MapReduce的常见输入格式之NlineInputFormat
有两个文件:
NlineInputFormat
-
切片策略: 读取配置文件中的参数
mapreduce.input.lineinputformat.linespermap
,默认为1,以文件为单位,切片每此参数行作为1片! -
既然有参数,那就可以修改,设置为每N行切为一片:
Configuration conf = new Configuration();
conf.set("mapreduce.input.lineinputformat.linespermap", "2")
RecordReader
:LineRecordReader
,一次处理一行,将一行内容的偏移量作为key,一行内容作为value
它们的数据类型:
LongWritable key
Text value
所以上面两个文件总共八行,若一行切一片,则有八片;两行切一片,则有四片。
WCMapper.java
public class WCMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
private Text out_key=new Text();
private IntWritable out_value=new IntWritable(1);
@Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
throws IOException, InterruptedException {
System.out.println("keyin:"+key+"----keyout:"+value);
String[] words = value.toString().split("\t");
for (String word : words) {
out_key.set(word);
//写出数据(单词,1)
context.write(out_key, out_value);
}
}
}
WCReducer.java
public class WCReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
private IntWritable out_value=new IntWritable();
// reduce一次处理一组数据,key相同的视为一组
@Override
protected void reduce(Text key, Iterable<IntWritable> values,
Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
int sum=0;
for (IntWritable intWritable : values) {
sum+=intWritable.get();
}
out_value.set(sum);
//将累加的值写出
context.write(key, out_value);
}
}
WCDriver.java
public class WCDriver {
public static void main(String[] args) throws Exception {
Path inputPath=new Path("e:/mrinput/nline");
Path outputPath=new Path("e:/mroutput/nline");
//作为整个Job的配置
Configuration conf = new Configuration();
conf.set("mapreduce.input.lineinputformat.linespermap", "2");//设置为每两行切一片
//保证输出目录不存在
FileSystem fs=FileSystem.get(conf);
if (fs.exists(outputPath)) {
fs.delete(outputPath, true);
}
// ①创建Job
Job job = Job.getInstance(conf);
job.setJarByClass(WCDriver.class);
// ②设置Job
// 设置Job运行的Mapper,Reducer类型,Mapper,Reducer输出的key-value类型
job.setMapperClass(WCMapper.class);
job.setReducerClass(WCReducer.class);
// Job需要根据Mapper和Reducer输出的Key-value类型准备序列化器,通过序列化器对输出的key-value进行序列化和反序列化
// 如果Mapper和Reducer输出的Key-value类型一致,直接设置Job最终的输出类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
// 声明使用NLineInputFormat
job.setInputFormatClass(NLineInputFormat.class);
// 设置输入目录和输出目录
FileInputFormat.setInputPaths(job, inputPath);
FileOutputFormat.setOutputPath(job, outputPath);
// ③运行Job
job.waitForCompletion(true);
}
}
如果真的不知道做什么 那就做好眼前的事情吧 你所希望的事情都会慢慢实现...