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Once a connection has been built it can be opened and closed at will, and properties (such as the command time-out length, or transaction, if one exists) can be set. The Connection String is composed of a set of key/value pairs as dictated by the data access interface and data provider being used.
In computing, a connection string is a string that specifies information about a data source and the means of connecting to it. It is passed in code to an underlying driver or provider in order to initiate the connection. Whilst commonly used for a database connection, the data source could also be a spreadsheet or text file.
Entity Framework (EF) is an open source [2] object–relational mapping (ORM) framework for ADO.NET.It was originally shipped as an integral part of .NET Framework, however starting with Entity Framework version 6.0 it has been delivered separately from the .NET Framework.
Without connection pooling mechanisms (e.g., HikariCP, pgbouncer), idle or excessive connections can strain database resources. Virtual Machine-Based Environments: AWS EC2 instances scale connection demand with the number of instances. Manual or automated tuning of connection pool parameters is essential to prevent exceeding database limits.
The Dynamic Language Runtime (DLR) from Microsoft runs on top of the Common Language Runtime (CLR) and provides computer language services for dynamic languages. These services include: A dynamic type system, to be shared by all languages using the DLR services; Dynamic method dispatch; Dynamic code generation; Hosting API
In information theory, linguistics, and computer science, the Levenshtein distance is a string metric for measuring the difference between two sequences. The Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.
Edit distance finds applications in computational biology and natural language processing, e.g. the correction of spelling mistakes or OCR errors, and approximate string matching, where the objective is to find matches for short strings in many longer texts, in situations where a small number of differences is to be expected.