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Hi/Lo is an algorithm and a key generation strategy used for generating unique keys for use in a database as a primary key. It uses a sequence-based hi-lo pattern to generate values. Hi/Lo is used in scenarios where an application needs its entities to have an identity prior to persistence. It is a value generation strategy.
In a relational database, a candidate key uniquely identifies each row of data values in a database table. A candidate key comprises a single column or a set of columns in a single database table. No two distinct rows or data records in a database table can have the same data value (or combination of data values) in those candidate key columns ...
The advantages of using a natural key to uniquely identify records in a relation include less disk space usage, the natural key is an attribute that is related to the business or the real world so in most cases, it is already being stored in the relation which saves disk space as compared to creating a new column for storing the surrogate key.
Database model for MediaWiki 1.28.0 (2017) Different types of database models. A database model is a type of data model that determines the logical structure of a database. It fundamentally determines in which manner data can be stored, organized and manipulated. The most popular example of a database model is the relational model, which uses a ...
In the relational data model a superkey is any set of attributes that uniquely identifies each tuple of a relation. [1] [2] Because superkey values are unique, tuples with the same superkey value must also have the same non-key attribute values.
A relational database schema is defined as a tuple S = (D, R, h) where D is the domain of atomic values (see relational model for more on the notions of domain and atomic value), R is a finite set of relation names, and h : R → 2 C. a function that associates a header with each relation name in R. (Note that this is a simplification from the ...
In-database processing, sometimes referred to as in-database analytics, refers to the integration of data analytics into data warehousing functionality. Today, many large databases, such as those used for credit card fraud detection and investment bank risk management, use this technology because it provides significant performance improvements over traditional methods.
For example, if you need to load data into two databases, you can run the loads in parallel (instead of loading into the first – and then replicating into the second). Sometimes processing must take place sequentially. For example, dimensional (reference) data are needed before one can get and validate the rows for main "fact" tables.