Comparing DBMSs: The 8 Most Popular Databases

Databases power nearly every digital platform on the planet, from websites and blogs to social media and streaming services. While many end-users view databases like MySQL as tools for storing data, this description, while accurate, only scratches the surface of what databases truly represent.

The Different Types of Databases

The term “database” often conflates two distinct components: the database itself, which stores data, and the Database Management System (DBMS), the set of tools used to manage that data. Here, we focus on the DBMS, which enables database administrators to interact with and govern the database effectively.

Database Management Systems consist of three main layers:

• Client: Initiates requests via command line or GUI screens using valid SQL queries.
• Server: Manages all logical functions of the DBMS.
• Storage: Handles data storage operations.

These layers include essential tools such as thread handlers, query languages, parsers, optimizers, query caches, buffers, table metadata caches, and key caches. Together, they form a robust system that facilitates data storage and retrieval for administrators, users, and software.

A critical component of any DBMS is its query language, a specialized language used to interact with databases according to the DBMS specifications. While some DBMSs have proprietary query languages, the most popular ones include:

• SQL: Structured Query Language, widely used by databases like MS SQL and MySQL.
• XQuery: Utilizes XML file formats for data extraction and manipulation.
• OQL: Object Query Language, default language for object-oriented databases often used in Big Data scenarios.
• SQL/XML: Combines SQL with XQuery, supporting SQL statements on XML data.
• GraphQL: Open-source language for querying APIs and existing data.
• LINQ: Language Integrated Query, extracts and processes data from various sources including XML documents and relational databases.

Relational and Non-Relational Databases

Database Management Systems (DBMSs) utilize two primary types of databases: Relational and Non-Relational. Understanding their differences is crucial for determining the best use case for a database.

A relational database organizes information into tables containing related data. What distinguishes a relational database is its ability to establish relationships between tables. These relationships link rows from different tables into a cohesive structure. Relational databases excel when data stability and accuracy are paramount, and the data structure remains relatively static.

Non-relational databases, also known as NoSQL Databases, store information in non-tabular formats. They employ various data models, with the four most common types being:

• Document-oriented: Stores data as JSON documents.
• Key-value: Stores data in key pairs.
• Graph: Stores data in a node-edge-node structure.
• Wide-column: Stores data in a tabular format with flexible columns that can vary between rows.

Due to their flexible data storage models, non-relational databases are highly versatile. They can accommodate diverse data types effectively, making them ideal for handling complex and voluminous data, such as in Big Data applications.

What to Look for in a Database

The initial question to consider is whether to use a relational or non-relational database. A relational database is well-suited for scenarios requiring ACID (Atomicity, Consistency, Isolation, Durability) compliance, data accuracy, normalization, and simplicity, but not necessarily scalability, flexibility, or high performance. A prime example of a relational database use case is a dynamic, database-driven website like WordPress.

Conversely, a non-relational database is preferable when data flexibility, speed, and scalability are critical. A typical example of a non-relational database use case is a cloud-based application that necessitates extensive scaling.

Eight Popular Databases

This list of the eight most popular databases is divided into 4 relational and 4 non-relational databases.

Relational Databases

The following are the most popular relational databases on the market today.

Oracle

Oracle Database, developed in 1977, stands as the oldest database on the list. As of January 2022, Oracle holds the top position as the most widely-used relational database management system globally, with a Statista ranking score of 1266.89.

Oracle Database offers five editions:

• Enterprise: Includes all DBMS features plus the Oracle Real Application Clusters option for high availability.
• Personal: Includes all features except the Oracle Real Application Clusters option.
• Standard: Provides base functionality.
• Express: A lightweight, free limited version available for Windows and Linux.
• Oracle Lite: Designed for mobile device use cases.

Oracle Database’s leading market share is attributed to its scalability, achieved through a split architecture between logical and physical components. This approach makes data location transparent, facilitating a modular structure that can be adjusted without impacting the database itself. Key features of Oracle Database include:

Real Application Clustering (RAC) for scalable performance and data consistency. Efficient memory caching. High-performance partitioning for managing large tables. Comprehensive backup solutions with Recovery Manager. Robust tools for data access control and management. Advantages of Oracle Database include:

Support for SQL query language. High performance and portability across multiple networking protocols and hardware platforms. Instance Caging for efficient resource utilization. Various editions tailored to business needs. Scalability features such as clustering and load balancing. Reliable failure recovery using RMAN. Support for PL/SQL. Disadvantages include:

• Proprietary: Oracle is not open-source.
• Complexity: Considered one of the more complex relational databases. -Cost: Can be significantly more expensive than alternatives like MS SQL.

MySQL

MySQL stands as one of the most popular open-source relational databases available today. According to DB-Engines, MySQL ranks second, just behind Oracle Database, among the most widely-used databases.

First released in May 1995, MySQL is renowned for its maturity and reliability. Written in C and C++, it runs on Linux, Solaris, macOS, Windows, and FreeBSD, and is licensed under GPLv2.

MySQL is a robust relational database, though its scalability does not match that of non-relational databases. It supports multi-threading, enabling it to handle databases with over 50 million rows, with a default file size limit of 4GB and a theoretical limit of 8TB.

Key features of MySQL include:

• Security: Implements a strong data security layer with encrypted passwords.
• Rollback: Supports transaction rollback capabilities.
• Memory Efficiency: Minimizes memory leakage.
• Productivity: Utilizes Triggers, Stored Procedures, and Views to enhance productivity.
• Partitioning: Supports partitioning to optimize performance for large databases.
• GUI Tools: MySQL Workbench provides a graphical interface for managing databases.

Advantages of MySQL include:

• Free and Open-Source: MySQL is freely available and can be installed on multiple server instances.
• SQL Compatibility: Uses SQL query language, making it familiar to database administrators.
• Speed: Known for its fast performance, aided by a unique storage engine.
• Integration: Easily integrates with thousands of third-party applications, including blogging systems, CRMs, HRMs, ERPs, and more.

Microsoft SQL Server

Microsoft SQL Server, developed by Microsoft, is a proprietary database management system (DBMS) available for installation on both Linux and Windows platforms. It was initially released on April 24, 1989, and is now offered in five distinct editions:

• Standard: Core functionality suitable for most applications.
• Web: A cost-effective option with specific limitations on memory and compute capacity.
• Enterprise: Supports extensive data warehouse capabilities, including advanced features such as data compression, enhanced security, and support for large data sizes.
• Developer: Intended for developers, featuring tools for creating stored procedures, functions, and views.
• Express: Designed for individuals or small organizations, lacking advanced functionality.

Microsoft SQL Server operates using the SQL query language and utilizes the SQL Server Operating System (SQLOS) for managing memory, I/O resources, jobs, and data processing.

Advantages of Microsoft SQL Server include:

• Native Visual Studio Support: Integrated data programming capabilities within Visual Studio for seamless database schema creation, viewing, and editing.
• Full-Text Search Service: Enables efficient word-based query searches.
• Multiple Version Support: Allows installation of multiple SQL Server versions on a single machine.
• Simple Installation: Can be installed with a single click.
• Data Restoration and Recovery: Built-in tools for effective data recovery.
• Community Support: Benefits from a large user community offering extensive help and support resources.

Considerations when adopting Microsoft SQL Server include:

• Cost and Pricing Complexity: Potential for high costs and complex pricing structures.
• User Interface: Some users find the interface less intuitive.
• Control Limitations: Offers partial control over databases compared to some other systems.

PostgreSQL

PostgreSQL, also known as Postgres, is a prominent free and open-source database management system that originally evolved from the Ingres database. Positioned as “The world’s most advanced open-source relational database,” PostgreSQL currently holds a 14.70% market share for relational databases.

Released in 1996, PostgreSQL benefits from an active development cycle and a robust support community. Setting it apart from other open-source relational databases, PostgreSQL operates as an object-relational database management system, combining relational database capabilities with object-oriented database features.

PostgreSQL is catalog-driven, allowing users to define custom data types, index types, and functional languages, thereby enhancing its extensibility compared to other relational databases.

Key features of PostgreSQL include:

ACID compliance. High concurrency. NoSQL support. Support for schema and query language extensions for objects, classes, inheritance, and function overloading. Common Table Expressions for temporary query results within larger queries. Declarative partitioning to simplify data partitioning tasks. Full-text search capabilities. Geographic Information System (GIS) / Spatial Reference System (SRS) support for managing geospatial data. JSON support. Logical replication for replicating data objects based on a primary key. Advantages of PostgreSQL include:

Well-suited for complex, high-volume data operations. Highly customizable through plugins and custom functions written in languages like C, C++, and Java. Multi-version concurrency control for optimized performance in multi-user environments. Scalability without requiring read locks, offering greater scalability compared to other relational databases. Cross-platform availability for BSD, Linux, macOS, Solaris, and Windows. Disadvantages of PostgreSQL include:

More complex setup compared to MySQL. Generally slower performance compared to MySQL in certain scenarios. Challenges in data migration from other RDBMS platforms. Limited built-in data compression capabilities. Complex horizontal scaling configurations. Limited built-in support for clustering. Lack of integrated support for machine learning.

For detailed instructions on installing PostgreSQL on an Ubuntu 20.04 server, refer to our guide on how to install and use PostgreSQL on Ubuntu 20.04.

Non-Relational Databases

The following sections cover the most popular non-relational databases on the market today.

Redis

Redis is an in-memory data structure store known for its role as a distributed, key-value NoSQL database. Derived from “Remote Dictionary Server,” Redis offers an advanced key-value store with optional durability features. It is often referred to as a data structure server because it supports storing strings, hashes, lists, sets, and sorted sets as keys.

As a volatile, in-memory database, Redis excels in environments with a substantial amount of frequently accessed data (“hot data”). It utilizes caching to ensure rapid read/write operations and consistently high data availability.

Key features that distinguish Redis include:

Minimal complexity compared to other NoSQL solutions. Lightweight and operates without external dependencies. Compatible with all POSIX environments. Supports synchronous, non-blocking master/slave replication for high availability. Utilizes a mapped key-value caching system akin to memcached. No rigid schema or table definitions required. Supports multiple data models and types. Includes sharding capabilities to distribute data across multiple Redis instances. Complements other databases to reduce load and enhance performance. Advantages of Redis include:

Supports storing key-value pairs as large as 512 MB. Implements its own hashing mechanism for efficient data storage. Provides resilient data replication, ensuring uninterrupted service during failures. Widely supported across popular programming languages. Facilitates rapid insertion of large data volumes into its cache. Can be deployed on resource-constrained hardware like Raspberry Pi and ARM devices due to its small footprint. Disadvantages of Redis include:

Requires all data to fit into memory, limiting the total manageable dataset size. Lacks a dedicated query language or support for relational algebra. Offers only two options for persistence (snapshots and append-only files). Basic security features compared to other databases. Operates in single-threaded mode on one CPU core, necessitating multiple Redis instances for scalability.

MongoDB

MongoDB is an open-source, document-oriented NoSQL database designed for high-volume data storage. Known for its schema-less architecture, MongoDB does not enforce a specific structure on documents within a collection. First released in 2009, this database uses JSON-like documents with optional schemas and can be deployed on-premises or as a fully managed cloud service. MongoDB is particularly well-suited for big data applications and is used by organizations of all sizes.

Key features of MongoDB include:

Support for field, range, and regex queries. High availability achieved through replica sets. Sharding support for horizontal scaling. GridFS, a file system capability for storing and retrieving files. Aggregation framework including pipeline and map-reduce functions. JavaScript support within queries. Capped collections for fixed-sized data storage. Indexing to enhance search performance. Operations on grouped data for single or computed results. Advantages of MongoDB:

Expressive query language. Schemaless design eliminates the need for a predefined database schema. Flexibility and high performance. Geospatial efficiency. Support for multiple document ACID transactions. Immune to SQL injection. Easy integration with Hadoop. Free and open-source. Disadvantages of MongoDB:

High memory requirements, particularly at scale. 16 MB limit on data document storage. 100-level limit on data nesting. Lack of support for multi-document transactions. Complex joining of documents. Potential for slow performance without proper indexing. Risk of duplicated data due to poorly defined relationships.

Apache Cassandra

Apache Cassandra is an open-source, distributed NoSQL database management system designed to handle massive amounts of data across commodity servers. Originally developed at Facebook to power its index search feature, Cassandra was open-sourced in July 2008 via Google Code and became an Apache Incubator project in March 2009.

Key features of Cassandra include:

• Distributed architecture: All nodes have the same role, eliminating a single point of failure.
• Replication and multi-data center replication: Ensures data availability and durability.
• Scalability: Linear read/write throughput increase as machines are added.
• Automatic data replication: Data is replicated across multiple distributed nodes.
• AP System: Prioritizes availability and partition tolerance over consistency (according to the CAP theorem).
• Hadoop integration: Supports MapReduce for large-scale data processing.
• Cassandra Query Language (CQL): A powerful query language tailored for Cassandra.

Advantages of Apache Cassandra:

• Elastic scalability: Can scale up or down without downtime.
• Peer-to-peer architecture: More resilient to failures compared to master-slave configurations.
• Data analytics: Four key methods including Solr-based integration and batch analysis with Hadoop.
• Near real-time analytics: Enables quick data analysis.
• Multi-data center and hybrid cloud support: Facilitates distributed and resilient data storage.
• Flexible data storage: Supports structured, semi-structured, and unstructured data.

Disadvantages of Apache Cassandra:

• Limited ACID support: May not be suitable for applications requiring strict transaction guarantees.
• Latency issues: High I/O operations can result in latency.
• Data modeling: Data is modeled around queries, leading to potential duplication.
• No join or subquery support: Limits complex query capabilities.
• Read performance: Writes are fast, but reads can be slow.
• Documentation: Official documentation is limited, which can pose challenges for new users.

CouchDB

CouchDB is an open-source, document-oriented NoSQL database known for storing data in JSON documents and using JavaScript as its query language with the help of MapReduce. It embraces web standards by accessing documents via HTTP, allowing them to be queried, combined, and transformed with JavaScript. CouchDB is well-suited for both web and mobile applications, offering on-the-fly document transformations and real-time change notifications.

Key features of CouchDB include:

• Database replication: Supports replication across multiple server instances.
• Fast indexing and retrieval: Ensures efficient data access.
• REST-like interface: Facilitates easy interaction with the database.
• Multiple language libraries: Enables the use of various programming languages.
• Browser-based GUI: Manages data, permissions, and configurations.
• Replication support: Ensures data redundancy and synchronization.
• ACID compliance: Follows all ACID properties for transactions.
• Authentication and session support: Provides secure access control.
• Database-level security: Ensures data protection.
• MapReduce support: Built-in support for processing and generating big data sets.

Advantages of using CouchDB:

• Data replication: Can store the same document in multiple database instances.
• JSON documents: Allows storage of serialized objects as unstructured data.
• Redundant data storage: Can replicate and sync with browsers via PouchDB.
• Sharding and clustering: Supports data distribution and scalability.
• Master-to-Master replication: Enables continuous backup and data synchronization.

Disadvantages of CouchDB:

• Performance: Slower than some NoSQL databases.
• Overhead: Requires significant overhead.
• Query cost: Arbitrary queries can be expensive.
• Temporary views: Slow on massive datasets.
• Transaction support: Lacks support for transactions.
• Large database replication: Can be unreliable.

Conclusion

No matter the project you’re working on, there’s a database that perfectly suits your needs. Whether you’re developing a small dynamic website that requires high levels of data consistency, making a relational database ideal, or an app that needs to scale to massive proportions, best served by a non-relational database, you have options. With Utho, you can work with any of these databases to effectively store your data and interact with your applications. It’s crucial, however, to understand your app’s specific database requirements before making a selection. Choosing the wrong database could be costly to retool later on.