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一道hadoop面试题，hadoop面试题
这题是网上找的，如果做的不对，请大家指正。
1 使用Hive或者自定义MR实现如下逻辑 product_no lac_id moment start_time user_id county_id staytime city_id 13429100031 22554 8 2013-03-11 08:55:19.151754088 571 571 282 571 13429100082 22540 8 2013-03-11 08:58:20.152622488 571 571 270 571 13429100082 22691 8 2013-03-11 08:56:37.149593624 571 571 103 571 13429100087 22705 8 2013-03-11 08:56:51.139539816 571 571 220 571 13429100087 22540 8 2013-03-11 08:55:45.150276800 571 571 66 571 13429100082 22540 8 2013-03-11 08:55:38.140225200 571 571 133 571 13429100140 26642 9 2013-03-11 09:02:19.151754088 571 571 18 571 13429100082 22691 8 2013-03-11 08:57:32.151754088 571 571 287 571 13429100189 22558 8 2013-03-11 08:56:24.139539816 571 571 48 571 13429100349 22503 8 2013-03-11 08:54:30.152622440 571 571 211 571 字段解释： product_no：用户手机号； lac_id：用户所在基站； start_time：用户在此基站的开始时间； staytime：用户在此基站的逗留时间。需求描述： 根据lac_id和start_time知道用户当时的位置，根据staytime知道用户各个基站的逗留时长。根据轨迹合并连续基站的staytime。 最终得到每一个用户按时间排序在每一个基站驻留时长
期望输出举例： 13429100082 22540 8 2013-03-11 08:58:20.152622488 571 571 270 571 13429100082 22691 8 2013-03-11 08:56:37.149593624 571 571 390 571 13429100082 22540 8 2013-03-11 08:55:38.140225200 571 571 133 571 13429100087 22705 8 2013-03-11 08:56:51.139539816 571 571 220 571 13429100087 22540 8 2013-03-11 08:55:45.150276800 571 571 66 571
说说我的思路：先按照TextInputFormat进行map，在map函数中再对每一行处理将手机号作为map的outputkey，行内容为outputvalue。在reduce的是按照时间排序。
package hadoop;
import java.io.IOException;
import java.net.URI;
import java.net.URISyntaxException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;
import java.util.List;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class HadoopTest1
{
public static String split = " +|\t"; //定义一个分隔符，空格和tab都可以
public static class MyComarator implements Comparator //由于不是按照整个字符串比较，所以实现一个Comparator接口，按时间来比较
{
@Override
public int compare(Object o1, Object o2)
{
// TODO Auto-generated method stub
String str1 = (String)o1;
String str2 = (String)o2;
String []arr1 = str1.split(split);
String []arr2 = str2.split(split);
return (arr1[3] + arr1[4]).compareTo((arr2[3] + arr2[4]));
}
}
public static class MyMapper extends Mapper<LongWritable, Text, Text, Text>
{
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException
{
if (key.equals(new LongWritable(0))) //过滤掉第一行
{
return;
}
String line = value.toString();
String[] elements = line.split(split);
context.write(new Text(elements[0]), value);
}
}
public static class MyReducer extends Reducer<Text, Text, NullWritable, Text>
{
public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException
{
List<String>list = new ArrayList<String>();
for (Text v : values)
{
list.add(v.toString());
}
list.sort(new MyComarator());
Collections.reverse(list);
for (int i =0; i < list.size(); ++i)
{
context.write(NullWritable.get(), new Text(list.get(i)));
}
}
}
public static void main(String[] args)
{
String HDFS_PATH = "hdfs://master:9000";
String INPUT_PATH = "/home/hadoop/hadoop-data/20150721/input";
String OUTT_PATH = "/home/hadoop/hadoop-data/20150721/output";
try
{
FileSystem fs = FileSystem.get(new URI(HDFS_PATH), new Configuration());
FSDataOutputStream out = fs.create(new Path(HDFS_PATH + INPUT_PATH + "/text"));
String text = "product_no lac_id moment start_time user_id county_id staytime city_id\n"
+ "13429100031 22554 8 2013-03-11 08:55:19.151754088 571 571 282 571\n"
+ "13429100082 22540 8 2013-03-11 08:58:20.152622488 571 571 270 571\n"
+ "13429100082 22691 8 2013-03-11 08:56:37.149593624 571 571 103 571\n"
+ "13429100087 22705 8 2013-03-11 08:56:51.139539816 571 571 220 571\n"
+ "13429100087 22540 8 2013-03-11 08:55:45.150276800 571 571 66 571\n"
+ "13429100082 22540 8 2013-03-11 08:55:38.140225200 571 571 133 571\n"
+ "13429100140 26642 9 2013-03-11 09:02:19.151754088 571 571 18 571\n"
+ "13429100082 22691 8 2013-03-11 08:57:32.151754088 571 571 287 571\n"
+ "13429100189 22558 8 2013-03-11 08:56:24.139539816 571 571 48 571\n"
+ "13429100349 22503 8 2013-03-11 08:54:30.152622440 571