MapReduce Old API - 将命令行参数传递给Map [英] MapReduce Old API - Passing Command Line Argument to Map
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问题描述
我编写了一个MapReduce作业，用于在使用旧API存储在HDFS中的输入文件中查找搜索字符串（通过命令行参数传递）。
下面是我的Driver类 -
public class StringSearchDriver
{
public static void main （String [] args）抛出IOException
{
JobConf jc = new JobConf（StringSearchDriver.class）;
jc.set（SearchWord，args [2]）;
jc.setJobName（String Search）;
FileInputFormat.addInputPath（jc，new Path（args [0]））;
FileOutputFormat.setOutputPath（jc，new Path（args [1]））;
jc.setMapperClass（StringSearchMap.class）;
jc.setReducerClass（StringSearchReduce.class）;
jc.setOutputKeyClass（Text.class）;
jc.setOutputValueClass（IntWritable.class）;
JobClient.runJob（jc）;
以下是我的Mapper类 -
public class StringSearchMap扩展MapReduceBase实现
Mapper< LongWritable，Text，Text，IntWritable>
{
字符串searchWord;
public void configure（JobConf jc）
{
searchWord = jc.get（SearchWord）;
$ b @Override
public void map（LongWritable key，Text value，
OutputCollector< Text，IntWritable> ; out，Reporter记者）
抛出IOException
{
String [] input = value.toString（）。split（）;
for（String word：input）
{
if（word.equalsIgnoreCase（searchWord））
out.collect（new Text（word），new IntWritable 1））;
$ b 运行时作业（传递的命令行字符串是hi），我得到下面的错误 -
14/09/21 22 ：35：41信息mapred.JobClient：任务ID：attempt_201409212134_0005_m_000001_2，状态：FAILED
java.lang.Class.asSubclass中的
java.lang.ClassCastException：接口javax.xml.soap.Text
at org.apache.hadoop.mapred.JobConf.getOutputKeyComparator（JobConf.java:795）
at org.apache.hadoop.mapred.MapTask $ MapOutputBuffer。< init>（MapTask .java：964）
at org.apache.hadoop.mapred.MapTask.runOldMapper（MapTask.java:422）
at org.apache.hadoop.mapred.MapTask.run（MapTask.java:366 ）
at org.apache.hadoop.mapred.Child $ 4.run（Child.java:255）
at java.security.AccessController.doPrivileged（Native Method）
at javax.security。 auth.Subject.doAs（Subject.java:416）
at org.apache.hadoop.security.UserGroupInformation.doAs（ UserGroupInformation.java:1190）
at org.apache.hadoop.mapred.Child.main（Child.java:249）
请建议。
解决方案
您自动导入了错误的导入。
代替 import org.apache.hadoop.io.Text ，您可以 import javax.xml.soap.Text
您可以在博客。
一点，最好采用新API
编辑
我使用了新的API
import java.io.IOException;
import java.util.StringTokenizer;
导入org.apache.hadoop.conf.Configuration;
导入org.apache.hadoop.conf.Configured;
导入org.apache.hadoop.fs.FileSystem;
导入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 org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
/ **
* @author Unmesha sreeveni
* @Date 23 sep 2014
* /
public class StringSearchDriver extends Configured implements Tool {
public static class Map扩展
Mapper< LongWritable，Text，Text，IntWritable> {
private static static IntWritable one = new IntWritable（1）;
私人文字=新文字（）;
public void map（LongWritable key，Text value，Context context）
throws IOException，InterruptedException {
Configuration conf = context.getConfiguration（）;
String line = value.toString（）;
String searchString = conf.get（word）;
StringTokenizer tokenizer = new StringTokenizer（line）;
while（tokenizer.hasMoreTokens（））{
String token = tokenizer.nextToken（）;
if（token.equals（searchString））{
word.set（token）;
context.write（word，one）;
$ b public static class Reduce extends
Reducer< Text，IntWritable，Text，IntWritable> ; {
$ b $ public void reduce（Text key，Iterable< IntWritable> values，
Context context）throws IOException，InterruptedException {
int sum = 0; （IntWritable val：values）
{
sum + = val.get（）;
}
context.write（key，new IntWritable（sum））;
$ b $ public static void main（String [] args）throws Exception {
Configuration conf = new Configuration（）;
int res = ToolRunner.run（conf，new StringSearchDriver（），args）;
System.exit（res）;
$ b @Override
public int run（String [] args）throws Exception {
// TODO自动生成的方法存根
if（args .length！= 3）{
System.out
.printf（Usage：Search String< input dir>< output dir>< search word> \\\
）;
System.exit（-1）;
}
String source = args [0];
String dest = args [1];
字符串searchword = args [2];
Configuration conf = new Configuration（）;
conf.set（word，searchword）;
工作作业=新作业（conf，搜索字符串）;
job.setJarByClass（StringSearchDriver.class）;
FileSystem fs = FileSystem.get（conf）;
Path in = new Path（source）;
Path out = new Path（dest）;
if（fs.exists（out））{
fs.delete（out，true）;
}
job.setMapOutputKeyClass（Text.class）;
job.setMapOutputValueClass（IntWritable.class）;
job.setOutputKeyClass（Text.class）;
job.setOutputValueClass（IntWritable.class）;
job.setMapperClass（Map.class）;
job.setReducerClass（Reduce.class）;
job.setInputFormatClass（TextInputFormat.class）;
job.setOutputFormatClass（TextOutputFormat.class）;
FileInputFormat.addInputPath（job，in）;
FileOutputFormat.setOutputPath（job，out）;
boolean sucess = job.waitForCompletion（true）;
return（成功？0：1）;
}
}
这是可行的。
I am coding a MapReduce job for finding the occurrence of a search string (passed through Command Line argument) in an input file stored in HDFS using old API.
Below is my Driver class -
public class StringSearchDriver
{
public static void main(String[] args) throws IOException
{
JobConf jc = new JobConf(StringSearchDriver.class);
jc.set("SearchWord", args[2]);
jc.setJobName("String Search");
FileInputFormat.addInputPath(jc, new Path(args[0]));
FileOutputFormat.setOutputPath(jc, new Path(args[1]));
jc.setMapperClass(StringSearchMap.class);
jc.setReducerClass(StringSearchReduce.class);
jc.setOutputKeyClass(Text.class);
jc.setOutputValueClass(IntWritable.class);
JobClient.runJob(jc);
}
}
Below is my Mapper Class -
public class StringSearchMap extends MapReduceBase implements
Mapper<LongWritable, Text, Text, IntWritable>
{
String searchWord;
public void configure(JobConf jc)
{
searchWord = jc.get("SearchWord");
}
@Override
public void map(LongWritable key, Text value,
OutputCollector<Text, IntWritable> out, Reporter reporter)
throws IOException
{
String[] input = value.toString().split("");
for(String word:input)
{
if (word.equalsIgnoreCase(searchWord))
out.collect(new Text(word), new IntWritable(1));
}
}
}
On running the job (command line string passed is "hi"), I am getting the below error -
14/09/21 22:35:41 INFO mapred.JobClient: Task Id : attempt_201409212134_0005_m_000001_2, Status : FAILED
java.lang.ClassCastException: interface javax.xml.soap.Text
at java.lang.Class.asSubclass(Class.java:3129)
at org.apache.hadoop.mapred.JobConf.getOutputKeyComparator(JobConf.java:795)
at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.<init>(MapTask.java:964)
at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:422)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:366)
at org.apache.hadoop.mapred.Child$4.run(Child.java:255)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:416)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1190)
at org.apache.hadoop.mapred.Child.main(Child.java:249)
Please suggest.
解决方案
You auto imported the wrong import. Instead of import org.apache.hadoop.io.Text you import javax.xml.soap.Text
You can find a sample wrong import in this blog.
One point , It is better to adopt New API
EDIT
I used New Api
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
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 org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
/**
* @author Unmesha sreeveni
* @Date 23 sep 2014
*/
public class StringSearchDriver extends Configured implements Tool {
public static class Map extends
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, Context context)
throws IOException, InterruptedException {
Configuration conf = context.getConfiguration();
String line = value.toString();
String searchString = conf.get("word");
StringTokenizer tokenizer = new StringTokenizer(line);
while (tokenizer.hasMoreTokens()) {
String token = tokenizer.nextToken();
if(token.equals(searchString)){
word.set(token);
context.write(word, one);
}
}
}
}
public static class Reduce extends
Reducer<Text, IntWritable, Text, IntWritable> {
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 {
Configuration conf = new Configuration();
int res = ToolRunner.run(conf, new StringSearchDriver(), args);
System.exit(res);
}
@Override
public int run(String[] args) throws Exception {
// TODO Auto-generated method stub
if (args.length != 3) {
System.out
.printf("Usage: Search String <input dir> <output dir> <search word> \n");
System.exit(-1);
}
String source = args[0];
String dest = args[1];
String searchword = args[2];
Configuration conf = new Configuration();
conf.set("word", searchword);
Job job = new Job(conf, "Search String");
job.setJarByClass(StringSearchDriver.class);
FileSystem fs = FileSystem.get(conf);
Path in =new Path(source);
Path out =new Path(dest);
if (fs.exists(out)) {
fs.delete(out, true);
}
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setMapperClass(Map.class);
job.setReducerClass(Reduce.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.addInputPath(job, in);
FileOutputFormat.setOutputPath(job, out);
boolean sucess = job.waitForCompletion(true);
return (sucess ? 0 : 1);
}
}
This works.
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