hive与hbase结合
一、hive与hbase的结合
Hive会经常和Hbase结合使用，把Hbase作为Hive的存储路径，所以Hive整合Hbase尤其重要。使用Hive读取Hbase中的数据，可以使用HQL语句在HBase表上进行查询、插入操作；甚至是进行Join和Union等复杂查询。此功能是从Hive 0.6.0开始引入的。Hive与HBase整合的实现是利用两者本身对外的API接口互相进行通信，相互通信主要是依靠hive-hbase-handler-*.jar工具里面的类实现的。使用Hive操作HBase中的表，只是提供了便捷性，hiveQL引擎使用的是MapReduce，对于性能上，表现不尽人意。
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步骤：
1、将hbase相关jar包复制到hive/lib下，操作如下：
[hadoop@bus-stable hive]$ cp /opt/hbase/lib/hbase-protocol-1.4.5.jar /opt/hive/lib/
[hadoop@bus-stable hive]$ cp /opt/hbase/lib/hbase-server-1.4.5.jar /opt/hive/lib/
[hadoop@bus-stable hive]$ cp /opt/hbase/lib/hbase-client-1.4.5.jar /opt/hive/lib/
[hadoop@bus-stable hive]$ cp /opt/hbase/lib/hbase-common-1.4.5.jar /opt/hive/lib/
[hadoop@bus-stable hive]$ cp /opt/hbase/lib/hbase-common-1.4.5-tests.jar /opt/hive/lib/
[hadoop@bus-stable hive]$ 2、在hive-site.xml文件中引用hbase，添加如下内容：
[hadoop@bus-stable hive]$ vim /opt/hive/conf/hive-site.xml
hive.aux.jars.path
file:///opt/hive/lib/hive-hbase-handler-2.3.3.jar,
file:///opt/hive/lib/hbase-protocol-1.4.5.jar,
file:///opt/hive/lib/hbase-server-1.4.5.jar,
file:///opt/hive/lib/hbase-client-1.4.5.jar,
file:///opt/hive/lib/hbase-common-1.4.5.jar,
file:///opt/hive/lib/hbase-common-1.4.5-tests.jar,
file:///opt/hive/lib/zookeeper-3.4.6.jar,
file:///opt/hive/lib/guava-14.0.1.jar
The location of the plugin jars that contain implementations of user defined functions and serdes.
hbase.zookeeper.quorum
open-stable,permission-stable,sp-stable
dfs.permissions.enabled
false
3、启动hive:
[hadoop@bus-stable hive]$ hive -hiveconf hbase.master=oversea-stable:60000
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/opt/apache-hive-2.3.3-bin/lib/log4j-slf4j-impl-2.6.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/opt/hadoop-2.9.1/share/hadoop/common/lib/slf4j-log4j12-1.7.25.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 jar:file:/opt/apache-hive-2.3.3-bin/lib/hive-common-2.3.3.jar!/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> create table htest(key int,value string) stored by 'org.apache.hadoop.hive.hbase.HBaseStorageHandler' with serdeproperties('hbase.columns.mapping'=':key,f:value') tblproperties('hbase.table.name'='htest');
OK
Time taken: 9.376 seconds
hive> show databases;
OK
default
inspiry
Time taken: 0.121 seconds, Fetched: 2 row(s)
hive> show tables;
OK
htest
Time taken: 0.047 seconds, Fetched: 1 row(s)
hive> select * from htest;
OK
Time taken: 1.967 seconds
hive> 4、在hbase中验证数据：
[hadoop@oversea-stable opt]$ hbase shell
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/opt/hbase-1.4.5/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/opt/hadoop-2.9.1/share/hadoop/common/lib/slf4j-log4j12-1.7.25.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.slf4j.impl.Log4jLoggerFactory]
HBase Shell
Use "help" to get list of supported commands.
Use "exit" to quit this interactive shell.
Version 1.4.5, rca99a9466415dc4cfc095df33efb45cb82fe5480, Wed Jun 13 15:13:00 EDT 2018
hbase(main):001:0> list
TABLE
htest
1 row(s) in 0.2970 seconds
=> ["htest"]
hbase(main):002:0> scan "htest"
ROW COLUMN+CELL
0 row(s) in 0.1410 seconds
hbase(main):003:0> 二、导入外部数据
(1) 数据文件如下：
[hadoop@bus-stable ~]$ cat score.csv
hive,85
hbase,90
hadoop,92
flume,89
kafka,95
spark,80
storm,70
[hadoop@bus-stable ~]$ hadoop fs -put score.csv /data/score.csv
[hadoop@bus-stable ~]$ hadoop fs -ls /data/
Found 2 items
-rw-r--r-- 3 hadoop supergroup 88822 2018-06-15 10:32 /data/notepad.txt
-rw-r--r-- 3 hadoop supergroup 70 2018-06-26 15:59 /data/score.csv
[hadoop@bus-stable ~]$
(2) 创建外部表
利用hdfs上的现有数据，创建hive外部表
hive> create external table if not exists course.testcourse(cname string,score int) row format delimited fields terminated by ',' stored as textfile location '/data';
OK
Time taken: 0.282 seconds
hive> show databases;
OK
course
default
inspiry
Time taken: 0.013 seconds, Fetched: 3 row(s)
hive> use course;
OK
Time taken: 0.021 seconds
hive> show tables;
OK
testcourse
Time taken: 0.036 seconds, Fetched: 1 row(s)
hive> select * from testcourse ;
OK
hive 85
hbase 90
hadoop 92
flume 89
kafka 95
spark 80
storm 70
Time taken: 2.272 seconds, Fetched: 7 row(s)
hive>
三、利用HQL语句创建hbase 表
使用HQL语句创建一个指向HBase的Hive表，语法如下：
CREATE TABLE tbl_name(key int, value string) //Hive中的表名tbl_name
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler' //指定存储处理器
WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key,cf1:val") //声明列族，列名
TBLPROPERTIES ("hbase.table.name" = "tbl_name", "hbase.mapred.output.outputtable" = "iteblog"); //hbase.table.name 声明HBase表名， 为可选属性默认与Hive的表名相同， hbase.mapred.output.outputtable 指定插入数据时写入的表， 如果以后需要往该表插入数据就需要指定该值(1) 创建语句如下
hive> create table course.hbase_testcourse(cname string,score int) STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler' WITH SERDEPROPERTIES("hbase.columns.mapping" = ":key,cf:score")TBLPROPERTIES("hbase.table.name" = "hbase_testcourse","hbase.mapred.output.outputtable" = "hbase_testcourse");
OK
Time taken: 3.745 seconds
hive> show databases;
OK
course
default
inspiry
Time taken: 0.019 seconds, Fetched: 3 row(s)
hive> use course;
OK
Time taken: 0.02 seconds
hive> show tables;
OK
hbase_testcourse
testcourse
Time taken: 0.025 seconds, Fetched: 2 row(s)
hive> select * from hbase_testcourse;
OK
Time taken: 1.883 seconds
hive> (2) 创建完内部表，可以通过Hive支持的insert overwrite 方式将一个表的数据导入 HBase
hive> insert overwrite table course.hbase_testcourse select cname,score from course.testcourse;
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.
Query ID = hadoop_20180626170540_c7eecb8d-2925-4ad2-be7f-237d9815d1cb
Total 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_1529932626564_0002, Tracking URL = http://oversea-stable:8088/proxy/application_1529932626564_0002/
Kill Command = /opt/hadoop/bin/hadoop job -kill job_1529932626564_0002
Hadoop job information for Stage-3: number of mappers: 1; number of reducers: 0
2018-06-26 17:06:02,793 Stage-3 map = 0%, reduce = 0%
2018-06-26 17:06:14,126 Stage-3 map = 100%, reduce = 0%, Cumulative CPU 6.12 sec
MapReduce Total cumulative CPU time: 6 seconds 120 msec
Ended Job = job_1529932626564_0002
MapReduce Jobs Launched:
Stage-Stage-3: Map: 1 Cumulative CPU: 6.12 sec HDFS Read: 4224 HDFS Write: 0 SUCCESS
Total MapReduce CPU Time Spent: 6 seconds 120 msec
OK
Time taken: 41.489 seconds
hive>
hive> select * from hbase_testcourse;
OK
flume 89
hadoop 92
hbase 90
hive 85
kafka 95
spark 80
storm 70
Time taken: 0.201 seconds, Fetched: 7 row(s)
hive> (3) 验证hbase
hbase(main):011:0> list
TABLE
hbase_testcourse
htest
2 row(s) in 0.0110 seconds
=> ["hbase_testcourse", "htest"]
hbase(main):012:0> scan "hbase_testcourse"
ROW COLUMN+CELL
flume column=cf:score, timestamp=1530003973026, value=89
hadoop column=cf:score, timestamp=1530003973026, value=92
hbase column=cf:score, timestamp=1530003973026, value=90
hive column=cf:score, timestamp=1530003973026, value=85
kafka column=cf:score, timestamp=1530003973026, value=95
spark column=cf:score, timestamp=1530003973026, value=80
storm column=cf:score, timestamp=1530003973026, value=70
7 row(s) in 0.0760 seconds
hbase(main):013:0> 四、使用Hive映射HBase中已经存在的表
(1) 在hbase中创建HBase表，进入HBase Shell客户端执行建表命令
hbase(main):036:0> create 'hbase_test',{ NAME => 'cf'}
0 row(s) in 2.2830 seconds
=> Hbase::Table - hbase_test(2) 插入数据
hbase(main):037:0> put 'hbase_test','hadoop','cf:score', '95'
0 row(s) in 0.1110 seconds
hbase(main):038:0> put 'hbase_test','storm','cf:score', '96'
0 row(s) in 0.0120 seconds
hbase(main):039:0> put 'hbase_test','spark','cf:score', '97'
0 row(s) in 0.0110 seconds(3) 查看数据
hbase(main):041:0> scan "hbase_test"
ROW COLUMN+CELL
hadoop column=cf:score, timestamp=1530004351399, value=95
spark column=cf:score, timestamp=1530004365368, value=97
storm column=cf:score, timestamp=1530004359169, value=96
3 row(s) in 0.0220 seconds
hbase(main):042:0> (4) 进入Hive Shell 客户端，创建外部表course.hbase_test，建表命令如下所示
hive> create external table course.hbase_test(cname string,score int) STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler' WITH SERDEPROPERTIES("hbase.columns.mapping" = ":key,cf:score") TBLPROPERTIES("hbase.table.name" = "hbase_test", "hbase.mapred.output.outputtable" = "hbase_test");
OK
Time taken: 0.221 seconds
hive> show tables;
OK
hbase_test
hbase_testcourse
testcourse
Time taken: 0.024 seconds, Fetched: 3 row(s)
备注：创建外部表和创建内部表的命令基本一致，唯一的区别就是：创建内部表使用create table，创建外部表使用create external table。
Hive 查看数据
hive> select * from hbase_test;
OK
hadoop 95
spark 97
storm 96
Time taken: 0.22 seconds, Fetched: 3 row(s)
hive> 该Hive表一个外部表，所以删除该表并不会删除HBase表中的数据，有几点需要注意的是：
a)、建表或映射表的时候如果没有指定:key则第一个列默认就是行键
b)、HBase对应的Hive表中没有时间戳概念，默认返回的就是最新版本的值
c)、由于HBase中没有数据类型信息，所以在存储数据的时候都转化为String类型
五、使用java连接hive操作hbase
pom.xml
4.0.0
cn.itcast.hbase
hbase
0.0.1-SNAPSHOT
org.apache.hadoop
hadoop-client
2.6.4
org.apache.hadoop
hadoop-common
2.6.4
junit
junit
4.12
org.apache.hbase
hbase-client
1.4.0
org.apache.hbase
hbase-server
1.4.0
org.apache.hive
hive-jdbc
1.2.1
org.apache.hive
hive-metastore
1.2.1
org.apache.hive
hive-exec
1.2.1
Hive_Hbase.java
package cn.itcast.bigdata.hbase;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.ResultSet;
import java.sql.SQLException;
import java.sql.Statement;
public class Hive_Hbase {
public static void main(String[] args) {
try {
Class.forName("org.apache.hive.jdbc.HiveDriver");
Connection connection = DriverManager.getConnection("jdbc:hive2://hadoop1:10000/shizhan02","hadoop","");
Statement statement = connection.createStatement();
String sql = "SELECT * FROM hive_hbase_table_kv";
ResultSet res = statement.executeQuery(sql);
while (res.next()) {
System.out.println(res.getString(2));
}
} catch (ClassNotFoundException | SQLException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
}当前题目：hive与hbase结合
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