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
