With the volume of time-stamped data still inundating sectors from finance and IoT to manufacturing and energy, companies have been presented with an existential decision: how they are best equipped to store and query their growing streams of data. Whether to use a time series database or a relational database to achieve that has been an increasingly acute issue. While both have their own strengths, one must understand their difference in structure and performance aspects to make the appropriate choice while storing time series data in relational database or moving to a time series specialization relational database.

What's Special about a Time Series Database?

A time series database (TSDB) is designed to handle a large amount of sequential, time-stamped data. It is especially well-suited for building up, storing, and querying measures that change over time—like sensor readings, stock prices, or app performance metrics.

Unlike general-purpose databases, a time series database optimizes data ingestion every second and retrieval based on the primary dimension. Self-healing capabilities like auto-compression, downsampling, and persistent aggregation make it best suited to be used in analytics in real-time. TSDBs support millions of writes per second and are hence best suited for predictive analysis use cases and monitoring.

Relational Databases and Their Drawbacks with Time Series Data

Traditional relational databases like MySQL, PostgreSQL, and Oracle follow a schema of rows and columns within tables and rely heavily on indexes for query performance. While the model excels very well in transactional systems (OLTP), it does not fare very well with the volume and velocity of time series workloads.

Users typically face the following issues in storing time series data in relational database:

• Write bottlenecks: Excessive writes of high-frequency inserts slow down indexes.

• Wasteful storage: Duplicate metadata (e.g., sensor ID) in every record is wasteful for storage.

• Slow complex queries: Retrieving and aggregating data over time periods is slow and resource-intensive.

Although technically time-stamped data can be warehoused in relational databases, they are neither horizontally scalable nor efficient compressors for time-based analysis.

The Middle Ground: Time Series Relational Database

To fill this void, a time series relational database combines the structure of relational systems and the performance optimizations of time series storage. These hybrid offerings place time-partitioning, auto-indexing, and compression over traditional relational engines.

Because TSDB has relational database support, companies are able to keep writing SQL syntax they are familiar with and have the scalability and query performance advantage of a TSDB without sacrificing anything in terms of performance.

Comparison: Time Series Database vs Relational Database

Why choose a Time Series Database?

For businesses processing billions of time-stamped data points daily, a time series database offers some advantages:

• Speed: Optimized for high-speed consumption and real-time queryability.

• Scalability: Scale out easily to manage large amounts of data.

• Efficiency: Saves data costs of storing by using compression and retention.

• Automation: Offers continuous aggregation and auto-rollup.

Storing Time Series Data in a Relational Database: When It Is Appropriate

Although it is not ideal, it is yet possible to hold time series data in a relational database for low flexibility infrastructures, low-frequency updates, or for tiny datasets. To illustrate, with some thousand data points daily, an excellent relational table can handle time-stamped data satisfactorily.

At the point of performance optimization, take into consideration:

• With the application of time-range-partitioned tables.

• By creating composite indexes on ID and timestamp columns.

• By applying data retention policies that clean out old data on a routine basis.

These approaches can cause standard relational databases to mimic much of the functionality of specialist time series systems—though not as fast or as big.

Conclusion

Whether to use a relational database or a time series database, in the end, it comes down to data volume, performance needs, and scaling needs. While time series data can be managed in a relational database for medium-range workloads, modern applications that need real-time analytics and high write throughput are well supported by time series databases.

In a world where increasingly more is driven by time-stamped data, the right database choice isn't simply a storage issue—a matter of unleashing the full potential of temporal analytics for smarter, faster decision-making.