Hive与Hbase结合使用
hive的启动需要使用到zookeeper, 所以, 要么自己搭建zookeeper, 要么跟其它东西一起使用, 我这里做的是跟hbase一起使用的zookeeper, 因为hbase自带zookeeper, hbase启动就会启动zookeeper, 而hive默认会连接本机的2181端口, 所以我这里选择在slaver3上使用hive.
集群的搭建以及机器的分配见hadoop搭建: http://phey.cc/multinode_hadoop20.html
以及hbase集群搭建http://phey.cc/Install_hbase_cluster.html
解压hive包后拷贝环境变量模板到指定文件
[cc@slaver3 ~]$ cp hive-0.12.0-cdh5.0.1/conf/hive-env.sh.template hive-0.12.0-cdh5.0.1/conf/hive-env.sh
[cc@slaver3 ~]$ ▊
编辑环境变量， 一个是hadoop的安装目录，一个是hbase的jar位置，如果hbase和hive的jar版本不对会报错
[cc@slaver3 ~]$ vim hive-0.12.0-cdh5.0.1/conf/hive-env.sh
export HADOOP_HOME=/home/cc/hadoop-2.3.0-cdh5.0.0
export HIVE_AUX_JARS_PATH=/home/cc/hbase-0.96.1.1-cdh5.0.1/lib
[cc@slaver3 ~]$ ▊
启动hive，指定hbase的RPC端口
[cc@slaver3 hive-0.12.0-cdh5.0.1]$ bin/hive -hiveconf hbase.master=master1:60000
14/08/21 21:34:07 INFO Configuration.deprecation: mapred.input.dir.recursive is deprecated. Instead, use mapreduce.input.fileinputformat.input.dir.recursive
14/08/21 21:34:07 INFO Configuration.deprecation: mapred.max.split.size is deprecated. Instead, use mapreduce.input.fileinputformat.split.maxsize
14/08/21 21:34:07 INFO Configuration.deprecation: mapred.min.split.size is deprecated. Instead, use mapreduce.input.fileinputformat.split.minsize
14/08/21 21:34:07 INFO Configuration.deprecation: mapred.min.split.size.per.rack is deprecated. Instead, use mapreduce.input.fileinputformat.split.minsize.per.rack
14/08/21 21:34:07 INFO Configuration.deprecation: mapred.min.split.size.per.node is deprecated. Instead, use mapreduce.input.fileinputformat.split.minsize.per.node
14/08/21 21:34:07 INFO Configuration.deprecation: mapred.reduce.tasks is deprecated. Instead, use mapreduce.job.reduces
14/08/21 21:34:07 INFO Configuration.deprecation: mapred.reduce.tasks.speculative.execution is deprecated. Instead, use mapreduce.reduce.speculative
Logging initialized using configuration in jar:file:/home/cc/hive-0.12.0-cdh5.0.1/lib/hive-common-0.12.0-cdh5.0.1.jar!/hive-log4j.properties
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/cc/hadoop-2.3.0-cdh5.0.0/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/cc/hive-0.12.0-cdh5.0.1/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/cc/hbase-0.96.1.1-cdh5.0.1/lib/slf4j-log4j12-1.7.5.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]
hive> ▊
在hive中建表。在hive新建一个名为cctabl的表，这个表映射到hbase的表名是cc，cctable表里面的int类型的key对应了cc表里面的row key，cctable里面类型是string的value对应了cc表里面的cf:val
hive> CREATE TABLE cctable (key int, value string) STORED BY \’org.apache.hadoop.hive.hbase.HBaseStorageHandler\’ WITH SERDEPROPERTIES (“hbase.columns.mapping” = “:key,cf:val”) TBLPROPERTIES (“hbase.table.name” = “cc”);
OK
Time taken: 9.302 seconds
hive> ▊
在hbase中可以看到结果, 创建了一个表
hbase(main):011:0> list
TABLE
cc
1 row(s) in 0.0250 seconds
=> [“cc”]
hbase(main):012:0> describe \’cc\’
DESCRIPTION ENABLED
\’cc\’, {NAME => \’cf\’, DATA_BLOCK_ENCODING => \’NONE\’, BLOOMFILTER => \’ROW\’, REPLICATION_SCOPE => \’0\’, VERSIO true
NS => \’1\’, COMPRESSION => \’NONE\’, MIN_VERSIONS => \’0\’, TTL => \’2147483647\’, KEEP_DELETED_CELLS => \’false\’,
BLOCKSIZE => \’65536\’, IN_MEMORY => \’false\’, BLOCKCACHE => \’true\’}
1 row(s) in 0.0390 seconds
hbase(main):013:0> ▊
如果不希望hive去创建表而是使用hbase已经有的表, 那么创建表的时候加上external参数就可以了, 例如
hive> CREATE EXTERNAL TABLE cctable (key int, value string) STORED BY \’org.apache.hadoop.hive.hbase.HBaseStorageHandler\’ WITH SERDEPROPERTIES (“hbase.columns.mapping” = “:key,cf:val”) TBLPROPERTIES (“hbase.table.name” = “cc”);
OK
Time taken: 9.302 seconds
hive> ▊
由于有那个映射关系，所以往hbase里面插入数据可以在hive里面查询，不过hive不支持类似于mysql的insert语句，所以方便的也只能往hbase里面插入数据
向hbase里面插入数据
hbase(main):013:0> put \’cc\’, \’1\’, \’cf:val\’, \’hello cc!\’
0 row(s) in 0.0120 seconds
hbase(main):014:0> ▊
在hive里面查询
hive> select * from cctable;
OK
1 hello cc!
Time taken: 30.838 seconds, Fetched: 1 row(s)
hive> ▊
虽然hbase不支持count方法去计算行数，但是hive可以，不过这个会被转换成mapreduce过程，去执行
hive> select count(*) from cctable;
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapred.reduce.tasks=<number>
Starting Job = job_1408532552242_0005, Tracking URL = http://master1:8088/proxy/application_1408532552242_0005/
Kill Command = /home/cc/hadoop-2.3.0-cdh5.0.0/bin/hadoop job -kill job_1408532552242_0005
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2014-08-22 10:15:00,861 Stage-1 map = 0%, reduce = 0%
2014-08-22 10:15:14,479 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.73 sec
2014-08-22 10:15:15,525 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.73 sec
2014-08-22 10:15:16,572 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.73 sec
2014-08-22 10:15:17,617 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.73 sec
2014-08-22 10:15:18,662 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.73 sec
2014-08-22 10:15:19,707 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.73 sec
2014-08-22 10:15:20,753 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.73 sec
2014-08-22 10:15:21,798 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.73 sec
2014-08-22 10:15:22,842 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.73 sec
2014-08-22 10:15:23,889 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.73 sec
2014-08-22 10:15:24,937 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.73 sec
2014-08-22 10:15:25,991 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.73 sec
2014-08-22 10:15:27,040 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.73 sec
2014-08-22 10:15:28,094 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 4.89 sec
2014-08-22 10:15:29,143 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 4.89 sec
MapReduce Total cumulative CPU time: 4 seconds 890 msec
Ended Job = job_1408532552242_0005
MapReduce Jobs Launched:
Job 0: Map: 1 Reduce: 1 Cumulative CPU: 4.89 sec HDFS Read: 236 HDFS Write: 2 SUCCESS
Total MapReduce CPU Time Spent: 4 seconds 890 msec
OK
1
Time taken: 77.571 seconds, Fetched: 1 row(s)
hive> ▊