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。