For years, open-source relational database engines have seen widespread use, particularly in web-based applications and open-source application software stacks (e.g. LAMP). This type of database management system uses a table-based dataset structure, consisting of rows and columns whose content and the relationships between them are predefined by a user according to need, with the SQL language used to access and manipulate said data.
Among available RDBMS options, MySQL, along with PostgreSQL and Oracle, has long been a frontrunner thanks to its full-featured database management, namely its wholly transactional relation data store. This functionality has made it a common choice for applications which necessitate strict data retention and consistency. Some well-known examples of applications that use MySQL include TYPO3, WordPress, Joomla, and MyBB, as well as websites like Facebook, Twitter, Flickr, and YouTube.
MySQL Limitations and the Introduction of NoSQL
Over time, with the steady growth in the size and complexity of the datasets being handled by database management systems, bottlenecks began to crop up. This has become a serious drawback to the relational structure of RDBMS, highlighting the significantly limited horizontal scalabilty of such systems. With an eye to implement cost-effective methods of building complex distributed systems, without limiting their horizontal scalability, many firms and developers in the late 1990s and early 2000s began using non-relational databases instead, also known as NoSQL. Essentially, such database management systems eschew the tabular relationship that characterizes RDBMS in favor of a simpler, more flexible, and more scalable design, as well as more fine-tuned control over data availability. Thanks to alternative means of structuring data (such as wide column, graph, and key-value, etc.), many operations in NoSQL systems are performed faster than in RDBMS, particularly in big data and real-time web applications.
MongoDB – Swift, Scalable, and Schemaless
Additionally, tossing out the approach of parsing content into numerous relational structures, MongoDB database engines have the advantage of representing everything as JSON documents that can vary in structure and data type. This means that a minimal number of documents can be used to store data, thus simplifying data entry and speeding up data retrieval. What is more, unlike MySQL, MongoDB does not impose any schema restrictions on documents, but only on the supported document types it will allow. This translates into much greater business flexibility, as data model integrations or overhauls can be achieved without any pricey and timely schema migrations.
A further advantage to MongoDB over MySQL is its scalability across multiple distributed data centers. Even with growing data volume and throughput, MongoDB is capable of scaling up without downtime to the system or time consuming and costly application re-engineering. This feat is accomplished thanks to a high availability of replica sets, as well as sharding, in which data is either divvied up into user-defined ranges and distributed across multiple shards (a master-slave relationship), or mapped to a single shard for even distribution. This capability allows multiple servers to run MongoDB database engines, balancing and duplicating data loads to ensure greater system stability in case of failure. Furthermore, new hardware can be set up and added to a running MongoDB database engines without any hassle.
So When Should I Use MongoDB database engines?
While MongoDB is a great choice for many big data and web applications, there are still instances where MySQL will prove a better choice. Chiefly, because MongoDB favors high transaction loads (millions of read-write executions) over data integrity and security, some sensitive applications are better suited to MySQL, with its tightly regulated relational structure. On the other hand, MongoDB is often the better choice in applications such as content management systems, analytics, blogs, inventories, and catalogs, thanks to its rapid developability, user-friendliness, and the ease and speed afforded by numerous replica sets, sharded clusters, and schemaless data relationships. Lastly, data queries and manipulation are a cinch, as no special language is needed to do either, which has led many companies looking for greater ease of development to switch over from MySQL to MongoDB. A few key examples of big name firms using MongoDB are eBay, Craigslist, Facebook, Expedia and many others, with wider adoption expected to grow steadily in the coming years.