3.hive的thriftserver服务

1.ThiftServer介绍

1. 正常的hive仅允许使用HiveQL执行查询、更新等操作，并且该方式比较笨拙单一。幸好Hive提供了轻客户端的实现，通过HiveServer或者HiveServer2，客户端可以在不启动CLI的情况下对Hive中的数据进行操作，两者都允许远程客户端使用多种编程语言如Java、Python向Hive提交请求，取回结果 使用jdbc协议连接hive的thriftserver服务器

2. 可以实现远程访问

3. 可以通过命令链接多个hive

2.ThiftServer启动

1. 启动hive的thriftserver

   #cd /soft/hive/bin/
   #./hiveserver2
   #默认启动非后台启动 需要开另外一个终端
   #端口号为10000
   

2. 使用beeline连接hiveserver2服务器，client端命令行程序

   #beeline
   Beeline version 2.1.1 by Apache Hive
   #输入要链接的服务
   beeline> !connect jdbc:hive2://localhost:10000
   SLF4J: Class path contains multiple SLF4J bindings.
   SLF4J: Found binding in [jar:file:/soft/apache-hive-2.1.1-bin/lib/log4j-slf4j-impl-2.4.1.jar!/org/slf4j/impl/StaticLoggerBinder.class]
   SLF4J: Found binding in [jar:file:/soft/hadoop-2.7.3/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
   SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
   SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]
   Connecting to jdbc:hive2://localhost:10000
   Enter username for jdbc:hive2://localhost:10000:
   Enter password for jdbc:hive2://localhost:10000:
   Connected to: Apache Hive (version 2.1.1)
   Driver: Hive JDBC (version 2.1.1)
   17/07/13 10:31:00 [main]: WARN jdbc.HiveConnection: Request to set autoCommit to false; Hive does not support autoCommit=false.
   Transaction isolation: TRANSACTION_REPEATABLE_READ
   0: jdbc:hive2://localhost:10000>
   

3.beeline的基本用法

1. 查看数据库

   0: jdbc:hive2://localhost:10000> show databases;
   +----------------+--+
   | database_name  |
   +----------------+--+
   | default        |
   | liuyao         |
   +----------------+--+
   2 rows selected (1.485 seconds)
   

2. 查看表

   0: jdbc:hive2://localhost:10000> use liuyao;
   No rows affected (0.123 seconds)
   0: jdbc:hive2://localhost:10000> show tables;
   +-----------+--+
   | tab_name  |
   +-----------+--+
   | test      |
   +-----------+--+
   1 row selected (0.283 seconds)
   或者使用
   0: jdbc:hive2://localhost:10000> !tables
   +------------+--------------+-------------+-------------+----------+-----------+-------------+------------+----------------------------+-----------------+--+
   | TABLE_CAT  | TABLE_SCHEM  | TABLE_NAME  | TABLE_TYPE  | REMARKS  | TYPE_CAT  | TYPE_SCHEM  | TYPE_NAME  | SELF_REFERENCING_COL_NAME  | REF_GENERATION  |
   +------------+--------------+-------------+-------------+----------+-----------+-------------+------------+----------------------------+-----------------+--+
   |            | liuyao       | test        | TABLE       | NULL     | NULL      | NULL        | NULL       | NULL                       | NULL            |
   +------------+--------------+-------------+-------------+----------+-----------+-------------+------------+----------------------------+-----------------+--+
   0: jdbc:hive2://localhost:10000>
   

3. 创建表

   CREATE TABLE emp0
   (
   name string,
   arr ARRAY<string>,
   stru1 STRUCT<sex:string,age:int>,
   map1 MAP<string,int>,
   map2 MAP<string,ARRAY<string>>
   )
   ;
   

4. 查看表结构

   0: jdbc:hive2://localhost:10000> desc emp0;
   +-----------+-----------------------------+----------+--+
   | col_name  |          data_type          | comment  |
   +-----------+-----------------------------+----------+--+
   | name      | string                      |          |
   | arr       | array<string>               |          |
   | stru1     | struct<sex:string,age:int>  |          |
   | map1      | map<string,int>             |          |
   | map2      | map<string,array<string>>   |          |
   +-----------+-----------------------------+----------+--+
   5 rows selected (0.296 seconds)
   

5. 删除表

   0: jdbc:hive2://localhost:10000> use default;
   No rows affected (0.092 seconds)
   0: jdbc:hive2://localhost:10000> drop table emp0;
   No rows affected (1.823 seconds)
   

4. 数据导入查询等演示

1. 生成数据

   #vim /root/hive.data
   放入以下数据
   Michael|Montreal,Toronto|Male,30|DB:80|Product:Developer^DLead
   Will|Montreal|Male,35|Perl:85|Product:Lead,Test:Lead
   Shelley|New York|Female,27|Python:80|Test:Lead,COE:Architect
   Lucy|Vancouver|Female,57|Sales:89,HR:94|Sales:Lead
   

2. 创建表

   CREATE TABLE emp1
   (
   name string,
   arr ARRAY<string>,
   stru1 STRUCT<sex:string,age:int>,
   map1 MAP<string,int>,
   map2 MAP<string,ARRAY<string>>
   )
   ROW FORMAT DELIMITED
   FIELDS TERMINATED BY '|'	       //字段分隔符
   COLLECTION ITEMS TERMINATED BY ','
   MAP KEYS TERMINATED BY ':'    //map的kv之间分隔符
   LINES TERMINATED BY '\n';    //集合元素分隔符
   

3. 导入数据

   如果数据在本地用
   0: jdbc:hive2://localhost:10000> load data local inpath '/root/hive.data' into table emp0;
   No rows affected (0.956 seconds)
   0: jdbc:hive2://localhost:10000>
   

4. 查询

   0: jdbc:hive2://localhost:10000> select * from emp0;
   +------------+-------------------------+----------------------------+-----------------------+----------------------------------------+--+
   | emp0.name  |        emp0.arr         |         emp0.stru1         |       emp0.map1       |               emp0.map2                |
   +------------+-------------------------+----------------------------+-----------------------+----------------------------------------+--+
   | Michael    | ["Montreal","Toronto"]  | {"sex":"Male","age":30}    | {"DB":80}             | {"Product":["Developer^DLead"]}        |
   | Will       | ["Montreal"]            | {"sex":"Male","age":35}    | {"Perl":85}           | {"Product":["Lead"],"Test":["Lead"]}   |
   | Shelley    | ["New York"]            | {"sex":"Female","age":27}  | {"Python":80}         | {"Test":["Lead"],"COE":["Architect"]}  |
   | Lucy       | ["Vancouver"]           | {"sex":"Female","age":57}  | {"Sales":89,"HR":94}  | {"Sales":["Lead"]}                     |
   |            | NULL                    | NULL                       | NULL                  | NULL                                   |
   +------------+-------------------------+----------------------------+-----------------------+----------------------------------------+--+
   5 rows selected (1.049 seconds)
   0: jdbc:hive2://localhost:10000> select arr[0] from emp0;
   +------------+--+
   |     c0     |
   +------------+--+
   | Montreal   |
   | Montreal   |
   | New York   |
   | Vancouver  |
   | NULL       |
   +------------+--+                                                                                                                       5 rows selected (0.656 seconds)
   0: jdbc:hive2://localhost:10000> select stru1 from emp0;
   +----------------------------+--+
   |           stru1            |
   +----------------------------+--+
   | {"sex":"Male","age":30}    |
   | {"sex":"Male","age":35}    |
   | {"sex":"Female","age":27}  |
   | {"sex":"Female","age":57}  |
   | NULL                       |
   +----------------------------+--+
   5 rows selected (0.193 seconds)
   0: jdbc:hive2://localhost:10000> select map1 from emp0;
   +-----------------------+--+
   |         map1          |
   +-----------------------+--+
   | {"DB":80}             |
   | {"Perl":85}           |
   | {"Python":80}         |
   | {"Sales":89,"HR":94}  |
   | NULL                  |
   +-----------------------+--+
   5 rows selected (0.216 seconds)
   0: jdbc:hive2://localhost:10000> select map1["DB"] from emp0;
   +-------+--+
   |  c0   |
   +-------+--+
   | 80    |
   | NULL  |
   | NULL  |
   | NULL  |
   | NULL  |
   +-------+--+
   5 rows selected (0.249 seconds)
   

5.使用api编程方式连接到thriftserver服务器

public class TestCURD {
    @Test
    public void select() throws  Exception{
        String driverClass = "org.apache.hive.jdbc.HiveDriver";
        String url = "jdbc:hive2://192.168.10.145:10000/liuyao";
        Class.forName(driverClass);
        Connection connection = DriverManager.getConnection(url);
        System.out.println(connection);
        Statement statement = connection.createStatement();
        ResultSet rs = statement.executeQuery("SELECT  * FROM emp0");
        while (rs.next()){
            int id = rs.getInt(1);
            String name = rs.getString(2);
            System.out.println(id + "," + name);
        }
        rs.close();
        connection.close();
    }
}