MongoDB 1.6 Release increases automatic partitioning and replication subset support

MongoDB is a product between relational database and non relational database, and is the most powerful and relational database in the relational database. The data structure he supports is very loose and is a JSON-like Bjson format, so you can store more complex data types. The biggest feature of MONGO is that the query language he supports is very powerful, and its syntax is somewhat similar to an object-oriented query language, which can almost achieve most of the functions of a single table query like relational database, and also supports indexing data.

The MongoDB 1.6 just released adds support for automatic partitioning Sharding and replication subsets.

The following illustration is an automatic Sharding schema:

Original link: Http://java.dzone.com/articles/mongodb-16-arrives-auto

Extended Reading

MongoDB Logo

MONGO is a high-performance, open source, modeless document database that can be used to replace traditional relational databases or key/value storage in many scenarios. MONGO uses C + + development to provide the following features:

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

Dynamic query: MONGO supports rich query expressions. Query directives use JSON-form tags to easily query objects and arrays embedded in the document.

Full index support: Includes document inline objects and arrays. The MONGO query optimizer analyzes query expressions 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, supporting master-from-mode and server replication. The primary goal of replication is to provide redundancy and automatic failover.

Efficient traditional storage: supports binary data and large objects such as photos or pictures.

Automatic fragmentation to support cloud-level scalability (in the early alpha phase): Automatic fragmentation supports a level of database clustering that can dynamically add additional machines.

The main goal of MongoDB is to set up a bridge between key/value storage (high performance and high scalability) and traditional RDBMS systems (rich functionality), combining the advantages of both. According to the official website description, MONGO is suitable for the following scenarios:

Site data: MONGO is ideal for real-time inserts, updates, and queries, as well as the replication and high scalability required for real-time data storage on the Web site.

Caching: Because of its high performance, MONGO is also suitable as a caching layer for the information infrastructure. After the system restarts, the persistent cache layer built by MONGO can avoid overloading the underlying data source.

Large size, low value data: It may be expensive to store some data using a traditional relational database, before which programmers often choose traditional files for storage.

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

Storage for objects and JSON data: MONGO's Bson data format is ideal for storing and querying in a documented format.

Naturally, the use of MongoDB can also have some limitations, such as it does not fit:

Highly transactional system: such as a bank or accounting system. Traditional relational databases are now more suitable for applications that require a large number of atomic complex transactions.

Traditional business intelligence applications: The BI database for a particular problem is a highly optimized query. For such applications, the Data warehouse may be a more appropriate choice.

Problems requiring SQL

MongoDB supports operating systems such as OS X, Linux, and Windows and provides drivers for Python,php,ruby,java and C + + languages, as well as drivers for Erlang and. NET platforms in the community.