hbase shell操作之scan+filter

转载于：https://blog.csdn.net/liuxiao723846/article/details/73823056

有点草率，最近课程紧

创建表

 

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

lf: column family of LONG values (binary value) 
-- sf: column family of STRING values

 

导入数据

 

  1.  
    put 'test1', 'user1|ts1', 'sf:c1', 'sku1'
  2.  
    put 'test1', 'user1|ts2', 'sf:c1', 'sku188'
  3.  
    put 'test1', 'user1|ts3', 'sf:s1', 'sku123'
  4.  
     
  5.  
    put 'test1', 'user2|ts4', 'sf:c1', 'sku2'
  6.  
    put 'test1', 'user2|ts5', 'sf:c2', 'sku288'
  7.  
    put 'test1', 'user2|ts6', 'sf:s1', 'sku222'


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

 

查询案例


1、谁的值=sku188

  1.  
    scan 'test1', FILTER=>"ValueFilter(=,'binary:sku188')"
  2.  
     
  3.  
    ROW COLUMN+CELL
  4.  
    user1|ts2 column=sf:c1, timestamp=1409122354918, value=sku188

2、谁的值包含88

 

 

  1.  
    scan 'test1', FILTER=>"ValueFilter(=,'substring:88')"
  2.  
     
  3.  
    ROW COLUMN+CELL
  4.  
    user1|ts2 column=sf:c1, timestamp=1409122354918, value=sku188
  5.  
    user2|ts5 column=sf:c2, timestamp=1409122355030, value=sku288


3、通过广告点击进来的(column为c2)值包含88的用户

 

 

  1.  
    scan 'test1', FILTER=>"ColumnPrefixFilter('c2') AND ValueFilter(=,'substring:88')"
  2.  
     
  3.  
    ROW COLUMN+CELL
  4.  
    user2|ts5 column=sf:c2, timestamp=1409122355030, value=sku288


4、通过搜索进来的(column为s)值包含123或者222的用户

 

 

  1.  
    scan 'test1', FILTER=>"ColumnPrefixFilter('s') AND ( ValueFilter(=,'substring:123') OR ValueFilter(=,'substring:222') )"
  2.  
     
  3.  
    ROW COLUMN+CELL
  4.  
    user1|ts3 column=sf:s1, timestamp=1409122354954, value=sku123
  5.  
    user2|ts6 column=sf:s1, timestamp=1409122355970, value=sku222

5、rowkey为user1开头的

 

 

  1.  
    scan 'test1', FILTER => "PrefixFilter ('user1')"
  2.  
     
  3.  
    ROW COLUMN+CELL
  4.  
    user1|ts1 column=sf:c1, timestamp=1409122354868, value=sku1
  5.  
    user1|ts2 column=sf:c1, timestamp=1409122354918, value=sku188
  6.  
    user1|ts3 column=sf:s1, timestamp=1409122354954, value=sku123


6、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, 只拿出key中的第一个column的第一个version 
KeyOnlyFilter: 只要key,不要value 

  1.  
    scan 'test1', FILTER=>"FirstKeyOnlyFilter() AND ValueFilter(=,'binary:sku188') AND KeyOnlyFilter()"
  2.  
     
  3.  
    ROW COLUMN+CELL
  4.  
    user1|ts2 column=sf:c1, timestamp=1409122354918, value=


7、从user1|ts2开始,找到所有的rowkey以user1开头的

 

 

  1.  
    scan 'test1', {STARTROW=>'user1|ts2', FILTER => "PrefixFilter ('user1')"}
  2.  
     
  3.  
    ROW COLUMN+CELL
  4.  
    user1|ts2 column=sf:c1, timestamp=1409122354918, value=sku188
  5.  
    user1|ts3 column=sf:s1, timestamp=1409122354954, value=sku123


8、从user1|ts2开始,找到所有的到rowkey以user2开头

 

 

  1.  
    scan 'test1', {STARTROW=>'user1|ts2', STOPROW=>'user2'}
  2.  
     
  3.  
    ROW COLUMN+CELL
  4.  
    user1|ts2 column=sf:c1, timestamp=1409122354918, value=sku188
  5.  
    user1|ts3 column=sf:s1, timestamp=1409122354954, value=sku123


9、查询rowkey里面包含ts3的

 

 

  1.  
    import org.apache.hadoop.hbase.filter.CompareFilter
  2.  
    import org.apache.hadoop.hbase.filter.SubstringComparator
  3.  
    import org.apache.hadoop.hbase.filter.RowFilter
  4.  
    scan 'test1', {FILTER => RowFilter.new(CompareFilter::CompareOp.valueOf('EQUAL'), SubstringComparator.new('ts3'))}
  5.  
     
  6.  
    ROW COLUMN+CELL
  7.  
    user1|ts3 column=sf:s1, timestamp=1409122354954, value=sku123

10、查询rowkey里面包含ts的

 

 

  1.  
    import org.apache.hadoop.hbase.filter.CompareFilter
  2.  
    import org.apache.hadoop.hbase.filter.SubstringComparator
  3.  
    import org.apache.hadoop.hbase.filter.RowFilter
  4.  
    scan 'test1', {FILTER => RowFilter.new(CompareFilter::CompareOp.valueOf('EQUAL'), SubstringComparator.new('ts'))}
  5.  
     
  6.  
    ROW COLUMN+CELL
  7.  
    user1|ts1 column=sf:c1, timestamp=1409122354868, value=sku1
  8.  
    user1|ts2 column=sf:c1, timestamp=1409122354918, value=sku188
  9.  
    user1|ts3 column=sf:s1, timestamp=1409122354954, value=sku123
  10.  
    user2|ts4 column=sf:c1, timestamp=1409122354998, value=sku2
  11.  
    user2|ts5 column=sf:c2, timestamp=1409122355030, value=sku288
  12.  
    user2|ts6 column=sf:s1, timestamp=1409122355970, value=sku222


加入一条测试数据

 

 

put 'test1', 'user2|err', 'sf:s1', 'sku999'


11、查询rowkey里面以user开头的，新加入的测试数据并不符合正则表达式的规则，故查询不出来 

  1.  
    import org.apache.hadoop.hbase.filter.RegexStringComparator
  2.  
    import org.apache.hadoop.hbase.filter.CompareFilter
  3.  
    import org.apache.hadoop.hbase.filter.SubstringComparator
  4.  
    import org.apache.hadoop.hbase.filter.RowFilter
  5.  
    scan 'test1', {FILTER => RowFilter.new(CompareFilter::CompareOp.valueOf('EQUAL'),RegexStringComparator.new('^userd+|tsd+$'))}
  6.  
     
  7.  
    ROW COLUMN+CELL
  8.  
    user1|ts1 column=sf:c1, timestamp=1409122354868, value=sku1
  9.  
    user1|ts2 column=sf:c1, timestamp=1409122354918, value=sku188
  10.  
    user1|ts3 column=sf:s1, timestamp=1409122354954, value=sku123
  11.  
    user2|ts4 column=sf:c1, timestamp=1409122354998, value=sku2
  12.  
    user2|ts5 column=sf:c2, timestamp=1409122355030, value=sku288
  13.  
    user2|ts6 column=sf:s1, timestamp=1409122355970, value=sku222


加入测试数据 

 

 

put 'test1', 'user1|ts9', 'sf:b1', 'sku1'


12、b1开头的列中并且值为sku1的：

 

 

  1.  
    scan 'test1', FILTER=>"ColumnPrefixFilter('b1') AND ValueFilter(=,'binary:sku1')"
  2.  
     
  3.  
    ROW COLUMN+CELL
  4.  
    user1|ts9 column=sf:b1, timestamp=1409124908668, value=sku1


13、SingleColumnValueFilter的使用，b1开头的列中并且值为sku1的

 

 

  1.  
    import org.apache.hadoop.hbase.filter.CompareFilter
  2.  
    import org.apache.hadoop.hbase.filter.SingleColumnValueFilter
  3.  
    import org.apache.hadoop.hbase.filter.SubstringComparator
  4.  
    scan 'test1', {COLUMNS => 'sf:b1', FILTER => SingleColumnValueFilter.new(Bytes.toBytes('sf'), Bytes.toBytes('b1'), CompareFilter::CompareOp.valueOf('EQUAL'), Bytes.toBytes('sku1'))}
  5.  
     
  6.  
    ROW COLUMN+CELL
  7.  
    user1|ts9 column=sf:b1, timestamp=1409124908668, value=sku1


hbase zkcli 的使用 

 

 

  1.  
    hbase zkcli
  2.  
    ls /
  3.  
    [hbase, zookeeper]
  4.  
     
  5.  
    [zk: hadoop000:2181(CONNECTED) 1] ls /hbase
  6.  
    [meta-region-server, backup-masters, table, draining, region-in-transition, running, table-lock, master, namespace, hbaseid, online-snapshot, replication, splitWAL, recovering-regions, rs]
  7.  
     
  8.  
    [zk: hadoop000:2181(CONNECTED) 2] ls /hbase/table
  9.  
    [member, test1, hbase:meta, hbase:namespace]
  10.  
     
  11.  
    [zk: hadoop000:2181(CONNECTED) 3] ls /hbase/table/test1
  12.  
    []
  13.  
     
  14.  
    [zk: hadoop000:2181(CONNECTED) 4] get /hbase/table/test1
  15.  
    ?master:60000}l$??lPBUF
  16.  
    cZxid = 0x107
  17.  
    ctime = Wed Aug 27 14:52:21 HKT 2014
  18.  
    mZxid = 0x10b
  19.  
    mtime = Wed Aug 27 14:52:22 HKT 2014
  20.  
    pZxid = 0x107
  21.  
    cversion = 0
  22.  
    dataVersion = 2
  23.  
    aclVersion = 0
  24.  
    ephemeralOwner = 0x0
  25.  
    dataLength = 31
  26.  
    numChildren = 0
【推广】 免费学中医，健康全家人
原文地址：https://www.cnblogs.com/LEPENGYANG/p/14088125.html