hadoop入门--使用MapReduce统计每个航班班次

案例基于hadoop 2.73，伪分布式集群

一，创建一个MapReduce应用

MapReduce应用结构如图：

1、引入maven依赖

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0modelVersion>

  <groupId>com.hadoopgroupId>
  <artifactId>beginnerartifactId>
  <version>1.0-SNAPSHOTversion>
  <packaging>jarpackaging>

  <name>beginnername>
  <url>http://maven.apache.orgurl>

  <properties>
    <project.build.sourceEncoding>UTF-8project.build.sourceEncoding>
  properties>

  <dependencies>

    <dependency>
      <groupId>org.apache.hadoopgroupId>
      <artifactId>hadoop-coreartifactId>
      <version>1.2.1version>
    dependency>
    <dependency>
      <groupId>org.apache.hadoopgroupId>
      <artifactId>hadoop-commonartifactId>
      <version>2.7.3version>
    dependency>

    <dependency>
      <groupId>org.apache.hadoopgroupId>
      <artifactId>hadoop-clientartifactId>
      <version>2.7.3version>
    dependency>

    <dependency>
      <groupId>au.com.bytecodegroupId>
      <artifactId>opencsvartifactId>
      <version>2.4version>
    dependency>
  dependencies>

  <build>
    <plugins>
      <plugin>
        <groupId>org.apache.maven.pluginsgroupId>
        <artifactId>maven-shade-pluginartifactId>
        <version>1.2.1version>
        <executions>
          <execution>
            <phase>packagephase>
            <goals>
              <goal>shadegoal>
            goals>
            <configuration>
              <transformers>
                <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                  <mainClass>com.hadoop.FlightsByCarriermainClass>
                transformer>
              transformers>
            configuration>
          execution>
        executions>
      plugin>
    plugins>
  build>

project>

2、MapReduce Driver代码

是用户与hadoop集群交互的客户端，在此配置MapReduce Job。

package com.hadoop;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class FlightsByCarrier {
    public static void main(String[] args)  throws Exception {

        Job job = new Job();
        job.setJarByClass(FlightsByCarrier.class);
        job.setJobName("FlightsByCarrier");

        TextInputFormat.addInputPath(job, new Path(args[0]));
        job.setInputFormatClass(TextInputFormat.class);

        job.setMapperClass(FlightsByCarrierMapper.class);
        job.setReducerClass(FlightsByCarrierReducer.class);

        TextOutputFormat.setOutputPath(job, new Path(args[1]));
        job.setOutputFormatClass(TextOutputFormat.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        job.waitForCompletion(true);
    }
}

3、MapReduce Mapper代码

package com.hadoop;

import au.com.bytecode.opencsv.CSVParser;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class FlightsByCarrierMapper extends Mapper{
    @Override
    protected void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {

        if (key.get() > 0) {
            String[] lines = new CSVParser().parseLine(value.toString());

            context.write(new Text(lines[8]), new IntWritable(1));
        }
    }
}

4、MapReduce Reducer代码

package com.hadoop;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

public class FlightsByCarrierReducer extends Reducer<Text, IntWritable, Text, IntWritable>{

    @Override
    protected void reduce(Text token, Iterable counts,
                          Context context) throws IOException, InterruptedException {
        int sum = 0;

        for (IntWritable count : counts) {
            sum+= count.get();
        }
        context.write(token, new IntWritable(sum));
    }
}

5、利用idea maven打jar包

jar包名称为：beginner-1.0-SNAPSHOT.jar

6、上传到linux虚拟机

代码是在window系统中的idea编写完成，需要上传到Linux虚拟机。

7、运行MapReduce Driver，处理航班数据

hadoop jar beginner-1.0-SNAPSHOT.jar  /user/root/2008.csv /user/root/output/flightsCount

运行情况如下：

18/01/09 02:29:52 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
18/01/09 02:29:52 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
18/01/09 02:29:53 INFO input.FileInputFormat: Total input paths to process : 1
18/01/09 02:29:54 INFO mapreduce.JobSubmitter: number of splits:6
18/01/09 02:29:54 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1515491426576_0002
18/01/09 02:29:54 INFO impl.YarnClientImpl: Submitted application application_1515491426576_0002
18/01/09 02:29:55 INFO mapreduce.Job: The url to track the job: http://slave1:8088/proxy/application_1515491426576_0002/
18/01/09 02:29:55 INFO mapreduce.Job: Running job: job_1515491426576_0002
18/01/09 02:30:01 INFO mapreduce.Job: Job job_1515491426576_0002 running in uber mode : false
18/01/09 02:30:01 INFO mapreduce.Job:  map 0% reduce 0%
18/01/09 02:30:17 INFO mapreduce.Job:  map 39% reduce 0%
18/01/09 02:30:19 INFO mapreduce.Job:  map 52% reduce 0%
18/01/09 02:30:21 INFO mapreduce.Job:  map 86% reduce 0%
18/01/09 02:30:22 INFO mapreduce.Job:  map 100% reduce 0%
18/01/09 02:30:31 INFO mapreduce.Job:  map 100% reduce 100%
18/01/09 02:30:32 INFO mapreduce.Job: Job job_1515491426576_0002 completed successfully
18/01/09 02:30:32 INFO mapreduce.Job: Counters: 49
    File System Counters
        FILE: Number of bytes read=63087558
        FILE: Number of bytes written=127016400
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=689434454
        HDFS: Number of bytes written=197
        HDFS: Number of read operations=21
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=2
    Job Counters 
        Launched map tasks=6
        Launched reduce tasks=1
        Data-local map tasks=6
        Total time spent by all maps in occupied slots (ms)=110470
        Total time spent by all reduces in occupied slots (ms)=7315
        Total time spent by all map tasks (ms)=110470
        Total time spent by all reduce tasks (ms)=7315
        Total vcore-milliseconds taken by all map tasks=110470
        Total vcore-milliseconds taken by all reduce tasks=7315
        Total megabyte-milliseconds taken by all map tasks=113121280
        Total megabyte-milliseconds taken by all reduce tasks=7490560
    Map-Reduce Framework
        Map input records=7009729
        Map output records=7009728
        Map output bytes=49068096
        Map output materialized bytes=63087588
        Input split bytes=630
        Combine input records=0
        Combine output records=0
        Reduce input groups=20
        Reduce shuffle bytes=63087588
        Reduce input records=7009728
        Reduce output records=20
        Spilled Records=14019456
        Shuffled Maps =6
        Failed Shuffles=0
        Merged Map outputs=6
        GC time elapsed (ms)=6818
        CPU time spent (ms)=38010
        Physical memory (bytes) snapshot=1807056896
        Virtual memory (bytes) snapshot=13627478016
        Total committed heap usage (bytes)=1370488832
    Shuffle Errors
        BAD_ID=0
        CONNECTION=0
        IO_ERROR=0
        WRONG_LENGTH=0
        WRONG_MAP=0
        WRONG_REDUCE=0
    File Input Format Counters 
        Bytes Read=689433824
    File Output Format Counters 
        Bytes Written=197

8、查看航班数据

hadoop fs -cat /user/root/output/flightsCount/part-r-00000

结果如下：

9E  262208
AA  604885
AQ  7800
AS  151102
B6  196091
CO  298455
DL  451931
EV  280575
F9  95762
FL  261684
HA  61826
MQ  490693
NW  347652
OH  197607
OO  567159
UA  449515
US  453589
WN  1201754
XE  374510
YV  254930

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参考资料：
1、《Hadoop For Dummies》