使用压缩文件作为输入运行hadoop。 hadoop读取的数据输入不是按顺序读取的。数字格式例外

时间：2014-06-25 06:28:04

标签： hadoop compression

在更改mapred-site.xml中的属性后，我将tar.bz2文件，.gz和tar.gz文件作为输入。以上都没有起作用。我假设在这里发生的是由hadoop作为输入读取的记录不按顺序，即。一列输入是字符串，另一列是整数但是由于某些乱序数据而从压缩文件读取它，在某些时候hadoop将字符串部分读作整数并生成非法格式异常。我只是个菜鸟。我想知道配置或代码中是否存在问题。

core-site.xml中的属性是

<property>
  <name>io.compression.codecs</name>
   <value>org.apache.hadoop.io.compress.BZip2Codec,org.apache.hadoop.io.compress.DefaultCodec,org.apache.hadoop.io.compress.GzipCodec,org.apac\
he.hadoop.io.compress.SnappyCodec</value>
   <description>A list of the compression codec classes that can be used for compression/decompression.</description>
</property>
mapred-site.xml中的

属性是

<property>
  <name>mapred.compress.map.output</name>
  <value>true</value>
</property>

<property>
   <name>mapred.map.output.compression.codec</name>
   <value>org.apache.hadoop.io.compress.BZip2Codec</value>
</property>
<property>
<name>mapred.output.compression.type</name>
<value>BLOCK</value>
</property>

这是我的代码

package org.myorg;

import java.io.IOException;
import java.util.*;
import org.apache.hadoop.util.NativeCodeLoader;        
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.io.compress.CompressionCodec;
import org.apache.hadoop.io.compress.CompressionInputStream;
import org.apache.hadoop.io.compress.CompressionOutputStream;
import org.apache.hadoop.io.compress.Decompressor;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;
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.io.compress.GzipCodec;        
import org.apache.hadoop.io.compress.*;
import org.apache.hadoop.io.compress.BZip2Codec;

public class MySort{
    public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
    private final static IntWritable Marks = new IntWritable();
    private Text name = new Text();
        String one,two;
    int num;
    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String line = value.toString();
        StringTokenizer tokenizer = new StringTokenizer(line);
        while (tokenizer.hasMoreTokens()) {
        one=tokenizer.nextToken();
        name.set(one);
        if(tokenizer.hasMoreTokens())
            two=tokenizer.nextToken();
        num=Integer.parseInt(two);
        Marks.set(num);
        context.write(name, Marks);
        }
    }
    } 

    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();
    //  conf.set("mapreduce.job.inputformat.class", "com.wizecommerce.utils.mapred.TextInputFormat");

    //  conf.set("mapreduce.job.outputformat.class", "com.wizecommerce.utils.mapred.TextOutputFormat");
    //  conf.setBoolean("mapreduce.map.output.compress",true);
    conf.setBoolean("mapred.output.compress",true);
    //conf.setBoolean("mapreduce.output.fileoutputformat.compress",false);
    //conf.setBoolean("mapreduce.map.output.compress",true);
    conf.set("mapred.output.compression.type", "BLOCK");     
    //conf.setClass("mapreduce.map.output.compress.codec", BZip2Codec.class, CompressionCodec.class);
    //      conf.setClass("mapred.map.output.compression.codec", GzipCodec.class, CompressionCodec.class);
    conf.setClass("mapred.map.output.compression.codec", BZip2Codec.class, CompressionCodec.class);
        Job job = new Job(conf, "mysort");
    job.setJarByClass(org.myorg.MySort.class);
    job.setJobName("mysort");
    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.setCompressInput(job,true);
    FileOutputFormat.setCompressOutput(job, true);
    //FileOutputFormat.setOutputCompressorClass(job, GzipCodec.class);
    //  conf.set("mapred.output.compression.type", CompressionType.BLOCK.toString()); 

    FileOutputFormat.setOutputCompressorClass(job, BZip2Codec.class);
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));

    job.waitForCompletion(true);
    }

}

这些都是makefile中的所有命令

run:    all
        -sudo ./a.out
        sudo chmod 777 -R Data
        -sudo rm data.tar.bz2
        sudo tar -cvjf data.tar.bz2 Data/data.txt
        sudo javac -classpath /home/hduser/12115_Select_Query/hadoop-core-1.1.2.jar -d mysort MySort.java
        sudo jar -cvf mysort.jar -C mysort/ .
        -hadoop fs -rmr MySort/output
        -hadoop fs -rmr MySort/input
        hadoop fs -mkdir MySort/input
        hadoop fs -put data.tar.bz2 MySort/input
        hadoop jar mysort.jar org.myorg.MySort MySort/input/ MySort/output
        -sudo rm /home/hduser/Out/sort.txt
        hadoop fs -copyToLocal MySort/output/part-r-00000 /home/hduser/Out/sort.txt
        sudo gedit /home/hduser/Out/sort.txt

all:    rdata.c
        -sudo rm a.out
        -gcc rdata.c -o a.out

exec:   run

.PHONY: exec run

命令：

hadoop jar mysort.jar org.myorg.MySort MySort/input/ MySort/output

这是输出：

Java HotSpot(TM) 64-Bit Server VM warning: You have loaded library /usr/local/hadoop/lib/native/libhadoop.so.1.0.0 which might have disabled stack guard. The VM will try to fix the stack guard now.
It's highly recommended that you fix the library with 'execstack -c <libfile>', or link it with '-z noexecstack'.
14/06/25 11:20:28 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
14/06/25 11:20:28 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
14/06/25 11:20:29 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
14/06/25 11:20:29 INFO input.FileInputFormat: Total input paths to process : 1
14/06/25 11:20:29 INFO mapreduce.JobSubmitter: number of splits:1
14/06/25 11:20:29 INFO Configuration.deprecation: mapred.output.compress is deprecated. Instead, use mapreduce.output.fileoutputformat.compress
14/06/25 11:20:29 INFO Configuration.deprecation: mapred.map.output.compression.codec is deprecated. Instead, use mapreduce.map.output.compress.codec
14/06/25 11:20:29 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1403675322820_0001
14/06/25 11:20:30 INFO impl.YarnClientImpl: Submitted application application_1403675322820_0001
14/06/25 11:20:30 INFO mapreduce.Job: The url to track the job: http://localhost:8088/proxy/application_1403675322820_0001/
14/06/25 11:20:30 INFO mapreduce.Job: Running job: job_1403675322820_0001
14/06/25 11:20:52 INFO mapreduce.Job: Job job_1403675322820_0001 running in uber mode : false
14/06/25 11:20:52 INFO mapreduce.Job:  map 0% reduce 0%
14/06/25 11:21:10 INFO mapreduce.Job: Task Id : attempt_1403675322820_0001_m_000000_0, Status : FAILED
Error: java.lang.NumberFormatException: For input string: "0ustar"
    at java.lang.NumberFormatException.forInputString(NumberFormatException.java:65)
    at java.lang.Integer.parseInt(Integer.java:580)
    at java.lang.Integer.parseInt(Integer.java:615)
    at org.myorg.MySort$Map.map(MySort.java:36)
    at org.myorg.MySort$Map.map(MySort.java:23)
    at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:145)
    at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:764)
    at org.apache.hadoop.mapred.MapTask.run(MapTask.java:340)
    at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:168)
    at java.security.AccessController.doPrivileged(Native Method)
    at javax.security.auth.Subject.doAs(Subject.java:422)
    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1548)
    at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:163)

14/06/25 11:21:29 INFO mapreduce.Job: Task Id : attempt_1403675322820_0001_m_000000_1, Status : FAILED
Error: java.lang.NumberFormatException: For input string: "0ustar"
    at java.lang.NumberFormatException.forInputString(NumberFormatException.java:65)
    at java.lang.Integer.parseInt(Integer.java:580)
    at java.lang.Integer.parseInt(Integer.java:615)
    at org.myorg.MySort$Map.map(MySort.java:36)
    at org.myorg.MySort$Map.map(MySort.java:23)
    at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:145)
    at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:764)
    at org.apache.hadoop.mapred.MapTask.run(MapTask.java:340)
    at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:168)
    at java.security.AccessController.doPrivileged(Native Method)
    at javax.security.auth.Subject.doAs(Subject.java:422)
    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1548)
    at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:163)

14/06/25 11:21:49 INFO mapreduce.Job: Task Id : attempt_1403675322820_0001_m_000000_2, Status : FAILED
Error: java.lang.NumberFormatException: For input string: "0ustar"
    at java.lang.NumberFormatException.forInputString(NumberFormatException.java:65)
    at java.lang.Integer.parseInt(Integer.java:580)
    at java.lang.Integer.parseInt(Integer.java:615)
    at org.myorg.MySort$Map.map(MySort.java:36)
    at org.myorg.MySort$Map.map(MySort.java:23)
    at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:145)
    at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:764)
    at org.apache.hadoop.mapred.MapTask.run(MapTask.java:340)
    at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:168)
    at java.security.AccessController.doPrivileged(Native Method)
    at javax.security.auth.Subject.doAs(Subject.java:422)
    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1548)
    at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:163)

14/06/25 11:22:10 INFO mapreduce.Job:  map 100% reduce 100%
14/06/25 11:22:10 INFO mapreduce.Job: Job job_1403675322820_0001 failed with state FAILED due to: Task failed task_1403675322820_0001_m_000000
Job failed as tasks failed. failedMaps:1 failedReduces:0

14/06/25 11:22:10 INFO mapreduce.Job: Counters: 9
    Job Counters 
        Failed map tasks=4
        Launched map tasks=4
        Other local map tasks=3
        Data-local map tasks=1
        Total time spent by all maps in occupied slots (ms)=69797
        Total time spent by all reduces in occupied slots (ms)=0
        Total time spent by all map tasks (ms)=69797
        Total vcore-seconds taken by all map tasks=69797
        Total megabyte-seconds taken by all map tasks=71472128

我也尝试过使用它：

hadoop jar /usr/local/hadoop/share/hadoop/tools/lib/hadoop-streaming-2.3.0.jar   -Dmapred.output.compress=true   -Dmapred.compress.map.output=true   -Dmapred.output.compression.codec=org.apache.hadoop.io.compress.BZip2Codec   -Dmapred.reduce.tasks=0   -input MySort/input/data.txt   -output MySort/zip1

成功创建压缩文件

hadoop fs -ls MySort/zip1

Found 3 items
-rw-r--r--   1 hduser supergroup          0 2014-06-25 10:43 MySort/zip1/_SUCCESS
-rw-r--r--   1 hduser supergroup   42488018 2014-06-25 10:43 MySort/zip1/part-00000.bz2
-rw-r--r--   1 hduser supergroup   42504084 2014-06-25 10:43 MySort/zip1/part-00001.bz2

然后运行它：

hadoop jar mysort.jar org.myorg.MySort MySort/input/ MySort/zip1

它仍然不起作用。我在这里缺少什么。

当我在不使用压缩文件bz2的情况下运行它并直接传递文本文件Data / data.txt即将其上传到hdfs中的MySort / input（hadoop fs -put Data / data.txt MySort / input）时，它工作正常

任何帮助都是赞赏的

1 个答案:

答案 0 :(得分：1)

我为此做了一个工作。我使用了工具运行器。

package org.myorg;

import java.io.IOException;
import java.util.*;
import org.apache.hadoop.util.NativeCodeLoader;        
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.io.compress.CompressionCodec;
import org.apache.hadoop.io.compress.CompressionInputStream;
import org.apache.hadoop.io.compress.CompressionOutputStream;
import org.apache.hadoop.io.compress.Decompressor;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;
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.io.compress.GzipCodec;        
import org.apache.hadoop.io.compress.*;
import org.apache.hadoop.io.compress.BZip2Codec;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
public class ToolMapReduce extends Configured implements Tool 
{


    public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> 
    {
        private final static IntWritable Marks = new IntWritable();
        private Text name = new Text();
        String one,two;
        int num;
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException 
        {
            String line = value.toString();
            StringTokenizer tokenizer = new StringTokenizer(line);
            while (tokenizer.hasMoreTokens()) 
            {
            one=tokenizer.nextToken();
            name.set(one);
            if(tokenizer.hasMoreTokens())
                two=tokenizer.nextToken();
            num=Integer.parseInt(two);
            Marks.set(num);
            context.write(name, Marks);
            }
        }
    } 

    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  
    {
        int res = ToolRunner.run(new Configuration(), new ToolMapReduce(), args);
        System.exit(res);
    }

    public int run(String[] args) throws Exception
    {   

        Configuration conf = this.getConf();
        //Configuration conf = new Configuration();
        //conf.setOutputFormat(SequenceFileOutputFormat.class); 
        //SequenceFileOutputFormat.setOutputCompressionType(conf, CompressionType.BLOCK); 
        //SequenceFileOutputFormat.setCompressOutput(conf, true); 
        //conf.set("mapred.output.compress","true");
        //  conf.set("mapred.output.compression","org.apache.hadoop.io.compress.SnappyCodec");

        //conf.set("mapred.output.compression.codec","org.apache.hadoop.io.compress.SnappyCodec");
        //  conf.set("mapreduce.job.inputformat.class", "com.wizecommerce.utils.mapred.TextInputFormat");

        //  conf.set("mapreduce.job.outputformat.class", "com.wizecommerce.utils.mapred.TextOutputFormat");
        //  conf.setBoolean("mapreduce.map.output.compress",true);
        conf.setBoolean("mapred.output.compress",true);
        //conf.setBoolean("mapreduce.output.fileoutputformat.compress",false);
        //conf.setBoolean("mapreduce.map.output.compress",true);
        conf.set("mapred.output.compression.type", "BLOCK");     
        //conf.setClass("mapreduce.map.output.compress.codec", BZip2Codec.class, CompressionCodec.class);
        //      conf.setClass("mapred.map.output.compression.codec", GzipCodec.class, CompressionCodec.class);
        conf.setClass("mapred.map.output.compression.codec", GzipCodec.class, CompressionCodec.class);
        Job job = new Job(conf, "mysort");
        job.setJarByClass(org.myorg.ToolMapReduce.class);
        //job.setJarByClass(org.myorg.MySort.class);
        job.setJobName("mysort");
        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.setCompressInput(job,true);
        FileOutputFormat.setCompressOutput(job, true);
        //FileOutputFormat.setOutputCompressorClass(job, GzipCodec.class);
        //  conf.set("mapred.output.compression.type", CompressionType.BLOCK.toString()); 

        FileOutputFormat.setOutputCompressorClass(job, GzipCodec.class);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        return job.waitForCompletion(true) ? 0 : 1;
        //job.waitForCompletion(true);
    }


}
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