Hive-0.5中SerDe概述

一、背景

1、当进程在进行远程通信时，彼此可以发送各种类型的数据，无论是什么类型的数据都会以二进制序列的形式在网络上传送。发送方需要把对象转化为字节序列才可在网络上传输，称为对象序列化；接收方则需要把字节序列恢复为对象，称为对象的反序列化。

2、Hive的反序列化是对key/value反序列化成hive table的每个列的值。

3、Hive可以方便的将数据加载到表中而不需要对数据进行转换，这样在处理海量数据时可以节省大量的时间。

二、技术细节

1、SerDe是Serialize/Deserilize的简称，目的是用于序列化和反序列化。

2、用户在建表时可以用自定义的SerDe或使用Hive自带的SerDe，SerDe能为表指定列，且对列指定相应的数据。

CREATE [EXTERNAL] TABLE [IF NOT EXISTS] table_name

[(col_name data_type [COMMENT col_comment], ...)]

[COMMENT table_comment]

[PARTITIONED BY (col_name data_type

[COMMENT col_comment], ...)]

[CLUSTERED BY (col_name, col_name, ...)

[SORTED BY (col_name [ASC|DESC], ...)]

INTO num_buckets BUCKETS]

[ROW FORMAT row_format]

[STORED AS file_format]

[LOCATION hdfs_path]

创建指定SerDe表时，使用row format row_format参数，例如：

a、添加jar包。在hive客户端输入：hive>add jar /run/serde_test.jar;

或者在linux shell端执行命令：${HIVE_HOME}/bin/hive -auxpath /run/serde_test.jar

b、建表：create table serde_table row format serde 'hive.connect.TestDeserializer';

3、编写序列化类TestDeserializer。实现Deserializer接口的三个函数：

a）初始化：initialize(Configuration conf, Properties tb1)。

b）反序列化Writable类型返回Object:deserialize(Writable blob)。

c）获取deserialize(Writable blob)返回值Object的inspector:getObjectInspector()。

public interface Deserializer {
/**
* Initialize the HiveDeserializer.
* @param conf System properties
* @param tbl table properties
* @throws SerDeException
*/
public void initialize(Configuration conf, Properties tbl) throws SerDeException;
/**
* Deserialize an object out of a Writable blob.
* In most cases, the return value of this function will be constant since the function
* will reuse the returned object.
* If the client wants to keep a copy of the object, the client needs to clone the
* returned value by calling ObjectInspectorUtils.getStandardObject().
* @param blob The Writable object containing a serialized object
* @return A Java object representing the contents in the blob.
*/
public Object deserialize(Writable blob) throws SerDeException;
/**
* Get the object inspector that can be used to navigate through the internal
* structure of the Object returned from deserialize(...).
*/
public ObjectInspector getObjectInspector() throws SerDeException;
}

实现一行数据

划分成hive表的time,userid,host,path四个字段

的反序列化类。例如：

package hive.connect;

import java.net.MalformedURLException;

import java.net.URL;

import java.util.ArrayList;

import java.util.List;

import java.util.Properties;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.hive.serde2.Deserializer;

import org.apache.hadoop.hive.serde2.SerDeException;

import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;

import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory;

import org.apache.hadoop.hive.serde2.objectinspector.-

ObjectInspectorFactory.ObjectInspectorOptions;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.io.Writable;

public class TestDeserializer implements Deserializer {

private static List<String> FieldNames = new ArrayList<String>();

private static List<ObjectInspector> FieldNamesObjectInspectors = new ArrayList<ObjectInspector>();

static {

FieldNames.add("time");

FieldNamesObjectInspectors.add(ObjectInspectorFactory

.getReflectionObjectInspector(Long.class,

ObjectInspectorOptions.JAVA));

FieldNames.add("userid");

FieldNamesObjectInspectors.add(ObjectInspectorFactory

.getReflectionObjectInspector(Integer.class,

ObjectInspectorOptions.JAVA));

FieldNames.add("host");

FieldNamesObjectInspectors.add(ObjectInspectorFactory

.getReflectionObjectInspector(String.class,

ObjectInspectorOptions.JAVA));

FieldNames.add("path");

FieldNamesObjectInspectors.add(ObjectInspectorFactory

.getReflectionObjectInspector(String.class,

ObjectInspectorOptions.JAVA));

}

@Override

public Object deserialize(Writable blob) {

try {

if (blob instanceof Text) {

String line = ((Text) blob).toString();

if (line == null)

return null;

String[] field = line.split("\t");

if (field.length != 3) {

return null;

}

List<Object> result = new ArrayList<Object>();

URL url = new URL(field[2]);

Long time = Long.valueOf(field[0]);

Integer userid = Integer.valueOf(field[1]);

//简易地对一行数据进行格式转换。

result.add(time);

result.add(userid);

result.add(url.getHost());

result.add(url.getPath());

return result;

}

} catch (MalformedURLException e) {

e.printStackTrace();

}

return null;

}

@Override

public ObjectInspector getObjectInspector() throws SerDeException {

return ObjectInspectorFactory.getStandardStructObjectInspector(

            FieldNames, FieldNamesObjectInspectors);

}

@Override

public void initialize(Configuration arg0, Properties arg1)

throws SerDeException {

}

}

测试HDFS上hive表数据，如下为一条测试数据：

1234567891012 123456 http://wiki.apache.org/hadoop/Hive/LanguageManual/UDF

hive> add jar /run/jar/merg_hua.jar;

Added /run/jar/merg_hua.jar to class path

hive> create table serde_table row format serde 'hive.connect.TestDeserializer';

Found class for hive.connect.TestDeserializer

OK

Time taken: 0.028 seconds

hive> describe serde_table;

OK

time bigint from deserializer

userid int from deserializer

host string from deserializer

path string from deserializer

Time taken: 0.042 seconds

hive> select * from serde_table;

OK

1234567891012 123456 wiki.apache.org /hadoop/Hive/LanguageManual/UDF

Time taken: 0.039 seconds

三、总结

1、创建Hive表使用序列化时，需要自写一个实现Deserializer的类，并且选用create命令的row format参数。

2、在处理海量数据的时候，如果数据的格式与表结构吻合，可以用到Hive的反序列化而不需要对数据进行转换，可以节省大量的时间。