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In computer programming, create, read, update, and delete (CRUD) are the four basic operations (actions) of persistent storage. [1] CRUD is also sometimes used to describe user interface conventions that facilitate viewing, searching, and changing information using computer-based forms and reports .
A document-oriented database is a specialized key-value store, which itself is another NoSQL database category. In a simple key-value store, the document content is opaque. A document-oriented database provides APIs or a query/update language that exposes the ability to query or update based on the internal structure in the document. This ...
MongoDB is a source-available, cross-platform, document-oriented database program. Classified as a NoSQL database product, MongoDB uses JSON -like documents with optional schemas . Released in February 2009 by 10gen (now MongoDB Inc. ), it supports features like sharding , replication , and ACID transactions (from version 4.0).
Amazon DocumentDB is a managed proprietary NoSQL database service that supports document data structures, with some compatibility with MongoDB version 3.6 (released by MongoDB in 2017) and version 4.0 (released by MongoDB in 2018). As a document database, Amazon DocumentDB can store, query, and index JSON data. It is available on Amazon Web ...
Consequently, while these databases excel at basic CRUD operations and key-based lookups, their suitability for complex queries involving joins or non-indexed filtering varies depending on the database type—document, key–value, wide-column, or graph—and the specific implementation. [35]
For example, Microsoft SQL Server comes with SQL Server Management Studio and Oracle has tools such as SQL*Plus and Oracle Enterprise Manager/Grid Control. In addition, 3rd parties such as BMC, Quest Software , Embarcadero Technologies , patchVantage and SQL Maestro Group offer GUI tools to monitor the DBMS and help DBAs carry out certain ...
For these types of common operations, graph databases would theoretically be faster. [20] The true value of the graph approach becomes evident when one performs searches that are more than one level deep. For example, consider a search for users who have "subscribers" (a table linking users to other users) in the "311" area code.
Compared to JSON, BSON is designed to be efficient both in storage space and scan-speed. Large elements in a BSON document are prefixed with a length field to facilitate scanning. In some cases, BSON will use more space than JSON due to the length prefixes and explicit array indices. [2]