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The relational model s domination of the database market is expected to continue into the foreseeable future, given the massive investment many large organizations have made in both the databases themselves and the staff required to manage them. The powerful and easy-to-understand relational databases are indeed the mainstay of a vast majority of organizations in today s world economy. Relational databases are based on the precepts laid down by E. F. Codd in the 1970s, when he was working for IBM. Codd s paper, which outlined the model, A Relational Model of Data for Large Shared Data Banks, was published in June 1970 in the Association of Computer Machinery (ACM) journal, Communications of the ACM, and Codd s model is accepted as the model for RDBMSs. D. L. Childs presented a similar set-oriented relational model in 1968, but it is Codd s exposition that made relational databases popular. There were (and still are) nonrelational database models that preceded the relational model specifically, the hierarchical and the network models. Both the network model and the hierarchical model use actual data links called pointers to process queries issued by users. These models, although powerful as far as performance goes, lead to a very complex database, and they are no longer adopted by most organizations. You can call relational databases second-generation database management systems.

Oracle Administrator s Guide. Additionally, the Oracle Data Warehousing Guide is an excellent source of information on the partitioning options and is a must-read for anyone planning to implement partitioning.

Partitioning facilitates the management of very large tables and indexes using divide and conquer logic. Partitioning introduces the concept of a partition key that is used to segregate data based on a certain

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Three key terms are used extensively in relational database models: relations, attributes, and domains A relation is a table with columns and rows The named columns of the relation are called the attributes, and the domain is the set of values the attributes are allowed to take The basic data structure of the relational model is the table, where information about the particular entity (say, an employee) is represented in columns and rows (also called tuples) Thus, the relation in relational database refers to the various tables in the database; a relation is a set of tuples The columns enumerate the various attributes of the entity (the employee s address or phone number, for example), and the rows are actual instances of the entity (specific employees) that is represented by the relation As a result, each tuple of the employee table represents various attributes of a single employee.

All relations (and thus tables) in a relational database have to adhere to some basic rules to qualify as relations First, the ordering of the columns is immaterial in a table Second, there can t be identical tuples or rows in a table And third, each tuple will contain a single value for each of its attributes (Remember that you can order the tuples and columns in any way you wish) Tables can have a single attribute or a set of attributes that can act as a key, which you can then use to uniquely identify each tuple in the table Keys serve many important functions They are commonly used to join or combine data from two or more tables Keys are also critical in the creation of indexes, which facilitate fast retrieval of data from large tables.

range value, a list of specific values, or the value of a hash function. If I were to put the benefits of partitioning in some sort of order, it would be 1. 2. Increases availability of data: This attribute is applicable to all system types, be they OLTP or warehouse systems by nature. Eases administration of large segments by removing them from the database: Performing administrative operations on a 100GB table, such as a reorganization to remove migrated rows or to reclaim whitespace left in the table after a purge of old information, would be much more onerous than performing the same operation ten times on individual 10GB table partitions. Additionally, using partitions, we might be able to conduct a purge routine without leaving whitespace behind at all, removing the need for a reorganization entirely! Improves the performance of certain queries: This is mainly beneficial in a large warehouse environment where we can use partitioning to eliminate large ranges of data from consideration, avoiding accessing this data at all. This will not be as applicable in a transactional system, since we are accessing small volumes of data in that system already. May reduce contention on high-volume OLTP systems by spreading out modifications across many separate partitions: If you have a segment experiencing high contention, turning it into many segments could have the side effect of reducing that contention proportionally.

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