自定义hadoop map/reduce输入文件切割InputFormat

hadoop会对原始输入文件进行文件切割，然后把每个split传入mapper程序中进行处理，FileInputFormat是所有以文件作为数据源的InputFormat实现的基类，FileInputFormat保存作为job输入的所有文件，并实现了对输入文件计算splits的方法。至于获得记录的方法是有不同的子类进行实现的。
那么，FileInputFormat是怎样将他们划分成splits的呢？FileInputFormat只划分比HDFS block大的文件，所以如果一个文件的大小比block小，将不会被划分，这也是Hadoop处理大文件的效率要比处理很多小文件的效率高的原因。

hadoop默认的InputFormat是TextInputFormat，重写了FileInputFormat中的createRecordReader和isSplitable方法。该类使用的reader是LineRecordReader，即以回车键(CR = 13)或换行符(LF = 10)为行分隔符。

但大多数情况下，回车键或换行符作为输入文件的行分隔符并不能满足我们的需求，通常用户很有可能会输入回车键、换行符，所以通常我们会定义不可见字符(即用户无法输入的字符)为行分隔符，这种情况下，就需要新写一个InputFormat。

又或者，一条记录的分隔符不是字符，而是字符串，这种情况相对麻烦；还有一种情况，输入文件的主键key已经是排好序的了，需要hadoop做的只是把相同的key作为一个数据块进行逻辑处理，这种情况更麻烦，相当于免去了mapper的过程，直接进去reduce，那么InputFormat的逻辑就相对较为复杂了，但并不是不能实现。

1、改变一条记录的分隔符，不用默认的回车或换行符作为记录分隔符，甚至可以采用[b]字符串作为记录分隔符。

[/b] 1）自定义一个InputFormat，继承FileInputFormat，重写createRecordReader方法，如果不需要分片或者需要改变分片的方式，则重写isSplitable方法，具体代码如下：

public class FileInputFormatB extends FileInputFormat<LongWritable, Text> {

@Override

public RecordReader<LongWritable, Text> createRecordReader( InputSplit split, TaskAttemptContext context) {

return new SearchRecordReader("\b");

}

@Override

protected boolean isSplitable(FileSystem fs, Path filename) {

// 输入文件不分片

return false;

}

}

2）关键在于定义一个新的SearchRecordReader继承RecordReader，支持自定义的行分隔符，即一条记录的分隔符。标红的地方为与hadoop默认的LineRecordReader不同的地方。

public class IsearchRecordReader extends RecordReader<LongWritable, Text> {

private static final Log LOG = LogFactory.getLog(IsearchRecordReader.class);

private CompressionCodecFactory compressionCodecs = null;

private long start;

private long pos;

private long end;

private LineReader in;

private int maxLineLength;

private LongWritable key = null;

private Text value = null;

//行分隔符，即一条记录的分隔符

private byte[] separator = {'\b'};

private int sepLength = 1;

‍ public IsearchRecordReader(){

}

public IsearchRecordReader(String seps){

this.separator = seps.getBytes();

sepLength = separator.length;

}

public void initialize(InputSplit genericSplit, TaskAttemptContext context) throws IOException {

FileSplit split = (FileSplit) genericSplit;

Configuration job = context.getConfiguration();

this.maxLineLength = job.getInt("mapred.linerecordreader.maxlength", Integer.MAX_VALUE);

this.start = split.getStart();

this.end = (this.start + split.getLength());

Path file = split.getPath();

this.compressionCodecs = new CompressionCodecFactory(job);

CompressionCodec codec = this.compressionCodecs.getCodec(file);

// open the file and seek to the start of the split

FileSystem fs = file.getFileSystem(job);

FSDataInputStream fileIn = fs.open(split.getPath());

boolean skipFirstLine = false;

if (codec != null) {

this.in = new LineReader(codec.createInputStream(fileIn), job);

this.end = Long.MAX_VALUE;

} else {

if (this.start != 0L) {

skipFirstLine = true;

this.start -= sepLength;

fileIn.seek(this.start);

}

this.in = new LineReader(fileIn, job);

}

if (skipFirstLine) { // skip first line and re-establish "start".

int newSize = in.readLine(new Text(), 0, (int) Math.min( (long) Integer.MAX_VALUE, end - start));

if(newSize > 0){

start += newSize;

}

}

this.pos = this.start;

}

public boolean nextKeyValue() throws IOException {

if (this.key == null) {

this.key = new LongWritable();

}

this.key.set(this.pos);

if (this.value == null) {

this.value = new Text();

}

int newSize = 0;

while (this.pos < this.end) {

newSize = this.in.readLine(this.value, this.maxLineLength, Math.max(

(int) Math.min(Integer.MAX_VALUE, this.end - this.pos), this.maxLineLength));

if (newSize == 0) {

break;

}

this.pos += newSize;

if (newSize < this.maxLineLength) {

break;

}

LOG.info("Skipped line of size " + newSize + " at pos " + (this.pos - newSize));

}

if (newSize == 0) {

//读下一个buffer

this.key = null;

this.value = null;

return false;

}

//读同一个buffer的下一个记录

return true;

}

public LongWritable getCurrentKey() {

return this.key;

}

public Text getCurrentValue() {

return this.value;

}

public float getProgress() {

if (this.start == this.end) {

return 0.0F;

}

return Math.min(1.0F, (float) (this.pos - this.start) / (float) (this.end - this.start));

}

public synchronized void close() throws IOException {

if (this.in != null)

this.in.close();

}

}

3）重写SearchRecordReader需要的LineReader，可作为SearchRecordReader内部类。特别需要注意的地方就是，读取文件的方式是按指定大小的buffer来读，必定就会遇到一条完整的记录被切成两半，甚至如果分隔符大于1个字符时分隔符也会被切成两半的情况，这种情况一定要加以拼接处理。

public class LineReader {

//回车键(hadoop默认)

//private static final byte CR = 13;

//换行符(hadoop默认)

//private static final byte LF = 10;

//按buffer进行文件读取

private static final int DEFAULT_BUFFER_SIZE = 32 * 1024 * 1024;

private int bufferSize = DEFAULT_BUFFER_SIZE;

private InputStream in;

private byte[] buffer;

private int bufferLength = 0;

private int bufferPosn = 0;

LineReader(InputStream in, int bufferSize) {

this.bufferLength = 0;

this.bufferPosn = 0;

this.in = in;

this.bufferSize = bufferSize;

this.buffer = new byte[this.bufferSize];

}

public LineReader(InputStream in, Configuration conf) throws IOException {

this(in, conf.getInt("io.file.buffer.size", DEFAULT_BUFFER_SIZE));

}

public void close() throws IOException {

in.close();

}

public int readLine(Text str, int maxLineLength) throws IOException {

return readLine(str, maxLineLength, Integer.MAX_VALUE);

}

public int readLine(Text str) throws IOException {

return readLine(str, Integer.MAX_VALUE, Integer.MAX_VALUE);

}

//以下是需要改写的部分_start，核心代码

public int readLine(Text str, int maxLineLength, int maxBytesToConsume) throws IOException{

str.clear();

Text record = new Text();

int txtLength = 0;

long bytesConsumed = 0L;

boolean newline = false;

int sepPosn = 0;

do {

//已经读到buffer的末尾了，读下一个buffer

if (this.bufferPosn >= this.bufferLength) {

bufferPosn = 0;

bufferLength = in.read(buffer);

//读到文件末尾了，则跳出，进行下一个文件的读取

if (bufferLength <= 0) {

break;

}

}

int startPosn = this.bufferPosn;

for (; bufferPosn < bufferLength; bufferPosn ++) {

//处理上一个buffer的尾巴被切成了两半的分隔符(如果分隔符中重复字符过多在这里会有问题)

if(sepPosn > 0 && buffer[bufferPosn] != separator[sepPosn]){

sepPosn = 0;

}

//遇到行分隔符的第一个字符

if (buffer[bufferPosn] == separator[sepPosn]) {

bufferPosn ++;

int i = 0;

//判断接下来的字符是否也是行分隔符中的字符

for(++ sepPosn; sepPosn < sepLength; i ++, sepPosn ++){

//buffer的最后刚好是分隔符，且分隔符被不幸地切成了两半

if(bufferPosn + i >= bufferLength){

bufferPosn += i - 1;

break;

}

//一旦其中有一个字符不相同，就判定为不是分隔符

if(this.buffer[this.bufferPosn + i] != separator[sepPosn]){

sepPosn = 0;

break;

}

}

//的确遇到了行分隔符

if(sepPosn == sepLength){

bufferPosn += i;

newline = true;

sepPosn = 0;

break;

}

}

}

int readLength = this.bufferPosn - startPosn;

bytesConsumed += readLength;

//行分隔符不放入块中

//int appendLength = readLength - newlineLength;

if (readLength > maxLineLength - txtLength) {

readLength = maxLineLength - txtLength;

}

if (readLength > 0) {

record.append(this.buffer, startPosn, readLength);

txtLength += readLength;

//去掉记录的分隔符

if(newline){

str.set(record.getBytes(), 0, record.getLength() - sepLength);

}

}

} while (!newline && (bytesConsumed < maxBytesToConsume));

if (bytesConsumed > (long)Integer.MAX_VALUE) {

throw new IOException("Too many bytes before newline: " + bytesConsumed);

}

return (int) bytesConsumed;

}

//以下是需要改写的部分_end

//以下是hadoop-core中LineReader的源码_start

public int readLine(Text str, int maxLineLength, int maxBytesToConsume) throws IOException{

str.clear();

int txtLength = 0;

int newlineLength = 0;

boolean prevCharCR = false;

long bytesConsumed = 0L;

do {

int startPosn = this.bufferPosn;

if (this.bufferPosn >= this.bufferLength) {

startPosn = this.bufferPosn = 0;

if (prevCharCR) bytesConsumed ++;

this.bufferLength = this.in.read(this.buffer);

if (this.bufferLength <= 0) break;

}

for (; this.bufferPosn < this.bufferLength; this.bufferPosn ++) {

if (this.buffer[this.bufferPosn] == LF) {

newlineLength = (prevCharCR) ? 2 : 1;

this.bufferPosn ++;

break;

}

if (prevCharCR) {

newlineLength = 1;

break;

}

prevCharCR = this.buffer[this.bufferPosn] == CR;

}

int readLength = this.bufferPosn - startPosn;

if ((prevCharCR) && (newlineLength == 0))

--readLength;

bytesConsumed += readLength;

int appendLength = readLength - newlineLength;

if (appendLength > maxLineLength - txtLength) {

appendLength = maxLineLength - txtLength;

}

if (appendLength > 0) {

str.append(this.buffer, startPosn, appendLength);

txtLength += appendLength; }

}

while ((newlineLength == 0) && (bytesConsumed < maxBytesToConsume));

if (bytesConsumed > (long)Integer.MAX_VALUE) throw new IOException("Too many bytes before newline: " + bytesConsumed);

return (int)bytesConsumed;

}

//以下是hadoop-core中LineReader的源码_end

}

2、已经按主键key排好序了，并保证相同主键key一定是在一起的，假设每条记录的第一个字段为主键，那么如果沿用上面的LineReader，需要在核心方法readLine中对前后两条记录的id进行equals判断，如果不同才进行split，如果相同继续下一条记录的判断。代码就不再贴了，但需要注意的地方，依旧是前后两个buffer进行交接的时候，非常有可能一条记录被切成了两半，一半在前一个buffer中，一半在后一个buffer中。

这种方式的好处在于少去了reduce操作，会大大地提高效率，其实mapper的过程相当的快，费时的通常是reduce。