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Highly scalable, wide column databases can handle petabytes of data, making them ideal for supporting real-time big data applications. An object-oriented database is based on object-oriented programming, so data and all of its attributes, are tied together as an object.
Like relational databases, object-oriented databases conform to ACID standards. One of the simplest types of NoSQL databases, key-value databases save data as a group of key-value pairs made up of two data items each. Key-value databases are highly scalable and can handle high volumes of traffic, making them ideal for processes such as session management for web applications, user sessions for massive multi-player online games, and online shopping carts.
Hierarchical databases use a parent-child model to store data. If you were to draw a picture of a hierarchical database, it would look like a family tree, with one object on top branching down to multiple objects beneath it. Originally developed by IBM in the early s, hierarchical databases are commonly used to support high-performance and high availability applications. Document databases, also known as document stores, use JSON-like documents to model data instead of rows and columns.
Sometimes referred to as document-oriented databases, document databases are designed to store and manage document-oriented information, also referred to as semi-structured data. Document databases are simple and scalable, making them useful for mobile apps that need fast iterations. Graph databases are a type of NoSQL database that are based on graph theory. Therefore graph databases are often used to analyze the relationships between heterogeneous data points, such as in fraud prevention or for mining data about customers from social media.
A time series database is a database optimized for time-stamped, or time series, data. Examples of this type of data include network data, sensor data, and application performance monitoring data. All of those Internet of Things sensors that are getting attached to everything put out a constant stream of time series data.
Given the increasing volume and complexity of data, and the speed and scale needed to handle it, the only place you can compete effectively—and cost-effectively—is in the cloud. MySQL is a mature relational database system, offering a familiar database environment for experienced IT professionals. MongoDB is a well-established, non-relational database system offering improved flexibility and horizontal scalability, but at the cost of some safety features of relational databases, such as referential integrity.
MongoDB is an attractive option to developers. Its data storage philosophy is simple and immediately understandable to anybody with programming experience.
MongoDB stores data in collections with no enforced schema. This flexible approach to storing data makes it particularly suitable for developers who may not be database experts, yet want to use a database to support the development of their applications.
Compared to MySQL, this flexibility is a significant advantage: To get the best out of a relational database, you must first understand the principles of normalization, referential integrity, and relational database design. A common example of such an application is a web application that doesn't depend on structured schemas; it can easily serve unstructured, semi-structured, or structured data, all from the same MongoDB collection. MySQL is a common choice for users who have extensive experience using traditional SQL scripting, designing solutions for relational databases, or who are modifying or updating existing applications that already work with a relational system.
Relational databases may also be a better choice for applications that require very complex but rigid data structures and database schemas across a large number of tables. A common example of such a system could be a banking application that requires very strong referential integrity and transactional guarantees to be enforced to maintain exact point-in-time integrity of data. However, it is important to clarify that MongoDB also supports ACID properties of transactions atomicity, consistency, isolation, and durability.
This enables greater flexibility in building a transactional data model that can horizontally scale in a distributed environment and has no impact on performance for multi-document transactions. A key benefit of the MongoDB design is that the database is extremely easy to scale.
Configuring a sharded cluster allows a portion of the database, called a shard, to also be configured as a replica set. In a sharded cluster, data is distributed across many servers. This highly flexible approach allows MongoDB to horizontally scale both read and write performance to cater to applications of any scale. A replica set is the replication of a group of MongoDB servers that hold the same data, ensuring high availability and disaster recovery. With a MySQL database system, options for scalability are much more limited.
Typically, you have two choices: vertical scalability, or adding read replicas. Scaling vertically involves adding more resources to the existing database server, but this has an inherent upper limit. Read replication involves adding read-only copies of the database to other servers. However, this is typically limited to five replicas in total, which can only be used for read operations.
Multi-master replication support has been added to MySQL, but its implementation is more limited than the functionality available in MongoDB. Assessing the performance of two completely different database systems is very difficult, since both management systems approach the task for data storage and retrieval in completely different ways. For example: MySQL is optimized for high performance joins across multiple tables that have been appropriately indexed.
MongoDB is also optimized for write performance, and features a specific insertMany API for rapidly inserting data, prioritizing speed over transaction safety wherein MySQL data needs to be inserted row by row. Observing some of the high-level query behaviors of the two systems, we can see that MySQL is faster at selecting a large number of records, while MongoDB is significantly faster at inserting or updating a large number of records.
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