創(chuàng)建表

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

lf: column family of LONG values (binary value)

-- sf: column family of STRING values

導(dǎo)入數(shù)據(jù)

put 'test1', 'user1|ts1', 'sf:c1', 'sku1'

put 'test1', 'user1|ts2', 'sf:c1', 'sku188'

put 'test1', 'user1|ts3', 'sf:s1', 'sku123'

put 'test1', 'user2|ts4', 'sf:c1', 'sku2'

put 'test1', 'user2|ts5', 'sf:c2', 'sku288'

put 'test1', 'user2|ts6', 'sf:s1', 'sku222'

一個(gè)用戶（userX），在什么時(shí)間（tsX），作為rowkey

對(duì)什么產(chǎn)品（value：skuXXX）奔脐，做了什么操作作為列名峦朗，比如，c1: click from homepage; c2: click from ad; s1: search from homepage; b1: buy

查詢案例

誰的值=sku188

scan 'test1', FILTER=>"ValueFilter(=,'binary:sku188')"

ROW??????????????????????????COLUMN+CELL????????????????????

user1|ts2???????????????????column=sf:c1, timestamp=1409122354918, value=sku188

誰的值包含88

scan 'test1', FILTER=>"ValueFilter(=,'substring:88')"

ROW??????????????????????????COLUMN+CELL????

user1|ts2???????????????????column=sf:c1, timestamp=1409122354918, value=sku188

user2|ts5???????????????????column=sf:c2, timestamp=1409122355030, value=sku288

通過廣告點(diǎn)擊進(jìn)來的(column為c2)值包含88的用戶

scan 'test1', FILTER=>"ColumnPrefixFilter('c2') AND ValueFilter(=,'substring:88')"

ROW??????????????????????????COLUMN+CELL

user2|ts5???????????????????column=sf:c2, timestamp=1409122355030, value=sku288

通過搜索進(jìn)來的(column為s)值包含123或者222的用戶

scan 'test1', FILTER=>"ColumnPrefixFilter('s') AND ( ValueFilter(=,'substring:123') OR ValueFilter(=,'substring:222') )"

ROW??????????????????????????COLUMN+CELL

user1|ts3???????????????????column=sf:s1, timestamp=1409122354954, value=sku123

user2|ts6???????????????????column=sf:s1, timestamp=1409122355970, value=sku222

rowkey為user1開頭的

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

ROW??????????????????????????COLUMN+CELL

user1|ts1???????????????????column=sf:c1, timestamp=1409122354868, value=sku1

user1|ts2???????????????????column=sf:c1, timestamp=1409122354918, value=sku188

user1|ts3???????????????????column=sf:s1, timestamp=1409122354954, value=sku123

FirstKeyOnlyFilter: 一個(gè)rowkey可以有多個(gè)version,同一個(gè)rowkey的同一個(gè)column也會(huì)有多個(gè)的值, 只拿出key中的第一個(gè)column的第一個(gè)version

KeyOnlyFilter: 只要key,不要value

scan 'test1', FILTER=>"FirstKeyOnlyFilter() AND ValueFilter(=,'binary:sku188') AND KeyOnlyFilter()"

ROW??????????????????????????COLUMN+CELL

user1|ts2???????????????????column=sf:c1, timestamp=1409122354918, value=

從user1|ts2開始,找到所有的rowkey以u(píng)ser1開頭的

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

ROW??????????????????????????COLUMN+CELL

user1|ts2???????????????????column=sf:c1, timestamp=1409122354918, value=sku188

user1|ts3???????????????????column=sf:s1, timestamp=1409122354954, value=sku123

從user1|ts2開始,找到所有的到rowkey以u(píng)ser2開頭

scan 'test1', {STARTROW=>'user1|ts2', STOPROW=>'user2'}

ROW??????????????????????????COLUMN+CELL

user1|ts2???????????????????column=sf:c1, timestamp=1409122354918, value=sku188

user1|ts3???????????????????column=sf:s1, timestamp=1409122354954, value=sku123

查詢r(jià)owkey里面包含ts3的

importorg.apache.hadoop.hbase.filter.CompareFilter

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

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

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

ROW??????????????????????????COLUMN+CELL

user1|ts3???????????????????column=sf:s1, timestamp=1409122354954, value=sku123

查詢r(jià)owkey里面包含ts的

importorg.apache.hadoop.hbase.filter.CompareFilter

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

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

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

ROW??????????????????????????COLUMN+CELL

user1|ts1???????????????????column=sf:c1, timestamp=1409122354868, value=sku1

user1|ts2???????????????????column=sf:c1, timestamp=1409122354918, value=sku188

user1|ts3???????????????????column=sf:s1, timestamp=1409122354954, value=sku123

user2|ts4???????????????????column=sf:c1, timestamp=1409122354998, value=sku2

user2|ts5???????????????????column=sf:c2, timestamp=1409122355030, value=sku288

user2|ts6???????????????????column=sf:s1, timestamp=1409122355970, value=sku222

加入一條測試數(shù)據(jù)

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

查詢r(jià)owkey里面以u(píng)ser開頭的店溢，新加入的測試數(shù)據(jù)并不符合正則表達(dá)式的規(guī)則床牧，故查詢不出來

import org.apache.hadoop.hbase.filter.RegexStringComparator

importorg.apache.hadoop.hbase.filter.CompareFilter

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

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

scan 'test1', {FILTER => RowFilter.new(CompareFilter::CompareOp.valueOf('EQUAL'),RegexStringComparator.new('^user\d+\|ts\d+$'))}

ROW??????????????????????????COLUMN+CELL

user1|ts1???????????????????column=sf:c1, timestamp=1409122354868, value=sku1

user1|ts2???????????????????column=sf:c1, timestamp=1409122354918, value=sku188

user1|ts3???????????????????column=sf:s1, timestamp=1409122354954, value=sku123

user2|ts4???????????????????column=sf:c1, timestamp=1409122354998, value=sku2

user2|ts5???????????????????column=sf:c2, timestamp=1409122355030, value=sku288

user2|ts6???????????????????column=sf:s1, timestamp=1409122355970, value=sku222

加入測試數(shù)據(jù)

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

b1開頭的列中并且值為sku1的

scan 'test1', FILTER=>"ColumnPrefixFilter('b1') AND ValueFilter(=,'binary:sku1')"

ROW??????????????????????????COLUMN+CELL???????????????????????????????????????????????????????????????????????

user1|ts9???????????????????column=sf:b1, timestamp=1409124908668, value=sku1

SingleColumnValueFilter的使用，b1開頭的列中并且值為sku1的

importorg.apache.hadoop.hbase.filter.CompareFilter

import org.apache.hadoop.hbase.filter.SingleColumnValueFilter

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

scan 'test1', {COLUMNS => 'sf:b1', FILTER => SingleColumnValueFilter.new(Bytes.toBytes('sf'), Bytes.toBytes('b1'), CompareFilter::CompareOp.valueOf('EQUAL'), Bytes.toBytes('sku1'))}

ROW??????????????????????????COLUMN+CELL

user1|ts9???????????????????column=sf:b1, timestamp=1409124908668, value=sku1

hbase zkcli 的使用

hbase zkcli

ls /

[hbase, zookeeper]

[zk: hadoop000:2181(CONNECTED) 1] ls /hbase

[meta-region-server, backup-masters, table, draining, region-in-transition, running, table-lock, master, namespace, hbaseid, online-snapshot, replication, splitWAL, recovering-regions, rs]

[zk: hadoop000:2181(CONNECTED) 2] ls /hbase/table

[member, test1, hbase:meta, hbase:namespace]

[zk: hadoop000:2181(CONNECTED) 3] ls /hbase/table/test1

[]

[zk: hadoop000:2181(CONNECTED) 4] get /hbase/table/test1

?master:60000}l$??lPBUF

cZxid = 0x107

ctime = Wed Aug 27 14:52:21 HKT 2014

mZxid = 0x10b

mtime = Wed Aug 27 14:52:22 HKT 2014

pZxid = 0x107

cversion = 0

dataVersion = 2

aclVersion = 0

ephemeralOwner = 0x0

dataLength = 31

numChildren = 0

hbase表操作命令

1册踩、認(rèn)證及進(jìn)入：

kinit?命令進(jìn)行認(rèn)證暂吉，進(jìn)入命令：hbase?shell? ? 查看當(dāng)前用戶（whoami）

2慕的、展示表：

list

3肮街、查看表結(jié)構(gòu)：

describe? "table.name"

4、掃描表

scan 'table.name',{LIMIT=>5}

5、值包含8888888

scan "table.name",FILTER=>"ValueFilter(=,'binary:888888')"

6擂红、值含有888888

scan "table.name",FILTER=>"ValueFilter(=,'substring:888888')"

7昵骤、column為：c2?的值包含 8888888

scan "table.name",FILTER=>"ColumPrefixFilter('c2') AND ValueFilter(=,'substring:88')"

8变秦、column?為：s1?的值為包含88或者66

scan? “table.name”FILTER=>"ColumPrefixFilter('s') AND (ValueFilter(=,'substring:88'）OR ValueFilter(='substring:66')) "

9、rowkey?為user1開頭的

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

10雨饺、get的用法（t為表名额港，r為row，c為行）

hbase> get ‘t1′, ‘r1′

hbase> get ‘t1′, ‘r1′,

{TIMERANGE => [ts1, ts2]}

hbase> get ‘t1′, ‘r1′, {COLUMN => ‘c1′}

hbase> get ‘t1′, ‘r1′, {COLUMN => ['c1', 'c2', 'c3']}

hbase> get ‘t1′, ‘r1′, {COLUMN => ‘c1′, TIMESTAMP => ts1}

hbase> get ‘t1′, ‘r1′, {COLUMN => ‘c1′, TIMERANGE => [ts1, ts2], VERSIONS => 4}

hbase> get ‘t1′, ‘r1′, {COLUMN => ‘c1′, TIMESTAMP => ts1, VERSIONS => 4}

hbase> get ‘t1′, ‘r1′, ‘c1′

hbase> get ‘t1′, ‘r1′, ‘c1′, ‘c2′

hbase> get ‘t1′, ‘r1′, ['c1', 'c2']

11、scan

hbase> scan ‘.META.'

hbase> scan ‘.META.', {COLUMNS => ‘info:regioninfo'}

hbase> scan ‘t1′, {COLUMNS => ['c1', 'c2'], LIMIT => 10, STARTROW => ‘xyz'}

hbase> scan ‘t1′, {COLUMNS => ‘c1′, TIMERANGE => [1303668804, 1303668904]}

hbase> scan ‘t1′, {FILTER => “(PrefixFilter (‘row2′) AND (QualifierFilter (>=, ‘binary:xyz'))) AND (TimestampsFilter ( 123, 456))”}

hbase> scan ‘t1′, {FILTER => org.apache.hadoop.hbase.filter.ColumnPaginationFilter.new(1, 0)}