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hive-TextInputformat自定义分隔符

2018-03-25 17:45 573 查看

hive-TextInputformat自定义分隔符

原创 2015年06月03日 11:38:273440

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 objectRow 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。[sql] view plain copytranswarp> create table test1(id int);
OK
Time taken: 0.062seconds
transwarp>describe extended test1;
OK
id                  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：[java] view plain copyinputFormat: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)[java] view plain copy//======================================================
  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。[java] view plain copy//======================================================
    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。[java] view plain copy//======================================================
    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：[java] view plain copypackage 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);
}
}

[java] view plain copypackage 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.Unstable
public 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。[sql] view plain copyset 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。[sql] view plain copycreate table test
(
id string,
name string
)
stored as
INPUTFORMAT'com.learn.util.hadoop.SQPTextInputFormat'
OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
3.      Load 语句加载数据