資訊專欄INFORMATION COLUMN
摘要：示例本地創建測試文件放入中此時在表中就可以查看到數據注意如果刪除外部表，里的文件并不會刪除也就是如果包刪除，下文件并不會被刪除。示例分組聚合構建測試數據建分區表，導入數據查看數據查看表分區每一行的變成大寫針對每一行進行運算
文章作者：foochane?
原文鏈接：https://foochane.cn/article/2019062501.html
Hive是一個可以將SQL翻譯為MR程序的工具，支持用戶將HDFS上的文件映射為表結構，然后用戶就可以輸入SQL對這些表（HDFS上的文件）進行查詢分析。Hive將用戶定義的庫、表結構等信息存儲hive的元數據庫（可以是本地derby，也可以是遠程mysql）中。
1.2 Hive的用途做數據分析，不用自己寫大量的MR程序，只需要寫SQL腳本即可
用于構建大數據體系下的數據倉庫
hive 2 以后 把底層引擎從MapReduce換成了Spark
啟動hive前要先啟動hdfs 和yarn
2 使用方式 2.1 方式1：直接使用hive服務端輸入命令 $ hive即可：
hadoop@Master:~$ hive SLF4J: Class path contains multiple SLF4J bindings. SLF4J: Found binding in [jar:file:/usr/local/bigdata/hive-2.3.5/lib/log4j-slf4j-impl-2.6.2.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: Found binding in [jar:file:/usr/local/bigdata/hadoop-2.7.1/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] Logging initialized using configuration in file:/usr/local/bigdata/hive-2.3.5/conf/hive-log4j2.properties Async: true Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases. hive>show databases; OK dbtest default Time taken: 3.539 seconds, Fetched: 2 row(s) hive>
技巧：
讓提示符顯示當前庫：
hive>set hive.cli.print.current.db=true;
顯示查詢結果是顯示自帶名稱：
hive>set hive.cli.print.header=true;
這樣設置只是對當前窗口有效，永久生效可以在當前用戶目錄下建一個.hiverc文件。
加入如下內容：
set hive.cli.print.current.db=true; set hive.cli.print.header=true;2.2 方式2：使用beeline客戶端
將hive啟動為一個服務端，然后可以在任意一臺機器上使用beeline客戶端連接hive服務，進行交互式查詢
hive是一個單機的服務端可以在任何一臺機器里安裝，它訪問的是hdfs集群。
啟動hive服務 ：
$ nohup hiveserver2 1>/dev/null 2>&1 &
啟動后，可以用beeline去連接，beeline是一個客戶端，可以在任意機器啟動,只要能夠跟hive服務端相連即可。
在本地啟動beeline
$ beeline -u jdbc:hive2://localhost:10000 -n hadoop -p hadoop
在啟動機器上啟動beeline
$ beeline -u jdbc:hive2://Master:10000 -n hadoop -p hadoop
示例：
hadoop@Master:~$ beeline -u jdbc:hive2://Master:10000 -n hadoop -p hadoop Connecting to jdbc:hive2://Master:10000 19/06/25 01:50:12 INFO jdbc.Utils: Supplied authorities: Master:10000 19/06/25 01:50:12 INFO jdbc.Utils: Resolved authority: Master:10000 19/06/25 01:50:13 INFO jdbc.HiveConnection: Will try to open client transport with JDBC Uri: jdbc:hive2://Master:10000 Connected to: Apache Hive (version 2.3.5) Driver: Hive JDBC (version 1.2.1.spark2) Transaction isolation: TRANSACTION_REPEATABLE_READ Beeline version 1.2.1.spark2 by Apache Hive 0: jdbc:hive2://Master:10000>
u ：指定連接方式
n ：登錄的用戶（系統用戶）
p ：用戶密碼
errorMessage:Failed to open new session: java.lang.RuntimeException: org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.security.authorize.AuthorizationException): User: hadoop is not allowed to impersonate hadoop), serverProtocolVersion:null)
在 hadoop配置文件中的core-site.xml 文件中添加如下內容,然后重啟hadoop集群：
2.3 方式3：使用hive命令運行sql? ? ? ? ?hadoop.proxyuser.hadoop.groups ? ? ?hadoop ? ? ?Allow the superuser oozie to impersonate any members of the group group1 and group2 ?? ? ? hadoop.proxyuser.hadoop.hosts ? ? ?Master,127.0.0.1,localhost ? ? ?The superuser can connect only from host1 and host2 to impersonate a user ?
接用 hive -e 在命令行中運行sql命令，該命令可以一起運行多條sql語句，用;隔開。
hive -e "sql1;sql2;sql3;sql4"
另外，還可以使用 hive -f命令。
事先將sql語句寫入一個文件比如 q.hql ，然后用hive -f命令執行：
bin/hive -f q.hql2.4 方式4：寫腳本
可以將方式3寫入一個xxx.sh腳本中,然后運行該腳本。
3 表的基本操作 3.1 新建數據庫create database db1;
示例：
0: jdbc:hive2://Master:10000> create database db1; No rows affected (1.123 seconds) 0: jdbc:hive2://Master:10000> show databases; +----------------+--+ | database_name | +----------------+--+ | db1 | | dbtest | | default | +----------------+--+
成功后，hive就會在/user/hive/warehouse/下建一個文件夾： db1.db
3.2 刪除數據庫drop database db1;
示例：
0: jdbc:hive2://Master:10000> drop database db1; No rows affected (0.969 seconds) 0: jdbc:hive2://Master:10000> show databases; +----------------+--+ | database_name | +----------------+--+ | dbtest | | default | +----------------+--+3.3 建內部表
use db1; create table t_test(id int,name string,age int) row format delimited fields terminated by ",";
示例：
0: jdbc:hive2://Master:10000> use db1; No rows affected (0.293 seconds) 0: jdbc:hive2://Master:10000> create table t_test(id int,name string,age int) 0: jdbc:hive2://Master:10000> row format delimited 0: jdbc:hive2://Master:10000> fields terminated by ","; No rows affected (1.894 seconds) 0: jdbc:hive2://Master:10000> desc db1.t_test; +-----------+------------+----------+--+ | col_name | data_type | comment | +-----------+------------+----------+--+ | id | int | | | name | string | | | age | int | | +-----------+------------+----------+--+ 3 rows selected (0.697 seconds)
建表后，hive會在倉庫目錄中建一個表目錄： /user/hive/warehouse/db1.db/t_test
3.4 建外部表create external table t_test1(id int,name string,age int) row format delimited fields terminated by "," location "/user/hive/external/t_test1";
這里的location指的是hdfs上的目錄，可以直接在該目錄下放入相應格式的文件，就可以在hive表中查看到。
示例：
0: jdbc:hive2://Master:10000> create external table t_test1(id int,name string,age int) 0: jdbc:hive2://Master:10000> row format delimited 0: jdbc:hive2://Master:10000> fields terminated by "," 0: jdbc:hive2://Master:10000> location "/user/hive/external/t_test1"; No rows affected (0.7 seconds) 0: jdbc:hive2://Master:10000> desc db1.t_test1; +-----------+------------+----------+--+ | col_name | data_type | comment | +-----------+------------+----------+--+ | id | int | | | name | string | | | age | int | | +-----------+------------+----------+--+ 3 rows selected (0.395 seconds)
本地創建測試文件user.data
1,xiaowang,28 2,xiaoli,18 3,xiaohong,23
放入hdfs中：
$ hdfs dfs -mkdir -p /user/hive/external/t_test1 $ hdfs dfs -put ./user.data /user/hive/external/t_test1
此時在hive表中就可以查看到數據：
0: jdbc:hive2://Master:10000> select * from db1.t_test1; +-------------+---------------+--------------+--+ | t_test1.id | t_test1.name | t_test1.age | +-------------+---------------+--------------+--+ | 1 | xiaowang | 28 | | 2 | xiaoli | 18 | | 3 | xiaohong | 23 | +-------------+---------------+--------------+--+ 3 rows selected (8 seconds)
注意：如果刪除外部表，hdfs里的文件并不會刪除
也就是如果包db1.t_test1刪除，hdfs下/user/hive/external/t_test1/user.data文件并不會被刪除。
3.5 導入數據本質上就是把數據文件放入表目錄；
可以用hive命令來做：
load data [local] inpath "/data/path" [overwrite] into table t_test;
加local代表導入本地數據。
導入本地數據
load data local inpath "/home/hadoop/user.data" into table t_test;
示例：
0: jdbc:hive2://Master:10000> load data local inpath "/home/hadoop/user.data" into table t_test; No rows affected (2.06 seconds) 0: jdbc:hive2://Master:10000> select * from db1.t_test; +------------+--------------+-------------+--+ | t_test.id | t_test.name | t_test.age | +------------+--------------+-------------+--+ | 1 | xiaowang | 28 | | 2 | xiaoli | 18 | | 3 | xiaohong | 23 | +------------+--------------+-------------+--+
導入hdfs中的數據
load data inpath "/user/hive/external/t_test1/user.data" into table t_test;
示例：
0: jdbc:hive2://Master:10000> load data inpath "/user/hive/external/t_test1/user.data" into table t_test; No rows affected (1.399 seconds) 0: jdbc:hive2://Master:10000> select * from db1.t_test; +------------+--------------+-------------+--+ | t_test.id | t_test.name | t_test.age | +------------+--------------+-------------+--+ | 1 | xiaowang | 28 | | 2 | xiaoli | 18 | | 3 | xiaohong | 23 | | 1 | xiaowang | 28 | | 2 | xiaoli | 18 | | 3 | xiaohong | 23 | +------------+--------------+-------------+--+ 6 rows selected (0.554 seconds)
注意：從本地導入數據，本地數據不是發生變化，從hdfs中導入數據，hdfs中的導入的文件會被移動到數據倉庫相應的目錄下
3.6 建分區表分區的意義在于可以將數據分子目錄存儲，以便于查詢時讓數據讀取范圍更精準
create table t_test1(id int,name string,age int,create_time bigint) partitioned by (day string,country string) row format delimited fields terminated by ",";
插入數據到指定分區：
> load data [local] inpath "/data/path1" [overwrite] into table t_test partition(day="2019-06-04",country="China"); > load data [local] inpath "/data/path2" [overwrite] into table t_test partition(day="2019-06-05",country="China"); > load data [local] inpath "/data/path3" [overwrite] into table t_test partition(day="2019-06-04",country="England");
導入完成后，形成的目錄結構如下：
/user/hive/warehouse/db1.db/t_test1/day=2019-06-04/country=China/... /user/hive/warehouse/db1.db/t_test1/day=2019-06-04/country=England/... /user/hive/warehouse/db1.db/t_test1/day=2019-06-05/country=China/...4 查詢語法 4.1 條件查詢
select * from t_table where a<1000 and b>0;4.2 join關聯查詢
各類join
測試數據：
a.txt：
a,1 b,2 c,3 d,4
b.txt:
b,16 c,17 d,18 e,19
建表導入數據：
create table t_a(name string,num int) row format delimited fields terminated by ","; create table t_b(name string,age int) row format delimited fields terminated by ","; load data local inpath "/home/hadoop/a.txt" into table t_a; load data local inpath "/home/hadoop/b.txt" into table t_b;
表數據如下：
0: jdbc:hive2://Master:10000> select * from t_a; +-----------+----------+--+ | t_a.name | t_a.num | +-----------+----------+--+ | a | 1 | | b | 2 | | c | 3 | | d | 4 | +-----------+----------+--+ 4 rows selected (0.523 seconds) 0: jdbc:hive2://Master:10000> select * from t_b; +-----------+----------+--+ | t_b.name | t_b.age | +-----------+----------+--+ | b | 16 | | c | 17 | | d | 18 | | e | 19 | +-----------+----------+--+ 4 rows selected (0.482 seconds)4.3 內連接
指定join條件
select a.*,b.* from t_a a join t_b b on a.name=b.name;
示例：
0: jdbc:hive2://Master:10000> select a.*,b.* 0: jdbc:hive2://Master:10000> from 0: jdbc:hive2://Master:10000> t_a a join t_b b on a.name=b.name; .... +---------+--------+---------+--------+--+ | a.name | a.num | b.name | b.age | +---------+--------+---------+--------+--+ | b | 2 | b | 16 | | c | 3 | c | 17 | | d | 4 | d | 18 | +---------+--------+---------+--------+--+4.4 左外連接（左連接）
select a.*,b.* from t_a a left outer join t_b b on a.name=b.name;
示例：
0: jdbc:hive2://Master:10000> select a.*,b.* 0: jdbc:hive2://Master:10000> from 0: jdbc:hive2://Master:10000> t_a a left outer join t_b b on a.name=b.name; ... +---------+--------+---------+--------+--+ | a.name | a.num | b.name | b.age | +---------+--------+---------+--------+--+ | a | 1 | NULL | NULL | | b | 2 | b | 16 | | c | 3 | c | 17 | | d | 4 | d | 18 | +---------+--------+---------+--------+--+4.5 右外連接（右連接）
select a.*,b.* from t_a a right outer join t_b b on a.name=b.name;
示例：
0: jdbc:hive2://Master:10000> select a.*,b.* 0: jdbc:hive2://Master:10000> from 0: jdbc:hive2://Master:10000> t_a a right outer join t_b b on a.name=b.name; .... +---------+--------+---------+--------+--+ | a.name | a.num | b.name | b.age | +---------+--------+---------+--------+--+ | b | 2 | b | 16 | | c | 3 | c | 17 | | d | 4 | d | 18 | | NULL | NULL | e | 19 | +---------+--------+---------+--------+--+4.6 全外連接
select a.*,b.* from t_a a full outer join t_b b on a.name=b.name;
示例：
0: jdbc:hive2://Master:10000> select a.*,b.* 0: jdbc:hive2://Master:10000> from 0: jdbc:hive2://Master:10000> t_a a full outer join t_b b on a.name=b.name; WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases. +---------+--------+---------+--------+--+ | a.name | a.num | b.name | b.age | +---------+--------+---------+--------+--+ | a | 1 | NULL | NULL | | b | 2 | b | 16 | | c | 3 | c | 17 | | d | 4 | d | 18 | | NULL | NULL | e | 19 | +---------+--------+---------+--------+--+4.7 左半連接
求存在于a表，且b表里也存在的數據。
select a.* from t_a a left semi join t_b b on a.name=b.name;
示例：
0: jdbc:hive2://Master:10000> select a.* 0: jdbc:hive2://Master:10000> from 0: jdbc:hive2://Master:10000> t_a a left semi join t_b b on a.name=b.name; ..... +---------+--------+--+ | a.name | a.num | +---------+--------+--+ | b | 2 | | c | 3 | | d | 4 | +---------+--------+--+4.8 group by分組聚合
構建測試數據
192.168.33.3,http://www.xxx.cn/stu,2019-08-04 15:30:20 192.168.33.3,http://www.xxx.cn/teach,2019-08-04 15:35:20 192.168.33.4,http://www.xxx.cn/stu,2019-08-04 15:30:20 192.168.33.4,http://www.xxx.cn/job,2019-08-04 16:30:20 192.168.33.5,http://www.xxx.cn/job,2019-08-04 15:40:20 192.168.33.3,http://www.xxx.cn/stu,2019-08-05 15:30:20 192.168.44.3,http://www.xxx.cn/teach,2019-08-05 15:35:20 192.168.33.44,http://www.xxx.cn/stu,2019-08-05 15:30:20 192.168.33.46,http://www.xxx.cn/job,2019-08-05 16:30:20 192.168.33.55,http://www.xxx.cn/job,2019-08-05 15:40:20 192.168.133.3,http://www.xxx.cn/register,2019-08-06 15:30:20 192.168.111.3,http://www.xxx.cn/register,2019-08-06 15:35:20 192.168.34.44,http://www.xxx.cn/pay,2019-08-06 15:30:20 192.168.33.46,http://www.xxx.cn/excersize,2019-08-06 16:30:20 192.168.33.55,http://www.xxx.cn/job,2019-08-06 15:40:20 192.168.33.46,http://www.xxx.cn/excersize,2019-08-06 16:30:20 192.168.33.25,http://www.xxx.cn/job,2019-08-06 15:40:20 192.168.33.36,http://www.xxx.cn/excersize,2019-08-06 16:30:20 192.168.33.55,http://www.xxx.cn/job,2019-08-06 15:40:20
建分區表，導入數據：
create table t_pv(ip string,url string,time string) partitioned by (dt string) row format delimited fields terminated by ","; load data local inpath "/home/hadoop/pv.log.0804" into table t_pv partition(dt="2019-08-04"); load data local inpath "/home/hadoop/pv.log.0805" into table t_pv partition(dt="2019-08-05"); load data local inpath "/home/hadoop/pv.log.0806" into table t_pv partition(dt="2019-08-06");
查看數據：
0: jdbc:hive2://Master:10000> select * from t_pv; +----------------+------------------------------+----------------------+-------------+--+ | t_pv.ip | t_pv.url | t_pv.time | t_pv.dt | +----------------+------------------------------+----------------------+-------------+--+ | 192.168.33.3 | http://www.xxx.cn/stu | 2019-08-04 15:30:20 | 2019-08-04 | | 192.168.33.3 | http://www.xxx.cn/teach | 2019-08-04 15:35:20 | 2019-08-04 | | 192.168.33.4 | http://www.xxx.cn/stu | 2019-08-04 15:30:20 | 2019-08-04 | | 192.168.33.4 | http://www.xxx.cn/job | 2019-08-04 16:30:20 | 2019-08-04 | | 192.168.33.5 | http://www.xxx.cn/job | 2019-08-04 15:40:20 | 2019-08-05 | | 192.168.33.3 | http://www.xxx.cn/stu | 2019-08-05 15:30:20 | 2019-08-05 | | 192.168.44.3 | http://www.xxx.cn/teach | 2019-08-05 15:35:20 | 2019-08-05 | | 192.168.33.44 | http://www.xxx.cn/stu | 2019-08-05 15:30:20 | 2019-08-05 | | 192.168.33.46 | http://www.xxx.cn/job | 2019-08-05 16:30:20 | 2019-08-05 | | 192.168.33.55 | http://www.xxx.cn/job | 2019-08-05 15:40:20 | 2019-08-06 | | 192.168.133.3 | http://www.xxx.cn/register | 2019-08-06 15:30:20 | 2019-08-06 | | 192.168.111.3 | http://www.xxx.cn/register | 2019-08-06 15:35:20 | 2019-08-06 | | 192.168.34.44 | http://www.xxx.cn/pay | 2019-08-06 15:30:20 | 2019-08-06 | | 192.168.33.46 | http://www.xxx.cn/excersize | 2019-08-06 16:30:20 | 2019-08-06 | | 192.168.33.55 | http://www.xxx.cn/job | 2019-08-06 15:40:20 | 2019-08-06 | | 192.168.33.46 | http://www.xxx.cn/excersize | 2019-08-06 16:30:20 | 2019-08-06 | | 192.168.33.25 | http://www.xxx.cn/job | 2019-08-06 15:40:20 | 2019-08-06 | | 192.168.33.36 | http://www.xxx.cn/excersize | 2019-08-06 16:30:20 | 2019-08-06 | | 192.168.33.55 | http://www.xxx.cn/job | 2019-08-06 15:40:20 | 2019-08-06 | +----------------+------------------------------+----------------------+-------------+--+
查看表分區：
show partitions t_pv;
0: jdbc:hive2://Master:10000> show partitions t_pv; +----------------+--+ | partition | +----------------+--+ | dt=2019-08-04 | | dt=2019-08-05 | | dt=2019-08-06 | +----------------+--+ 3 rows selected (0.575 seconds)
針對每一行進行運算
select ip,upper(url),time from t_pv
0: jdbc:hive2://Master:10000> select ip,upper(url),time 0: jdbc:hive2://Master:10000> from t_pv +----------------+------------------------------+----------------------+--+ | ip | _c1 | time | +----------------+------------------------------+----------------------+--+ | 192.168.33.3 | HTTP://WWW.XXX.CN/STU | 2019-08-04 15:30:20 | | 192.168.33.3 | HTTP://WWW.XXX.CN/TEACH | 2019-08-04 15:35:20 | | 192.168.33.4 | HTTP://WWW.XXX.CN/STU | 2019-08-04 15:30:20 | | 192.168.33.4 | HTTP://WWW.XXX.CN/JOB | 2019-08-04 16:30:20 | | 192.168.33.5 | HTTP://WWW.XXX.CN/JOB | 2019-08-04 15:40:20 | | 192.168.33.3 | HTTP://WWW.XXX.CN/STU | 2019-08-05 15:30:20 | | 192.168.44.3 | HTTP://WWW.XXX.CN/TEACH | 2019-08-05 15:35:20 | | 192.168.33.44 | HTTP://WWW.XXX.CN/STU | 2019-08-05 15:30:20 | | 192.168.33.46 | HTTP://WWW.XXX.CN/JOB | 2019-08-05 16:30:20 | | 192.168.33.55 | HTTP://WWW.XXX.CN/JOB | 2019-08-05 15:40:20 | | 192.168.133.3 | HTTP://WWW.XXX.CN/REGISTER | 2019-08-06 15:30:20 | | 192.168.111.3 | HTTP://WWW.XXX.CN/REGISTER | 2019-08-06 15:35:20 | | 192.168.34.44 | HTTP://WWW.XXX.CN/PAY | 2019-08-06 15:30:20 | | 192.168.33.46 | HTTP://WWW.XXX.CN/EXCERSIZE | 2019-08-06 16:30:20 | | 192.168.33.55 | HTTP://WWW.XXX.CN/JOB | 2019-08-06 15:40:20 | | 192.168.33.46 | HTTP://WWW.XXX.CN/EXCERSIZE | 2019-08-06 16:30:20 | | 192.168.33.25 | HTTP://WWW.XXX.CN/JOB | 2019-08-06 15:40:20 | | 192.168.33.36 | HTTP://WWW.XXX.CN/EXCERSIZE | 2019-08-06 16:30:20 | | 192.168.33.55 | HTTP://WWW.XXX.CN/JOB | 2019-08-06 15:40:20 | +----------------+------------------------------+----------------------+--+
select url ,count(1) --對分好組的數據進行逐行運算 from t_pv group by url;
0: jdbc:hive2://Master:10000> select url ,count(1) 0: jdbc:hive2://Master:10000> from t_pv 0: jdbc:hive2://Master:10000> group by url; ····· +------------------------------+------+--+ | url | _c1 | +------------------------------+------+--+ | http://www.xxx.cn/excersize | 3 | | http://www.xxx.cn/job | 7 | | http://www.xxx.cn/pay | 1 | | http://www.xxx.cn/register | 2 | | http://www.xxx.cn/stu | 4 | | http://www.xxx.cn/teach | 2 | +------------------------------+------+--+
可以給_c1加入字段名稱：
select url ,count(1) as count from t_pv group by url;
select url,max(ip) from t_pv group by url;
0: jdbc:hive2://Master:10000> select url,max(ip) 0: jdbc:hive2://Master:10000> from t_pv 0: jdbc:hive2://Master:10000> group by url; WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases. +------------------------------+----------------+--+ | url | _c1 | +------------------------------+----------------+--+ | http://www.xxx.cn/excersize | 192.168.33.46 | | http://www.xxx.cn/job | 192.168.33.55 | | http://www.xxx.cn/pay | 192.168.34.44 | | http://www.xxx.cn/register | 192.168.133.3 | | http://www.xxx.cn/stu | 192.168.33.44 | | http://www.xxx.cn/teach | 192.168.44.3 | +------------------------------+----------------+--+
select ip,url,max(time) from t_pv group by ip,url;
0: jdbc:hive2://Master:10000> select ip,url,max(time) 0: jdbc:hive2://Master:10000> from t_pv 0: jdbc:hive2://Master:10000> group by ip,url; ..... +----------------+------------------------------+----------------------+--+ | ip | url | _c2 | +----------------+------------------------------+----------------------+--+ | 192.168.111.3 | http://www.xxx.cn/register | 2019-08-06 15:35:20 | | 192.168.133.3 | http://www.xxx.cn/register | 2019-08-06 15:30:20 | | 192.168.33.25 | http://www.xxx.cn/job | 2019-08-06 15:40:20 | | 192.168.33.3 | http://www.xxx.cn/stu | 2019-08-05 15:30:20 | | 192.168.33.3 | http://www.xxx.cn/teach | 2019-08-04 15:35:20 | | 192.168.33.36 | http://www.xxx.cn/excersize | 2019-08-06 16:30:20 | | 192.168.33.4 | http://www.xxx.cn/job | 2019-08-04 16:30:20 | | 192.168.33.4 | http://www.xxx.cn/stu | 2019-08-04 15:30:20 | | 192.168.33.44 | http://www.xxx.cn/stu | 2019-08-05 15:30:20 | | 192.168.33.46 | http://www.xxx.cn/excersize | 2019-08-06 16:30:20 | | 192.168.33.46 | http://www.xxx.cn/job | 2019-08-05 16:30:20 | | 192.168.33.5 | http://www.xxx.cn/job | 2019-08-04 15:40:20 | | 192.168.33.55 | http://www.xxx.cn/job | 2019-08-06 15:40:20 | | 192.168.34.44 | http://www.xxx.cn/pay | 2019-08-06 15:30:20 | | 192.168.44.3 | http://www.xxx.cn/teach | 2019-08-05 15:35:20 | +----------------+------------------------------+----------------------+--+
select dt,"http://www.xxx.cn/job",count(1),max(ip) from t_pv where url="http://www.xxx.cn/job" group by dt having dt>"2019-08-04"; select dt,max(url),count(1),max(ip) from t_pv where url="http://www.xxx.cn/job" group by dt having dt>"2019-08-04"; select dt,url,count(1),max(ip) from t_pv where url="http://www.xxx.cn/job" group by dt,url having dt>"2019-08-04"; select dt,url,count(1),max(ip) from t_pv where url="http://www.xxx.cn/job" and dt>"2019-08-04" group by dt,url;
select dt,url,count(1),max(ip) from t_pv where dt>"2019-08-04" group by dt,url;
方式1：
select dt,url,count(1) as cnts,max(ip) from t_pv where dt>"2019-08-04" group by dt,url having cnts>2;
方式2：用子查詢
select dt,url,cnts,max_ip from (select dt,url,count(1) as cnts,max(ip) as max_ip from t_pv where dt>"2019-08-04" group by dt,url) tmp where cnts>2;5 基本數據類型 5.1 數字類型
TINYINT (1-byte signed integer, from -128 to 127)
SMALLINT (2-byte signed integer, from -32,768 to 32,767)
INT/INTEGER (4-byte signed integer, from -2,147,483,648 to 2,147,483,647)
BIGINT (8-byte signed integer, from -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807)
FLOAT (4-byte single precision floating point number)
DOUBLE (8-byte double precision floating point number)
示例：
create table t_test(a string ,b int,c bigint,d float,e double,f tinyint,g smallint)5.2 日期類型
TIMESTAMP (Note: Only available starting with Hive 0.8.0)
DATE (Note: Only available starting with Hive 0.12.0)
示例，假如有以下數據文件：
1,zhangsan,1985-06-30 2,lisi,1986-07-10 3,wangwu,1985-08-09
那么，就可以建一個表來對數據進行映射
create table t_customer(id int,name string,birthday date) row format delimited fields terminated by ",";
然后導入數據
load data local inpath "/root/customer.dat" into table t_customer;
然后，就可以正確查詢
5.3 字符串類型STRING
VARCHAR (Note: Only available starting with Hive 0.12.0)
CHAR (Note: Only available starting with Hive 0.13.0)
5.4 雜類型BOOLEAN
BINARY (Note: Only available starting with Hive 0.8.0)
5.5 復合類型 5.5.1 數組類型有如下數據：
玩具總動員4,湯姆·漢克斯:蒂姆·艾倫:安妮·波茨,2019-06-21 流浪地球,屈楚蕭:吳京:李光潔:吳孟達,2019-02-05 千與千尋,柊瑠美:入野自由:夏木真理:菅原文太,2019-06-21 戰狼2,吳京:弗蘭克·格里羅:吳剛:張翰:盧靖姍,2017-08-16
建表導入數據：
--建表映射： create table t_movie(movie_name string,actors array,first_show date) row format delimited fields terminated by "," collection items terminated by ":"; --導入數據 load data local inpath "/home/hadoop/actor.dat" into table t_movie;
0: jdbc:hive2://Master:10000> select * from t_movie; +---------------------+-----------------------------------+---------------------+--+ | t_movie.movie_name | t_movie.actors | t_movie.first_show | +---------------------+-----------------------------------+---------------------+--+ | 玩具總動員4 | ["湯姆·漢克斯","蒂姆·艾倫","安妮·波茨"] | 2019-06-21 | | 流浪地球 | ["屈楚蕭","吳京","李光潔","吳孟達"] | 2019-02-05 | | 千與千尋 | ["柊瑠美","入野自由","夏木真理","菅原文太"] | 2019-06-21 | | 戰狼2 | ["吳京","弗蘭克·格里羅","吳剛","張翰","盧靖姍"] | 2017-08-16 | +---------------------+-----------------------------------+---------------------+--+
select movie_name,actors[0],first_show from t_movie;
0: jdbc:hive2://Master:10000> select movie_name,actors[0],first_show from t_movie; +-------------+---------+-------------+--+ | movie_name | _c1 | first_show | +-------------+---------+-------------+--+ | 玩具總動員4 | 湯姆·漢克斯 | 2019-06-21 | | 流浪地球 | 屈楚蕭 | 2019-02-05 | | 千與千尋 | 柊瑠美 | 2019-06-21 | | 戰狼2 | 吳京 | 2017-08-16 | +-------------+---------+-------------+--+
select movie_name,actors,first_show from t_movie where array_contains(actors,"吳京");
0: jdbc:hive2://Master:10000> select movie_name,actors,first_show 0: jdbc:hive2://Master:10000> from t_movie where array_contains(actors,"吳京"); +-------------+-----------------------------------+-------------+--+ | movie_name | actors | first_show | +-------------+-----------------------------------+-------------+--+ | 流浪地球 | ["屈楚蕭","吳京","李光潔","吳孟達"] | 2019-02-05 | | 戰狼2 | ["吳京","弗蘭克·格里羅","吳剛","張翰","盧靖姍"] | 2017-08-16 | +-------------+-----------------------------------+-------------+--+
select movie_name ,size(actors) as actor_number ,first_show from t_movie;
0: jdbc:hive2://Master:10000> from t_movie; +-------------+---------------+-------------+--+ | movie_name | actor_number | first_show | +-------------+---------------+-------------+--+ | 玩具總動員4 | 3 | 2019-06-21 | | 流浪地球 | 4 | 2019-02-05 | | 千與千尋 | 4 | 2019-06-21 | | 戰狼2 | 5 | 2017-08-16 | +-------------+---------------+-------------+--+5.5.2 map類型
1,zhangsan,father:xiaoming#mother:xiaohuang#brother:xiaoxu,28 2,lisi,father:mayun#mother:huangyi#brother:guanyu,22 3,wangwu,father:wangjianlin#mother:ruhua#sister:jingtian,29 4,mayun,father:mayongzhen#mother:angelababy,26
導入數據