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SDKs such as those for .NET, Java, and Python implement connection pooling to reuse HTTP connections to the database endpoint, optimizing resource usage and performance. This functionality applies to all Cosmos DB account types, including provisioned throughput and serverless models.
Objectivity/DB is a commercial object database produced by Objectivity, Inc. It allows applications to store standard C++, C#, Java, or Python objects persistently, without converting them into the rows and columns used by a relational database management system (RDBMS).
The connection object obtained from the connection pool is often a wrapper around the actual database connection. The wrapper understands its relationship with the pool, and hides the details of the pool from the application. For example, the wrapper object can implement a "close" method that can be called just like the "close" method on the ...
RDFLib is a Python library for working with RDF, [2] a simple yet powerful language for representing information. This library contains parsers/serializers for almost all of the known RDF serializations, such as RDF/XML, Turtle, N-Triples, & JSON-LD, many of which are now supported in their updated form (e.g. Turtle 1.1).
XtraDB – storage engine for the MariaDB and Percona Server databases, and is intended as a drop-in replacement to InnoDB, which is one of the default engines available on the MySQL database. Comparison of MySQL database engines – comparison between the available database engines for the MySQL database management system (DBMS). A database ...
In computer science, a database cursor is a mechanism that enables traversal over the records in a database. Cursors facilitate processing in conjunction with the traversal, such as retrieval, addition and removal of database records. The database cursor characteristic of traversal makes cursors akin to the programming language concept of iterator.
Unlike the later ODBC, Blueprint was a purely code-based system, lacking anything approximating a command language like SQL. Instead, programmers used data structures to store the query information, constructing a query by linking many of these structures together. Lotus referred to these compound structures as query trees. [2]
The simplest solution to the NNS problem is to compute the distance from the query point to every other point in the database, keeping track of the "best so far". This algorithm, sometimes referred to as the naive approach, has a running time of O(dN), where N is the cardinality of S and d is the dimensionality of S. There are no search data ...