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Query by Example (QBE) is a database query language for relational databases. It was devised by Moshé M. Zloof at IBM Research during the mid-1970s, in parallel to the development of SQL . [ 1 ] It is the first graphical query language, using visual tables where the user would enter commands, example elements and conditions.
Pros: The cost is predictable, as every time database system needs to scan full table row by row. When table is less than 2 percent of database block buffer, the full scan table is quicker. Cons: Full table scan occurs when there is no index or index is not being used by SQL. And the result of full scan table is usually slower that index table ...
In situations where the number of unique values of a column is far less than the number of rows in the table, column-oriented storage allow significant savings in space through data compression. Columnar storage also allows fast execution of range queries (e.g., show all records where a particular column is between X and Y, or less than X.)
Both approaches have their pros and cons. Using the strongly typed retrieval methods can be more cumbersome, especially without specific knowledge of the underlying data. Numeric values in the database can translate to several .NET types: Int16, Int32, Int64, Float, Decimal, or Currency. Trying to retrieve a value using the wrong type results ...
A true fully (database, schema, and table) qualified query is exemplified as such: SELECT * FROM database. schema. table. Both a schema and a database can be used to isolate one table, "foo", from another like-named table "foo". The following is pseudo code: SELECT * FROM database1. foo vs. SELECT * FROM database2. foo (no explicit schema ...
The choice of data orientation is a trade-off and an architectural decision in databases, query engines, and numerical simulations. [1] As a result of these tradeoffs, row-oriented formats are more commonly used in Online transaction processing (OLTP) and column-oriented formats are more commonly used in Online analytical processing (OLAP).
An entity–attribute–value model (EAV) is a data model optimized for the space-efficient storage of sparse—or ad-hoc—property or data values, intended for situations where runtime usage patterns are arbitrary, subject to user variation, or otherwise unforeseeable using a fixed design.
The result of Wagner's objective evaluation of the SQL/XML:2006 standard compliance of Oracle 11g Release 1, MS SQL Server 2008 and MySQL 5.1.30 is shown in the following table [2], to which the data for PostgreSQL 9.1, [5] [6] and IBM DB2 has been added: