环境：centos7+hadoop2.5.2
1.使用ECLIPS具打包运行WORDCOUNT实例，统计莎士比亚文集各单词计数（文件SHAKESPEARE.TXT）。
①WorldCount.java 中的main函数修改如下：
public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = new Job(conf, "word count"); job.setJarByClass(WordCount.class); job.setMapperClass(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); //设置输入文本路径 FileInputFormat.addInputPath(job, new Path("/input")); //设置mp结果输出路径 FileOutputFormat.setOutputPath(job, new Path("/output/wordcount")); System.exit(job.waitForCompletion(true) ? 0 : 1); }
②导出WordCount的jar包：
export->jar file->next->next->Main class里面选择WordCount->Finish。
③使用scp将wc.jar拷贝到node1机器，创建目录：hadoop fs –mkdir /input,将shakespeare.txt上传到hdfs上，运行wc.jar文件：hadoop jar wc.jar
④使用hadoop fs -cat /output/wordcount/part-r-00000 grep|head -n 30 查看前30条输出结果：
2.对于SOGOU_500W_UTF文件，完成下列程序设计内容：
（1） 统计每个用户搜索的关键字总长度
Mapreduce程序：
public class sougou3 { public static class Sougou3Map extends Mapper<Object, Text, Text, Text> { public void map(Object key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); String[] vals = line.split(" "); String uid = vals[1]; String search = vals[2]; context.write(new Text(uid), new Text(search+"|"+search.length())); } } public static class Sougou3Reduce extends Reducer<Text, Text, Text, Text> { public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException { String result = ""; for (Text value : values) { String strVal = value.toString(); result += (strVal+" "); } context.write(new Text(key + " "), new Text(result)); } } }
输出结果：
（2） 统计2011年12月30日1点到2点之间，搜索过的UID有哪些？
Mapreduce程序：
public class sougou1 { public static class Sougou1Map extends Mapper<Object, Text, Text, Text> { public void map(Object key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); String[] vals = line.split(" "); String time = vals[0]; String uid = vals[1]; //2008-07-10 19:20:00 String formatTime = time.substring(0,4)+"-"+time.substring(4,6)+"-"+time.substring(6,8)+" " +time.substring(8,10)+":"+time.substring(10,12)+":"+time.substring(12,14); SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss"); Date date; try { date = sdf.parse(formatTime); Date date1 = sdf.parse("2011-12-30 01:00:00"); Date date2 = sdf.parse("2011-12-30 02:00:00"); //日期在范围区间上 if (date.getTime() > date1.getTime() && date.getTime() < date2.getTime()){ context.write(new Text(uid), new Text(formatTime)); } } catch (ParseException e) { e.printStackTrace(); } } } public static class Sougou1Reduce extends Reducer<Text, Text, Text, Text> { public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException { String result = ""; for (Text value : values) { result += value.toString()+"|"; } context.write(key, new Text(result)); } } }
输出结果：
左边是用户id，右边分别是时间，以“|”作为分割。
（3） 统计搜索过‘仙剑奇侠’的每个UID搜索该关键词的次数。
Mapreduce程序：
public class sougou2 { public static class Sougou2Map extends Mapper<Object, Text, Text, IntWritable> { public void map(Object key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); String[] vals = line.split(" "); String uid = vals[1]; String search = vals[2]; if (search.equals("仙剑奇侠")){ context.write(new Text(uid), new IntWritable(1)); } } } public static class Sougou2Reduce extends Reducer<Text, IntWritable, Text, IntWritable> { public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int result = 0; for (IntWritable value : values) { result += value.get(); } context.write(new Text(key+" "), new IntWritable(result)); } } }
输出结果：
UID为：6856e6e003a05cc912bfe13ebcea8a04的用户搜索过“仙剑奇侠”共1次。
3.使用MAPREDUCE程序设计实现对文件中下列数据的排序操作78 11 56 87 25 63 19 22 55
Mapreduce程序：
public class Sort { //map将输入中的value化成IntWritable类型，作为输出的key public static class Map extends Mapper<Object,Text,IntWritable,NullWritable>{ private static IntWritable data=new IntWritable(); //实现map函数 public void map(Object key,Text value,Context context) throws IOException,InterruptedException{ String line=value.toString(); data.set(Integer.parseInt(line)); context.write(data, NullWritable.get()); } } //reduce将输入中的key复制到输出数据的key上， //然后根据输入的value-list中元素的个数决定key的输出次数 //用全局linenum来代表key的位次 public static class Reduce extends Reducer<IntWritable,NullWritable,IntWritable,NullWritable>{ //实现reduce函数 public void reduce(IntWritable key,Iterable<NullWritable> values,Context context) throws IOException,InterruptedException{ for(NullWritable val:values){ context.write(key, NullWritable.get()); } } } }
输出内容为:
4.学生成绩文件TXT内容（字段用TAB键分隔）如下，使用MAPREDUCE计算每个学生的平均成绩
李平 87 89 98 75
张三 66 78 69 70
李四 96 82 78 90
王五 82 77 74 86
赵六 88 72 81 76
Mapreduce 程序：
public class Score { public static class ScoreMap extends Mapper<Object, Text, Text, NullWritable> { public void map(Object key, Text value, Context context) throws IOException, InterruptedException { context.write(value, NullWritable.get()); } } public static class ScoreReduce extends Reducer<Text, NullWritable, Text, IntWritable> { public void reduce(Text key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException { for (NullWritable nullWritable : values) { String line = key.toString(); String[] vals = line.split(" "); String name = vals[0]; int val1 = Integer.parseInt(vals[1]); int val2 = Integer.parseInt(vals[2]); int val3 = Integer.parseInt(vals[3]); int average = (val1 + val2 + val3) / 3; context.write(new Text(name), new IntWritable(average)); } } } }
输出结果为
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