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1、 Hive索引概述
Hive的索引目的是提高Hive表指定列的查询速度。
没有索引时。类似‘WHERE tab1.col1 = 10‘ 的查询。Hive会载入整张表或分区。然后处理全部的rows，可是假设在字段col1上面存在索引时。那么仅仅会载入和处理文件的一部分。
与其它传统数据库一样。添加索引在提升查询速度时。会消耗额外资源去创建索引和须要很多其它的磁盘空间存储索引。
Hive 0.7.0版本号中，添加了索引。Hive 0.8.0版本号中添加了bitmap索引。
2、 索引相关的配置參数
Default Value: false
Added In: Hive 0.7.0 withHIVE-1889
在索引文件里存储的hdfs地址将在执行时被忽略，假设开启的话；假设数据被迁移。那么索引文件依旧可用，默认是false
hive.optimize.index.filter
Default Value: false
Added In: Hive 0.8.0 withHIVE-1644
是否自己主动使用索引, 默认是false
hive.optimize.index.filter.compact.minsize
Default Value: 5368709120
Added In: Hive 0.8.0 withHIVE-1644
hive.optimize.index.filter.compact.maxsize
Default Value: -1
Added In: Hive 0.8.0 withHIVE-1644
压缩索引自己主动应用的最大输入大小,负值代表正无穷
hive.index.compact.query.max.size
Default Value: 10737418240
Added In: Hive 0.8.0 withHIVE-2096
一个使用压缩索引做的查询能取到的最大数据量。默认是10737418240 个byte；负值代表无穷大；
hive.index.compact.query.max.entries
Default Value: 10000000
Added In: Hive 0.8.0 withHIVE-2096
使用压缩索引查询时能读到的最大索引项数，默认是10000000；负值代表无穷大；
hive.exec.concatenate.check.index
Default Value: true
Added In: Hive 0.8.0 withHIVE-2125
假设设置为true，那么在做ALTER TABLE tbl_name CONCATENATE on a table/partition（有索引） 操作时，抛出错误；能够帮助用户避免index的删除和重建；
Default Value: false
Added In: Hive 0.8.1 withHIVE-1694
hive.index.compact.binary.search
Default Value: true
Added In: Hive 0.8.1with HIVE-2535
在索引表中是否开启二分搜索进行索引项查询，默认是true。
3、 索引演示样例
注意：在Hive 0.12.0以及之前版本号中，索引名称在create index和drop index语句中是大写和小写敏感的。然而，alter index 须要一个小写的索引名字。
此bug在Hive 0.13.0解决，此版本号開始使索引名字大写和小写不敏感。
对于Hive 0.13.0之前的版本号，最好使用小写的索引名字。
以下介绍索引的常见使用方法：
A、 Create/build，show和drop index
create index table01_index ontable table01(column2) as ‘COMPACT‘ with deferred rebuild;
show index on table01;
drop index table01_index ontable01;
B、 Create then build。show formatted和drop index
create index table02_index ontable table02(column3) as ‘compact‘ with deferred rebuild;
alter index table02_index ontable02 rebuild;
show formatted index ontable02;
drop index table02_index ontable02;
C、 创建bitmap索引，build,show 和drop
createindex table03_index on table table03 (column4) as ‘bitmap‘ with deferred rebuild;
alter index table03_index ontable03 rebuild;
show formatted index ontable03;
drop index table03_index on table03;
D、 在一张新表上创建索引
createindex table04_index on table table04 (column5) as ‘compact‘with deferred rebuild in tabletable04_index_table;
E、 创建索引，存储格式为RCFile
create index table05_index ontable table05 (column6) as ‘compact‘ with deferred rebuildstored as rcfile;
F、 创建索引。存储格式为TextFile
create index table06_index ontable table06 (column7) as ‘compact‘ with deferredrebuild row format delimited fields terminated by ‘\t‘ stored as textfile;
G、 创建带有索引属性的索引
create index table07_index ontable table07 (column8) as ‘compact‘ with deferred rebuild idxproperties("prop1"="value1", "prop2"="value2");
H、 创建带有表属性的索引
create index table08_index ontable table08 (column9) as ‘compact‘ withdeferred rebuild tblproperties("prop3"="value3", "prop4"="value4");
I、 假设索引存在，则删除
drop index if exists table09_indexon table09;
J、 在分区上重建索引
alter index table10_index on table10partition (columnx=‘valueq‘, columny=‘valuer‘) rebuild;
4、 索引測试
(1) 查询表中行数
hive (hive)> select count(1)from userbook;
4409365
(2) 表中未创建索引前查询
hive (hive)> select * fromuserbook where book_id = ‘15999998838‘;
Query ID =hadoop_20150627165551_595da79a-0e27-453b-9142-7734912934c4
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks is setto 0 since there‘s no reduce operator
Starting Job =job_1435392961740_0012, Tracking URL =http://gpmaster:8088/proxy/application_1435392961740_0012/
Kill Command =/home/hadoop/hadoop-2.6.0/bin/hadoop job -kill job_1435392961740_0012
Hadoop job information forStage-1: number of mappers: 2; number of reducers: 0
2015-06-27 16:56:04,666 Stage-1map = 0%, reduce = 0%
2015-06-27 16:56:28,974 Stage-1map = 50%, reduce = 0%, Cumulative CPU4.36 sec
2015-06-27 16:56:31,123 Stage-1map = 78%, reduce = 0%, Cumulative CPU6.21 sec
2015-06-27 16:56:34,698 Stage-1map = 100%, reduce = 0%, Cumulative CPU7.37 sec
MapReduce Total cumulative CPUtime: 7 seconds 370 msec
Ended Job =job_1435392961740_0012
MapReduce Jobs Launched:
Stage-Stage-1: Map: 2 Cumulative CPU: 7.37 sec HDFS Read: 348355875 HDFS Write: 76 SUCCESS
Total MapReduce CPU Time Spent:7 seconds 370 msec
OK
userbook.book_id userbook.book_name userbook.author userbook.public_date userbook.address
15999998838 uviWfFJ KwCrDOA 2009-12-27 3b74416d-eb69-48e2-9d0d-09275064691b
Time taken: 45.678 seconds, Fetched: 1 row(s)
(3) 创建索引
hive (hive)> create indexuserbook_bookid_idx on table userbook(book_id) as ‘COMPACT‘ WITH DEFERREDREBUILD;
(4) 创建索引后再运行查询
hive (hive)> select * fromuserbook where book_id = ‘15999998838‘;
Query ID =hadoop_20150627170019_5bb5514a-4c8e-4c47-9347-ed0657e1f2ff
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks is setto 0 since there‘s no reduce operator
Starting Job =job_1435392961740_0013, Tracking URL = http://gpmaster:8088/proxy/application_1435392961740_0013/
Kill Command =/home/hadoop/hadoop-2.6.0/bin/hadoop job -kill job_1435392961740_0013
Hadoop job information forStage-1: number of mappers: 2; number of reducers: 0
2015-06-27 17:00:30,429 Stage-1map = 0%, reduce = 0%
2015-06-27 17:00:54,003 Stage-1map = 50%, reduce = 0%, Cumulative CPU7.43 sec
2015-06-27 17:00:56,181 Stage-1map = 78%, reduce = 0%, Cumulative CPU9.66 sec
2015-06-27 17:00:58,417 Stage-1map = 100%, reduce = 0%, Cumulative CPU10.83 sec
MapReduce Total cumulative CPUtime: 10 seconds 830 msec
Ended Job =job_1435392961740_0013
MapReduce Jobs Launched:
Stage-Stage-1: Map: 2 Cumulative CPU: 10.83 sec HDFS Read: 348356271 HDFS Write: 76 SUCCESS
Total MapReduce CPU Time Spent:10 seconds 830 msec
OK
userbook.book_id userbook.book_name userbook.author userbook.public_date userbook.address
15999998838 uviWfFJ KwCrDOA 2009-12-27 3b74416d-eb69-48e2-9d0d-09275064691b
Time taken: 40.549 seconds, Fetched: 1 row(s)
能够看到创建索引后，速度还是稍快一点的。
事实上对于这样的简单的查询，通过我们的设置，能够不用启动Map/Reduce的，而是启动Fetch task，直接从HDFS文件里filter过滤出须要的数据。须要设置例如以下參数：
set hive.fetch.task.conversion=more;
hive (hive)> select * fromuserbook where book_id = ‘15999998838‘;
OK
userbook.book_id userbook.book_name userbook.author userbook.public_date userbook.address
15999998838 uviWfFJ KwCrDOA 2009-12-27 3b74416d-eb69-48e2-9d0d-09275064691b
Time taken: 0.093 seconds,Fetched: 1 row(s)
能够看到速度更快了。毕竟省略掉了开启MR任务，运行效率提高不少。
參考：https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Indexing
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原文地址：http://www.cnblogs.com/yxwkf/p/5197802.html
