反向索引主要用于全文搜索，就是形成一个word url这样的结构 file1: MapReduce is simple file2: MapReduce is powerful is simple file3: Hello MapReduce bye MapReduce 那么经过反向索引后就是： Hello     file3.txt:1;

MapReduce     file3.txt:2;fil1.txt:1;fil2.txt:1;

bye     file3.txt:1; 

is     fil1.txt:1;fil2.txt:2;

powerful     fil2.txt:1;

simple     fil2.txt:1;fil1.txt:1; 主要的方法就是，对每个文件的内容进行遍历，形成的key为word+filename，value=1然后在combiner中将key相同的进行累加，这样就得到在同一个文件中word的字数了。最后在reduce中将filename进行分割即可。不过这里有个小的bug，一般来说combiner是在同一个节点上进行reduce，但是我这里却是用于统计同一个文件了，如果说文件很大，那么很有可能一个文件的内容会被分配到两个不同的节点上，那么就有会bug了。所以这里只能适合小的文件。 PS：获得文件名String filename = ((FileSplit) context.getInputSplit()).getPath().getName();别的似乎没有了。
public 
class 
MyMapper 
extends 
Mapper<LongWritable, Text, Text, Text> {
 
                 
public 
void 
map(LongWritable ikey, Text ivalue, Context context)
                                                 
throws 
IOException, InterruptedException {
                                StringTokenizer st= 
new 
StringTokenizer(ivalue.toString());
                                FileSplit split=
new 
FileSplit();
                                split = (FileSplit) context.getInputSplit();
                                InputSplit isplit=context.getInputSplit();
                                String filename = ((FileSplit) context.getInputSplit()).getPath().getName();
                                 
while
(st.hasMoreTokens()){
                                                 
//int splitIndex = split.getPath().toString().indexOf(“file”);
                                                String key=st.nextToken()+
“:” 
+filename;
                                                context.write( 
new 
Text(key),
new 
Text(
“1”
));
                                }
                }
 
}
 
 
public 
class 
MyCombiner 
extends 
Reducer<Text, Text, Text, Text> {
 
                 
public 
void 
reduce(Text _key, Iterable<Text> values, Context context)
                                                 
throws 
IOException, InterruptedException {
                                 
// process values
                                 
int 
sum=0;
                                 
for 
(Text val : values) {
                                                sum++;
                                }
                                StringTokenizer st= 
new 
StringTokenizer(_key.toString(),
“:”
);
                                String key=st.nextToken();
                                String value=st.nextToken();
                                value=value+ 
“:”
+sum;
                                context.write( 
new 
Text(key),
new 
Text(value));
                }
 
}    
public 
class 
MyReducer 
extends 
Reducer<Text, Text, Text, Text> {
 
                 
public 
void 
reduce(Text _key, Iterable<Text> values, Context context)
                                                 
throws 
IOException, InterruptedException {
                                 
// process values
                                String filelist= 
new 
String();
                                 
for 
(Text val : values) {
                                                filelist=filelist+val.toString()+ 
“;  “
;
                                }
                                context.write(_key, 
new 
Text(filelist));
                                 
//System.out.println(_key.toString()+filelist);
                }
 
}