线上的zipkin的存储是利用的HBase0.94.6，一开始Dev想直接写MR来做离线分析，后来聊了下发现走Hive会提高开发的效率（当然，这里查询HBase的SQL接口还有phoenix，Impala等，只不过都还不够成熟，并且是离线分析不是adhocquery，BTW，前阶段和intel的聊过他们的Hive Over HBase是跳过MR的，效率非常赞，不过钱也略贵了=.=）；

潜江ssl适用于网站、小程序/APP、API接口等需要进行数据传输应用场景，ssl证书未来市场广阔！成为创新互联的ssl证书销售渠道，可以享受市场价格4-6折优惠！如果有意向欢迎电话联系或者加微信：18982081108（备注：SSL证书合作）期待与您的合作！

其实用Hive查询HBase非常简单：

//首先在HBase里建一张表并插入几条数据
hbase(main):003:0> create 'table_inhbase','cf'
0 row(s) in 1.2060 seconds
=> Hbase::Table - table_inhbase
hbase(main):004:0> list
TABLE                                                               
table_inhbase                                                        
1 row(s) in 0.0350 seconds
hbase(main):005:0> put 'table_inhbase','row1','cf:a','value1'
0 row(s) in 0.0830 seconds
hbase(main):006:0> put 'table_inhbase','row2','cf:a','value2'
0 row(s) in 0.0200 seconds
hbase(main):007:0> put 'table_inhbase','row3','cf:b','value3'
0 row(s) in 0.0180 seconds
hbase(main):008:0> scan 'table_inhbase'
ROW                                        COLUMN+CELL              
 row1                                      column=cf:a, timestamp=1383736436773,value=value1                                                             
 row2                                      column=cf:a, timestamp=1383736462917,value=value2                                                             
 row3                                      column=cf:b, timestamp=1383736476017,value=value3                                                            
3 row(s) in 0.0660 seconds
//在Hive里创建一个外部表，注意要在hive-site.xml加入ZK,否则会hang住，一直去重试localhost:2181
CREATE EXTERNAL TABLE ext_table_inhbase(key string, avalue string,bvaluestring) 
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler' 
WITH SERDEPROPERTIES ("hbase.columns.mapping" ="cf:a,cf:b") 
TBLPROPERTIES("hbase.table.name" = "table_inhbase");
hive> CREATE EXTERNAL TABLE ext_table_inhbase(key string, avaluestring,bvalue string) 
    > STORED BY'org.apache.hadoop.hive.hbase.HBaseStorageHandler' 
    > WITH SERDEPROPERTIES("hbase.columns.mapping" = "cf:a,cf:b") 
    > TBLPROPERTIES("hbase.table.name" ="table_inhbase");
OK
//注意，这里要加入这2个jar包：hbase-0.94.6-cdh5.4.0.jar，hive-hbase-handler-0.10.0-cdh5.4.0.jar否则会抛出异常
hive> select * from ext_table_inhbase;
OK
row1    value1  NULL
row2    value2  NULL
row3    NULL    value3
Time taken: 0.609 seconds
hive> select key,avalue from ext_table_inhbase;
java.io.IOException: Cannot create an instance of InputSplit.apache.hadoop.hive.hbase.HBaseSplit:Classorg.apache.hadoop.hive.hbase.HBaseSplit not found
        at org.apache.hadoop.hive.ql.io.HiveInputFormat$HiveInputSplit.readFields(HiveInputFormat.java:146)
        atorg.apache.hadoop.io.serializer.WritableSerialization$WritableDeserializer.deserialize(WritableSerialization.java:73)
        atorg.apache.hadoop.io.serializer.WritableSerialization$WritableDeserializer.deserialize(WritableSerialization.java:44)
        atorg.apache.hadoop.mapred.MapTask.getSplitDetails(MapTask.java:356)
        atorg.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:388)
        at org.apache.hadoop.mapred.MapTask.run(MapTask.java:332)
        atorg.apache.hadoop.mapred.Child$4.run(Child.java:268)
        atjava.security.AccessController.doPrivileged(Native Method)
        atjavax.security.auth.Subject.doAs(Subject.java:396)
        at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1408)
hive> select key,avalue from ext_table_inhbase;
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Hadoop job information for Stage-1: number of mappers: 1; number ofreducers: 0
19:33:55,386 Stage-1 map = 0%,  reduce = 0%
19:34:01,472 Stage-1 map = 100%,  reduce = 0%, CumulativeCPU 2.73 sec
19:34:02,495 Stage-1 map = 100%,  reduce = 0%, CumulativeCPU 2.73 sec
19:34:03,512 Stage-1 map = 100%,  reduce = 100%,Cumulative CPU 2.73 sec
MapReduce Total cumulative CPU time: 2 seconds 730 msec
Ended Job = job_201311061424_0003
MapReduce Jobs Launched:
Job 0: Map: 1   Cumulative CPU: 2.73 sec   HDFS Read:255 HDFS Write: 39 SUCCESS
Total MapReduce CPU Time Spent: 2 seconds 730 msec
OK
row1    value1
row2    value2
//尝试通过HiveServer去查询
beeline> !connect jdbc:hive2://test-2:10000 hdfs hdfsorg.apache.hive.jdbc.HiveDriver        
Connecting to jdbc:hive2://test-2:10000
Connected to: Hive (version 0.10.0)
Driver: Hive (version 0.10.0-cdh5.4.0)
Transaction isolation: TRANSACTION_REPEATABLE_READ
0: jdbc:hive2://test-2:10000> show databases;
+----------------+
| database_name  |
+----------------+
| default        |
+----------------+
1 row selected (1.483 seconds)
0: jdbc:hive2://test-2:10000> show tables;
+--------------------+
|      tab_name      |
+--------------------+
| ext_table_inhbase  |
|test              |
+--------------------+
2 rows selected (0.657 seconds)
0: jdbc:hive2://test-2:10000> select count(*) from ext_table_inhbase;
+------+
| _c0  |
+------+
| 3    |
+------+

新闻名称：通过Hive查询HBase
URL分享：http://kswjz.com/article/jepsjs.html