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  • hive原生和复合类型的数据加载和使用

    原生类型

    原生类型包括TINYINT,SMALLINT,INT,BIGINT,BOOLEAN,FLOAT,DOUBLE,STRING,BINARY (Hive 0.8.0以上才可用),TIMESTAMP (Hive 0.8.0以上才可用),这些数据加载很容易,只要设置好列分隔符,按照列分隔符输出到文件就可以了。

    假设有这么一张用户登陆表

    CREATE TABLE login (
      uid  BIGINT,
      ip  STRING
    )
    ROW FORMAT DELIMITED
    FIELDS TERMINATED BY ','
    STORED AS TEXTFILE;

    这表示登陆表ip字段和uid字段以分隔符','隔开。

    输出hive表对应的数据

    # printf "%s,%s\n" 3105007001 192.168.1.1 >> login.txt
    # printf "%s,%s\n" 3105007002 192.168.1.2 >> login.txt

    login.txt的内容:

    # cat login.txt                                                                                                                        
    3105007001,192.168.1.1
    3105007002,192.168.1.2

    加载数据到hive表

    LOAD DATA LOCAL INPATH '/home/hadoop/login.txt' OVERWRITE INTO TABLE login PARTITION (dt='20130101'); 

    查看数据

    select uid,ip from login where dt='20130101';
    3105007001    192.168.1.1
    3105007002    192.168.1.2

    array

    假设登陆表是

    CREATE TABLE login_array (
      ip  STRING,
      uid  array<BIGINT>
    )
    PARTITIONED BY (dt STRING)
    ROW FORMAT DELIMITED
    FIELDS TERMINATED BY ','
    COLLECTION ITEMS TERMINATED BY '|'
    STORED AS TEXTFILE;

    这表示登陆表每个ip有多个用户登陆,ip和uid字段之间使用','隔开,而uid数组之间的元素以'|'隔开。

    输出hive表对应的数据

    # printf "%s,%s|%s|%s\n" 192.168.1.1 3105007010 3105007011 3105007012 >> login_array.txt
    # printf "%s,%s|%s|%s\n" 192.168.1.2 3105007020 3105007021 3105007022 >> login_array.txt

    login_array.txt的内容:

    cat login_array.txt                                                                                                                    
    192.168.1.1,3105007010|3105007011|3105007012
    192.168.1.2,3105007020|3105007021|3105007022

    加载数据到hive表

    LOAD DATA LOCAL INPATH '/home/hadoop/login_array.txt' OVERWRITE INTO TABLE login_array PARTITION (dt='20130101'); 

    查看数据

    select ip,uid from login_array where dt='20130101';
    192.168.1.1    [3105007010,3105007011,3105007012]
    192.168.1.2    [3105007020,3105007021,3105007022]

    使用数组

    select ip,uid[0] from login_array where dt='20130101'; --使用下标访问数组
    
    select ip,size(uid) from login_array where dt='20130101'; #查看数组长度
    
    select ip from login_array where dt='20130101'  where array_contains(uid,'3105007011');#数组查找

    更多操作参考 https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF#LanguageManualUDF-CollectionFunctions

    map

    假设登陆表是

    CREATE TABLE login_map (
      ip  STRING,
      uid  STRING,
      gameinfo map<string,bigint>
    )
    PARTITIONED BY (dt STRING)
    ROW FORMAT DELIMITED
    FIELDS TERMINATED BY ','
    COLLECTION ITEMS TERMINATED BY '|'
    MAP KEYS TERMINATED BY ':'
    STORED AS TEXTFILE;

    这表示登陆表每个用户都会有游戏信息,而用户的游戏信息有多个,key是游戏名,value是游戏的积分。map中的key和value以'':"分隔,map的元素以'|'分隔。 

    输出hive表对应的数据

    # printf "%s,%s,%s:%s|%s:%s|%s:%s\n" 192.168.1.1  3105007010 wow 10 cf 1 qqgame 2  >> login_map.txt
    # printf "%s,%s,%s:%s|%s:%s|%s:%s\n" 192.168.1.2  3105007012 wow 20 cf 21 qqgame 22  >> login_map.txt

    login_map.txt的内容:

    # cat login_map.txt
    192.168.1.1,3105007010,wow:10|cf:1|qqgame:2
    192.168.1.2,3105007012,wow:20|cf:21|qqgame:22

    加载数据到hive表

    LOAD DATA LOCAL INPATH '/home/hadoop/login_map.txt' OVERWRITE INTO TABLE login_map PARTITION (dt='20130101'); 

    查看数据

    select ip,uid,gameinfo from login_map where dt='20130101';
    192.168.1.1    3105007010    {"wow":10,"cf":1,"qqgame":2}
    192.168.1.2    3105007012    {"wow":20,"cf":21,"qqgame":22}

    使用map

    select ip,uid,gameinfo['wow'] from login_map where dt='20130101'; --使用下标访问map
    
    select ip,uid,size(gameinfo) from login_map where dt='20130101'; #查看map长度
    
    select ip,uid from login_map where dt='20130101'  where array_contains(map_keys(gameinfo),'wow');#查看map的key,找出有玩wow游戏的记录

    更多操作参考 https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF#LanguageManualUDF-CollectionFunctions

    struct

    假设登陆表是

    CREATE TABLE login_struct (
      ip  STRING,
      user  struct<uid:bigint,name:string>
    )
    PARTITIONED BY (dt STRING)
    ROW FORMAT DELIMITED
    FIELDS TERMINATED BY ','
    COLLECTION ITEMS TERMINATED BY '|'
    MAP KEYS TERMINATED BY ':'
    STORED AS TEXTFILE;

    user是一个struct,分别包含用户uid和用户名。

    输出hive表对应的数据

    printf "%s,%s|%s|\n" 192.168.1.1  3105007010 blue  >> login_struct.txt
    printf "%s,%s|%s|\n" 192.168.1.2  3105007012 ggjucheng  >> login_struct.txt

    login_struct.txt的内容:

    # cat login_struct.txt
    192.168.1.1,3105007010,wow:10|cf:1|qqgame:2
    192.168.1.2,3105007012,wow:20|cf:21|qqgame:22

    加载数据到hive表

    LOAD DATA LOCAL INPATH '/home/hadoop/login_struct.txt' OVERWRITE INTO TABLE login_struct PARTITION (dt='20130101'); 

    查看数据

    select ip,user from login_struct where dt='20130101';
    192.168.1.1    {"uid":3105007010,"name":"blue"}
    192.168.1.2    {"uid":3105007012,"name":"ggjucheng"}

    使用struct

    select ip,user.uid,user.name from login_map where dt='20130101'; 

    union

    用的比较少,暂时不讲

    嵌套复合类型

    之前讲的array,map,struct这几种复合类型,里面的元素都是原生类型,如果元素是复合类型,那该怎么加载数据呢。

    假设登陆表是

    CREATE TABLE login_game_complex (
      ip STRING,
      uid STRING,
      gameinfo map<bigint,struct<name:string,score:bigint,level:string>> ) 
    PARTITIONED BY (dt STRING) 
    ROW FORMAT DELIMITED 
    STORED AS TEXTFILE;

    这表示登陆表每个用户都会有游戏信息,而用户的游戏信息有多个,key是游戏id,value是一个struct,包含游戏的名字,积分,等级。

    这种复杂类型的入库格式很麻烦,而且复合嵌套层次很多时,要生成的正确的格式也比较复杂,很容易出错。这里稍微提下,在嵌套层次多的情况下,分隔符会会随着复合类型嵌套层次的递增,分隔符默认会以\0,\1,\2....变化。

    这里不介绍从shell下生成文件load data入库,感兴趣的同学,可以看看hive的源代码的org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe的serialize方法。

    这里介绍使用另一种数据操作方式:insert,先把一个简单的表的数据,加载load到hive,再使用insert插入数据到一个嵌套复杂类型的表。

    创建简单的表

    CREATE TABLE login_game_simple (
      ip STRING,
      uid STRING,
      gameid bigint,
      gamename string,
      gamescore bigint,
      gamelevel string 
    ) 
    PARTITIONED BY (dt STRING) 
    ROW FORMAT DELIMITED 
    FIELDS TERMINATED BY ','
    STORED AS TEXTFILE;

    生成login_game_simple.txt的内容:

    192.168.1.0,3105007010,1,wow,100,v1
    192.168.1.0,3105007010,2,cf,100,v2
    192.168.1.0,3105007010,3,qqgame,100,v3
    192.168.1.2,3105007011,1,wow,101,v1
    192.168.1.2,3105007011,3,qqgame,101,v3
    192.168.1.2,3105007012,1,wow,102,v1
    192.168.1.2,3105007012,2,cf,102,v2
    192.168.1.2,3105007012,3,qqgame,102,v3

    load data到hive后,再生成复杂的gameinfo map结构,插入到表login_game_complex

    INSERT OVERWRITE TABLE login_game_complex PARTITION (dt='20130101')  
    select ip,uid,map(gameid, named_struct('name',gamename,'score',gamescore,'level',gamelevel) ) FROM login_game_simple  where dt='20130101' ;

    查询数据

    select ip,uid,gameinfo from login_game_complex where dt='20130101';
    192.168.1.0    3105007010    {1:{"name":"wow","score":100,"level":"v1"}}
    192.168.1.0    3105007010    {2:{"name":"cf","score":100,"level":"v2"}}
    192.168.1.0    3105007010    {3:{"name":"qqgame","score":100,"level":"v3"}}
    192.168.1.2    3105007011    {1:{"name":"wow","score":101,"level":"v1"}}
    192.168.1.2    3105007011    {3:{"name":"qqgame","score":101,"level":"v3"}}
    192.168.1.2    3105007012    {1:{"name":"wow","score":102,"level":"v1"}}
    192.168.1.2    3105007012    {2:{"name":"cf","score":102,"level":"v2"}}
    192.168.1.2    3105007012    {3:{"name":"qqgame","score":102,"level":"v3"}}

    这里只是演示了嵌套复杂类型的入库方式,所以这里只是例子。真正要完美入库,还是需要写一个自定义函数,根据ip和uid做group by,然后把gameinfo合并起来。hive没有这样的自定义函数,篇幅着想,不引进复杂的自定义函数编写。

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  • 原文地址:https://www.cnblogs.com/ggjucheng/p/2868941.html
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