HBase

OUTLINE Basic Data Model Implementation – Architecture of HDFS Hbase Server HRegionServer 2

Basic HBase directly uses or subclasses the parent Hadoop implementation

Basic 4 Linux

Basic DataBase of problem: – Grown of Data – Complexity of install and maintain Mutil-RDBMS of poblem:(for nodes ) – JOIN – not effective – rebalance Solution : Relational DataBase Management System(RDBMS) Solution : NoSQL DataBase

Basic NoSQL DataBase : – Distributed – Scalability – Easy to use (EX:put, get,alter etc.)

Basic List of NoSQL: – OpenSource HBase (Yahoo!) Cassandra (Facebook) SimpleDB (Amazon) – Commercial BigTable (Google)

Basic Hbase: – Hadoop’s DataBase. – Reversion of released – Usage with Map/Reduce

OUTLINE Basic Data Model Implementation – Architecture of HDFS Hbase Server HRegionServer 9

Table member : Row, Column, TimeStamp Row key Time Stamep Column”Contents” “com.yahoo.news.tw” t3 “ 我研發水下 6 千公尺機器人 ” t2 “ 蚊子怎麼搜尋人肉 ” t1 “… Wang 40…” “com.cnn.www”t1 “ 用腦波「發聲」 ”

Table Add column ” Anchor ” Row key Time Stamep ”Contents” “com.yahoo. news.tw” t3 “ 我研發水下 6 千公尺 機器人 ” t2 “ 蚊子怎麼搜尋人肉 ” t1 “… Wang 40…” “com.cnn.w ww” t1 “ 用腦波「發聲」 ” Add

Table Row key Time Stamep ”Contents”‘’ Anchor ’’ “com.yahoo.ne ws.tw” t5 “Anchor:tech” “Silvia” t4 “Anchor:sports” “Eric” t3 “ 我研發水下 6 千公尺機器 人 ” t2 “ 蚊子怎麼搜尋人肉 ” t1“… Wang 40…” “com.cnn.ww w”t1 “ 用腦波「發聲」 ” ‘’ Anchor_tech ’’‘’ Anchor_sports ’’ Silva Eric

Region Row key Time Stamep ”Contents”‘’ Anchor ’’ “com.ya hoo.new s.tw” t5 “Anchor:tech” “Silvia” t4“Anchor:sports”“Eric” t3 “ 我研發水下 6 千公 尺機器人 ” t2 “ 蚊子怎麼搜尋人肉 ” t1“… Wang 40…” “com.cn n.www”t1 “ 用腦波「發聲」 ” “com.ab c.www” “com.de f.www” region1region1 region2region2 Region1(com.yahoo.ne w.tw,com.def.www>,ID Express: Region(start row key, end row key>& identifier

Sort Sort by row key – byte-ordered Add label on family column

Locking Row key Time Stamep ”Contents”‘’ Anchor ’’ “com.ya hoo.new s.tw” t5 “Anchor:tech” “Silvia” t4“Anchor:sports”“Eric” t3 “ 我研發水下 6 千公 尺機器人 ” t2 “ 蚊子怎麼搜尋人肉 ” t1“… Wang 40…” “com.cn n.www”t1 “ 用腦波「發聲」 ” “com.ab c.www” “com.de f.www” User1 update User2 update User3 update User4 update

OUTLINE Basic Data Model Implementation – Architecture of Hbase Hbase Server HRegionServer 16

Architecture of Hbase NN: NameNode DN: DataNode HM: Hmaster HR:HRegion Cluster HDFS Client NNDN HMHR ZooKeeper

rebalance a single host grows the regions – it split a row into two new regions of approximately equal size. Until not across threshold automatic

Hbase Master The master node is lightly loaded. assignment of the replacement daughters Recovering regionserver failures.

RegionServer carry zero or more regions client read/write/scan requests – Random access Automatic split regions Send HeartBeat to Master

RegionInfo. Region of metadata – the current list, state, recent history, and location of all regions afloat on the cluster. {NAME => ‘docs’, FAMILIES => [{NAME => ‘cache’, COMPRESSION => ‘NONE’, VERSIONS => ’3′, TTL => ′ ′, BLOCKSIZE => ’65536′, IN_MEMORY => ‘false’, BLOCKCACHE => ‘false’}

.MATA.

HBase in operation memory size of 256MB and each row is 1KB size -ROOT-.META. useregion 1.8 x (2 64 ) bytes of user data 2.6 x 10 5 META regions 6.9 x user regions

HBase in operation NN: NameNode DN: DataNode HM: Hmaster HR:Regionsever Cluster HBase Client NNDN HMRR RRR ZooKeeper ROOTMETA Request consult Step 3. User region Step 1. Step 2 Read Requests - Step 1.location of -ROOT- - Step 2.location of the.META. Region - Step3.user region space

HBase in operation NN: NameNode DN: DataNode HM: Hmaster HR:Regionsever Cluster HBase Client NNDN HMRR RRR ZooKeeper Interacts with RegionServer Read Requests -clients cache save information of ROOT, META and User Region

HBase in operation Interacts with RegionServer HBase Client HLog table Region server of state Region Serser Region Serser Hstore Region Hstore HFile Hfile Mem Store

HBase in operation RegionServer HBase Client HLog Client request to save data in table Region Serser Region Serser Hstore Region Hstore HFile Hfile Mem Store

Hbase of characteristic Fault tolerance Batch processing Automatic partitioning Scale linearly with new nodes