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 {
public void initialize(Configuration conf, Properties tbl) throws SerDeException;
public Object deserialize(Writable blob) throws SerDeException;
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]);
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。