hive-TextInputformat自定义分隔符

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前言

在一次利用sqoop将关系型数据库Oracle中的数据导入到hive的测试中，出现了一个分割符的问题。oracle中有字段中含有\n换行符，由于hive默认是以’\n’作为换行分割符的，所以用sqoop将oracle中数据导入到hive中导致hive中的数据条目跟原始数据库不一致，当时的处理方式是数据在导入到HDFS之前，用sqoop的参数将字段中的换行符都替换掉。

Sqoop在将数据从关系型数据库导入到HDFS时，支持将\n替换成自定义换行符（支持单字符自定义换行符），但是在hive中建表时用语句<row format delimited lines terminated by>指定自定义换行符会提示如下错误：

< linesterminated by>参数目前仅支持’\n’。不能指定自定义换行符，这样自定义换行符的数据就不能导入到hive中，基于以上考虑，本文简单说明了如何让hive实现自定义多个字符的换行和字段分割符，供大家参考。如有不足请批评指正。

环境

Hadoop：2.2
Hive：0.12（星环inceptor，支持原生hive）

目标

分析hive自定义多字符串换行符；
实现hive自定义多字符串字段分隔符；
实现hivetextinputformat自定义编码格式的设置。

1.hive的序列化与反序列化

Hive中，默认使用的是TextInputFormat，一行表示一条记录。在每条记录(一行中)，默认使用^A分割各个字段。

在有些时候，我们往往面对多行，结构化的文档，并需要将其导入Hive处理，此时，就需要自定义InputFormat、OutputFormat，以及SerDe了。

首先来理清这三者之间的关系，我们直接引用Hive官方说法：

SerDe is a short name for “Serializer and Deserializer.”

Hive uses SerDe (and !FileFormat) to read and write table rows.

HDFS files –> InputFileFormat –> <key, value> –> Deserializer –> Row object

Row object –> Serializer –> <key, value> –> OutputFileFormat –> HDFS files

总结一下，面对一个HDFS上的文件，Hive将如下处理（以读为例）：

(1) 调用InputFormat，将文件切成不同的文档。每篇文档即一行(Row)。

(2) 调用SerDe的Deserializer，将一行(Row)，切分为各个字段。

当HIVE执行INSERT操作，将Row写入文件时，主要调用OutputFormat、SerDe的Seriliazer，顺序与读取相反。

针对含有自定义换行符和字段分隔符的HDFS文件，本文仅介绍hive读取的过程的修改。

2 Hive默认采用的TextInputFormat类

首先建一个简单的表，然后用<describe extended >命令查看该表的详细信息。

transwarp> create table test1(id int);OKTime taken: 0.062secondstranswarp>describe extended test1;OKid                  int                   None                                 Detailed Table Information     Table(tableName:test1, dbName:default, owner:root,createTime:1409300219, lastAccessTime:0, retention:0,sd:StorageDescriptor(cols:[FieldSchema(name:id, type:int,comment:null)],location:hdfs://leezq-vm3:8020/inceptor1/user/hive/warehouse/test1,inputFormat:org.apache.hadoop.mapred.TextInputFormat,outputFormat:org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat,compressed:false,numBuckets:-1,serdeInfo:SerDeInfo(name:null,serializationLib:org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe,parameters:{serialization.format=1}),bucketCols:[], sortCols:[], parameters:{},skewedInfo:SkewedInfo(skewedColNames:[], skewedColValues:[],skewedColValueLocationMaps:{}), storedAsSubDirectories:false),partitionKeys:[],parameters:{transient_lastDdlTime=1409300219},viewOriginalText:null, viewExpandedText:null,tableType:MANAGED_TABLE)       Time taken: 0.121 seconds, Fetched: 3 row(s)

从上面可以看出，默认状态下，hive的输入和输出调用的类分别为：

inputFormat:org.apache.hadoop.mapred.TextInputFormat,outputFormat:org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat,

虽然现在现在hadoop现在升级到2.X版本，hive依然采用老版的mapred接口。

我们要改写的就是类TextInputFormat。

2.1类 TextInputFormat

类TextInputFormat在hadoop-mapreduce-client-core-2.2.0.jar中。

重点看类中getRecordReader方法，该方法返回LineRecordReader对象。并且该方法中已经实现了接收自定义字符串作为换行符的代码，只要建表前在hive的CLI界面上输入set textinputformat.record.delimiter=<自定义换行字符串>;即可实现自定义多字符换行符。

2.2类LineRecordReader

为了进一步查看其实现原理，我们进一步看LineRecordReader（package org.apache.hadoop.mapred. LineRecordReader）类。

查看该类的构造函数，该类调用org.apache.hadoop.util.LineReader(在包hadoop-common-2.2.0.jar中)获取每行的数据，把参数recordDelimiter传给类对象LineReader，类LineReader中的readLine(Text str, int maxLineLength, intmaxBytesToConsume)方法负责按照用户自定义分隔符返回每行的长度，如果用户不设定textinputformat.record.delimiter的值，recordDelimiter的值为null，这时readLine方法就会按照默认’\n’分割每行。readLine的代码如下：

通过读源码可以看到，原始的hive可以通过设置参数的方法实现多字符自定义换行符（textFile的存储方式），通过上图中readCustomLine方法获得用户自定义换行符的字符串实现自动换行，每行最大可支持2147483648大小。但是要想实现自定义多字符的字段分隔符和自定义编码格式的设置，还需要对源码进行改写。下面就讲一下改写的步骤。

3 自定义TextInputFormat

实现自定义多字符串的字段分割符
实现自定义编码格式的设置

首先建一个空的java工程，添加必须的五个包

然后新建两个类SQPTextInputFormat和SQPRecordReader，将TextInputFormat和LineRecordReader的代码分别拷贝过来。

在SQPTextInputFormat中添加对自定义编码格式的设置。（对换行符的参数进行了更名，将textinputformat.record.delimiter改成了textinputformat.record.linesep)

//======================================================  String delimiter = job.get("textinputformat.record.linesep");  this.encoding = job.get("textinputformat.record.encoding",defaultEncoding);  byte[] recordDelimiterBytes = null;  if (null != delimiter) {//Charsets.UTF_8    recordDelimiterBytes = delimiter.getBytes(this.encoding);  }  return new SQPRecordReader(job, (FileSplit)genericSplit, recordDelimiterBytes);

在SQPRecordReader构造函数中添加对字段分隔符和编码格式的设置。

//======================================================    this.FieldSep = job.get("textinputformat.record.fieldsep",defaultFSep);this.encoding = job.get("textinputformat.record.encoding",defaultEncoding);

在SQPRecordReader的next（）方法中添加对字段分割符的替换和对编码格式的设置。

//======================================================    if (encoding.compareTo(defaultEncoding) != 0) {              String str = new String(value.getBytes(), 0,value.getLength(), encoding);              value.set(str);         }      if (FieldSep.compareTo(defaultFSep) != 0) {              String replacedValue = value.toString().replace(FieldSep, defaultFSep);              value.set(replacedValue);

详细的代码如下：

package com.learn.util.hadoop;//import com.google.common.base.Charsets;import java.io.IOException;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.io.compress.SplittableCompressionCodec;import org.apache.hadoop.mapred.*;public class SQPTextInputFormat extends FileInputFormat<LongWritable, Text>implements JobConfigurable{private CompressionCodecFactory compressionCodecs = null;private final static String defaultEncoding = "UTF-8";//"US-ASCII""ISO-8859-1""UTF-8""UTF-16BE""UTF-16LE""UTF-16"private String encoding = null;public void configure(JobConf conf) {  this.compressionCodecs = new CompressionCodecFactory(conf);}protected boolean isSplitable(FileSystem fs, Path file) {  CompressionCodec codec = this.compressionCodecs.getCodec(file);  if (null == codec) {    return true;  }  return codec instanceof SplittableCompressionCodec;}public RecordReader<LongWritable, Text> getRecordReader(InputSplit genericSplit, JobConf job, Reporter reporter)  throws IOException{  reporter.setStatus(genericSplit.toString());  String delimiter = job.get("textinputformat.record.linesep");  this.encoding = job.get("textinputformat.record.encoding",defaultEncoding);  byte[] recordDelimiterBytes = null;  if (null != delimiter) {//Charsets.UTF_8    recordDelimiterBytes = delimiter.getBytes(this.encoding);  }  return new SQPRecordReader(job, (FileSplit)genericSplit, recordDelimiterBytes);}}

package com.learn.util.hadoop;import java.io.IOException;import java.io.InputStream;import org.apache.commons.logging.Log;import org.apache.commons.logging.LogFactory;import org.apache.hadoop.classification.InterfaceAudience.LimitedPrivate;import org.apache.hadoop.classification.InterfaceStability.Unstable;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.io.compress.SplittableCompressionCodec.READ_MODE;import org.apache.hadoop.util.LineReader;import org.apache.hadoop.mapred.RecordReader;import org.apache.hadoop.mapred.FileSplit;//@InterfaceAudience.LimitedPrivate({"MapReduce", "Pig"})//@InterfaceStability.Unstablepublic class SQPRecordReader  implements RecordReader<LongWritable, Text>{  private static final Log LOG = LogFactory.getLog(SQPRecordReader.class.getName());  private CompressionCodecFactory compressionCodecs = null;  private long start;  private long pos;  private long end;  private LineReader in;  private FSDataInputStream fileIn;  private final Seekable filePosition;  int maxLineLength;  private CompressionCodec codec;  private Decompressor decompressor;  private String FieldSep;//field separator  private static final String defaultFSep="\001";  private final static String defaultEncoding = "UTF-8";//"US-ASCII""ISO-8859-1""UTF-8""UTF-16BE""UTF-16LE""UTF-16"  private String encoding = null;  public SQPRecordReader(Configuration job, FileSplit split)    throws IOException  {    this(job, split, null);  }  public SQPRecordReader(Configuration job, FileSplit split, byte[] recordDelimiter) throws IOException  {    this.maxLineLength = job.getInt("mapreduce.input.linerecordreader.line.maxlength", 2147483647);    this.FieldSep = job.get("textinputformat.record.fieldsep",defaultFSep);    this.encoding = job.get("textinputformat.record.encoding",defaultEncoding);    this.start = split.getStart();    this.end = (this.start + split.getLength());    Path file = split.getPath();    this.compressionCodecs = new CompressionCodecFactory(job);    this.codec = this.compressionCodecs.getCodec(file);    FileSystem fs = file.getFileSystem(job);    this.fileIn = fs.open(file);    if (isCompressedInput()) {      this.decompressor = CodecPool.getDecompressor(this.codec);      if ((this.codec instanceof SplittableCompressionCodec)) {        SplitCompressionInputStream cIn = ((SplittableCompressionCodec)this.codec).createInputStream(this.fileIn, this.decompressor, this.start, this.end, SplittableCompressionCodec.READ_MODE.BYBLOCK);        this.in = new LineReader(cIn, job, recordDelimiter);        this.start = cIn.getAdjustedStart();        this.end = cIn.getAdjustedEnd();        this.filePosition = cIn;      } else {        this.in = new LineReader(this.codec.createInputStream(this.fileIn, this.decompressor), job, recordDelimiter);        this.filePosition = this.fileIn;      }    } else {      this.fileIn.seek(this.start);      this.in = new LineReader(this.fileIn, job, recordDelimiter);      this.filePosition = this.fileIn;    }    if (this.start != 0L) {      this.start += this.in.readLine(new Text(), 0, maxBytesToConsume(this.start));    }    this.pos = this.start;  }  public SQPRecordReader(InputStream in, long offset, long endOffset, int maxLineLength)  {    this(in, offset, endOffset, maxLineLength, null);  }  public SQPRecordReader(InputStream in, long offset, long endOffset, int maxLineLength, byte[] recordDelimiter)  {    this.maxLineLength = maxLineLength;    this.in = new LineReader(in, recordDelimiter);    this.start = offset;    this.pos = offset;    this.end = endOffset;    this.filePosition = null;  }  public SQPRecordReader(InputStream in, long offset, long endOffset, Configuration job)    throws IOException  {    this(in, offset, endOffset, job, null);  }  public SQPRecordReader(InputStream in, long offset, long endOffset, Configuration job, byte[] recordDelimiter)    throws IOException  {    this.maxLineLength = job.getInt("mapreduce.input.linerecordreader.line.maxlength", 2147483647);    this.in = new LineReader(in, job, recordDelimiter);    this.start = offset;    this.pos = offset;    this.end = endOffset;    this.filePosition = null;  }  public LongWritable createKey() {    return new LongWritable();  }  public Text createValue() {    return new Text();  }  private boolean isCompressedInput() {    return this.codec != null;  }  private int maxBytesToConsume(long pos) {    return isCompressedInput() ? 2147483647 : (int)Math.min(2147483647L, this.end - pos);  }  private long getFilePosition()    throws IOException  {    long retVal;    if ((isCompressedInput()) && (null != this.filePosition))      retVal = this.filePosition.getPos();    else {      retVal = this.pos;    }    return retVal;  }  public synchronized boolean next(LongWritable key, Text value)    throws IOException  {    while (getFilePosition() <= this.end) {      key.set(this.pos);      int newSize = this.in.readLine(value, this.maxLineLength, Math.max(maxBytesToConsume(this.pos), this.maxLineLength));            if (newSize == 0) {        return false;      }            if (encoding.compareTo(defaultEncoding) != 0) {String str = new String(value.getBytes(), 0, value.getLength(), encoding);value.set(str);}            if (FieldSep.compareTo(defaultFSep) != 0) {String replacedValue = value.toString().replace(FieldSep, defaultFSep);value.set(replacedValue);}            this.pos += newSize;      if (newSize < this.maxLineLength) {        return true;      }      LOG.info("Skipped line of size " + newSize + " at pos " + (this.pos - newSize));    }    return false;  }  public synchronized float getProgress()    throws IOException  {    if (this.start == this.end) {      return 0.0F;    }    return Math.min(1.0F, (float)(getFilePosition() - this.start) / (float)(this.end - this.start));  }  public synchronized long getPos() throws IOException  {    return this.pos;  }  public synchronized void close() throws IOException {    try {      if (this.in != null)        this.in.close();    }    finally {      if (this.decompressor != null)        CodecPool.returnDecompressor(this.decompressor);    }  }}

4 自定义InputFormat的使用

1. 将程序打成jar包，放在/usr/lib/hive/lib和各个节点的/usr/lib/hadoop-mapreduce目录下。

在hvie的CLI命令行界面可以设置如下参数，分别修改编码格式、自定义字段分隔符和自定义换行符。

set textinputformat.record.encoding=UTF-8;//"US-ASCII""ISO-8859-1""UTF-8""UTF-16BE""UTF-16LE""UTF-16"set textinputformat.record.fieldsep=,;set textinputformat.record.linesep=|+|;

2. 建表，标示采用的Inputformat和OutputFormat，其中org.apach…noreKeyTextOutputFormat 是hive默认的OutputFormat分隔符。

create table test(id string,name string)stored asINPUTFORMAT'com.learn.util.hadoop.SQPTextInputFormat'OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'

3. Load 语句加载数据