MySQL vs TimescaleDB: What are the differences?

Introduction

TimescaleDB is an open-source relational database management system designed to handle time-series data. It is built on top of PostgreSQL and provides enhanced scalability and performance for time-series workloads. In comparison, MySQL is a popular open-source relational database management system that offers comprehensive features and support for various applications.

1. Architecture: MySQL follows a traditional RDBMS architecture, while TimescaleDB extends PostgreSQL's architecture to support time-series data efficiently. TimescaleDB introduces hypertables, a partitioning mechanism that enables time-based partitioning of data for optimal query performance.

2. Scalability: MySQL can horizontally scale using replication and sharding techniques, but it requires additional effort and maintenance. TimescaleDB, on the other hand, comes with built-in automatic data partitioning and parallel query processing, allowing for seamless scalability.

3. Compression: TimescaleDB provides built-in time-series data compression techniques, including delta compression and run-length encoding. This compression reduces storage requirements and improves query performance. MySQL does not offer native compression for time-series data.

4. Continuous Aggregations: TimescaleDB supports continuous aggregations, which allow users to precompute and store aggregated results. This feature significantly speeds up queries that require aggregations over large time-series datasets. MySQL does not have native support for continuous aggregations.

5. Data Retention Policies: TimescaleDB offers data retention policies that allow automatic removal of old data based on criteria such as time or size. This feature simplifies data lifecycle management for time-series data. MySQL requires manual intervention or custom scripts to implement similar functionality.

6. Time-Series Specific Functions: TimescaleDB provides several time-series-specific functions and operators, such as time_bucket, time_bucket_gapfill, and time_bucket_overlap. These functions simplify time-series data manipulation and analysis. While MySQL offers some date and time functions, it lacks dedicated functions for time-series analysis.

In summary, TimescaleDB extends PostgreSQL with features specific to time-series data management, such as optimized partitioning, built-in compression, continuous aggregations, and data retention policies. MySQL is a generic RDBMS that requires additional effort to handle time-series data effectively.