Hive 安裝及使用攻略

時間  2019-11-12
標籤 hive 安裝 使用 攻略 欄目 Hadoop 简体版
原文   原文鏈接

前言java

Hive是Hadoop一個程序接口，Hive讓數據分析人員快速上手，Hive使用了類SQL的語法，Hive讓JAVA的世界變得簡單而輕巧，Hive讓Hadoop普及到了程序員之外的人。node

從Hive開始，讓分析師們也能玩轉大數據。mysql

目錄程序員

  1. Hive的安裝
  2. Hive的基本使用:CRUD
  3. Hive交互式模式
  4. 數據導入
  5. 數據導出
  6. Hive查詢HiveQL
  7. Hive視圖
  8. Hive分區表

1. Hive的安裝

系統環境
裝好hadoop的環境後，咱們能夠把Hive裝在namenode機器上(c1)。
hadoop的環境，請參考：讓Hadoop跑在雲端系列文章，RHadoop實踐系列之一:Hadoop環境搭建sql

下載: hive-0.9.0.tar.gz
解壓到： /home/cos/toolkit/hive-0.9.0shell

hive配置數據庫

~ 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的元數據存儲到MySQL中apache

~ vi conf/hive-site.xml

<property>
<name>javax.jdo.option.ConnectionURL</name>
<value>jdbc:mysql://c1:3306/hive_metadata?createDatabaseIfNotExist=true</value>
<description>JDBC connect string for a JDBC metastore</description>
</property>

<property>
<name>javax.jdo.option.ConnectionDriverName</name>
<value>com.mysql.jdbc.Driver</value>
<description>Driver class name for a JDBC metastore</description>
</property>

<property>
<name>javax.jdo.option.ConnectionUserName</name>
<value>hive</value>
<description>username to use against metastore database</description>
</property>

<property>
<name>javax.jdo.option.ConnectionPassword</name>
<value>hive</value>
<description>password to use against metastore database</description>
</property>

<property>
<name>hive.metastore.warehouse.dir</name>
<value>/user/hive/warehouse</value>
<description>location of default database for the warehouse</description>
</property>

修改hive-log4j.properties緩存

#log4j.appender.EventCounter=org.apache.hadoop.metrics.jvm.EventCounter
log4j.appender.EventCounter=org.apache.hadoop.log.metrics.EventCounter

設置環境變量app

~ 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，建立目錄

$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中建立數據庫

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;

手動上傳mysql的jdbc庫到hive/lib

~ ls /home/cos/toolkit/hive-0.9.0/lib
mysql-connector-java-5.1.22-bin.jar

啓動hive

#啓動metastore服務
~ bin/hive --service metastore &
Starting Hive Metastore Server

#啓動hiveserver服務
~ bin/hive --service hiveserver &
Starting Hive Thrift Server

#啓動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數據庫中的元數據

~ 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已經成功安裝，下面是hive的使用攻略。

2. Hive的基本使用

1. 進入hive控制檯

~ 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

#建立數據(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

#建立新表
hive> CREATE TABLE t_hive (a int, b int, c int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';
OK
Time taken: 0.489 seconds

#導入數據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

查看錶和數據

#查看錶 
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

#查看錶數據
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

#查看錶結構
hive> desc t_hive;
OK
a       int
b       int
c       int
Time taken: 0.1 seconds

修改表

#增長一個字段
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: 重置配置爲默認值
  • set <key>=<value> : 修改特定變量的值(若是變量名拼寫錯誤，不會報錯)
  • set :  輸出用戶覆蓋的hive配置變量
  • set -v : 輸出全部Hadoop和Hive的配置變量
  • add FILE[S] *, add JAR[S] *, add ARCHIVE[S] * : 添加 一個或多個 file, jar, archives到分佈式緩存
  • list FILE[S], list JAR[S], list ARCHIVE[S] : 輸出已經添加到分佈式緩存的資源。
  • list FILE[S] *, list JAR[S] *,list ARCHIVE[S] * : 檢查給定的資源是否添加到分佈式緩存
  • delete FILE[S] *,delete JAR[S] *,delete ARCHIVE[S] * : 從分佈式緩存刪除指定的資源
  • ! <command> : 
  • dfs <dfs command> :  從Hive shell執行一個dfs命令
  • <query string> : 執行一個Hive 查詢，而後輸出結果到標準輸出
  • source FILE <filepath>:  在CLI裏執行一個hive腳本文件

4. 數據導入

還以剛纔的t_hive爲例。

#建立表結構
hive> CREATE TABLE t_hive (a int, b int, c int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';

從操做本地文件系統加載數據(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中查找剛剛導入的數據
~ 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加載數據

建立表t_hive2
hive> CREATE TABLE t_hive2 (a int, b int, c int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';

#從HDFS加載數據
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

#查看數據
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

從其餘表導入數據

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

建立表並從其餘表導入數據

#刪除表
hive> DROP TABLE t_hive;

#建立表並從其餘表導入數據
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

僅複製表結構不導數據

hive> CREATE TABLE t_hive3 LIKE t_hive;
hive> select * from t_hive3;
OK
Time taken: 0.077 seconds

從MySQL數據庫導入數據
咱們將在介紹Sqoop時講。

5. 數據導出

從HDFS複製到HDFS其餘位置

~ 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

經過Hive導出到本地文件系統

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

#查看本地操做系統
hive> ! cat /tmp/t_hive/000000_0;
hive> 1623
611213
41231
17213
71231
11234
11234

6. Hive查詢HiveQL

注：如下代碼將去掉map,reduce的日誌輸出部分。

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

鏈接查詢：JOIN

hive> 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, avg

hive> SELECT count(*), avg(a) FROM t_hive;
7       31.142857142857142

聚合查詢2：count, distinct

hive> 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視圖和數據庫視圖的概念是同樣的，咱們還以t_hive爲例。

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分區表

分區表是數據庫的基本概念，但不少時候數據量不大，咱們徹底用不到分區表。Hive是一種OLAP數據倉庫軟件，涉及的數據量是很是大的，因此分區表在這個場景就顯得很是重要！！

下面咱們從新定義一個數據表結構：t_hft

建立數據

~ 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

建立數據表

DROP TABLE IF EXISTS t_hft;
CREATE TABLE t_hft(
SecurityID STRING,
tradeTime STRING,
PreClosePx DOUBLE
) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';

建立分區數據表
根據業務：按天和股票ID進行分區設計

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 ',';

導入數據

#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)

查看分區表

hive> SHOW PARTITIONS t_hft;
tradedate=20130627
tradedate=20130628
Time taken: 0.082 seconds

查詢數據

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基於使用完成，這些都是平常的操做。後面我會繼續講一下，HiveQL優化及Hive的運維。

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