Hbase——练习2

1、创建表

lf: column family of LONG values (binary value)

sf: column family of STRING values

create 'test1', 'lf', 'sf'

2、导入数据

一个用户（userX），在什么时间（tsX），作为rowkey

对什么产品（value：skuXXX），做了什么操作作为列名

比如，c1: click from homepage; c2: click from ad; s1: search from homepage; b1: buy

put 'test:test1', 'user1|ts1', 'sf:c1', 'sku1'
put 'test:test1', 'user1|ts2', 'sf:c1', 'sku2'
put 'test:test1', 'user1|ts3', 'sf:s1', 'sku3'
put 'test:test1', 'user2|ts4', 'sf:c1', 'sku11'
put 'test:test1', 'user2|ts5', 'sf:c2', 'sku22'
put 'test:test1', 'user2|ts6', 'sf:s1', 'sku33'

 3、查询案例，谁的值=sku1

scan 'test:test1', FILTER=>"ValueFilter(=,'binary:sku1')"

 4、查询案例，谁的值包含2

scan 'test:test1', FILTER=>"ValueFilter(=,'substring:2')"

 5、通过搜索点击进来的(column为s2)值包含2的用户

scan 'test:test1', FILTER=>"ColumnPrefixFilter('s2') AND ValueFilter(=,'substring:2')"

 6、通过搜索进来的(column为c1)值包含1或者2的用户

scan 'test:test1', FILTER=>"ColumnPrefixFilter('c1') AND ( ValueFilter(=,'substring:1') OR ValueFilter(=,'substring:2') )"

 

 7、rowkey为user1开头的

scan 'test:test1', FILTER => "PrefixFilter ('user1')"

 8、只拿出key中的第一个column的第一个version

FirstKeyOnlyFilter: 一个rowkey可以有多个version,同一个rowkey的同一个column也会有多个的值

KeyOnlyFilter: 只要key,不要value

scan 'test:test1', FILTER=>"FirstKeyonlyFilter() AND ValueFilter(=,'binary:sku1') AND KeyonlyFilter()"

 9、从user1|ts3开始,找到所有的rowkey以user1开头的

scan 'test1', {STARTROW=>'user1|ts2', FILTER => "PrefixFilter ('user1')"

10、查询rowkey里面包含ts3的

import org.apache.hadoop.hbase.filter.CompareFilter
import org.apache.hadoop.hbase.filter.SubstringComparator
import org.apache.hadoop.hbase.filter.RowFilter

scan 'test:test1', {FILTER => RowFilter.new(CompareFilter::CompareOp.valueOf('EQUAL'), SubstringComparator.new('ts3'))}

 11、查询rowkey里面包含ts的

import org.apache.hadoop.hbase.filter.CompareFilter
import org.apache.hadoop.hbase.filter.SubstringComparator
import org.apache.hadoop.hbase.filter.RowFilter
scan 'test:test1', {FILTER => RowFilter.new(CompareFilter::CompareOp.valueOf('EQUAL'), SubstringComparator.new('ts'))}