我对这个hadoop map reduce概念全新了
我的文件由':::'分隔，而不是由空格（“”）分隔。
默认情况下，hadoop map是否会将空格作为分隔符。如果是这样，那么如何更改它以接受用户定义的分隔符。
由于
感谢Praveen，100gods和Eric指导我。再次出现了一个问题。我将编写我的代码和错误
我想我可能做错了。所以请清除我的锥体
再次感谢
enter code here
package com;
import java.io.IOException;
import java.util.Iterator;
import java.util.StringTokenizer;
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.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.KeyValueTextInputFormat;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
public class WordCount {
public static class Map extends MapReduceBase implements
Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(LongWritable key, Text value,
OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {
String line = value.toString();
StringTokenizer tokenizer = new StringTokenizer(line);
while (tokenizer.hasMoreTokens()) {
word.set(tokenizer.nextToken());
output.collect(word, one);
}
}
}
public static class Reduce extends MapReduceBase implements
Reducer<Text, IntWritable, Text, IntWritable> {
public void reduce(Text key, Iterator<IntWritable> values,
OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {
int sum = 0;
while (values.hasNext()) {
sum += values.next().get();
}
output.collect(key, new IntWritable(sum));
}
}
public static void main(String[] args) throws Exception {
/*
* JobConf conf = new JobConf(WordCount.class);
* conf.setJobName("wordcount");
*/
Configuration configuration = new Configuration();
JobConf conf = new JobConf(configuration);
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(Map.class);
conf.setCombinerClass(Reduce.class);
conf.setReducerClass(Reduce.class);
conf.setInputFormat(KeyValueTextInputFormat.class);
conf.set("key.value.separator.in.input.line", ":::");
// conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
// conf.set("mapreduce.input.keyvaluelinerecordreader.key.value.separator",
// ":::");
FileInputFormat.setInputPaths(conf, "/home/vishal/note.txt");
FileOutputFormat.setOutputPath(conf, new Path("/home/vishal/output"));
JobClient.runJob(conf);
}
}
以下是错误
12/10/30 12:23:04 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
12/10/30 12:23:04 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
12/10/30 12:23:04 WARN mapred.JobClient: No job jar file set. User classes may not be found. See JobConf(Class) or JobConf#setJar(String).
12/10/30 12:23:04 WARN snappy.LoadSnappy: Snappy native library not loaded
12/10/30 12:23:04 INFO mapred.FileInputFormat: Total input paths to process : 1
12/10/30 12:23:04 INFO mapred.JobClient: Running job: job_local_0001
12/10/30 12:23:04 INFO util.ProcessTree: setsid exited with exit code 0
12/10/30 12:23:04 INFO mapred.Task: Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@193722c
12/10/30 12:23:04 INFO mapred.MapTask: numReduceTasks: 1
12/10/30 12:23:04 INFO mapred.MapTask: io.sort.mb = 100
12/10/30 12:23:05 INFO mapred.MapTask: data buffer = 79691776/99614720
12/10/30 12:23:05 INFO mapred.MapTask: record buffer = 262144/327680
12/10/30 12:23:05 WARN mapred.LocalJobRunner: job_local_0001
`java.lang.ClassCastException: org.apache.hadoop.io.Text cannot be cast to org.apache.hadoop.io.LongWritable
at com.WordCount$Map.map(WordCount.java:1)
at org.apache.hadoop.mapred.MapRunner.run(MapRunner.java:50)
at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:436)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:372)
at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:212)
12/10/30 12:23:05 INFO mapred.JobClient: map 0% reduce 0%
12/10/30 12:23:05 INFO mapred.JobClient: Job complete: job_local_0001
12/10/30 12:23:05 INFO mapred.JobClient: Counters: 0
12/10/30 12:23:05 INFO mapred.JobClient: Job Failed: NA
Exception in thread "main" java.io.IOException: Job failed!
at org.apache.hadoop.mapred.JobClient.runJob(JobClient.java:1327)
at com.WordCount.main(WordCount.java:84)
答案 0 :(得分：1)
Hadoop中用户定义的map function将Key和Value作为输入。对于FileInputFormat，键是文件中的行偏移（通常被忽略），值是输入文件中的一行。映射器可以将输入行（也就是值）与任何分隔符分开。否则KeyValueTextInputFormat可以像其他查询中提到的那样使用。
答案 1 :(得分：0)
使用KeyValueTextInputFormat，因为它允许您选择分隔符。这将允许您使用Configuration对象设置分隔符：
conf.set("mapreduce.input.keyvaluelinerecordreader.key.value.separator", ":::");
答案 2 :(得分：0)
您正在使用 KeyValueTextInputFormat 并指定了分隔符。键=＆gt;发送到地图作业的值对是文本类型。
尝试将Mapper类定义更改为：
public static class Map extends MapReduceBase implements
Mapper<Text, Text, Text, IntWritable> {
.....
public void map(Text key, Text value,
OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {
.....
}
}
注意：您使用的是旧版API。尝试切换到新的，它简化了很多东西（例如： OutputCollector 和 Reporter 已经融合到 Context ）。
答案 3 :(得分：0)
当您使用KeyValuetextInputFormat.class
public static class Map extends MapReduceBase implements
Mapper<Text, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Text key, Text value,
OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {
采用新的Api
public class Map extends Mapper<Text, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Text key, Text value, Context context)
throws IOException, InterruptedException {
答案 4 :(得分：-2)
您必须使用旧的Hadoop API才能使用KeyValueTextInputFormat。 以下代码将完成工作。
Configuration configuration=new Configuration();
JobConf conf=new JobConf(configuration);
conf.setInputFormat(KeyValueTextInputFormat.class);
conf.set("key.value.separator.in.input.line", ":::");
注意： KeyValueTextInputFormat已从新的hadoop API中删除。