第一步，启动hadoop，命令：./start-all.sh
第二步，启动hive，命令：
./hive --auxpath /home/dream-victor/hive-0.6.0/lib/hive_hbase-handler.jar,/home/dream-victor/hive-0.6.0/lib/hbase-0.20.3.jar,/home/dream-victor/hive-0.6.0/lib/zookeeper-3.2.2.jar -hiveconf hbase.master=127.0.0.1:60000
这里，-hiveconf hbase.master=指向自己在hbase-site.xml中hbase.master的值
第三步，启动hbase，命令：./start-hbase.sh
第四步，建立关联表，这里我们要查询的表在hbase中已经存在所以，使用CREATE EXTERNAL TABLE来建立，如下：
Java代码
- CREATE EXTERNAL TABLE hbase_table_2(key string, value string)
- STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
- WITH SERDEPROPERTIES ("hbase.columns.mapping" = "data:1")
- TBLPROPERTIES("hbase.table.name" = "test");
CREATE EXTERNAL TABLE hbase_table_2(key string, value string)
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
WITH SERDEPROPERTIES ("hbase.columns.mapping" = "data:1")
TBLPROPERTIES("hbase.table.name" = "test");
hbase.columns.mapping指向对应的列族；多列时，data:1，data:2；多列族时，data1:1,data2:1；
hbase.table.name指向对应的表；
hbase_table_2(key string, value string)，这个是关联表
我们看一下HBase中要查询的表的结构，
Java代码
- hbase(main):001:0> describe 'test'
- DESCRIPTION ENABLED
- {NAME => 'test', FAMILIES => [{NAME => 'data', COMPRESSION => 'NONE', true
- VERSIONS => '3', TTL => '2147483647', BLOCKSIZE => '65536', IN_MEMORY
- => 'false', BLOCKCACHE => 'true'}]}
- 1 row(s) in 0.0810 seconds
- hbase(main):002:0>
hbase(main):001:0> describe 'test'
DESCRIPTION ENABLED
{NAME => 'test', FAMILIES => [{NAME => 'data', COMPRESSION => 'NONE', true
VERSIONS => '3', TTL => '2147483647', BLOCKSIZE => '65536', IN_MEMORY
=> 'false', BLOCKCACHE => 'true'}]}
1 row(s) in 0.0810 seconds
hbase(main):002:0>
在看一下表中的数据，
Java代码
- hbase(main):002:0> scan 'test'
- ROW COLUMN+CELL
- row1 column=data:1, timestamp=1300847098583, value=value1
- row12 column=data:1, timestamp=1300849056637, value=value3
- row2 column=data:2, timestamp=1300847106880, value=value2
- 3 row(s) in 0.0160 seconds
- hbase(main):003:0>
hbase(main):002:0> scan 'test' ROW COLUMN+CELL row1 column=data:1, timestamp=1300847098583, value=value1 row12 column=data:1, timestamp=1300849056637, value=value3 row2 column=data:2, timestamp=1300847106880, value=value2 3 row(s) in 0.0160 seconds hbase(main):003:0>
列族：data:1、data:2两个
Key：row1、row12、row2
value：value1、value3、value2
hbase_table_2(key string, value string)中对应的test表中的row，value字段对应的是test表中的value
OK，现在可以来看看查询结果了，
我们在hive命令行中先查看一下hbase_table_2，
Java代码
- hive> select * from hbase_table_2;
- OK
- row1 value1
- row12 value3
- Time taken: 0.197 seconds
- hive>
hive> select * from hbase_table_2; OK row1 value1 row12 value3 Time taken: 0.197 seconds hive>
对比一下test表中的列族为data:1的数据，
Java代码
- row1 column=data:1, timestamp=1300847098583, value=value1
- row12 column=data:1, timestamp=1300849056637, value=value3
row1 column=data:1, timestamp=1300847098583, value=value1 row12 column=data:1, timestamp=1300849056637, value=value3
和查询结果相符，没问题，然后我们在hbase中在给列族data:1新增一条数据，
Java代码
- hbase(main):003:0> put 'test','row13','data:1','value4'
- 0 row(s) in 0.0050 seconds
- hbase(main):004:0>
hbase(main):003:0> put 'test','row13','data:1','value4' 0 row(s) in 0.0050 seconds hbase(main):004:0>
再查看hbase_table_2表，
Java代码
- hive> select * from hbase_table_2;
- OK
- row1 value1
- row12 value3
- row13 value4
- Time taken: 0.165 seconds
- hive>
hive> select * from hbase_table_2; OK row1 value1 row12 value3 row13 value4 Time taken: 0.165 seconds hive>
新增数据value4出现了，说明可以通过hbase_table_2查询hbase的test表
下面我们来查询一下test表中value值为value3的数据，
Java代码
- hive> select * From hbase_table_2 where value='value3';
- 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_201103231022_0001, Tracking URL = http://localhost:50030/jobdetails.jsp?jobid=job_201103231022_0001
- Kill Command = /home/dream-victor/hadoop-0.20.2/bin/hadoop job -Dmapred.job.tracker=localhost:9001 -kill job_201103231022_0001
- 2011-03-23 11:23:27,807 Stage-1 map = 0%, reduce = 0%
- 2011-03-23 11:23:30,824 Stage-1 map = 100%, reduce = 0%
- 2011-03-23 11:23:33,854 Stage-1 map = 100%, reduce = 100%
- Ended Job = job_201103231022_0001
- OK
- row12 value3
- Time taken: 11.929 seconds
- hive>
hive> select * From hbase_table_2 where value='value3'; 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_201103231022_0001, Tracking URL = http://localhost:50030/jobdetails.jsp?jobid=job_201103231022_0001 Kill Command = /home/dream-victor/hadoop-0.20.2/bin/hadoop job -Dmapred.job.tracker=localhost:9001 -kill job_201103231022_0001 2011-03-23 11:23:27,807 Stage-1 map = 0%, reduce = 0% 2011-03-23 11:23:30,824 Stage-1 map = 100%, reduce = 0% 2011-03-23 11:23:33,854 Stage-1 map = 100%, reduce = 100% Ended Job = job_201103231022_0001 OK row12 value3 Time taken: 11.929 seconds hive>
和hbase的test表对比一下，
Java代码
- row12 column=data:1, timestamp=1300849056637, value=value3
row12 column=data:1, timestamp=1300849056637, value=value3
OK，这样我们就可以使用SQL来对hbase进行查询了。
以上只是在命令行里左对应的查询，我们的目的是使用JAVA代码来查询出有用的数据，其实这个也很简单，
首先，启动Hive的命令有点变化，使用如下命令：
Java代码
- ./hive --service hiveserver
./hive --service hiveserver
这里我们默认使用嵌入的Derby数据库，这里可以在hive-site.xml文件中查看到：
Java代码
- <property>
- <name>javax.jdo.option.ConnectionURL</name>
- <value>jdbc:derby:;databaseName=metastore_db;create=true</value>//指定了数据库默认的名字和地址
- </property>
- <property>
- <name>javax.jdo.option.ConnectionDriverName</name>
- <value>org.apache.derby.jdbc.EmbeddedDriver</value>
- </property>
<property> <name>javax.jdo.option.ConnectionURL</name> <value>jdbc:derby:;databaseName=metastore_db;create=true</value>//指定了数据库默认的名字和地址 </property> <property> <name>javax.jdo.option.ConnectionDriverName</name> <value>org.apache.derby.jdbc.EmbeddedDriver</value> </property>
在此，数据库链接的URL可以使用默认的：jdbc:hive://localhost:10000/default
有了上面的准备，下面我们就可以使用JAVA代码来读取数据了，如下：
Java代码
- public class HiveTest extends TestCase {
- private static String driverName = "org.apache.hadoop.hive.jdbc.HiveDriver";
- private Connection con;
- private boolean standAloneServer = true;
- public void testSelect() throws SQLException {
- Statement stmt = con.createStatement();
- ResultSet res = stmt.executeQuery("select * from hbase_table_2");
- boolean moreRow = res.next();
- while (moreRow) {
- System.out.println(res.getString(1)+","+res.getString(2));
- moreRow = res.next();
- }
- }
- @Override
- protected void setUp() throws Exception {
- super.setUp();
- Class.forName(driverName);
- con = DriverManager.getConnection(
- "jdbc:hive://localhost:10000/default", "", "");
- }
- }
public class HiveTest extends TestCase {
private static String driverName = "org.apache.hadoop.hive.jdbc.HiveDriver";
private Connection con;
private boolean standAloneServer = true;
public void testSelect() throws SQLException {
Statement stmt = con.createStatement();
ResultSet res = stmt.executeQuery("select * from hbase_table_2");
boolean moreRow = res.next();
while (moreRow) {
System.out.println(res.getString(1)+","+res.getString(2));
moreRow = res.next();
}
}
@Override
protected void setUp() throws Exception {
super.setUp();
Class.forName(driverName);
con = DriverManager.getConnection(
"jdbc:hive://localhost:10000/default", "", "");
}
}
结果，
Java代码
- row1,value1
- row12,value3
- row13,value4
- row14,test
row1,value1 row12,value3 row13,value4 row14,test
查看一下hbase中的结果，
Java代码
- ROW COLUMN+CELL
- row1 column=data:1, timestamp=1300847098583, value=value1
- row12 column=data:1, timestamp=1300849056637, value=value3
- row13 column=data:1, timestamp=1300850443699, value=value4
- row14 column=data:1, timestamp=1300867550502, value=test
ROW COLUMN+CELL row1 column=data:1, timestamp=1300847098583, value=value1 row12 column=data:1, timestamp=1300849056637, value=value3 row13 column=data:1, timestamp=1300850443699, value=value4 row14 column=data:1, timestamp=1300867550502, value=test
OK，完美了，不过还是希望这样的需求少一点，毕竟Hbase产生的初衷不是为了支持结构化查询。