搭建单节点Hadoop, 加载股票数据到HDFS
使用的环境为Ubuntu 16 64/位系统。
环境搭建
首先参照Hadoop的官方文档搭建单节点的系统，下载压缩包之后解压， 配置JDK即可运行。这里使用在单机运行的伪集群模式，如下修改configuration文件， NameNode为localhost:9000。
etc/hadoop/core-site.xml
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| <configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://localhost:9005</value>
</property>
<property>
<name>hadoop.tmp.dir</name>
<value>/home/vagrant/download/hadoop-2.7.2/data</value>
</property>
</configuration>
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etc/hadoop/hdfs-site.xml
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| <configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
</configuration>
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在Bash 下运行命令：
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| $ bin/hdfs namenode -format
$ sbin/start-dfs.sh
$ sbin/stop-dfs.sh
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上传股票数据到HDFS
股票数据来源为网易财经， 运行一个python script将所有股票的最新价格保存为一个文件， 文件名以日期命名， 形如2016-08-21.csv。
python script: save_price_daily_csv.py
使用Hadoop提供的工具将生成的csv保存到HDFS:
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| $ bin/hdfs dfs -mkdir -p /user/vagrant/stock-daily-price
$ bin/hdfs dfs -put /vagrant/tmp/2016-08-21.csv stock-daily-price/
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统计涨跌数的MapReduce程序
参考Wordcount的代码， 依葫芦画瓢， 写出下面一段代码， 添加hadoop相关的jar包，编译打包成HapTet-1.0-SNAPSHOT.jar. 代码部分只需要重写map()函数即可。
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| $ bin/hadoop jar /vagrant/tmp/HapTet-1.0-SNAPSHOT.jar com.viifly.hadoop.ta1.StockCount stock-daily-price/2016-08-21.csv out/
$ bin/hdfs dfs -cat ./out/*
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java代码：
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| package com.viifly.hadoop.ta1;
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class StockCount {
public static final String UP3 = "UP3";
public static final String UP0 = "UP0";
public static final String DW0 = "DW0";
public static final String DW3 = "DW3";
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
String line = value.toString();
String[] fields = line.split(",");
if (fields.length > 5 && (fields[0].charAt(0) == '0' || fields[0].charAt(0) == '6' )) {
Float percent = Float.parseFloat(fields[3]) * 100;
String t = null;
if (percent >= 3) {
t = UP3;
} else if (percent >= 0) {
t = UP0;
} else if (percent >= -3) {
t = DW0;
} else {
t = DW3;
}
word.set(t);
context.write(word, one);
}
}
}
public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "Stock Count");
job.setJarByClass(StockCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
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