MySQL vs PostgreSQL

Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of MySQL and PostgreSQL so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how MySQL and PostgreSQL perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.

MySQL Overview

MySQL is an open source relational database management system that was first released in 1995. It is one of the most popular databases worldwide due to its ease of use, reliability, and performance. MySQL is widely used for web applications, online transaction processing, and data warehousing. Oracle Corporation acquired MySQL in 2010, but it remains open source software with an active community of contributors.

PostgreSQL Overview

PostgreSQL, also known as Postgres, is an open-source relational database management system that was first released in 1996. It has a long history of being a robust, reliable, and feature-rich database system, widely used in various industries and applications. PostgreSQL is known for its adherence to the SQL standard and extensibility, which allows users to define their own data types, operators, and functions. It is developed and maintained by a dedicated community of contributors and is available on multiple platforms, including Windows, Linux, and macOS.

MySQL for Time Series Data

MySQL can be used for storing and analyzing time series data, but it will not be as efficient as a dedicated time series databases. MySQL’s flexibility and support for various indexing techniques can make it a suitable choice for small to medium sized time series datasets. For large-scale time series data workloads, with high write throughput or use cases where low latency queries are required, MySQL will tend to struggle unless highly customized.

PostgreSQL for Time Series Data

PostgreSQL can be used for time series data storage and analysis, although it was not specifically designed for this use case. With its rich set of data types, indexing options, and window function support, PostgreSQL can handle time series data. However, Postgres will not be as optimized for time series data as specialized time series databases when it comes to things like data compression, write throughput, and query speed. PostgreSQL also lacks a number of features that are useful for working with time series data like downsampling, retention policies, and custom SQL functions for time series data analysis.

MySQL Key Concepts

• Table: A collection of related data organized in rows and columns, which is the primary structure for storing data in MySQL.
• Primary Key: A unique identifier for each row in a table, used to enforce data integrity and enable efficient querying.
• Foreign Key: A column or set of columns in a table that refers to the primary key in another table, used to establish relationships between tables.

PostgreSQL Key Concepts

• MVCC: Multi-Version Concurrency Control is a technique used by PostgreSQL to allow multiple transactions to be executed concurrently without conflicts or locking.
• WAL: Write-Ahead Logging is a method used to ensure data durability by logging changes to a journal before they are written to the main data files.
• TOAST: The Oversized-Attribute Storage Technique is a mechanism for storing large data values in a separate table to reduce the main table’s disk space consumption.

MySQL Architecture

MySQL is a relational database management system that uses SQL for defining and manipulating data. It follows the client-server model, where a MySQL server accepts connections from multiple clients and processes their queries. MySQL’s architecture includes a storage engine framework that allows users to choose from different storage engines, such as InnoDB, MyISAM, or Memory, to optimize the database for specific use cases.

PostgreSQL Architecture

PostgreSQL is a client-server relational database system that uses the SQL language for querying and manipulation. It employs a process-based architecture, with each connection to the database being handled by a separate server process. This architecture provides isolation between different users and sessions. PostgreSQL supports ACID transactions and uses a combination of MVCC, WAL, and other techniques to ensure data consistency, durability, and performance. It also supports various extensions and external modules to enhance its functionality.