mapreduce 去重的问题怎么解决？
john 89
tom 100
mary 100
mary 200
tom 20
———–
我刚学mapreduce，正在练习，上面这个我计算了很久也不对，就是对第一列去重，去重后应该是3
如果用mapreduce计算成功后，part-00000 的文件内容 是：
请问下，这个mapreduce怎么写啊？
map按第一列为key，value无所谓
reduce class中初始化一个计数器
每个reduce方法中计数器每次加一
reduce 的cleanup方法中commit计数器就可以了
map 知道怎么写了，那reduce的具体怎么写啊？直接一个Map,在Map里面定义一个全局的HashSet,map方法里面把key加入进去，cleanup方法里面把结果写入就行了。直接一个Map,在Map里面定义一个全局的HashSet,map方法里面把key加入进去，cleanup方法里面把结果写入就行了。
只用map不可能解决这个问题
如果在不同的map中都用同一个key，怎么解决？
必须用reduce去group后的key才能得到去重效果直接一个Map,在Map里面定义一个全局的HashSet,map方法里面把key加入进去，cleanup方法里面把结果写入就行了。
只用map不可能解决这个问题
如果在不同的map中都用同一个key，怎么解决？
必须用reduce去group后的key才能得到去重效果
嗯，对，没想那么仔细，谢谢指正。public static class Map extends Mapper<LongWritable, Text, Text, Text> {
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String line = value.toString();
try {
String[] lineSplit = line.split("\t");
context.write(new Text(lineSplit[0]), new Text(""));
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
context.getCounter(Counter.LINESKIP).increment(1);
return;
}
}
}
public static class Reduce extends Reducer<Text, Text, Text, Text> {
private Set<String> count = new HashSet<String>();
public void reduce(Text key, Iterable<Text> values, Context context)
throws IOException, InterruptedException {
for(Text value:values){
count.add(value.toString());
}
context.write(key, new Text(""));
}
}
------------------------- 这个问题纠结我2周了，这个方面的学习资料太少了，我的map和reduce是这样写的，但是数据量大一些，就会内存溢出，我想我这个思路是错误的
你说的 “必须用reduce去group后的key才能得到去重效果 ”，这个 map和reduce是具体怎么写的啊？public static class Map extends Mapper<LongWritable, Text, Text, Text> {
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String line = value.toString();
try {
String[] lineSplit = line.split("\t");
context.write(new Text(lineSplit[0]), new Text(""));
context.write(new Text("uniq") ,new Text(lineSplit[0]) );
} catch (java.lang.ArrayIndexOutOfBoundsException e) {
context.getCounter(Counter.LINESKIP).increment(1);
return;
}
}
}
public static class Reduce extends Reducer<Text, Text, Text, Text> {
private Set<String> count = new HashSet<String>();
public void reduce(Text key, Iterable<Text> values, Context context)
throws IOException, InterruptedException {
for(Text value:values){
count.add(value.toString());
}
context.write("uniq", new Text(count.size()+""));
}
}
------------------------- 这个问题纠结我2周了，这个方面的学习资料太少了，我的map和reduce是这样写的，但是数据量大一些，就会内存溢出，我想我这个思路是错误的
你说的 “必须用reduce去group后的key才能得到去重效果 ”，这个 map和reduce是具体怎么写的啊？
-------------刚才写的mapreduce错了，以这个为准map按第一列为key，value无所谓
reduce class中初始化一个计数器
每个reduce方法中计数器每次加一
reduce 的cleanup方法中commit计数器就可以了
谢谢了，请教下，你说的这个map我知道怎么写了，但是这个reduce怎么写啊？reduce阶段只用一个计数器就行了import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
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.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class wzl189_distinct {
public static class MyMapper extends
Mapper<Object, Text, Text, NullWritable> {
Text outKey = new Text();
@Override
public void map(Object key, Text value, Context context)
throws IOException, InterruptedException {
String tmp[] = value.toString().split( " " );
if (tmp.length != 2 )
return ;
outKey.set(tmp[ 0 ]);
context.write(outKey, NullWritable.get());
}
}
public static class MyReducer extends
Reducer<Text, NullWritable, LongWritable, NullWritable> {
long myCount = 0l;
@Override
public void reduce(Text key, Iterable<NullWritable> values,
Context context) throws IOException, InterruptedException {
++myCount;
}
@Override
public void cleanup(Context context) throws IOException,
InterruptedException {
context.write( new LongWritable(myCount), NullWritable.get());
};
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
if (args.length != 2 ) {
System.err.println( "Usage: <in> <out>" );
System.exit( 2 );
}
conf.set( "mapred.child.java.opts" , "-Xmx350m -Xmx1024m" );
@SuppressWarnings ( "deprecation" )
Job job = new Job(conf, "wzl189_distinct" );
job.setNumReduceTasks( 1 );
job.setInputFormatClass(TextInputFormat. class );
job.setJarByClass(wzl189_distinct. class );
job.setMapperClass(MyMapper. class );
job.setMapOutputKeyClass(Text. class );
job.setMapOutputValueClass(NullWritable. class );
job.setReducerClass(MyReducer. class );
job.setOutputKeyClass(Text. class );
job.setOutputValueClass(NullWritable. class );
FileInputFormat.addInputPath(job, new Path(args[ 0 ]));
FileOutputFormat.setOutputPath(job, new Path(args[ 1 ]));
System.exit(job.waitForCompletion( true ) ? 0 : 1 );
}
}import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
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.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class wzl189_distinct {
public static class MyMapper extends
Mapper<Object, Text, Text, NullWritable> {
Text outKey = new Text();
@Override
public void map(Object key, Text value, Context context)
throws IOException, InterruptedException {
String tmp[] = value.toString().split( " " );
if (tmp.length != 2 )
return ;
outKey.set(tmp[ 0 ]);
context.write(outKey, NullWritable.get());
}
}
public static class MyReducer extends
Reducer<Text, NullWritable, LongWritable, NullWritable> {
long myCount = 0l;
@Override
public void reduce(Text key, Iterable<NullWritable> values,
Context context) throws IOException, InterruptedException {
++myCount;
}
@Override
public void cleanup(Context context) throws IOException,
InterruptedException {
context.write( new LongWritable(myCount), NullWritable.get());
};
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
if (args.length != 2 ) {
System.err.println( "Usage: <in> <out>" );
System.exit( 2 );
}
conf.set( "mapred.child.java.opts" , "-Xmx350m -Xmx1024m" );
@SuppressWarnings ( "deprecation" )
Job job = new Job(conf, "wzl189_distinct" );
job.setNumReduceTasks( 1 );
job.setInputFormatClass(TextInputFormat. class );
job.setJarByClass(wzl189_distinct. class );
job.setMapperClass(MyMapper. class );
job.setMapOutputKeyClass(Text. class );
job.setMapOutputValueClass(NullWritable. class );
job.setReducerClass(MyReducer. class );
job.setOutputKeyClass(Text. class );
job.setOutputValueClass(NullWritable. class );
FileInputFormat.addInputPath(job, new Path(args[ 0 ]));
FileOutputFormat.setOutputPath(job, new Path(args[ 1 ]));
System.exit(job.waitForCompletion( true ) ? 0 : 1 );
}
}
reduce阶段只用一个计数器就行了
太感谢了，你了解这么多啊，我都搞了2周，没有结果，想再请教最后一个问题：
假如 第一列是姓名，第二列是班级（先不管我这个需求是否合理）
john 100
john 100
mary 100
mary 200
tom 200
想统计处如下结果，就是按班级人数去重
100 2
200 2
这个mapreduce怎么写啊？ 望高手最后再解答下，万分感谢了。map 输出key 用 班级 + 分隔符 + 姓名
重写 grouping 实现二次排序，如果reduce num > 1 还需要重写 partition
reduce略作修改，增个姓名变量 ，比较当前姓名是否和前一个姓名是否一致，如果不一致 计数器+=1
代码就不贴了，LZ多思考一下，这种简单的MR不难解决
标签：怎么,Text,reduce,mapreduce,hadoop,job,解决,import,class From： https://blog.51cto.com/u_16115638/7025766