String job_name = "keywordcount";
boolean flag = HdUtil.removeDir("/user/hadoop/out/" + job_name);
System.out.println(flag);
Configuration conf = new Configuration();
conf.set("fs.default.name", "hdfs://bigdata:9000");
conf.set("mapred.job.tracker", "bigdata:9001");
if (args.length < 1) {
System.out
.println("Usage: wordcount <input_path> <output_path> <keyword_list>");
return;
}
/** 控制台进行输入 */
Scanner scanner = new Scanner(System.in);
String str = scanner.next();
// String[] target_words = str.split(",");
// for (String word : target_words) {
// add(word.toLowerCase());
// }

conf.set("keywords", str);//将输入的值存入conf中，以备map取用

/** 命令行进行输入 */
// String[] target_words = args[0].split(",");
// for (String word : target_words) {
// add(word.toLowerCase());
// }

Job job = new Job(conf, job_name);
job.setJarByClass(Run.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);

job.setMapperClass(WordMap.class);
job.setReducerClass(Myreduce.class);

job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);

FileInputFormat.addInputPath(job, new Path("/user/hadoop/input/"
+ job_name + "/"));
FileOutputFormat.setOutputPath(job, new Path("/user/hadoop/out/"
+ job_name));

job.waitForCompletion(true);
}

}

以上是对指定关键词进行统计的程序。

原数据文件

There,are,moments,in,life,when,you,miss,someone,so,much,that
you,just,want,to,pick,them,from,your,dreams,and,hug,them
for,real!,Dream,what,you,want,to,dream;go,where,you,want
to,go;be,what,you,want,to,be,because,you,have,only,one,life
and,one
c935
,chance,to,do,all,the,things,you,want,to,do

运行日志文件

文件夹是否存在：true
true
you,do,to,and,what
17/01/10 14:21:12 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
17/01/10 14:21:12 INFO input.FileInputFormat: Total input paths to process : 1
17/01/10 14:21:12 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/01/10 14:21:12 WARN snappy.LoadSnappy: Snappy native library not loaded
17/01/10 14:21:12 INFO mapred.JobClient: Running job: job_201701100106_0025
17/01/10 14:21:13 INFO mapred.JobClient:  map 0% reduce 0%
17/01/10 14:21:17 INFO mapred.JobClient:  map 100% reduce 0%
17/01/10 14:21:24 INFO mapred.JobClient:  map 100% reduce 33%
17/01/10 14:21:26 INFO mapred.JobClient:  map 100% reduce 100%
17/01/10 14:21:26 INFO mapred.JobClient: Job complete: job_201701100106_0025
17/01/10 14:21:26 INFO mapred.JobClient: Counters: 26
17/01/10 14:21:26 INFO mapred.JobClient:   Job Counters
17/01/10 14:21:26 INFO mapred.JobClient:     SLOTS_MILLIS_MAPS=4622
17/01/10 14:21:26 INFO mapred.JobClient:     Launched reduce tasks=1
17/01/10 14:21:26 INFO mapred.JobClient:     Total time spent by all reduces waiting after reserving slots (ms)=0
17/01/10 14:21:26 INFO mapred.JobClient:     Total time spent by all maps waiting after reserving slots (ms)=0
17/01/10 14:21:26 INFO mapred.JobClient:     Launched map tasks=1
17/01/10 14:21:26 INFO mapred.JobClient:     Data-local map tasks=1
17/01/10 14:21:26 INFO mapred.JobClient:     SLOTS_MILLIS_REDUCES=8716
17/01/10 14:21:26 INFO mapred.JobClient:   FileSystemCounters
17/01/10 14:21:26 INFO mapred.JobClient:     FILE_BYTES_READ=190
17/01/10 14:21:26 INFO mapred.JobClient:     HDFS_BYTES_READ=416
17/01/10 14:21:26 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=116939
17/01/10 14:21:26 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=29
17/01/10 14:21:26 INFO mapred.JobClient:   Map-Reduce Framework
17/01/10 14:21:26 INFO mapred.JobClient:     Map input records=5
17/01/10 14:21:26 INFO mapred.JobClient:     Reduce shuffle bytes=190
17/01/10 14:21:26 INFO mapred.JobClient:     Spilled Records=38
17/01/10 14:21:26 INFO mapred.JobClient:     Map output bytes=146
17/01/10 14:21:26 INFO mapred.JobClient:     Total committed heap usage (bytes)=160501760
17/01/10 14:21:26 INFO mapred.JobClient:     CPU time spent (ms)=1020
17/01/10 14:21:26 INFO mapred.JobClient:     Combine input records=0
17/01/10 14:21:26 INFO mapred.JobClient:     SPLIT_RAW_BYTES=127
17/01/10 14:21:26 INFO mapred.JobClient:     Reduce input records=19
17/01/10 14:21:26 INFO mapred.JobClient:     Reduce input groups=5
17/01/10 14:21:26 INFO mapred.JobClient:     Combine output records=0
17/01/10 14:21:26 INFO mapred.JobClient:     Physical memory (bytes) snapshot=248950784
17/01/10 14:21:26 INFO mapred.JobClient:     Reduce output records=5
17/01/10 14:21:26 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=3875409920
17/01/10 14:21:26 INFO mapred.JobClient:     Map output records=19

输出结果文件

and 2
do  2
to  6
what    2
you 7

本博客参考了 数据手艺人的博客

http://www.cnblogs.com/zhengrunjian/p/4536572.html