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Hive 动态分区以及分区以及中文分区字段

Hive 动态分区

hive 提供了一个动态分区功能，其可以基于查询参数的位置去推断分区的名称，从而建立分区。

1、配置参数

1.1 主要配置参数

以下为 Hive 动态分区涉及的配置参数：

set hive.exec.dynamic.partition=true   --表示开启动态分区功能，默认是false
set hive.exec.dynamic.partition.mode=nonstrict  --表示允许所有分区都是动态的，否则必须有静态分区字段，默认strict。

1.2 调优相关参数

动态分区相关的调优参数：

set hive.exec.max.dynamic.partitions.pernode=100
--默认100，一般可以设置大一点，比如1000。表示每个maper或reducer可以允许创建的最大动态分区个数，默认是100，超出则会报错。

set hive.exec.max.dynamic.partitions =1000(默认值)
--表示一个动态分区语句可以创建的最大动态分区个数，超出报错。

set hive.exec.max.created.files =10000(默认)
--全局可以创建的最大文件个数，超出报错。

2、实现基本动态分区

2.1 准备数据

以下为准备数据的步骤，以及数据中的内容。

hive> create table user_3g (id int, name string, city string) row format delimited fields terminated by '\t';
OK
Time taken: 0.259 seconds
hive> load data local inpath '/root/book/3guser.txt' overwrite into table user_3g;
Loading data to table test.user_3g
Table test.user_3g stats: [numFiles=1, numRows=0, totalSize=127, rawDataSize=0]
OK
Time taken: 1.379 seconds
hive> select * from user_3g;
OK
1    刘备    shu
2    关羽    shu
3    张飞    shu
4    曹操    wei
5    曹丕    wei
6    孙权    wu
7    孙策    wu
8    孙尚香    wu
9    夏侯惇    wei
Time taken: 0.354 seconds, Fetched: 9 row(s)

2.2 创建分区表

以下为创建内部分区表的语句：

hive> create table user_3g_area(id int, name string) partitioned by(city string) row format delimited fields terminated by '\t';
OK
Time taken: 0.081 seconds

以下为创建外部分区表的语句：

本人这里有现成 test 目录，如果没有，记得加 - r 参数。


#创建外部表，首先创建一个数据目录
[root@hadoop ~]# hadoop fs -mkdir /test/user_xy_area_out/

#然后建表

hive> create external table user_3g_area_out (id int, name string) partitioned by (city string) row format delimited fields terminated by '\t' location '/test/user_xy_area_out';
OK
Time taken: 0.336 seconds

2.3 设置动态分区参数

这里只配置了主要的动态分区参数，其他参数，根据需要自己进行配置。

hive> set hive.exec.dynamic.partition=true;
hive> set hive.exec.dynamic.partition.mode=nonstrict;

2.4 开始导入数据

内部分区表数据导入：

hive> insert into table user_3g_area partition(city) select id, name, city from user_3g;
Query ID = root_20200614162430_25d9fa2d-7811-484a-bb52-efa9dae1fa72
Total jobs = 3
Launching Job 1 out of 3
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1592121918794_0001, Tracking URL = http://hadoop:8088/proxy/application_1592121918794_0001/
Kill Command = /home/software/hadoop-2.7.1/bin/hadoop job  -kill job_1592121918794_0001
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2020-06-14 16:24:44,429 Stage-1 map = 0%,  reduce = 0%
2020-06-14 16:24:52,638 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.19 sec
MapReduce Total cumulative CPU time: 1 seconds 190 msec
Ended Job = job_1592121918794_0001
Stage-4 is selected by condition resolver.
Stage-3 is filtered out by condition resolver.
Stage-5 is filtered out by condition resolver.
Moving data to: hdfs://hadoop:9000/user/hive/warehouse/test.db/user_3g_area/.hive-staging_hive_2020-06-14_16-24-30_674_3273172977143207137-1/-ext-10000
Loading data to table test.user_3g_area partition (city=null)
     Time taken for load dynamic partitions : 451
    Loading partition {city=wu}
    Loading partition {city=shu}
    Loading partition {city=wei}
     Time taken for adding to write entity : 7
Partition test.user_3g_area{city=shu} stats: [numFiles=1, numRows=3, totalSize=27, rawDataSize=24]
Partition test.user_3g_area{city=wei} stats: [numFiles=1, numRows=3, totalSize=30, rawDataSize=27]
Partition test.user_3g_area{city=wu} stats: [numFiles=1, numRows=3, totalSize=30, rawDataSize=27]
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 1   Cumulative CPU: 1.19 sec   HDFS Read: 3768 HDFS Write: 258 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 190 msec
OK
Time taken: 25.395 seconds

外部分区表数据导入：

hive> insert into table user_3g_area_out partition(city) select id,name,city from user_3g;
Query ID = root_20200615232438_f6a9d195-c416-4d5e-bd31-b96fb9c0133e
Total jobs = 3
Launching Job 1 out of 3
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1592152196890_0001, Tracking URL = http://hadoop:8088/proxy/application_1592152196890_0001/
Kill Command = /home/software/hadoop-2.7.1/bin/hadoop job  -kill job_1592152196890_0001
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2020-06-15 23:24:53,191 Stage-1 map = 0%,  reduce = 0%
2020-06-15 23:25:02,148 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.19 sec
MapReduce Total cumulative CPU time: 1 seconds 190 msec
Ended Job = job_1592152196890_0001
Stage-4 is selected by condition resolver.
Stage-3 is filtered out by condition resolver.
Stage-5 is filtered out by condition resolver.
Moving data to: hdfs://hadoop:9000/test/user_xy_area_out/.hive-staging_hive_2020-06-15_23-24-38_208_8331054339743666004-1/-ext-10000
Loading data to table test.user_3g_area_out partition (city=null)
     Time taken for load dynamic partitions : 466
    Loading partition {city=wu}
    Loading partition {city=wei}
    Loading partition {city=shu}
     Time taken for adding to write entity : 8
Partition test.user_3g_area_out{city=shu} stats: [numFiles=1, numRows=3, totalSize=27, rawDataSize=24]
Partition test.user_3g_area_out{city=wei} stats: [numFiles=1, numRows=3, totalSize=30, rawDataSize=27]
Partition test.user_3g_area_out{city=wu} stats: [numFiles=1, numRows=3, totalSize=30, rawDataSize=27]
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1   Cumulative CPU: 1.19 sec   HDFS Read: 3718 HDFS Write: 270 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 190 msec
OK
Time taken: 26.51 seconds

2.5 查看分区

查看内部分区表分区：

hive> show partitions user_3g_area;
OK
city=shu
city=wei
city=wu
Time taken: 0.6 seconds, Fetched: 3 row(s)

查看外部分区表分区：

#查看分区：
hive> show partitions user_3g_area_out;
OK
city=shu
city=wei
city=wu
Time taken: 0.138 seconds, Fetched: 3 row(s)

#查看数据目录
[root@hadoop ~]# hadoop fs -ls /test/user_xy_area_out/
20/06/15 23:29:13 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Found 3 items
drwxr-xr-x   - root supergroup          0 2020-06-15 23:25 /test/user_xy_area_out/city=shu
drwxr-xr-x   - root supergroup          0 2020-06-15 23:25 /test/user_xy_area_out/city=wei
drwxr-xr-x   - root supergroup          0 2020-06-15 23:25 /test/user_xy_area_out/city=wu

经过上面的演示可以看出，动态分区的内部表和外部表的实现过程几乎是一样的，不一样的地方就是数据存储的位置。

为什么说这个是基础的，因为这个分区是英文字符的，下面实现以下中文字符的。

注意： 使用 insert...select 往表中导入数据时，查询的字段个数必须和目标的字段个数相同，不能多，也不能少，否则会报错。但是如果字段的类型不一致的话，则会使用 null 值填充，不会报错。 使用 load data 形式往 hive 表中装载数据时，则不会检查。如果字段多了则会丢弃，少了则会 null 值填充。同样如果字段类型不一致，也是使用 null 值填充。

3、实现中文动态分区

实现包含中文的动态分区和英文的动态分区是一样的步骤，不一样的地方在于 Hive 和 MySQL 的支持，以及乱码问题。

3.1 MySQL

在 MySQL5.7 版本之前的 MySQL 数据库支持不了 Hive 元数据超长字符的索引字段，所以在 MySQL5.7 之前版本的 MySQL 中的 Hive 元数据库默认使用的是 latin1 字符集，元数据信息存储就不支持中文。

如果强行更改 MySQL5.7 版本之前的 MySQL 数据库的 Hive 元数据库的字符集或者更改其中某些表的字段的字符集的话，会出现下列错误：

Specified key was too long; max key length is 767 bytes

所以字符类型不能更改是因为字段长度问题，也可以先进行字段长度的调整，在进行字符集的更改。这个方法这里不做赘述。

当要在 Hive 的元数据信息中出现中文内容时，首先需要保证 MySQL 数据库使用 5.7 以上版本，因为 MySQL5.7 支持最大字符长度做了调整。能够达到 Hive 的需求。

如果安装了 MySQL5.7 之前的版本，考虑数据问题，就升级数据库版本到 5.7。数据库升级请参考：MySQL5.6.29 升级到 MySQL5.7.29

如果是新的环境直接安装 5.7 以上版本即可，安装 Hive，请参考：HIVE 简介。

如下是数据库 Hive 库涉及的更改字符集的命令：

#修改数据库编码
alter database hive default character set utf8;
#修改表的编码
alter table BUCKETING_COLS default character set utf8;
alter table CDS default character set utf8;
alter table COLUMNS_V2 default character set utf8;
alter table DATABASE_PARAMS default character set utf8;
alter table DBS default character set utf8;
alter table FUNCS default character set utf8;
alter table FUNC_RU default character set utf8;
alter table GLOBAL_PRIVS default character set utf8;
alter table PARTITIONS default character set utf8;
alter table PARTITION_KEYS default character set utf8;
alter table PARTITION_KEY_VALS default character set utf8;
alter table PARTITION_PARAMS default character set utf8;
alter table PART_COL_STATS default character set utf8;
alter table ROLES default character set utf8;
alter table SDS default character set utf8;
alter table SD_PARAMS default character set utf8;
alter table SEQUENCE_TABLE default character set utf8;
alter table SERDES default character set utf8;
alter table SERDE_PARAMS default character set utf8;
alter table SKEWED_COL_NAMES default character set utf8;
alter table SKEWED_COL_VALUE_LOC_MAP default character set utf8;
alter table SKEWED_STRING_LIST default character set utf8;
alter table SKEWED_STRING_LIST_VALUES default character set utf8;
alter table SKEWED_VALUES default character set utf8;
alter table SORT_COLS default character set utf8;
alter table TABLE_PARAMS default character set utf8;
alter table TAB_COL_STATS default character set utf8;
alter table TBLS default character set utf8;
alter table VERSION default character set utf8;
#修改列编码
alter table BUCKETING_COLS convert to character set utf8;
alter table CDS convert to character set utf8;
alter table COLUMNS_V2 convert to character set utf8;
alter table DATABASE_PARAMS convert to character set utf8;
alter table DBS convert to character set utf8;
alter table FUNCS convert to character set utf8;
alter table FUNC_RU convert to character set utf8;
alter table GLOBAL_PRIVS convert to character set utf8;
alter table PARTITIONS convert to character set utf8;
alter table PARTITION_KEYS convert to character set utf8;
alter table PARTITION_KEY_VALS convert to character set utf8;
alter table PARTITION_PARAMS convert to character set utf8;
alter table PART_COL_STATS convert to character set utf8;
alter table ROLES convert to character set utf8;
alter table SDS convert to character set utf8;
alter table SD_PARAMS convert to character set utf8;
alter table SEQUENCE_TABLE convert to character set utf8;
alter table SERDES convert to character set utf8;
alter table SERDE_PARAMS convert to character set utf8;
alter table SKEWED_COL_NAMES convert to character set utf8;
alter table SKEWED_COL_VALUE_LOC_MAP convert to character set utf8;
alter table SKEWED_STRING_LIST convert to character set utf8;
alter table SKEWED_STRING_LIST_VALUES convert to character set utf8;
alter table SKEWED_VALUES convert to character set utf8;
alter table SORT_COLS convert to character set utf8;
alter table TABLE_PARAMS convert to character set utf8;
alter table TAB_COL_STATS convert to character set utf8;
alter table TBLS convert to character set utf8;
alter table VERSION convert to character set utf8;
alter table PART_COL_STATS convert to character set utf8;
#修改数据库属性，如果数据库默认设置不是UTF8可执行。
SET character_set_client = utf8 ;
SET character_set_connection = utf8; 
SET character_set_database = utf8; 
SET character_set_results = utf8; 
SET character_set_server = utf8; 
SET collation_connection = utf8; 
SET collation_database = utf8; 
SET collation_server = utf8; 
SET NAMES 'utf8';

3.2 实现

以上都准备好了，就可以进行下面的步骤了：

#准备数据
hive> create table user_xy(id int, name string, city string) row format delimited fields terminated by '\t';
OK
Time taken: 0.102 seconds
hive> load data local inpath '/root/book/user.txt' overwrite into table user_xy;
Loading data to table test.user_xy
Table test.user_xy stats: [numFiles=1, numRows=0, totalSize=232, rawDataSize=0]
OK
Time taken: 0.33 seconds
hive> select * from user_xy;
OK
1    孙悟空    花果山
2    猪八戒    高老庄
3    沙和尚    流沙河
4    唐玄奘    西安
5    小白龙    清潭涧
6    唐玄宗    西安
7    观世音    南海
8    玉皇大帝    凌霄宝殿
9    黄风怪    小雷音寺
10    如来佛祖    雷音寺
Time taken: 0.094 seconds, Fetched: 10 row(s)
#设置参数
hive> set hive.exec.dynamic.partition=true;
hive> set hive.exec.dynamic.partition.mode=nonstrict;
#开始动态分区导入数据
hive> insert into table user_xy_area partition(city) select id,name,city from user_xy;
Query ID = root_20200614163041_ff9e15f6-ce41-4be2-bb86-ea569cab795e
Total jobs = 3
Launching Job 1 out of 3
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1592121918794_0003, Tracking URL = http://hadoop:8088/proxy/application_1592121918794_0003/
Kill Command = /home/software/hadoop-2.7.1/bin/hadoop job  -kill job_1592121918794_0003
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2020-06-14 16:30:49,649 Stage-1 map = 0%,  reduce = 0%
2020-06-14 16:30:57,202 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.26 sec
MapReduce Total cumulative CPU time: 1 seconds 260 msec
Ended Job = job_1592121918794_0003
Stage-4 is selected by condition resolver.
Stage-3 is filtered out by condition resolver.
Stage-5 is filtered out by condition resolver.
Moving data to: hdfs://hadoop:9000/user/hive/warehouse/test.db/user_xy_area/.hive-staging_hive_2020-06-14_16-30-41_545_7128885006398250032-1/-ext-10000
Loading data to table test.user_xy_area partition (city=null)
     Time taken for load dynamic partitions : 793
    Loading partition {city=花果山}
    Loading partition {city=凌霄宝殿}
    Loading partition {city=雷音寺}
    Loading partition {city=南海}
    Loading partition {city=清潭涧}
    Loading partition {city=高老庄}
    Loading partition {city=流沙河}
    Loading partition {city=西安}
    Loading partition {city=小雷音寺}
     Time taken for adding to write entity : 1
Partition test.user_xy_area{city=凌霄宝殿} stats: [numFiles=1, numRows=1, totalSize=15, rawDataSize=14]
Partition test.user_xy_area{city=南海} stats: [numFiles=1, numRows=1, totalSize=12, rawDataSize=11]
Partition test.user_xy_area{city=小雷音寺} stats: [numFiles=1, numRows=1, totalSize=12, rawDataSize=11]
Partition test.user_xy_area{city=流沙河} stats: [numFiles=1, numRows=1, totalSize=12, rawDataSize=11]
Partition test.user_xy_area{city=清潭涧} stats: [numFiles=1, numRows=1, totalSize=12, rawDataSize=11]
Partition test.user_xy_area{city=花果山} stats: [numFiles=1, numRows=1, totalSize=12, rawDataSize=11]
Partition test.user_xy_area{city=西安} stats: [numFiles=1, numRows=2, totalSize=24, rawDataSize=22]
Partition test.user_xy_area{city=雷音寺} stats: [numFiles=1, numRows=1, totalSize=16, rawDataSize=15]
Partition test.user_xy_area{city=高老庄} stats: [numFiles=1, numRows=1, totalSize=12, rawDataSize=11]
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1   Cumulative CPU: 1.26 sec   HDFS Read: 4053 HDFS Write: 632 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 260 msec
OK
Time taken: 19.53 seconds

#查看数据，没问题
hive> select * from user_xy_area;
OK
8    玉皇大帝    凌霄宝殿
7    观世音    南海
9    黄风怪    小雷音寺
3    沙和尚    流沙河
5    小白龙    清潭涧
1    孙悟空    花果山
4    唐玄奘    西安
6    唐玄宗    西安
10    如来佛祖    雷音寺
2    猪八戒    高老庄
Time taken: 0.242 seconds, Fetched: 10 row(s)

3.3 乱码问题

查看分区：

hive> show partitions user_xy_area;
OK
city=� ��
city=Ww
city=���
city=A��
city=m�
city=��q
city�
city=���
city=� �
Time taken: 0.135 seconds, Fetched: 9 row(s)

乱码了，崩溃不？坑一个接一个！

那么修改一下配置文件：hive-site-xml

[root@hadoop conf]# vim hive-site.xml
<configuration>
<property>
<name>javax.jdo.option.ConnectionURL</name>
<value>jdbc:mysql://hadoop:3306/hive?createDatabaseIfNotExist=true&amp;useUnicode=true&amp;characterEncoding=UTF-8</value>
</property>

<property>
<name>javax.jdo.option.ConnectionDriverName</name>
<value>com.mysql.jdbc.Driver</value>
</property>

<property>
<name>javax.jdo.option.ConnectionUserName</name>
<value>root</value>
</property>

<property>
<name>javax.jdo.option.ConnectionPassword</name>
<value>root</value>
</property>
</configuration>

在配置中添加的是如下这一段：

&amp;useUnicode=true&amp;characterEncoding=UTF-8

注意分隔符，在 xml 文件中 & 符号需要转义。

如果没有转义，就会出现如下错误：

[root@hadoop ~]# hive
[Fatal Error] hive-site.xml:21:105: The reference to entity "characterEncoding" must end with the ';' delimiter.

所有都搞完，重启之后，结果如下：

hive> show partitions user_xy_area;
OK
city=� ��
city=Ww
city=���
city=A��
city=m�
city=��q
city�
city=���
city=� �
Time taken: 0.135 seconds, Fetched: 9 row(s)

还是乱码，这个问题，本人暂时没能解决！哪位大神有解决方案，麻烦告知一下，谢谢！

标签： hive hadoop 数据仓库
本文转载自: https://blog.csdn.net/m0_73098549/article/details/131070542
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