hadoop中mapreduce如何自定义InputFormat
这篇文章主要介绍了hadoop中mapreduce如何自定义InputFormat,具有一定借鉴价值,感兴趣的朋友可以参考下,希望大家阅读完这篇文章之后大有收获,下面让小编带着大家一起了解一下。
网站建设哪家好,找成都创新互联公司!专注于网页设计、网站建设、微信开发、小程序制作、集团企业网站建设等服务项目。为回馈新老客户创新互联还提供了石鼓免费建站欢迎大家使用!
首先我们要先定义一个类继承FileInputFormat,并重写createRecordReader方法返回RecordReader,然后定义一个类继承RecordReader,createRecordReader方法返回也就是我们定义的RecordReader的子类的对象。
代码如下
public class TrackInputFormat extends FileInputFormat{ @Override public RecordReader createRecordReader(InputSplit arg0, TaskAttemptContext arg1) throws IOException, InterruptedException { // TODO Auto-generated method stub return new TrackRecordReader(); } }
package input; import java.io.IOException; import java.io.InputStream; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FSDataInputStream; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.io.compress.CompressionCodec; import org.apache.hadoop.io.compress.CompressionCodecFactory; import org.apache.hadoop.mapreduce.InputSplit; import org.apache.hadoop.mapreduce.RecordReader; import org.apache.hadoop.mapreduce.TaskAttemptContext; import org.apache.hadoop.mapreduce.lib.input.FileSplit; import org.apache.log4j.Logger; /** * Treats keys as offset in file and value as line. * * @deprecated Use * {@link org.apache.hadoop.mapreduce.lib.input.LineRecordReader} * instead. */ public class TrackRecordReader extends RecordReader{ Logger logger = Logger.getLogger(TrackRecordReader.class.getName()); private CompressionCodecFactory compressionCodecs = null; private long start; private long pos; private long end; private NewLineReader 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 = "]@\n".getBytes(); // -------------------- public void initialize(InputSplit genericSplit, TaskAttemptContext context) throws IOException { FileSplit split = (FileSplit) genericSplit; Configuration job = context.getConfiguration(); //mapreduce.input.linerecordreader.line.maxlength this.maxLineLength = job.getInt("mapred.linerecordreader.maxlength",Integer.MAX_VALUE); start = split.getStart(); end = start + split.getLength(); final Path file = split.getPath(); //logger.info("path========================="+file.toString()); compressionCodecs = new CompressionCodecFactory(job); final CompressionCodec codec = compressionCodecs.getCodec(file); FileSystem fs = file.getFileSystem(job); FSDataInputStream fileIn = fs.open(split.getPath()); boolean skipFirstLine = false; //logger.info("codec========================="+codec); if (codec != null) { in = new NewLineReader(codec.createInputStream(fileIn), job); end = Long.MAX_VALUE; } else { if (start != 0) { skipFirstLine = true; this.start -= separator.length;// // --start; fileIn.seek(start); } in = new NewLineReader(fileIn, job); } if (skipFirstLine) { // skip first line and re-establish "start". start += in.readLine(new Text(), 0, (int) Math.min((long) Integer.MAX_VALUE, end - start)); } this.pos = start; /*if (skipFirstLine) { int newSize = in.readLine(new Text(), 0, (int) Math.min( (long) Integer.MAX_VALUE, end - start)); if(newSize > 0){ start += newSize; } }*/ } public boolean nextKeyValue() throws IOException { if (key == null) { key = new LongWritable(); } key.set(pos); if (value == null) { value = new Text(); } int newSize = 0; while (pos < end) { newSize = in.readLine(value, maxLineLength, Math.max((int) Math.min(Integer.MAX_VALUE, end - pos), maxLineLength)); if (newSize == 0) { break; } pos += newSize; if (newSize < maxLineLength) { break; } } if (newSize == 0) { //读取下一个buffer key = null; value = null; return false; } else { //读同一个buffer的下一个记录 return true; } } @Override public LongWritable getCurrentKey() { return key; } @Override public Text getCurrentValue() { return value; } /** * Get the progress within the split */ public float getProgress() { if (start == end) { return 0.0f; } else { return Math.min(1.0f, (pos - start) / (float) (end - start)); } } public synchronized void close() throws IOException { if (in != null) { in.close(); } } public class NewLineReader { private static final int DEFAULT_BUFFER_SIZE = 256 * 1024* 1024; private int bufferSize = DEFAULT_BUFFER_SIZE; private InputStream in; private byte[] buffer; private int bufferLength = 0; private int bufferPosn = 0; public NewLineReader(InputStream in) { this(in, DEFAULT_BUFFER_SIZE); } public NewLineReader(InputStream in, int bufferSize) { this.in = in; this.bufferSize = bufferSize; this.buffer = new byte[this.bufferSize]; } public NewLineReader(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, 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 < separator.length; 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 == separator.length) { bufferPosn += i; newline = true; sepPosn = 0; break; } } } int readLength = this.bufferPosn - startPosn; bytesConsumed += readLength; // 行分隔符不放入块中 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() - separator.length); } } } while (!newline && (bytesConsumed < maxBytesToConsume)); if (bytesConsumed > (long) Integer.MAX_VALUE) { throw new IOException("Too many bytes before newline: " + bytesConsumed); } return (int) bytesConsumed; } 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); } } }
private byte[] separator = "]@\n".getBytes();
感谢你能够认真阅读完这篇文章,希望小编分享的“hadoop中mapreduce如何自定义InputFormat”这篇文章对大家有帮助,同时也希望大家多多支持创新互联,关注创新互联行业资讯频道,更多相关知识等着你来学习!
分享题目:hadoop中mapreduce如何自定义InputFormat
本文来源:http://cdiso.cn/article/jeesoe.html