Hive HBase 整合
hive hbase整合，要求比较多，1.hive的得是0.6.0（当前最新的版本）
2.hive本身要求hadoop的最高版本是hadoop-0.20.2
3.要求hbase的版本是0.20.3，其他版本需要重新编译hive_hbase-handler
但是新版的hbase（0.90）变动特别大，根本无法从编译。这点比较恶心，hbase目前升级比较快，当前是0.90（从0.20.6直接跳到0.89），至于为什么这样跳跃，参考官方的解释http://wiki.apache.org/hadoop/Hbase/HBaseVersions
1)启动Hbase,
要求hbase-0.20.3，zookeeper-3.2.2
如果使用的不是hbase-0.20.3需要重新编译hive_hbase-handler.jar
2)单节点HBase的连接
./bin/hive -hiveconf hbase.master=master:60000
3)集群HBase的连接
1.启动zookeeper
2.启动hbase
3.启动hive，添加zookeeper的支持
Java代码
- ./bin/hive -hiveconf hbase.zookeeper.quorum= master,slave-A,slave-B
./bin/hive -hiveconf hbase.zookeeper.quorum= master,slave-A,slave-B
//所有的zookeeper节点
二、插入数据
启动
Java代码
- ./bin/hive --auxpath /data/soft/hive/lib/hive_hbase-handler.jar,/data/soft/hive/lib/hbase-0.20.3.jar,/data/soft/hive/lib/zookeeper-3.2.2.jar -hiveconf hbase.zookeeper.quorum=slave-001,slave-002,slave-003
./bin/hive --auxpath /data/soft/hive/lib/hive_hbase-handler.jar,/data/soft/hive/lib/hbase-0.20.3.jar,/data/soft/hive/lib/zookeeper-3.2.2.jar -hiveconf hbase.zookeeper.quorum=slave-001,slave-002,slave-003
hive
1.创建hbase识别的数据库
Java代码
- CREATE TABLE hbase_table_1(key int, value string)
- STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
- WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key,cf1:val")
- TBLPROPERTIES ("hbase.table.name" = "xyz");
CREATE TABLE hbase_table_1(key int, value string)
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key,cf1:val")
TBLPROPERTIES ("hbase.table.name" = "xyz");
hbase.table.name 定义在hbase的table名称
hbase.columns.mapping 定义在hbase的列族
2.使用sql导入数据
i.预先准备数据
a)新建hive的数据表
Java代码
- CREATE TABLE pokes (foo INT, bar STRING);
CREATE TABLE pokes (foo INT, bar STRING);
b）批量插入数据
Java代码
- hive> LOAD DATA LOCAL INPATH './examples/files/kv1.txt' OVERWRITE INTO TABLE pokes;
hive> LOAD DATA LOCAL INPATH './examples/files/kv1.txt' OVERWRITE INTO TABLE pokes;
这个文件位于hive的安装目录下，examples/files/kv1.txt
Java代码
- ii.使用sql导入hbase_table_1
ii.使用sql导入hbase_table_1
Java代码
- INSERT OVERWRITE TABLE hbase_table_1 SELECT * FROM pokes WHERE foo=86;
INSERT OVERWRITE TABLE hbase_table_1 SELECT * FROM pokes WHERE foo=86;
注意，默认的启动会报错的
FAILED: Execution Error, return code 2 from org.apache.hadoop.hive.ql.exec.ExecDriver
启动的时候要添加
Java代码
- -auxpath /data/soft/hive/lib/hive_hbase-handler.jar,/data/soft/hive/lib/hbase-0.20.3.jar,/data/soft/hive/lib/zookeeper-3.2.2.jar
-auxpath /data/soft/hive/lib/hive_hbase-handler.jar,/data/soft/hive/lib/hbase-0.20.3.jar,/data/soft/hive/lib/zookeeper-3.2.2.jar
3查看数据
Java代码
- hive> select * from hbase_table_1;
hive> select * from hbase_table_1;
会显示刚刚插入的数据
86 val_86
hbase
1.登录hbase
Java代码
- [root@master hbase]# ./bin/hbase shell
[root@master hbase]# ./bin/hbase shell
2.查看表结构
Java代码
- hbase(main):001:0> describe 'xyz'
- DESCRIPTION ENABLED
- {NAME => 'xyz', FAMILIES => [{NAME => 'cf1', COMPRESSION => 'NONE', VE true
- RSIONS => '3', TTL => '2147483647', BLOCKSIZE => '65536', IN_MEMORY =>
- 'false', BLOCKCACHE => 'true'}]}
- 1 row(s) in 0.7460 seconds
hbase(main):001:0> describe 'xyz'
DESCRIPTION ENABLED
{NAME => 'xyz', FAMILIES => [{NAME => 'cf1', COMPRESSION => 'NONE', VE true
RSIONS => '3', TTL => '2147483647', BLOCKSIZE => '65536', IN_MEMORY =>
'false', BLOCKCACHE => 'true'}]}
1 row(s) in 0.7460 seconds
3.查看加载的数据
Java代码
- hbase(main):002:0> scan 'xyz'
- ROW COLUMN+CELL
- 86 column=cf1:val, timestamp=1297690405634, value=val_86
hbase(main):002:0> scan 'xyz'
ROW COLUMN+CELL
86 column=cf1:val, timestamp=1297690405634, value=val_86
1 row(s) in 0.0540 seconds
可以看到，在hive中添加的数据86，已经在hbase中了
4.添加数据
Java代码
- ' hbase(main):008:0> put 'xyz','100','cf1:val','www.360buy.com'
- 0 row(s) in 0.0630 seconds
' hbase(main):008:0> put 'xyz','100','cf1:val','www.360buy.com'
0 row(s) in 0.0630 seconds
Hive
参看hive中的数据
Java代码
- hive> select * from hbase_table_1;
- OK
- 100 www.360buy.com
- 86 val_86
- Time taken: 8.661 seconds
hive> select * from hbase_table_1;
OK
100 www.360buy.com
86 val_86
Time taken: 8.661 seconds
刚刚在hbase中插入的数据，已经在hive里了
hive访问已经存在的hbase
使用CREATE EXTERNAL TABLE
Java代码
- CREATE EXTERNAL TABLE hbase_table_2(key int, value string)
- STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
- WITH SERDEPROPERTIES ("hbase.columns.mapping" = "cf1:val")
- TBLPROPERTIES("hbase.table.name" = "some_existing_table");
CREATE EXTERNAL TABLE hbase_table_2(key int, value string)
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
WITH SERDEPROPERTIES ("hbase.columns.mapping" = "cf1:val")
TBLPROPERTIES("hbase.table.name" = "some_existing_table");
三、多列和多列族（Multiple Columns and Families）
1．创建数据库
Java代码
- CREATE TABLE hbase_table_2(key int, value1 string, value2 int, value3 int)
- STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
- WITH SERDEPROPERTIES (
- "hbase.columns.mapping" = ":key,a:b,a:c,d:e"
- );
CREATE TABLE hbase_table_2(key int, value1 string, value2 int, value3 int)
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
WITH SERDEPROPERTIES (
"hbase.columns.mapping" = ":key,a:b,a:c,d:e"
);
2.插入数据
Java代码
- INSERT OVERWRITE TABLE hbase_table_2 SELECT foo, bar, foo+1, foo+2
- FROM pokes WHERE foo=98 OR foo=100;
INSERT OVERWRITE TABLE hbase_table_2 SELECT foo, bar, foo+1, foo+2
FROM pokes WHERE foo=98 OR foo=100;
这个有3个hive的列（value1和value2，value3），2个hbase的列族（a，d）
Hive的2列（value1和value2）对应1个hbase的列族（a，在hbase的列名称b，c），hive的另外1列（value3）对应列（e）位于列族（d）
3.登录hbase查看结构
Java代码
- hbase(main):003:0> describe "hbase_table_2"
- DESCRIPTION ENABLED
- {NAME => 'hbase_table_2', FAMILIES => [{NAME => 'a', COMPRESSION => 'N true
- ONE', VERSIONS => '3', TTL => '2147483647', BLOCKSIZE => '65536', IN_M
- EMORY => 'false', BLOCKCACHE => 'true'}, {NAME => 'd', COMPRESSION =>
- 'NONE', VERSIONS => '3', TTL => '2147483647', BLOCKSIZE => '65536', IN
- _MEMORY => 'false', BLOCKCACHE => 'true'}]}
- 1 row(s) in 1.0630 seconds
hbase(main):003:0> describe "hbase_table_2"
DESCRIPTION ENABLED
{NAME => 'hbase_table_2', FAMILIES => [{NAME => 'a', COMPRESSION => 'N true
ONE', VERSIONS => '3', TTL => '2147483647', BLOCKSIZE => '65536', IN_M
EMORY => 'false', BLOCKCACHE => 'true'}, {NAME => 'd', COMPRESSION =>
'NONE', VERSIONS => '3', TTL => '2147483647', BLOCKSIZE => '65536', IN
_MEMORY => 'false', BLOCKCACHE => 'true'}]}
1 row(s) in 1.0630 seconds
4.查看hbase的数据
Java代码
- hbase(main):004:0> scan 'hbase_table_2'
- ROW COLUMN+CELL
- 100 column=a:b, timestamp=1297695262015, value=val_100
- 100 column=a:c, timestamp=1297695262015, value=101
- 100 column=d:e, timestamp=1297695262015, value=102
- 98 column=a:b, timestamp=1297695242675, value=val_98
- 98 column=a:c, timestamp=1297695242675, value=99
- 98 column=d:e, timestamp=1297695242675, value=100
- 2 row(s) in 0.0380 seconds
hbase(main):004:0> scan 'hbase_table_2'
ROW COLUMN+CELL
100 column=a:b, timestamp=1297695262015, value=val_100
100 column=a:c, timestamp=1297695262015, value=101
100 column=d:e, timestamp=1297695262015, value=102
98 column=a:b, timestamp=1297695242675, value=val_98
98 column=a:c, timestamp=1297695242675, value=99
98 column=d:e, timestamp=1297695242675, value=100
2 row(s) in 0.0380 seconds
5.在hive中查看
Java代码
- hive> select * from hbase_table_2;
- OK
- 100 val_100 101 102
- 98 val_98 99 100
- Time taken: 3.238 seconds
hive> select * from hbase_table_2;
OK
100 val_100 101 102
98 val_98 99 100
Time taken: 3.238 seconds