HIVE攻略 JFK_Hive安装及使用攻略
目錄Hive的安裝
Hive的基本使用:CRUD
Hive交互式模式
數(shù)據(jù)導(dǎo)入
數(shù)據(jù)導(dǎo)出
Hive查詢HiveQL
Hive視圖
Hive分區(qū)表
1. Hive的安裝
系統(tǒng)環(huán)境裝好hadoop的環(huán)境后，我們可以把Hive裝在namenode機(jī)器上(c1)。hadoop的環(huán)境，請(qǐng)參考：讓Hadoop跑在云端系列文章，RHadoop實(shí)踐系列之一:Hadoop環(huán)境搭建
下載: hive-0.9.0.tar.gz解壓到： /home/cos/toolkit/hive-0.9.0
hive配置~ cd /home/cos/toolkit/hive-0.9.0
~ cp hive-default.xml.template hive-site.xml
~ cp hive-log4j.properties.template hive-log4j.properties
修改hive-site.xml配置文件把Hive的元數(shù)據(jù)存儲(chǔ)到MySQL中~ vi conf/hive-site.xml
javax.jdo.option.ConnectionURL
jdbc:mysql://c1:3306/hive_metadata?createDatabaseIfNotExist=true
JDBC connect string for a JDBC metastore
javax.jdo.option.ConnectionDriverName
com.mysql.jdbc.Driver
Driver class name for a JDBC metastore
javax.jdo.option.ConnectionUserName
hive
username to use against metastore database
javax.jdo.option.ConnectionPassword
hive
password to use against metastore database
hive.metastore.warehouse.dir
/user/hive/warehouse
location of default database for the warehouse
修改hive-log4j.properties#log4j.appender.EventCounter=org.apache.hadoop.metrics.jvm.EventCounter
log4j.appender.EventCounter=org.apache.hadoop.log.metrics.EventCounter
設(shè)置環(huán)境變量~ sudo vi /etc/environment
PATH="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/home/cos/toolkit/ant184/bin:/home/cos/toolkit/jdk16/bin:/home/cos/toolkit/maven3/bin:/home/cos/toolkit/hadoop-1.0.3/bin:/home/cos/toolkit/hive-0.9.0/bin"
JAVA_HOME=/home/cos/toolkit/jdk16
ANT_HOME=/home/cos/toolkit/ant184
MAVEN_HOME=/home/cos/toolkit/maven3
HADOOP_HOME=/home/cos/toolkit/hadoop-1.0.3
HIVE_HOME=/home/cos/toolkit/hive-0.9.0
CLASSPATH=/home/cos/toolkit/jdk16/lib/dt.jar:/home/cos/toolkit/jdk16/lib/tools.jar
在hdfs上面，創(chuàng)建目錄$HADOOP_HOME/bin/hadoop fs -mkidr /tmp
$HADOOP_HOME/bin/hadoop fs -mkidr /user/hive/warehouse
$HADOOP_HOME/bin/hadoop fs -chmod g+w /tmp
$HADOOP_HOME/bin/hadoop fs -chmod g+w /user/hive/warehouse
在MySQL中創(chuàng)建數(shù)據(jù)庫(kù)create database hive_metadata;
grant all on hive_metadata.* to hive@'%' identified by 'hive';
grant all on hive_metadata.* to hive@localhost identified by 'hive';
ALTER DATABASE hive_metadata CHARACTER SET latin1;
手動(dòng)上傳mysql的jdbc庫(kù)到hive/lib~ ls /home/cos/toolkit/hive-0.9.0/lib
mysql-connector-java-5.1.22-bin.jar
啟動(dòng)hive#啟動(dòng)metastore服務(wù)
~ bin/hive --service metastore &
Starting Hive Metastore Server
#啟動(dòng)hiveserver服務(wù)
~ bin/hive --service hiveserver &
Starting Hive Thrift Server
#啟動(dòng)hive客戶端
~ bin/hive shell
Logging initialized using configuration in file:/root/hive-0.9.0/conf/hive-log4j.properties
Hive history file=/tmp/root/hive_job_log_root_201211141845_1864939641.txt
hive> show tables
OK
查詢MySQL數(shù)據(jù)庫(kù)中的元數(shù)據(jù)~ mysql -uroot -p
mysql> use hive_metadata;
Database changed
mysql> show tables;
+-------------------------+
| Tables_in_hive_metadata |
+-------------------------+
| BUCKETING_COLS |
| CDS |
| COLUMNS_V2 |
| DATABASE_PARAMS |
| DBS |
| IDXS |
| INDEX_PARAMS |
| PARTITIONS |
| PARTITION_KEYS |
| PARTITION_KEY_VALS |
| PARTITION_PARAMS |
| PART_COL_PRIVS |
| PART_PRIVS |
| SDS |
| SD_PARAMS |
| SEQUENCE_TABLE |
| SERDES |
| SERDE_PARAMS |
| SORT_COLS |
| TABLE_PARAMS |
| TBLS |
| TBL_COL_PRIVS |
| TBL_PRIVS |
+-------------------------+
23 rows in set (0.00 sec)
Hive已經(jīng)成功安裝，下面是hive的使用攻略。
2. Hive的基本使用
1. 進(jìn)入hive控制臺(tái)~ cd /home/cos/toolkit/hive-0.9.0
~ bin/hive shell
Logging initialized using configuration in file:/home/cos/toolkit/hive-0.9.0/conf/hive-log4j.properties
Hive history file=/tmp/cos/hive_job_log_cos_201307160003_95040367.txt
hive>
新建表#創(chuàng)建數(shù)據(jù)(文本以tab分隔)
~ vi /home/cos/demo/t_hive.txt
16 2 3
61 12 13
41 2 31
17 21 3
71 2 31
1 12 34
11 2 34
#創(chuàng)建新表
hive> CREATE TABLE t_hive (a int, b int, c int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';
OK
Time taken: 0.489 seconds
#導(dǎo)入數(shù)據(jù)t_hive.txt到t_hive表
hive> LOAD DATA LOCAL INPATH '/home/cos/demo/t_hive.txt' OVERWRITE INTO TABLE t_hive ;
Copying data from file:/home/cos/demo/t_hive.txt
Copying file: file:/home/cos/demo/t_hive.txt
Loading data to table default.t_hive
Deleted hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive
OK
Time taken: 0.397 seconds
查看表和數(shù)據(jù)#查看表
hive> show tables;
OK
t_hive
Time taken: 0.099 seconds
#正則匹配表名
hive>show tables '*t*';
OK
t_hive
Time taken: 0.065 seconds
#查看表數(shù)據(jù)
hive> select * from t_hive;
OK
16 2 3
61 12 13
41 2 31
17 21 3
71 2 31
1 12 34
11 2 34
Time taken: 0.264 seconds
#查看表結(jié)構(gòu)
hive> desc t_hive;
OK
a int
b int
c int
Time taken: 0.1 seconds
修改表#增加一個(gè)字段
hive> ALTER TABLE t_hive ADD COLUMNS (new_col String);
OK
Time taken: 0.186 seconds
hive> desc t_hive;
OK
a int
b int
c int
new_col string
Time taken: 0.086 seconds
#重命令表名
~ ALTER TABLE t_hive RENAME TO t_hadoop;
OK
Time taken: 0.45 seconds
hive> show tables;
OK
t_hadoop
Time taken: 0.07 seconds
刪除表hive> DROP TABLE t_hadoop;
OK
Time taken: 0.767 seconds
hive> show tables;
OK
Time taken: 0.064 seconds
3. Hive交互式模式quit,exit: ?退出交互式shell
reset: 重置配置為默認(rèn)值
set =: 修改特定變量的值(如果變量名拼寫(xiě)錯(cuò)誤，不會(huì)報(bào)錯(cuò))
set?:? 輸出用戶覆蓋的hive配置變量
set -v : 輸出所有Hadoop和Hive的配置變量
add FILE[S] *,?add JAR[S] *,?add ARCHIVE[S] * : 添加 一個(gè)或多個(gè) file, jar, archives到分布式緩存
list FILE[S],?list JAR[S],?list ARCHIVE[S] : 輸出已經(jīng)添加到分布式緩存的資源。
list FILE[S] *,?list JAR[S] *,list ARCHIVE[S] * : 檢查給定的資源是否添加到分布式緩存
delete FILE[S] *,delete JAR[S] *,delete ARCHIVE[S] * : 從分布式緩存刪除指定的資源
! :??從Hive shell執(zhí)行一個(gè)shell命令
dfs : ?從Hive shell執(zhí)行一個(gè)dfs命令
: 執(zhí)行一個(gè)Hive 查詢，然后輸出結(jié)果到標(biāo)準(zhǔn)輸出
source FILE : ?在CLI里執(zhí)行一個(gè)hive腳本文件
4. 數(shù)據(jù)導(dǎo)入
還以剛才的t_hive為例。#創(chuàng)建表結(jié)構(gòu)
hive> CREATE TABLE t_hive (a int, b int, c int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';
從操作本地文件系統(tǒng)加載數(shù)據(jù)(LOCAL)hive> LOAD DATA LOCAL INPATH '/home/cos/demo/t_hive.txt' OVERWRITE INTO TABLE t_hive ;
Copying data from file:/home/cos/demo/t_hive.txt
Copying file: file:/home/cos/demo/t_hive.txt
Loading data to table default.t_hive
Deleted hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive
OK
Time taken: 0.612 seconds
#在HDFS中查找剛剛導(dǎo)入的數(shù)據(jù)
~ hadoop fs -cat /user/hive/warehouse/t_hive/t_hive.txt
16 2 3
61 12 13
41 2 31
17 21 3
71 2 31
1 12 34
11 2 34
從HDFS加載數(shù)據(jù)創(chuàng)建表t_hive2
hive> CREATE TABLE t_hive2 (a int, b int, c int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';
#從HDFS加載數(shù)據(jù)
hive> LOAD DATA INPATH '/user/hive/warehouse/t_hive/t_hive.txt' OVERWRITE INTO TABLE t_hive2;
Loading data to table default.t_hive2
Deleted hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive2
OK
Time taken: 0.325 seconds
#查看數(shù)據(jù)
hive> select * from t_hive2;
OK
16 2 3
61 12 13
41 2 31
17 21 3
71 2 31
1 12 34
11 2 34
Time taken: 0.287 seconds
從其他表導(dǎo)入數(shù)據(jù)hive> INSERT OVERWRITE TABLE t_hive2 SELECT * FROM t_hive ;
Total MapReduce jobs = 2
Launching Job 1 out of 2
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_201307131407_0002, Tracking URL = http://c1.wtmart.com:50030/jobdetails.jsp?jobid=job_201307131407_0002
Kill Command = /home/cos/toolkit/hadoop-1.0.3/libexec/../bin/hadoop job -Dmapred.job.tracker=hdfs://c1.wtmart.com:9001 -kill job_201307131407_0002
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2013-07-16 10:32:41,979 Stage-1 map = 0%, reduce = 0%
2013-07-16 10:32:48,034 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:49,050 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:50,068 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:51,082 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:52,093 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:53,102 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:54,112 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 1.03 sec
MapReduce Total cumulative CPU time: 1 seconds 30 msec
Ended Job = job_201307131407_0002
Ended Job = -314818888, job is filtered out (removed at runtime).
Moving data to: hdfs://c1.wtmart.com:9000/tmp/hive-cos/hive_2013-07-16_10-32-31_323_5732404975764014154/-ext-10000
Loading data to table default.t_hive2
Deleted hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive2
Table default.t_hive2 stats: [num_partitions: 0, num_files: 1, num_rows: 0, total_size: 56, raw_data_size: 0]
7 Rows loaded to t_hive2
MapReduce Jobs Launched:
Job 0: Map: 1 Cumulative CPU: 1.03 sec HDFS Read: 273 HDFS Write: 56 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 30 msec
OK
Time taken: 23.227 seconds
hive> select * from t_hive2;
OK
16 2 3
61 12 13
41 2 31
17 21 3
71 2 31
1 12 34
11 2 34
Time taken: 0.134 seconds
創(chuàng)建表并從其他表導(dǎo)入數(shù)據(jù)#刪除表
hive> DROP TABLE t_hive;
#創(chuàng)建表并從其他表導(dǎo)入數(shù)據(jù)
hive> CREATE TABLE t_hive AS SELECT * FROM t_hive2 ;
Total MapReduce jobs = 2
Launching Job 1 out of 2
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_201307131407_0003, Tracking URL = http://c1.wtmart.com:50030/jobdetails.jsp?jobid=job_201307131407_0003
Kill Command = /home/cos/toolkit/hadoop-1.0.3/libexec/../bin/hadoop job -Dmapred.job.tracker=hdfs://c1.wtmart.com:9001 -kill job_201307131407_0003
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2013-07-16 10:36:48,612 Stage-1 map = 0%, reduce = 0%
2013-07-16 10:36:54,648 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.13 sec
2013-07-16 10:36:55,657 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.13 sec
2013-07-16 10:36:56,666 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.13 sec
2013-07-16 10:36:57,673 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.13 sec
2013-07-16 10:36:58,683 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.13 sec
2013-07-16 10:36:59,691 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 1.13 sec
MapReduce Total cumulative CPU time: 1 seconds 130 msec
Ended Job = job_201307131407_0003
Ended Job = -670956236, job is filtered out (removed at runtime).
Moving data to: hdfs://c1.wtmart.com:9000/tmp/hive-cos/hive_2013-07-16_10-36-39_986_1343249562812540343/-ext-10001
Moving data to: hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive
Table default.t_hive stats: [num_partitions: 0, num_files: 1, num_rows: 0, total_size: 56, raw_data_size: 0]
7 Rows loaded to hdfs://c1.wtmart.com:9000/tmp/hive-cos/hive_2013-07-16_10-36-39_986_1343249562812540343/-ext-10000
MapReduce Jobs Launched:
Job 0: Map: 1 Cumulative CPU: 1.13 sec HDFS Read: 272 HDFS Write: 56 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 130 msec
OK
Time taken: 20.13 seconds
hive> select * from t_hive;
OK
16 2 3
61 12 13
41 2 31
17 21 3
71 2 31
1 12 34
11 2 34
Time taken: 0.109 seconds
僅復(fù)制表結(jié)構(gòu)不導(dǎo)數(shù)據(jù)hive> CREATE TABLE t_hive3 LIKE t_hive;
hive> select * from t_hive3;
OK
Time taken: 0.077 seconds
從MySQL數(shù)據(jù)庫(kù)導(dǎo)入數(shù)據(jù)我們將在介紹Sqoop時(shí)講。
5. 數(shù)據(jù)導(dǎo)出
從HDFS復(fù)制到HDFS其他位置~ hadoop fs -cp /user/hive/warehouse/t_hive /
~ hadoop fs -ls /t_hive
Found 1 items
-rw-r--r-- 1 cos supergroup 56 2013-07-16 10:41 /t_hive/000000_0
~ hadoop fs -cat /t_hive/000000_0
1623
611213
41231
17213
71231
11234
11234
通過(guò)Hive導(dǎo)出到本地文件系統(tǒng)hive> INSERT OVERWRITE LOCAL DIRECTORY '/tmp/t_hive' SELECT * FROM t_hive;
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_201307131407_0005, Tracking URL = http://c1.wtmart.com:50030/jobdetails.jsp?jobid=job_201307131407_0005
Kill Command = /home/cos/toolkit/hadoop-1.0.3/libexec/../bin/hadoop job -Dmapred.job.tracker=hdfs://c1.wtmart.com:9001 -kill job_201307131407_0005
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2013-07-16 10:46:24,774 Stage-1 map = 0%, reduce = 0%
2013-07-16 10:46:30,823 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:31,833 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:32,844 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:33,856 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:34,865 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:35,873 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:36,884 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 0.87 sec
MapReduce Total cumulative CPU time: 870 msec
Ended Job = job_201307131407_0005
Copying data to local directory /tmp/t_hive
Copying data to local directory /tmp/t_hive
7 Rows loaded to /tmp/t_hive
MapReduce Jobs Launched:
Job 0: Map: 1 Cumulative CPU: 0.87 sec HDFS Read: 271 HDFS Write: 56 SUCCESS
Total MapReduce CPU Time Spent: 870 msec
OK
Time taken: 23.369 seconds
#查看本地操作系統(tǒng)
hive> ! cat /tmp/t_hive/000000_0;
hive> 1623
611213
41231
17213
71231
11234
11234
6. Hive查詢HiveQL
注：以下代碼將去掉map,reduce的日志輸出部分。
普通查詢：排序，列別名，嵌套子查詢hive> FROM (
> SELECT b,c as c2 FROM t_hive
> ) t
> SELECT t.b, t.c2
> WHERE b>2
> LIMIT 2;
12 13
21 3
連接查詢：JOINhive> SELECT t1.a,t1.b,t2.a,t2.b
> FROM t_hive t1 JOIN t_hive2 t2 on t1.a=t2.a
> WHERE t1.c>10;
1 12 1 12
11 2 11 2
41 2 41 2
61 12 61 12
71 2 71 2
聚合查詢1：count, avghive> SELECT count(*), avg(a) FROM t_hive;
7 31.142857142857142
聚合查詢2：count, distincthive> SELECT count(DISTINCT b) FROM t_hive;
3
聚合查詢3：GROUP BY, HAVING#GROUP BY
hive> SELECT avg(a),b,sum(c) FROM t_hive GROUP BY b,c
16.0 2 3
56.0 2 62
11.0 2 34
61.0 12 13
1.0 12 34
17.0 21 3
#HAVING
hive> SELECT avg(a),b,sum(c) FROM t_hive GROUP BY b,c HAVING sum(c)>30
56.0 2 62
11.0 2 34
1.0 12 34
7. Hive視圖
Hive視圖和數(shù)據(jù)庫(kù)視圖的概念是一樣的，我們還以t_hive為例。hive> CREATE VIEW v_hive AS SELECT a,b FROM t_hive where c>30;
hive> select * from v_hive;
41 2
71 2
1 12
11 2
刪除視圖hive> DROP VIEW IF EXISTS v_hive;
OK
Time taken: 0.495 seconds
8. Hive分區(qū)表
分區(qū)表是數(shù)據(jù)庫(kù)的基本概念，但很多時(shí)候數(shù)據(jù)量不大，我們完全用不到分區(qū)表。Hive是一種OLAP數(shù)據(jù)倉(cāng)庫(kù)軟件，涉及的數(shù)據(jù)量是非常大的，所以分區(qū)表在這個(gè)場(chǎng)景就顯得非常重要！！
下面我們重新定義一個(gè)數(shù)據(jù)表結(jié)構(gòu)：t_hft
創(chuàng)建數(shù)據(jù)~ vi /home/cos/demo/t_hft_20130627.csv
000001,092023,9.76
000002,091947,8.99
000004,092002,9.79
000005,091514,2.2
000001,092008,9.70
000001,092059,9.45
~ vi /home/cos/demo/t_hft_20130628.csv
000001,092023,9.76
000002,091947,8.99
000004,092002,9.79
000005,091514,2.2
000001,092008,9.70
000001,092059,9.45
創(chuàng)建數(shù)據(jù)表DROP TABLE IF EXISTS t_hft;
CREATE TABLE t_hft(
SecurityID STRING,
tradeTime STRING,
PreClosePx DOUBLE
) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';
創(chuàng)建分區(qū)數(shù)據(jù)表根據(jù)業(yè)務(wù)：按天和股票ID進(jìn)行分區(qū)設(shè)計(jì)DROP TABLE IF EXISTS t_hft;
CREATE TABLE t_hft(
SecurityID STRING,
tradeTime STRING,
PreClosePx DOUBLE
) PARTITIONED BY (tradeDate INT)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';
導(dǎo)入數(shù)據(jù)#20130627
hive> LOAD DATA LOCAL INPATH '/home/cos/demo/t_hft_20130627.csv' OVERWRITE INTO TABLE t_hft PARTITION (tradeDate=20130627);
Copying data from file:/home/cos/demo/t_hft_20130627.csv
Copying file: file:/home/cos/demo/t_hft_20130627.csv
Loading data to table default.t_hft partition (tradedate=20130627)
#20130628
hive> LOAD DATA LOCAL INPATH '/home/cos/demo/t_hft_20130628.csv' OVERWRITE INTO TABLE t_hft PARTITION (tradeDate=20130628);
Copying data from file:/home/cos/demo/t_hft_20130628.csv
Copying file: file:/home/cos/demo/t_hft_20130628.csv
Loading data to table default.t_hft partition (tradedate=20130628)
查看分區(qū)表hive> SHOW PARTITIONS t_hft;
tradedate=20130627
tradedate=20130628
Time taken: 0.082 seconds
查詢數(shù)據(jù)hive> select * from t_hft where securityid='000001';
000001 092023 9.76 20130627
000001 092008 9.7 20130627
000001 092059 9.45 20130627
000001 092023 9.76 20130628
000001 092008 9.7 20130628
000001 092059 9.45 20130628
hive> select * from t_hft where tradedate=20130627 and PreClosePx<9;
000002 091947 8.99 20130627
000005 091514 2.2 20130627
Hive基于使用完成，這些都是日常的操作。后面我會(huì)繼續(xù)講一下，HiveQL優(yōu)化及Hive的運(yùn)維。
參照：http://blog.fens.me/hadoop-hive-intro/
創(chuàng)作挑戰(zhàn)賽新人創(chuàng)作獎(jiǎng)勵(lì)來(lái)咯，堅(jiān)持創(chuàng)作打卡瓜分現(xiàn)金大獎(jiǎng)總結(jié)
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