标签：hadoop 气象分析
1.下载气象数据集
wget -r -c
查看数据集
[root@hadoop-master 2008]# ls
010030-99999-2008.gz 010231-99999-2008.gz 010460-99999-2008.gz 010570-99999-2008.gz 010881-99999-2008.gz
010070-99999-2008.gz 010260-99999-2008.gz 010490-99999-2008.gz 010750-99999-2008.gz 010883-99999-2008.gz
010150-99999-2008.gz 010330-99999-2008.gz 010520-99999-2008.gz 010780-99999-2008.gz 010890-99999-2008.gz
010230-99999-2008.gz 010450-99999-2008.gz 010550-99999-2008.gz 010830-99999-2008.gz
2.将数据解压并导入到example文件中
[root@hadoop-master 2008]# zcat *.gz > example
查看文件是否正确
[root@hadoop-master 2008]# tail -10 example
0101010980999992008031013004+70367+031100FM-12+001599999V0201801N006019999999N9999999N1-00081-00291099591ADDMA1999999099411MD1710101+9999REMSYN060AAXX 10131 01098 46/// /1806 11008 21029 39941 49959 57010;
0101010980999992008031014004+70367+031100FM-12+001599999V0201901N006019999999N9999999N1-00071-00241099601ADDMA1999999099411MD1710051+9999REMSYN060AAXX 10141 01098 46/// /1906 11007 21024 39941 49960 57005;
0171010980999992008031015004+70367+031100FM-12+001599999V0202201N004010042019N0060001N1-00151-00261099611ADDAY171031AY221031GF107991061071004501021999MA1999999099431MD1510021+9999MW1221REMSYN082AAXX 10151 01098 41456 72204 11015 21026 39943 49961 55002 72272 8672/ 333 4////;
0101010980999992008031016004+70367+031100FM-12+001599999V0202101N005019999999N9999999N1-00121-00211099581ADDMA1999999099401MD1010011+9999REMSYN060AAXX 10161 01098 46/// /2105 11012 21021 39940 49958 50001;
0101010980999992008031017004+70367+031100FM-12+001599999V0202201N004019999999N9999999N1-00131-00231099591ADDMA1999999099411MD1410001+9999REMSYN060AAXX 10171 01098 46/// /2204 11013 21023 39941 49959 54000;
0213010980999992008031018004+70367+031100FM-12+001599999V0201901N004010042019N0150001N1-00061-00151099601ADDAA112000021AY171061AY221061GF107991051071004501021999KA1120M-00061MA1999999099421MD1510011+9999MW1701REMSYN100AAXX 10181 01098 11465 71904 11006 21015 39942 49960 55001 69912 77072 8572/ 333 11006 4//// 91107;
0101010980999992008031019004+70367+031100FM-12+001599999V0201901N006019999999N9999999N1+00001-00101099591ADDMA1999999099411MD1210011+9999REMSYN060AAXX 10191 01098 46/// /1906 10000 21010 39941 49959 52001;
0101010980999992008031020004+70367+031100FM-12+001599999V0201801N006019999999N9999999N1+00041-00091099621ADDMA1999999099441MD1210031+9999REMSYN060AAXX 10201 01098 46/// /1806 10004 21009 39944 49962 52003;
0171010980999992008031021004+70367+031100FM-12+001599999V0201901N005010042019N0300001N1+00071-00061099621ADDAY171031AY221031GF107991071071004501999999MA1999999099441MD1210021+9999MW1021REMSYN082AAXX 10211 01098 41480 71905 10007 21006 39944 49962 52002 70272 877// 333 4////;
0101010980999992008031022004+70367+031100FM-12+001599999V0201901N005019999999N9999999N1+00091-0004
3.将数据集导入到hadoop的in目录的test文件中
/root/hadoop-1.1.2/bin/hadoop fs -put ./example ./in/test
4.编写MapReduce程序
cd /root/hadoop-1.1.2/myclass
[root@hadoop-master myclass]# cat MaxTemperature.java
// cc MaxTemperature Application to find the maximum temperature in the weather dataset
// vv 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);
}
}
// ^^ MaxTemperature
[root@hadoop-master myclass]# cat MaxTemperatureMapper.java
// cc MaxTemperatureMapper Mapper for maximum temperature example
// vv MaxTemperatureMapper
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));
}
}
}
// ^^ MaxTemperatureMapper
[root@hadoop-master myclass]# cat MaxTemperatureMapper.java
// cc MaxTemperatureMapper Mapper for maximum temperature example
// vv MaxTemperatureMapper
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));
}
}
}
// ^^ MaxTemperatureMapper
[root@hadoop-master myclass]# cat MaxTemperatureReducer.java
// cc MaxTemperatureReducer Reducer for maximum temperature example
// vv MaxTemperatureReducer
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));
}
}
// ^^ MaxTemperatureReducer
4.编译程序
javac -classpath ../hadoop-core-1.1.2.jar *.java
查看
[root@hadoop-master myclass]# ls
MaxTemperature.class MaxTemperatureMapper.class MaxTemperatureReducer.class
MaxTemperature.java MaxTemperatureMapper.java MaxTemperatureReducer.java
5.制作jar包
[root@hadoop-master myclass]# jar cvf ../MaxTemperature.jar *.class
added manifest
adding: MaxTemperature.class(in = 1413) (out= 799)(deflated 43%)
adding: MaxTemperatureMapper.class(in = 1876) (out= 805)(deflated 57%)
adding: MaxTemperatureReducer.class(in = 1687) (out= 717)(deflated 57%)
删除类文件：rm -rf *.class
6.运行程序
分析上述导入文件至in/test的数据集，并将分析结果导出到./out_result中
[root@hadoop-master hadoop-1.1.2]# ./bin/hadoop jar MaxTemperature.jar MaxTemperature ./in/test ./out_result
16/07/13 23:07:54 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
16/07/13 23:07:55 INFO input.FileInputFormat: Total input paths to process : 1
16/07/13 23:07:55 INFO util.NativeCodeLoader: Loaded the native-hadoop library
16/07/13 23:07:55 WARN snappy.LoadSnappy: Snappy native library not loaded
16/07/13 23:07:59 INFO mapred.JobClient: Running job: job_201607131558_0001
16/07/13 23:08:00 INFO mapred.JobClient: map 0% reduce 0%
16/07/13 23:08:23 INFO mapred.JobClient: map 100% reduce 0%
16/07/13 23:08:38 INFO mapred.JobClient: map 100% reduce 100%
16/07/13 23:08:40 INFO mapred.JobClient: Job complete: job_201607131558_0001
16/07/13 23:08:40 INFO mapred.JobClient: Counters: 29
16/07/13 23:08:40 INFO mapred.JobClient: Map-Reduce Framework
16/07/13 23:08:40 INFO mapred.JobClient: Spilled Records=300506
16/07/13 23:08:40 INFO mapred.JobClient: Map output materialized bytes=1652789
16/07/13 23:08:40 INFO mapred.JobClient: Reduce input records=150253
16/07/13 23:08:40 INFO mapred.JobClient: Virtual memory (bytes) snapshot=3868651520
16/07/13 23:08:40 INFO mapred.JobClient: Map input records=150656
16/07/13 23:08:40 INFO mapred.JobClient: SPLIT_RAW_BYTES=108
16/07/13 23:08:40 INFO mapred.JobClient: Map output bytes=1352277
16/07/13 23:08:40 INFO mapred.JobClient: Reduce shuffle bytes=1652789
16/07/13 23:08:40 INFO mapred.JobClient: Physical memory (bytes) snapshot=295931904
16/07/13 23:08:40 INFO mapred.JobClient: Reduce input groups=1
16/07/13 23:08:40 INFO mapred.JobClient: Combine output records=0
16/07/13 23:08:40 INFO mapred.JobClient: Reduce output records=1
16/07/13 23:08:40 INFO mapred.JobClient: Map output records=150253
16/07/13 23:08:40 INFO mapred.JobClient: Combine input records=0
16/07/13 23:08:40 INFO mapred.JobClient: CPU time spent (ms)=12220
16/07/13 23:08:40 INFO mapred.JobClient: Total committed heap usage (bytes)=177016832
16/07/13 23:08:40 INFO mapred.JobClient: File Input Format Counters
16/07/13 23:08:40 INFO mapred.JobClient: Bytes Read=35197493
16/07/13 23:08:40 INFO mapred.JobClient: FileSystemCounters
16/07/13 23:08:40 INFO mapred.JobClient: HDFS_BYTES_READ=35197601
16/07/13 23:08:40 INFO mapred.JobClient: FILE_BYTES_WRITTEN=3409028
16/07/13 23:08:40 INFO mapred.JobClient: FILE_BYTES_READ=1652789
16/07/13 23:08:40 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=9
16/07/13 23:08:40 INFO mapred.JobClient: Job Counters
16/07/13 23:08:40 INFO mapred.JobClient: Launched map tasks=1
16/07/13 23:08:40 INFO mapred.JobClient: Launched reduce tasks=1
16/07/13 23:08:40 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=12976
16/07/13 23:08:40 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0
16/07/13 23:08:40 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=18769
16/07/13 23:08:40 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0
16/07/13 23:08:40 INFO mapred.JobClient: Data-local map tasks=1
16/07/13 23:08:40 INFO mapred.JobClient: File Output Format Counters
16/07/13 23:08:40 INFO mapred.JobClient: Bytes Written=9
7. 查看结果
[root@hadoop-master hadoop-1.1.2]# ./bin/hadoop fs -ls ./out_result
Found 3 items
-rw-r--r-- 3 root supergroup 0 2016-07-13 23:08 /user/root/out_result/_SUCCESS
drwxr-xr-x - root supergroup 0 2016-07-13 23:08 /user/root/out_result/_logs
-rw-r--r-- 3 root supergroup 9 2016-07-13 23:08 /user/root/out_result/part-r-00000
log文件为日志，part文件为结果
查看part文件内容：
[root@hadoop-master hadoop-1.1.2]# ./bin/hadoop fs -cat ./out_result/part-r-00000
2008 290本文出自 “杜先生” 博客，请务必保留此出处http://duxiansheng.blog.51cto.com/11062970/1826395
【大数据】Hadoop 成长之路 (二) 利用Hadoop分析气象数据集
标签：hadoop 气象分析
原文地址：http://duxiansheng.blog.51cto.com/11062970/1826395
