MongoDB is an opensource database that uses a document-oriented data model. It was created
by Dwight Merriman and Eliot Horowitz, who had encountered development and
scalability issues with traditional relational database approaches while
building Web applications at
DoubleClick, an Internet advertising company that is now owned by Google Inc.
According to Merriman, the name of the database was derived from the word humongous to
represent the idea of supporting large amounts of data. Merriman and Horowitz
helped form 10Gen Inc. in 2007 to commercialize MongoDB and related software.
The company was renamed MongoDB Inc. in 2013.
MongoDB
is a document database with the scalability and flexibility that you want with
the querying and indexing that you need.
Instead of using tables and rows as in relational
databases, MongoDB is built on architecture of collections and documents.
Documents comprise sets of key-value pairs and are the basic unit of
data in MongoDB. Collections contain sets of documents and function as the
equivalent of relational database tables.
Like other NoSQL databases, MongoDB supports dynamic schema design,
allowing the documents in a collection to have different fields and structures.
The database uses a document storage and data interchange format called BSON,
which provides a binary representation of JSON-like documents.
The database
was released to open source in 2009 and is available under the terms of the
Free Software Foundation's GNU AGPL Version 3.0 commercial license. Among
other users, the Micra Systems an IT company in Noida is using MongoDB for web
development applications, the website Craigslist is using it for archiving
data, the CERN physics lab is using it for data aggregation and discovery and
the The New York Times newspaper is using MongoDB to support a
form-building application for photo submissions.
Ad hoc queries:
MongoDB supports
field, range queries, regular expression searches. Queries can return specific
fields of documents and also include user-defined Javascript functions.
Queries can also be configured to return a random sample of results of a given
size.
Indexing
Fields in a MongoDB document can be indexed
with primary and secondary indices.
Replication
MongoDB provides high availability with
replica sets.A
replica set consists of two or more copies of the data. Each replica set member
may act in the role of primary or secondary replica at any time. All writes and
reads are done on the primary replica by default. Secondary replicas maintain a
copy of the data of the primary using built-in replication.
Load balancing
MongoDB scales horizontally using sharding.
The user 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.). Alternatively, the shard key can be hashed to map to a shard –
enabling an even data distribution.
MongoDB can run over multiple servers,
balancing the load or duplicating data to keep the system up and running in
case of hardware failure.
File storage
MongoDB can be used as a file system with
load balancing and data replication features over multiple machines for storing
files.
This function, called Grid File System is
included with MongoDB drivers. MongoDB exposes functions for file manipulation
and content to developers. GridFS is used in plugins for NGINX and lighttpd GridFS divides a file into parts, or chunks, and stores each of those chunks as a
separate document.
Aggregation
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. Aggregation operators can be strung together to form a pipeline –
analogous to Unix pipes. The aggregation framework includes
the $lookup operator which can join documents from multiple documents, as well
as statistical operators such as standard deviation.
Server-side
JavaScript execution
JavaScript can be used in queries, aggregation
functions (such as MapReduce), and 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.
Main purpose to build MongoDB:
- Scalability
- Performance
- High
Availability
- Scaling from single server deployments to large, complex multi-site architectures.
- It Develops Faster
- It Deploy
Easier
- The Scale is Bigger
