MongoDB Database Learning

A popular type of non-relational database Mongodb,nosql database

High performance, open source, non-modal document database

Alternative to traditional relational databases or key/value storage methods

Nosql,not only SQL, non-relational database. Next-generation databases: Non-relational, distributed, open-source, extensible.

Large-scale Web applications

Characteristics:

High performance, easy to deploy, easy to use, easy to store data

Collection-oriented storage for easy storage of object-type data

Mode freedom

Dynamic Query

Full index support, including internal objects

Support Query

Supports replication, failure recovery

Use efficient binary data storage, including large objects (video)

Automates fragmentation and supports cloud-level scaling

Supports Python, Php,ruby,java,c,c#,javascript, perl,c++

File storage Format Bson

Access over the network

Function:

Collection-oriented storage: data that is suitable for storing objects and JSON forms

Dynamic query: Supports rich query expressions. Query directives use JSON-style markup to easily query objects and arrays embedded in a document

Full index support: Includes embedded objects and arrays in the document. The MONGO query optimizer parses the query expression and generates an efficient query plan.

Query monitoring: MONGO contains a monitoring tool for analyzing the performance of database operations.

Replication and automatic failover: The MONGO database supports data replication between servers and supports replication between master-slave mode and server. The primary goal of replication is to provide redundancy and automatic failover.

Efficient traditional storage: supports binary data and large objects (image, video)

Auto-sharding to support cloud-scale scalability: Automatic sharding supports a level of database clustering, adding additional machines dynamically.

Applicable occasions

Website data: MONGO is ideal for real-time inserts, updates and queries, with the replication and high scalability required for real-time data storage on the site.

Caching: High performance, MONGO also suitable as a buffer layer of information infrastructure. After the system restarts, the persistent cache layer built by MONGO can avoid overloading the underlying data sources.

Large-scale, low-value data: Storing some data using a traditional relational database can be more expensive.

Highly scalable scenario: The MONGO is ideal for databases consisting of dozens of or hundreds of servers. The MONGO roadmap contains the MapReduce engine built-in.

Storage for objects and JSON data: MONGO's Bson data format is ideal for storing and querying document formats.

MongoDB Database Learning