补充:hive 读取数据的机制:
1、 首先用 InputFormat<默认是:org.apache.hadoop.mapred.TextInputFormat >的一个具体实 现类读入文件数据,返回一条一条的记录(可以是行,或者是你逻辑中的“行”)
2、 然后利用 SerDe<默认:org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe>的一个具体 实现类,对上面返回的一条一条的记录进行字段切割
Hive 对文件中字段的分隔符默认情况下只支持单字节分隔符,如果数据文件中的分隔符是多 字符的,如下所示:
01||huangbo
02||xuzheng
03||wangbaoqiang
1.使用RegexSerDe通过正则表达式来抽取字段
create table t_bi_reg(id string,name string) row format serde 'org.apache.hadoop.hive.serde2.RegexSerDe' with serdeproperties('input.regex'='(.*)\\|\\|(.*)','output.format.string'='%1$s %2$s') stored as textfile; hive>select * from t_bi_reg; |
2、通过自定义 InputFormat 解决特殊分隔符问题
其原理是在 inputformat 读取行的时候将数据中的“多字节分隔符”替换为 hive 默认的分隔 符(ctrl+A 亦即 \001)或用于替代的单字符分隔符,以便 hive 在 serde 操作时按照默认的 单字节分隔符进行字段抽取
com.ghgj.hive.delimit2.BiDelimiterInputFormat
package com.ghgj.hive.delimit2;
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.RecordReader;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;
public class BiDelimiterInputFormat extends TextInputFormat {
@Override public RecordReader getRecordReader(InputSplit genericSplit, JobConf job, Reporter reporter)throws IOException {
reporter.setStatus(genericSplit.toString());
BiRecordReader reader = new BiRecordReader(job,(FileSplit)genericSplit);
// MyRecordReader reader = new MyRecordReader(job,(FileSplit)genericSplit);
return reader; }
}
com.ghgj.hive.delimit2.BiRecordReader
package com.ghgj.hive.delimit2;
import java.io.IOException;
import java.io.InputStream;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
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.fs.Seekable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.compress.CodecPool;
import org.apache.hadoop.io.compress.CompressionCodec;
import org.apache.hadoop.io.compress.CompressionCodecFactory;
import org.apache.hadoop.io.compress.Decompressor;
import org.apache.hadoop.io.compress.SplitCompressionInputStream;
import org.apache.hadoop.io.compress.SplittableCompressionCodec;
import org.apache.hadoop.mapred.FileSplit;
import org.apache.hadoop.mapred.LineRecordReader;
import org.apache.hadoop.mapred.RecordReader;
public class BiRecordReader implements RecordReader {
private static final Log LOG = LogFactory.getLog(LineRecordReader.class
.getName());
private CompressionCodecFactory compressionCodecs = null;
private long start;
private long pos;
private long end;
private LineReader in;
int maxLineLength;
private Seekable filePosition;
private CompressionCodec codec;
private Decompressor decompressor;
/**
* A class that provides a line reader from an input stream.
* @deprecated Use {@link org.apache.hadoop.util.LineReader} instead.
*/
@Deprecated
public static class LineReader extends org.apache.hadoop.util.LineReader {
LineReader(InputStream in) {
super(in);
}
LineReader(InputStream in, int bufferSize) {
super(in, bufferSize);
}
public LineReader(InputStream in, Configuration conf)
throws IOException {
super(in, conf);
}
}
public BiRecordReader(Configuration job, FileSplit split) throws IOException {
this.maxLineLength = job.getInt("mapred.linerecordreader.maxlength",
Integer.MAX_VALUE);
start = split.getStart();
end = start + split.getLength();
final Path file = split.getPath();
compressionCodecs = new CompressionCodecFactory(job);
codec = 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());
if (isCompressedInput()) {
decompressor = CodecPool.getDecompressor(codec);
if (codec instanceof SplittableCompressionCodec) {
final SplitCompressionInputStream cIn = ((SplittableCompressionCodec) codec)
.createInputStream(fileIn, decompressor, start, end,
SplittableCompressionCodec.READ_MODE.BYBLOCK);
in = new LineReader(cIn, job);
start = cIn.getAdjustedStart();
end = cIn.getAdjustedEnd();
filePosition = cIn;
// take pos from compressed stream
} else {
in = new LineReader(codec.createInputStream(fileIn, decompressor), job);
filePosition = fileIn;
}
} else {
fileIn.seek(start);
in = new LineReader(fileIn, job);
filePosition = fileIn; }
// If this is not the first split, we always throw away first record
// because we always (except the last split) read one extra line in
// next() method.
if (start != 0) {
start += in.readLine(new Text(), 0, maxBytesToConsume(start));
}
this.pos = start;
}
private boolean isCompressedInput() {
return (codec != null); }
private int maxBytesToConsume(long pos) {
return isCompressedInput() ? Integer.MAX_VALUE : (int) Math.min(
Integer.MAX_VALUE, end - pos); }
private long getFilePosition() throws IOException { long retVal;
if (isCompressedInput() && null != filePosition) {
retVal = filePosition.getPos();
} else {
retVal = pos; }
return retVal; }
public BiRecordReader(InputStream in, long offset, long endOffset,
int maxLineLength) {
this.maxLineLength = maxLineLength;
this.in = new LineReader(in);
this.start = offset;
this.pos = offset;
this.end = endOffset;
this.filePosition = null; }
public BiRecordReader(InputStream in, long offset, long endOffset,
Configuration job) throws IOException {
this.maxLineLength = job.getInt("mapred.linerecordreader.maxlength",
Integer.MAX_VALUE);
this.in = new LineReader(in, job);
this.start = offset;
this.pos = offset;
this.end = endOffset;
this.filePosition = null; }
public LongWritable createKey() {
return new LongWritable();
}
public Text createValue() {
return new Text(); }
/** Read a line. */
public synchronized boolean next(LongWritable key, Text value)
throws IOException {
// We always read one extra line, which lies outside the upper
// split limit i.e. (end - 1)
while (getFilePosition() <= end) {
key.set(pos);
int newSize = in.readLine(value,
maxLineLength,Math.max(maxBytesToConsume(pos), maxLineLength));
String str = value.toString().replaceAll("\\|\\|", "\\|");
value.set(str);
pos += newSize;
if (newSize == 0) {
return false; }
if (newSize < maxLineLength) {
return true; }
// line too long. try again
LOG.info("Skipped line of size " + newSize + " at pos " + (pos -
newSize)); }
return false;
}
/** * Get the progress within the split
*/ public float getProgress() throws IOException {
if (start == end) {
return 0.0f;
} else {
return Math.min(1.0f, (getFilePosition() - start)
/ (float) (end - start)); } }
public synchronized long getPos() throws IOException {
return pos;
}
public synchronized void close() throws IOException {
try {
if (in != null) {
in.close();
}
} finally {
if (decompressor != null) {
CodecPool.returnDecompressor(decompressor);
}
}
}
}
注意:上述代码中的 api 全部使用 hadoop 的老 api 接口 org.apache.hadoop.mapred„. 然后将工程打包,并拷贝至 hive 安装目录的 lib 文件夹中,并重启 hive,使用以下语句建表 即可:
注:还需要在 hive 中使用 add jar,才能在执行 hql 查询该表时把自定义 jar 包传递给 maptask hive>add jar /home/hadoop/apps/hive/lib/myinput.jar