HBase is the Hadoop database. Its an open-source, distributed, column-oriented store modeled after the Google paper, Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Hadoop. HBase's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Try it if your plans for a data store run to big.
HBase: Bigtable-like structured storage for Hadoop HDFS Just as Google's [WWW] Bigtable leverages the distributed data storage provided by the [WWW] Google File System, HBase provides Bigtable-like capabilities on top of Hadoop Core. Data is organized into tables, rows and columns. An Iterator-like interface is available for scanning through a row range (and of course there is the ability to retrieve a column value for a specific key). Any particular column may have multiple versions for the same row key.
Katta is a scalable, failure tolerant, distributed, data storage for real time access.
Katta serves large, replicated, indices as shards to serve high loads and very large data sets. These indices can be of different type. Currently implementations are available for Lucene and Hadoop mapfiles.
* Makes serving large or high load indices easy
* Serves very large Lucene or Hadoop Mapfile indices as index shards on many servers
* Replicate shards on different servers for performance and fault-tolerance
* Supports pluggable network topologies
* Master fail-over
* Fast, lightweight, easy to integrate
* Plays well with Hadoop clusters
* Apache Version 2 License
Apache's Hadoop project aims to solve these problems by providing a framework for running large data processing applications on clusters of commodity hardware. Combined with Amazon EC2 for running the application, and Amazon S3 for storing the data, we can run large jobs very economically. This paper describes how to use Amazon Web Services and Hadoop to run an ad hoc analysis on a large collection of web access logs that otherwise would have cost a prohibitive amount in either time or money.
H. chih Yang, A. Dasdan, R. Hsiao, und D. Parker. SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data, Seite 1029--1040. New York, NY, USA, ACM, (2007)
H. chih Yang, A. Dasdan, R. Hsiao, und D. Parker. SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data, Seite 1029--1040. New York, NY, USA, ACM, (2007)