MongoDB (from "humongous") is an open-source document database, and the leading NoSQL database. It is part of the NoSQL family of database systems. Instead of storing data in tables as is done in a "classical" relational database, MongoDB stores structured data as JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster. Written in C++.
Features:
Document-Oriented Storage
JSON-style documents with dynamic schemas offer simplicity and power.
Full Index Support
Index on any attribute. Any field in a MongoDB document can be indexed (indices in MongoDB are conceptually similar to those in RDBMSes). Secondary indices are also available.
Replication & High Availability
Mirror across LANs and WANs for scale and peace of mind. MongoDB supports master-slave replication. A master can perform reads and writes. A slave copies data from the master and can only be used for reads or backup (not writes). The slaves have the ability to select a new master if the current one goes down.
Auto-Sharding
Scale horizontally without compromising functionality.
Querying, Ad hoc queries
Rich, document-based queries. MongoDB supports search by field, range queries, regular expression searches. Queries can return specific fields of documents and also include user-defined JavaScript functions.
Fast In-Place Updates
Atomic modifiers for contention-free performance.
Aggregation, Map/Reduce
Flexible aggregation and data processing. MapReduce can be used for batch processing of data and aggregation operations. The aggregation framework enables users to obtain the kind of results for which the SQL GROUP BY clause is used.
GridFS
Store files of any size without complicating your stack. MongoDB could be used as a file system, taking advantage of load balancing and data replication features over multiple machines for storing files. This function, called GridFS, is included with MongoDB drivers and available with no difficulty for development languages (see "Language Support" for a list of supported languages). MongoDB exposes functions for file manipulation and content to developers. GridFS is used, for example, in plugins for NGINX and lighttpd. In a multi-machine MongoDB system, files can be distributed and copied multiple times between machines transparently, thus effectively creating a load balanced and fault tolerant system.
Load balancing
MongoDB scales horizontally using sharding. The developer chooses a shard key, which determines how the data in a collection will be distributed. The data is split into ranges (based on the shard key) and distributed across multiple shards. (A shard is a master with one or more slaves.) MongoDB can run over multiple servers, balancing the load and/or duplicating data to keep the system up and running in case of hardware failure. Automatic configuration is easy to deploy and new machines can be added to a running database.
Server-side JavaScript execution
JavaScript can be used in queries, aggregation functions (such as MapReduce), are sent directly to the database to be executed.
Capped collections
MongoDB supports fixed-size collections called capped collections. This type of collection maintains insertion order and, once the specified size has been reached, behaves like a circular queue.
Document-Oriented Storage
JSON-style documents with dynamic schemas offer simplicity and power.
Full Index Support
Index on any attribute. Any field in a MongoDB document can be indexed (indices in MongoDB are conceptually similar to those in RDBMSes). Secondary indices are also available.
Replication & High Availability
Mirror across LANs and WANs for scale and peace of mind. MongoDB supports master-slave replication. A master can perform reads and writes. A slave copies data from the master and can only be used for reads or backup (not writes). The slaves have the ability to select a new master if the current one goes down.
Auto-Sharding
Scale horizontally without compromising functionality.
Querying, Ad hoc queries
Rich, document-based queries. MongoDB supports search by field, range queries, regular expression searches. Queries can return specific fields of documents and also include user-defined JavaScript functions.
Fast In-Place Updates
Atomic modifiers for contention-free performance.
Aggregation, Map/Reduce
Flexible aggregation and data processing. MapReduce can be used for batch processing of data and aggregation operations. The aggregation framework enables users to obtain the kind of results for which the SQL GROUP BY clause is used.
GridFS
Store files of any size without complicating your stack. MongoDB could be used as a file system, taking advantage of load balancing and data replication features over multiple machines for storing files. This function, called GridFS, is included with MongoDB drivers and available with no difficulty for development languages (see "Language Support" for a list of supported languages). MongoDB exposes functions for file manipulation and content to developers. GridFS is used, for example, in plugins for NGINX and lighttpd. In a multi-machine MongoDB system, files can be distributed and copied multiple times between machines transparently, thus effectively creating a load balanced and fault tolerant system.
Load balancing
MongoDB scales horizontally using sharding. The developer chooses a shard key, which determines how the data in a collection will be distributed. The data is split into ranges (based on the shard key) and distributed across multiple shards. (A shard is a master with one or more slaves.) MongoDB can run over multiple servers, balancing the load and/or duplicating data to keep the system up and running in case of hardware failure. Automatic configuration is easy to deploy and new machines can be added to a running database.
Server-side JavaScript execution
JavaScript can be used in queries, aggregation functions (such as MapReduce), are sent directly to the database to be executed.
Capped collections
MongoDB supports fixed-size collections called capped collections. This type of collection maintains insertion order and, once the specified size has been reached, behaves like a circular queue.
Data manipulation:
MongoDB stores structured data as JSON-like documents, using dynamic schemas (called BSON), rather than predefined schemas. In MongoDB, an element of data is called a document, and documents are stored in collections. One collection may have any number of documents. The arrangement of data in a MongoDB instance is innovative compared to traditional relational databases. A typical MongoDB collection would look like this:
MongoDB stores structured data as JSON-like documents, using dynamic schemas (called BSON), rather than predefined schemas. In MongoDB, an element of data is called a document, and documents are stored in collections. One collection may have any number of documents. The arrangement of data in a MongoDB instance is innovative compared to traditional relational databases. A typical MongoDB collection would look like this:
{
"_id": ObjectId("4efa8d2b7d284dad101e4bc9"),
"Last Name": "DUMONT",
"First Name": "Jean",
"Date of Birth": "01-22-1963"
},
{
"_id": ObjectId("4efa8d2b7d284dad101e4bc7"),
"Last Name": "PELLERIN",
"First Name": "Franck",
"Date of Birth": "09-19-1983",
"Address": "1 chemin des Loges",
"City": "VERSAILLES"
}
"_id": ObjectId("4efa8d2b7d284dad101e4bc9"),
"Last Name": "DUMONT",
"First Name": "Jean",
"Date of Birth": "01-22-1963"
},
{
"_id": ObjectId("4efa8d2b7d284dad101e4bc7"),
"Last Name": "PELLERIN",
"First Name": "Franck",
"Date of Birth": "09-19-1983",
"Address": "1 chemin des Loges",
"City": "VERSAILLES"
}
