1、MapReduce代码入口
FileInputFormat.setInputPaths(job, new Path(input)); //设置MapReduce输入格式
job.waitForCompletion(true);
2、InputFormat分析
public abstract class InputFormat<K, V> {
//获取输入文件的分片，仅是逻辑分片，并没有物理分片
public abstract List<InputSplit> getSplits(JobContext context);
//创建RecordReader，从InputSplit中读取数据
public abstract RecordReader<K,V> createRecordReader(InputSplit split,TaskAttemptContext context) ;
}不同的InputFormat会各自实现不同的文件读取方式以及分片方式，每个输入分片(InputSplit)会被单独的map task作为数据源
3、InputSplit
Mapper的输入是一个一个的输入分片(InputSplit)
public abstract class InputSplit {
public abstract long getLength();
public abstract String[] getLocations();
}
public class FileSplit extends InputSplit implements Writable{
private Path file; //文件路径
private long start; //分片起始位置
private long length; //分片长度
private String[] hosts; //存储分片的hosts
public FileSplit(Path file, long start, long length, String[] hosts) {
this.file = file;
this.start = start;
this.length = length;
this.hosts = hosts;
}
}一个FileSplit对应Mapper的一个输入文件，不管这个文件有多么的小，也是作为一个单独的InputSplit来处理；
在输入文件是由大量小文件组成的场景下，就会有大量的InputSplit，从而需要大量的Mapper的处理；
大量的Mapper Task创建和销毁开销将是巨大的；可以采用CombineFileSplit将多个小文件进行合并再交由Mapper Task处理；
4、FileInputFormat
public List<InputSplit> getSplits(JobContext job) throws IOException {
/**
* getFormatMinSplitSize() = 1
* job.getConfiguration().getLong(SPLIT_MINSIZE, 1L)
* SPLIT_MINSIZE = "mapreduce.input.fileinputformat.split.minsize"
* mapred-default.xml中参数为0
*/
long minSize = Math.max(getFormatMinSplitSize(), getMinSplitSize(job)); //计算分片的最小值： max(1,0) = 1
/**
* SPLIT_MAXSIZE = "mapreduce.input.fileinputformat.split.maxsize"
* mapred-default.xml中参数为空
*/
long maxSize = getMaxSplitSize(job); //计算分片的最大值：Long.MAX_VALUE
//存储输入文件的分片结果
List<InputSplit> splits = new ArrayList<InputSplit>();
List<FileStatus> files = listStatus(job);
for (FileStatus file: files) {
Path path = file.getPath();
long length = file.getLen();
if (length != 0) {
...
if (isSplitable(job, path)) { //能分片
long blockSize = file.getBlockSize();
long splitSize = computeSplitSize(blockSize, minSize, maxSize);{
//max(1, min(Long.MAX_VALUE, 64M)) = 64M 默认情况下splitSize=blockSize
return Math.max(minSize, Math.min(maxSize, blockSize));
}
//循环分片，当剩余数据与分片大小比值大于Split_Slop时，继续分片，小于等于时，停止分片
long bytesRemaining = length;
while (((double) bytesRemaining)/splitSize > SPLIT_SLOP) { //SPLIT_SLOP = 1.1
int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
splits.add(makeSplit(path, length-bytesRemaining, splitSize, blkLocations[blkIndex].getHosts()));
bytesRemaining -= splitSize;
}
//处理余下的数据
if (bytesRemaining != 0) {
int blkIndex = getBlockIndex(blkLocations, length-bytesRemaining);
splits.add(makeSplit(path, length-bytesRemaining, bytesRemaining, blkLocations[blkIndex].getHosts()));
}
} else { // 不可分片，整块返回（有些压缩后是不能分片处理的）
splits.add(makeSplit(path, 0, length, blkLocations[0].getHosts()));
}
} else {
splits.add(makeSplit(path, 0, length, new String[0]));
}
}
job.getConfiguration().setLong(NUM_INPUT_FILES, files.size()); // 设置输入文件数量
LOG.debug("Total # of splits: " + splits.size());
return splits;
}5、PathFilter
protected List<FileStatus> listStatus(JobContext job) throws IOException {
......
List<PathFilter> filters = new ArrayList<PathFilter>();
filters.add(hiddenFileFilter);
PathFilter jobFilter = getInputPathFilter(job);
if (jobFilter != null) {
filters.add(jobFilter);
}
PathFilter inputFilter = new MultiPathFilter(filters);
......
}PathFilter文件筛选器接口，使用它我们可以控制哪些文件要作为输入，哪些不作为输入；
PathFilter有一个accept(Path)方法，当接收的Path要被包含进来，就返回true，否则返回false；
public interface PathFilter {
boolean accept(Path path);
}
//过滤掉文件名以_或者.开头的文件
private static final PathFilter hiddenFileFilter = new PathFilter(){
public boolean accept(Path p){
String name = p.getName();
return !name.startsWith("_") && !name.startsWith(".");
}
}; 6、RecordReader
RecordReader将InputSplit拆分成KEY-VALUE对
public abstract class RecordReader<KEYIN, VALUEIN> implements Closeable {
//InputSplit初始化
public abstract void initialize(InputSplit split,TaskAttemptContext context) ;
//读取分片下一个<key, value>对
public abstract boolean nextKeyValue() throws IOException, InterruptedException;
//获得当前读取到的KEY
public abstract KEYIN getCurrentKey() throws IOException, InterruptedException;
//获得当前读取到的VALUE
public abstract VALUEIN getCurrentValue() throws IOException, InterruptedException;
//跟踪读取分片的进度
public abstract float getProgress() throws IOException, InterruptedException;
//关闭RecordReader
public abstract void close() throws IOException;
}7、Mapper
public class Mapper<KEYIN, VALUEIN, KEYOUT, VALUEOUT> {
public abstract class Context implements MapContext<KEYIN,VALUEIN,KEYOUT,VALUEOUT> {
}
//预处理，仅在map task启动时运行一次
protected void setup(Context context) throws IOException, InterruptedException {
}
//对于InputSplit中的每一对<key, value>都会运行一次
protected void map(KEYIN key, VALUEIN value, Context context) throws IOException, InterruptedException {
context.write((KEYOUT) key, (VALUEOUT) value);
}
//扫尾工作，比如关闭流等
protected void cleanup(Context context) throws IOException, InterruptedException {
}
public void run(Context context) throws IOException, InterruptedException {
setup(context);
try {
while (context.nextKeyValue()) {
map(context.getCurrentKey(), context.getCurrentValue(), context);
}
} finally {
cleanup(context);
}
}
}模板模式的应用：run方法：
1）setup
2）循环从InputSplit中获得到的KV对调用map函数进行处理
3）cleanup
至此完成了MapReduce的输入文件是如何被过滤、分片、读取、读出“K-V对”，然后交给Mapper类来处理