1. 背景

2.hbase查询的确是不太方便，除了指定rowkey，或者通过指定startkey stopkey进行scan之外，没有更有效的查询方式 如果想通过列值过滤，只能全表扫描了 如果要搞什么group by或者order by（除非你的rowkey做了相应设计） 更是没法弄 在传统的mysql/oracle得心应手的查询在hbase上就是束手束脚

3.当然可以通过写hadoop job解决问题，但为了查询去写job，代价未免有点高 于是hive出现了

4.有两个方法可以集成hive和hbase

1.使用HBaseStorageHandler，这个会直接操作HBase,可能会对线上产生影响

2.将HBase定期导入到HDFS，再通过hive访问HDFS

下面将详述第二种方法

HDFS导入

1.使用datax将HBase表导入到HDFS上,比如/group/wireless-arctic/task/arctic_task

2.hive产生外部表，从而避免导入数据
CREATE EXTERNAL TABLE task_history (
biz_type string,
cid string,
content string,
ctime string,
gmt_create string,
hostName string,
item string,
mtime string,
otags string,
priority string,
retry string,
result string,
srcImages string,
src_url string,
status string,
summary string,
task_type string,
title string,
userId string,
userNick string,
utags string,
writer string
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\001'
LOCATION '/group/wireless-arctic/task';location是云梯文件的目录

3.测试
select cid,result from task_history limit 10;
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Selecting distributed mode: Input Size (= 2578823293 = 2 gigabytes 411 megabytes 366 kilobytes 125 bytes) is larger than hive.exec.mode.local.auto.inputbytes.max (= 134217728 = 128 megabytes 0 kilobytes 0 bytes)
Starting Job = job_201311281255_6734353, Tracking URL = http://hdpjt2.alibaba-inc.com/jobdetails.jsp jobid=job_201311281255_6734353
Kill Command = /home/hadoop/hadoop-current/bin/../bin/hadoop job -Dmapred.job.tracker=hdpjt:9001 -kill job_201311281255_6734353
Hadoop job information for Stage-1: number of mappers: 10; number of reducers: 0
2013-12-19 18:53:02,891 Stage-1 map = 0%, reduce = 0%
2013-12-19 18:53:11,017 Stage-1 map = 50%, reduce = 0%
2013-12-19 18:53:12,033 Stage-1 map = 90%, reduce = 0%
2013-12-19 18:53:19,394 Stage-1 map = 100%, reduce = 100%
Ended Job = job_201311281255_6734353
OK
200011928538 success
200011928538 success
200011909281 success
200011928474 success
200011909281 success
200011928474 success
110010569498 failure:userId:1782836127,contentId:110010569498 ImageFlow,call error and ret:1
110010523403 success
110010523921 success
110010524299 success
Time taken: 23.137 seconds = 23 seconds 137 milliseconds添加分区及自动化

1.完成了上面的步骤，你就可以查询数据了，但面临一个问题，数据更新怎么办？

一个比较通用的做法就是每天跑一个定时任务将HBase表dump到HDFS，即每天一个快照每天的快照可以存放在以日期命名的目录中，这样可以保存多份快照，出了问题也好追踪2.hive如何利用这每天的快照

那就是hive分区

分区的本意是数据量大了切分数据,但目前我们并未如此使用，而是利用分区来区分快照删除之前的表

drop table task_history;产生一张分区表
CREATE EXTERNAL TABLE task_history (
biz_type string,
cid string,
content string,
ctime string,
gmt_create string,
hostName string,
item string,
mtime string,
otags string,
priority string,
retry string,
result string,
srcImages string,
src_url string,
status string,
summary string,
task_type string,
title string,
userId string,
userNick string,
utags string,
writer string
)
PARTITIONED BY (dt string)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\001'
LOCATION '/group/wireless-arctic/task';其实就是在之前的建表语句中加了一行PARTITIONED BY (dt string)

添加分区

ALTER TABLE task_history ADD PARTITION(dt='20131223') LOCATION '/group/wireless-arctic/task/20131223';3.如何自动化

通过工具比如datax或者其他导出工具将HBase表导出到HDFS,正如前面提到的每天一个目录（以日期命名）