hive不支持多个字符作为分隔符的解决方案

题记：

　　近期在做某个大型银行的大数据项目，当在处理非结构化数据时，却发现他们给的数据并不符合hive和pig的处理要求，数据每行必须需要多个分割符才能完美处理，一下午也没有想到完美的办法解决，今天重新审视了一下整个过程。看来hive的命令行没法搞定了。于是乎，只能通过代码来搞定。

1、重新实现hive的InputFormat了，别急放码过来

package hiveStream;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileSplit;
import org.apache.hadoop.mapred.InputSplit;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.JobConfigurable;
import org.apache.hadoop.mapred.RecordReader;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;

public class MyHiveInputFormat  extends TextInputFormat implements JobConfigurable{

	@Override
	public RecordReader<LongWritable, Text> getRecordReader(
			InputSplit genericSplit, JobConf job, Reporter reporter)
			throws IOException {
		 reporter.setStatus(genericSplit.toString());
	       return new MyRecordReader((FileSplit) genericSplit, job);
	}

}

2、仔细看看下面的方法，不解释，自己领悟。

package hiveStream;

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.mapred.FileSplit;
import org.apache.hadoop.mapred.RecordReader;
import org.apache.hadoop.util.LineReader;

public class MyRecordReader implements RecordReader<LongWritable, Text>{

	private CompressionCodecFactory compressionCodecs = null;
    private long start;
    private long pos;
    private long end;
    private LineReader lineReader;
    int maxLineLength;

    public MyRecordReader(InputStream in, long offset, long endOffset,
            int maxLineLength) {
        this.maxLineLength = maxLineLength;
        this.start = offset;
        this.lineReader = new LineReader(in);
        this.pos = offset;
        this.end = endOffset;
    }

    public MyRecordReader(InputStream in, long offset, long endOffset,
            Configuration job) throws IOException {
        this.maxLineLength = job.getInt(
                "mapred.mutilCharRecordReader.maxlength", Integer.MAX_VALUE);
        this.lineReader = new LineReader(in, job);
        this.start = offset;
        this.end = endOffset;
    }

    // 构造方法
    public MyRecordReader(FileSplit inputSplit, Configuration job)
            throws IOException {
        maxLineLength = job.getInt("mapred.mutilCharRecordReader.maxlength",
                Integer.MAX_VALUE);
        start = inputSplit.getStart();
        end = start + inputSplit.getLength();
        final Path file = inputSplit.getPath();
        // 创建压缩器
        compressionCodecs = new CompressionCodecFactory(job);
        final CompressionCodec codec = compressionCodecs.getCodec(file);
        // 打开文件系统
        FileSystem fs = file.getFileSystem(job);
        FSDataInputStream fileIn = fs.open(file);
        boolean skipFirstLine = false;

        if (codec != null) {
            lineReader = new LineReader(codec.createInputStream(fileIn), job);
            end = Long.MAX_VALUE;
        } else {
            if (start != 0) {
                skipFirstLine = true;
                --start;
                fileIn.seek(start);
            }
            lineReader = new LineReader(fileIn, job);
        }

        if (skipFirstLine) {
            start += lineReader.readLine(new Text(), 0,
                    (int) Math.min((long) Integer.MAX_VALUE, end - start));
        }
        this.pos = start;
    }

    @Override
	public void close() throws IOException {
    	if (lineReader != null)
            lineReader.close();
	}

	@Override
	public LongWritable createKey() {
		return new LongWritable();
	}

	@Override
	public Text createValue() {
		 return new Text();
	}

	@Override
	public long getPos() throws IOException {
		  return pos;
	}

	@Override
	public float getProgress() throws IOException {
	   if (start == end) {
            return 0.0f;
        } else {
            return Math.min(1.0f, (pos - start) / (float) (end - start));
        }
	}

	@Override
	public boolean next(LongWritable key, Text value) throws IOException {
		 while (pos < end) {
	            key.set(pos);
	            int newSize = lineReader.readLine(value, maxLineLength,
	                    Math.max((int) Math.min(Integer.MAX_VALUE, end - pos),
	                            maxLineLength));
	            // 把字符串中的"##"转变为"#"
	            String strReplace = value.toString().replaceAll("\\s+", "\001");
	            Text txtReplace = new Text();
	            txtReplace.set(strReplace);
	            value.set(txtReplace.getBytes(), 0, txtReplace.getLength());
	            if (newSize == 0)
	                return false;
	            pos += newSize;
	            if (newSize < maxLineLength)
	                return true;
	        }
	        return false;
	    }
	}

3、处理实例：如下

数据处理要求：

    12 afd   fewf	fewfe  we
    76 vee   ppt	wfew  wefw
    83 tyutr   ppt	wfew  wefw
    45 vbe   ppt	wfew  wefw
    565 wee   ppt	wfew  wefw
    12 sde   ppt	wfew  wefw
注意：字段之间的空格不一致

1、建表：
    create table micmiu_blog(author int, category string, url string,town string,oop string) stored as inputformat ‘hiveStream.MyHiveInputFormat‘ outputformat  ‘org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat‘;
注意：输出咱可没有重写哦

2、加载数据：
    LOAD DATA LOCAL INPATH‘/mnt/test‘ OVERWRITE INTO TABLE micmiu_blog;

3、看看的成果：
    select * from micmiu_blog;

自己去试试，不解释