Background
After the first release of Oracle Database in 1979, the relational database management system (RDBMS) quickly became the go-to choice for software developers and systems architects around the world. By now, the maturity, standardization, and flexibility of major RDBMS solutions has solidified their place in the world of data — even with the rise of NoSQL databases like MongoDB, DB-Engines shows that seven of the top ten most popular database management systems are still relational as of July 2025.
At the same time, RRDtool, probably the first time-series database (TSDB), came out 20 years after Oracle in 1999, and adoption of TSDBs did not really pick up until around 2015 with the growth of the IoT. And although the trend toward specialized databases has continued and more advanced time-series databases have been released in recent years, when designing systems, many developers and architects tend to stick with what they know. In the database world, this often means traditional relational databases like Oracle, MySQL, and SQL Server. And when these products are used to store and query time-series data, they seem to work well — at first.
In fact, while initial testing and proof of concept are often done on small amounts of data to speed up the development process, real-world time-series datasets grow rapidly over time, especially when new sites or devices are added as the business grows. Despite the best efforts of DBAs, there comes a time when the performance of relational databases just can’t keep up with the scale of the data: queries slow to a crawl, and reports that were once generated in seconds aren’t ready till the end of the day. Businesses that built their time-series systems on relational databases are then presented with two unsavory choices: either spend huge amounts of money to upgrade hardware until performance is acceptable, or rearchitect the entire system around a purpose-built time-series database.
Comparison of Relational and Time-Series Databases
The following table provides a comparison of relational databases and time-series databases.
| Item | Relational Databases | Time-Series Databases |
|---|---|---|
| Primary Use Case | General-purpose data storage across many domains | High-frequency, time-stamped data (e.g. metrics, logs) |
| Data Structure | Tables with flexible schemas | Optimized for time-series format: timestamp + tags + values |
| Schema Flexibility | Structured schema; rigid columns and relationships | Often fixed schema; tags + fields pattern; some support dynamic fields |
| Write Patterns | Moderate, transactional writes | High ingestion rate, append-only writes |
| Read Patterns | Optimized for joins, complex queries | Optimized for aggregations, rollups, downsampling |
| Storage Optimization | Row-based or columnar (depending on engine) | Time-partitioned, compressed, and optimized for sequential writes |
| Query Language | SQL | SQL (TDengine, TimescaleDB) or custom (InfluxQL, PromQL) |
| Performance with Time-Series | Not optimized for time-series workloads | Purpose-built for large-scale time-series performance |
| Retention Policies | Manual implementation | Built-in retention and downsampling features |
| Examples | MySQL, PostgreSQL, Oracle, Microsoft SQL Server | TDengine, InfluxDB, TimescaleDB, OpenTSDB |
TDengine’s Solution
While TDengine is a purpose-built time-series database, it offers many advantages for developers more accustomed to relational databases.
- Standard SQL interface
- TDengine supports standard SQL, not a custom query language.
- Developers and users accustomed to MySQL or PostgreSQL can write queries without needing to learn something new.
- Common operations like
SELECT,JOIN,GROUP BY, andWHEREwork as expected.
- Relational data model
- TDengine uses tables and columns, not unfamiliar document or key-value models.
- It introduces supertables (like templates) to manage many similar time-series sources, which feel like regular tables with added convenience.
For even more convenience, TDengine Enterprise offers zero-code data replication from four popular RDBMS solutions: MySQL, PostgreSQL, Oracle, and Microsoft SQL Server. Choose your system to learn more:
