Hadoop2.x编程入门实例：MaxTemperature

@(HADOOP)[hadoop]

Hadoop2x编程入门实例MaxTemperature 一前期准备二编写代码 1创建Map2创建Reduce3创建main方法4导出成MaxTempjar并上传至运行程序的服务器 三运行程序 1创建input目录并将sampletxt复制到input目录2运行程序3查看结果

注意：以下内容在2.x版本与1.x版本同样适用，已在2.4.1与1.2.0进行测试。

一、前期准备

1、创建伪分布Hadoop环境，请参考官方文档。或者http://blog.csdn.net/jediael_lu/article/details/38637277

2、准备数据文件如下sample.txt：

123456798676231190101234567986762311901012345679867623119010123456798676231190101234561+00121534567890356 123456798676231190101234567986762311901012345679867623119010123456798676231190101234562+01122934567890456 123456798676231190201234567986762311901012345679867623119010123456798676231190101234562+02120234567893456 123456798676231190401234567986762311901012345679867623119010123456798676231190101234561+00321234567803456 123456798676231190101234567986762311902012345679867623119010123456798676231190101234561+00429234567903456 123456798676231190501234567986762311902012345679867623119010123456798676231190101234561+01021134568903456 123456798676231190201234567986762311902012345679867623119010123456798676231190101234561+01124234578903456 123456798676231190301234567986762311905012345679867623119010123456798676231190101234561+04121234678903456 123456798676231190301234567986762311905012345679867623119010123456798676231190101234561+00821235678903456

二、编写代码

1、创建Map

package org.jediael.hadoopDemo.maxtemperature; import java.io.IOException; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; public class MaxTemperatureMapper extends Mapper<LongWritable, Text, Text, IntWritable> { private static final int MISSING = 9999; @Override public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); String year = line.substring(15, 19); int airTemperature; if (line.charAt(87) == '+') { // parseInt doesn't like leading plus // signs airTemperature = Integer.parseInt(line.substring(88, 92)); } else { airTemperature = Integer.parseInt(line.substring(87, 92)); } String quality = line.substring(92, 93); if (airTemperature != MISSING && quality.matches("[01459]")) { context.write(new Text(year), new IntWritable(airTemperature)); } } }

2、创建Reduce

package org.jediael.hadoopDemo.maxtemperature; import java.io.IOException; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; public class MaxTemperatureReducer extends Reducer<Text, IntWritable, Text, IntWritable> { @Override public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int maxValue = Integer.MIN_VALUE; for (IntWritable value : values) { maxValue = Math.max(maxValue, value.get()); } context.write(key, new IntWritable(maxValue)); } }

3、创建main方法

package org.jediael.hadoopDemo.maxtemperature; 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.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class MaxTemperature { public static void main(String[] args) throws Exception { if (args.length != 2) { System.err .println("Usage: MaxTemperature <input path> <output path>"); System.exit(-1); } Job job = new Job(); job.setJarByClass(MaxTemperature.class); job.setJobName("Max temperature"); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); job.setMapperClass(MaxTemperatureMapper.class); job.setReducerClass(MaxTemperatureReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); System.exit(job.waitForCompletion(true) ? 0 : 1); } }

4、导出成MaxTemp.jar，并上传至运行程序的服务器。

三、运行程序

1、创建input目录并将sample.txt复制到input目录

hadoop fs -put sample.txt /

2、运行程序

export HADOOP_CLASSPATH=MaxTemp.jar hadoop org.jediael.hadoopDemo.maxtemperature.MaxTemperature /sample.txt output10

注意输出目录不能已经存在，否则会创建失败。

3、查看结果

（1）查看结果

[jediael@jediael44 code]$ hadoop fs -cat output10/* 14/07/09 14:51:35 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 1901 42 1902 212 1903 412 1904 32 1905 102

（2）运行时输出

[jediael@jediael44 code]$ hadoop org.jediael.hadoopDemo.maxtemperature.MaxTemperature /sample.txt output10 14/07/09 14:50:40 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 14/07/09 14:50:41 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032 14/07/09 14:50:42 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this. 14/07/09 14:50:43 INFO input.FileInputFormat: Total input paths to process : 1 14/07/09 14:50:43 INFO mapreduce.JobSubmitter: number of splits:1 14/07/09 14:50:44 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1404888618764_0001 14/07/09 14:50:44 INFO impl.YarnClientImpl: Submitted application application_1404888618764_0001 14/07/09 14:50:44 INFO mapreduce.Job: The url to track the job: http://jediael44:8088/proxy/application_1404888618764_0001/ 14/07/09 14:50:44 INFO mapreduce.Job: Running job: job_1404888618764_0001 14/07/09 14:50:57 INFO mapreduce.Job: Job job_1404888618764_0001 running in uber mode : false 14/07/09 14:50:57 INFO mapreduce.Job: map 0% reduce 0% 14/07/09 14:51:05 INFO mapreduce.Job: map 100% reduce 0% 14/07/09 14:51:15 INFO mapreduce.Job: map 100% reduce 100% 14/07/09 14:51:15 INFO mapreduce.Job: Job job_1404888618764_0001 completed successfully 14/07/09 14:51:16 INFO mapreduce.Job: Counters: 49 File System Counters FILE: Number of bytes read=94 FILE: Number of bytes written=185387 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=1051 HDFS: Number of bytes written=43 HDFS: Number of read operations=6 HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Launched map tasks=1 Launched reduce tasks=1 Data-local map tasks=1 Total time spent by all maps in occupied slots (ms)=5812 Total time spent by all reduces in occupied slots (ms)=7023 Total time spent by all map tasks (ms)=5812 Total time spent by all reduce tasks (ms)=7023 Total vcore-seconds taken by all map tasks=5812 Total vcore-seconds taken by all reduce tasks=7023 Total megabyte-seconds taken by all map tasks=5951488 Total megabyte-seconds taken by all reduce tasks=7191552 Map-Reduce Framework Map input records=9 Map output records=8 Map output bytes=72 Map output materialized bytes=94 Input split bytes=97 Combine input records=0 Combine output records=0 Reduce input groups=5 Reduce shuffle bytes=94 Reduce input records=8 Reduce output records=5 Spilled Records=16 Shuffled Maps =1 Failed Shuffles=0 Merged Map outputs=1 GC time elapsed (ms)=154 CPU time spent (ms)=1450 Physical memory (bytes) snapshot=303112192 Virtual memory (bytes) snapshot=1685733376 Total committed heap usage (bytes)=136515584 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=954 File Output Format Counters Bytes Written=43