MongoDB is the database system developed in 10gen by Geir Magnusson and Dwight Merriman. It is a JSON document oriented database , except that it is designed to be a true object database, rather than pure key / value storage.
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- 1 Features
- 1 How it operates
- 2 Features and benefits
- 3 Sources
It is a non-relational database, that is, it does not use SQL . The name comes from the English term “humongous” (colossal) and can be defined as a schematic, scalable, high-performance document database. Some specialists have cataloged it as the “ MySQL of NoSQL databases ”, seeing that it is a very fast database, simple in the offered functionality and that, as MySQL did years ago, it is gaining the attention of many people.
How it operates
To store the documents, it uses a binary JSON serialization called BSON , which is an ordered list of simple elements. The core of the database is capable of interpreting its content, so that what appears at first glance to be binary content is actually a document that contains several elements. These data are limited to a maximum size of 4 MB; for larger sizes the use of GridFS is required.
Similar to CouchDB , this database allows you to store documents without a predefined schema, but offers some slight differences in the organization of the information. Part of the database concept that could be analogous to that used by previously used databases and also has “collections” that would be like tables. But unlike MySQL or the like, here you can save documents with different fields within the same collection, so this structure has more of a sense of categorizing the elements it contains without forcing them to have the same data.
Importantly, MongoDB offers much higher insert performance than CouchDB (and even better than MySQL). The strength of this database is a mix between hash tables (key-value) with certain characteristics of relational databases (RDBMS).
Features and benefits
- Document-oriented storage
- JSON-style documents with dynamic schemas offer simplicity and power.
- Full index support
- Indexes on any attribute, as we are used to.
- Replication and high availability
- Mirrors between LANs and WANs
- Horizontal scalability without compromising functionality, is currently limited to 20 nodes, although the goal is to reach a figure close to 1,000.
- Rich and document-based, fast for basic read and write queries.
- Quick updates in context
- Mapping and reduction
- Flexible aggregation and data processing.
- Store files of any size without complicating your “stack”.
- Commercial support
- Commercial support, training and consulting available.
- Supports dynamic queries
- They can be formulated on any value of the documents and not only on the indexed ones.