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HBaseA column-centered database

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Overview

•An Apache project• Influenced by Google’s BigTable•Built on Hadoop

▫A distributed file system▫Supports Map-Reduce

•Goals▫Scalability▫Versions▫Compression▫In memory tables

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Architectural issues

•Cluster of nodes is general architecture•Standalone mode for single machine•There is a Java API accessed with JRuby •There is a JRuby shell

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Modeling constructs

•Table▫Has a row key▫A series of column families

Each has a column name and a value•Operations

▫Create table▫Insert a row with “Put” command

Only one column at a time▫Query a table with a “Get” command

(uses a table name and a row key)

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Filters

•Scan▫can get a series of rows based on two key

values ▫Can provide a filter for such things as

column families, timestamps▫Filters can be pushed to the server

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Updating

•When a column value is written to the db, old values are kept and organized by timestamp▫Each such value is a cell

•You can explicitly assign timestamps manually▫Otherwise, current timestamp with insert▫When getting, uses most recent version

•Operations that alter column family structures is expensive

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Other characteristics

•Text compression•Rows are stored in order by key value•A region is some set of rows

▫Each is stored in a single region server▫Regions can be automatically merged and

split•Uses write-ahead logging to prevent loss

of data with node failures▫This is called journaling in Unix file

systems•Supports a master/slave multi-cluster

strategy

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An HBase clustertaken from: http://www.packtpub.com/article/hbase-basic-performance-tuning

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Tasks of components

•Zookeeper cluster is a coordination service for the HBase cluster▫Finds the correct server▫Selects the master

•Master allocates regions & load balancing•Region servers hold the regions•Hadoop supports Map-Reduce

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Some key concepts

•De-normalization•Fast random, key-row retrieval•Use of a multi-component architecture to

leverage existing software tools•Controllable in-memory selection