MongoDB
Published by MongoDB on 3/5/2017
MongoDB is an open source database that uses a document-oriented data model.
Introduction

MongoDB is an open source database that uses a document-oriented data model.


MongoDB is one of several database types to arise in the mid-2000s under the NoSQL banner. Instead of using tables and rows as in relational databases, MongoDB is built on an 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. Automatic sharding enables data in a collection to be distributed across multiple systems for horizontal scalability as data volumes increase.

Features
  • 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.


  • Indexing


    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. When a primary replica fails, the replica set automatically conducts an election process to determine which secondary should become the primary. Secondaries can optionally serve read operations, but that data is only eventually consistent by default.


  • 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


    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


    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


    Capped collecMongoDB 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.

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