Published at Dec 9, 2024

Why Consider Using PostgreSQL for Time-Series Data?

Written by Sarah Conway

With applications in just about every field, including IoT, cryptocurrency, finance, transport, climate, healthcare, and many others, you’ve likely encountered the need for time-series data no matter what industry you’re in.

Put simply, time-series data is characterized by data points continuously collected over time (we’ve written extensively about it here). This data allows businesses to track changes (across any span of time, from seconds to years!) to obtain critical business insights, perform historical analysis, and empower informed decision-making. 

The volume of time-series data for organizations is growing exponentially every day, and so is the need to collect, manage, and analyze this information. This raises essential questions: with the emergence of specialized databases tailored for time-series data management, why consider using a general-purpose relational database management system (RDBMS) like PostgreSQL? Which solution is best suited for these use cases where anywhere from gigabytes to petabytes (or more) of data might require processing or management on a daily basis? 

Considering PostgreSQL for Time Series

You may have heard of PostgreSQL before, but maybe not. It’s an excellent open-source RDBMS with over 35 years of active development from a thriving community. It’s renowned for being flexible, reliable, and consistent, with attributes like ACID compliance and multi-version concurrency control (MVCC). 

Not to mention—it is extremely extensible with popular extensions created and maintained by the community, such as PostGIS (a top solution for processing and managing geospatial data vs. other databases) or pgai and pgvector (open-source tools that enable seamless development of retrieval-augmented generation or RAG, semantic search, and other AI applications using PostgreSQL).

Traditionally, it’s been a solution primarily leveraged for OLTP workloads given its particular strengths in that area. Yet, in recent years, more and more improvements have been introduced that make it suitable for OLAP and time-series data—particularly in the latest release (as of 2024), PostgreSQL 17. 

Using PostgreSQL Specifically for Time-Series Data

Referencing the extensibility of PostgreSQL mentioned previously, TimescaleDB is a 100 percent open-source extension that optimizes PostgreSQL for rapid ingest rates and efficient querying, especially for complex operations and high volumes of data. 

TimescaleDB is packaged as a PostgreSQL extension, so it wraps PostgreSQL and inherits all of the benefits and reliability for any workload—general purpose or specialized. Yet, it has several features that distinguish it as a solution specifically for time-series data, including:

• Dramatically improved ingest rates

• Query performance that is either equivalent to PostgreSQL or orders of magnitude greater

• Columnar compression for enhanced scalability with impressive compression rates not found in any other relational database solution

Using TimescaleDB with PostgreSQL gives you a streamlined time-series data management experience with specialized features:

• Data retention policies for automatically discarding unnecessary data 

• Continuous aggregates for faster retrieval of aggregated results and reduced storage

• Hypertables to scale PostgreSQL via automated partitioning

• A hybrid-row columnar storage engine (combined with specialized compression algorithms) that achieves unparalleled compression rates (95+ %) compared to any other relational database and can handle real-time analytics use cases

• Time buckets to aggregate data within hypertables by time interval (such as minute, hour, or day buckets) for calculating summary values

• Native job scheduling for automated workflows (to automatically handle compression, continuous aggregates, data retention, and other built-in features)

• Over 100 hyperfunctions that make data analysis easy in PostgreSQL, including time-weighted averages, statistical aggregates, percentile approximations, and much more

These features dramatically improve PostgreSQL query performance at scale, lower costs, increase storage efficiency, and give developers tools designed for managing time-series data. This is all accompanied by the reliable and robust foundation of PostgreSQL, along with all of the native features, functionality, and ecosystem that come with it.

Interested in learning more? Check out our introductory articles for developers on each of these features, including hypertables and continuous aggregates.

Comparing PostgreSQL to Specialized Database Solutions

Because PostgreSQL has been developed for over 35 years, it is a very mature, “boring” technology. It’s been heavily battle-tested with common “gotchas” ironed out and an intensive review and quality assurance process. This, combined with robust backup and recovery functionality, helps ensure you can trust that your data is safe—no matter what.

Full compliance with the SQL standard means it’s quick and easy for your team to get up to speed on working with PostgreSQL. Not to mention, as an advanced and general-purpose RDBMS, PostgreSQL can process many different data types alongside your time-series data (including geospatial, transactional, metadata, vector, JSON/JSONB, and much more).

Depending on your use case, the significant improvements in speed and scalability that can come with an alternative database solution fully designed for solely time-series data may be worth it. You need to consider that there may be lost or duplicated blocks of data across enormous time-series datasets; your data will eventually reach consistency through alternative methods to ACID, but it might not be straight away.

However, when it comes to consistency and reliability being top concerns in addition to needing to accommodate significant performance requirements, PostgreSQL tends to be the way to go. It’s an excellent option for everything from e-commerce to analytics, with accommodations for a full range of data types, letting you simplify your data stack without needing the expertise, training, and support for handling many different database solutions. 

Give PostgreSQL + TimescaleDB a Try

The TimescaleDB extension itself is fully open source and is free and open for use. Leverage multiple options for community support from the largest community of time-series developers in the world on Slack, GitHub, and the Timescale community forum. If you do decide to self-host and eventually want professional support, you can always reach out to our team for self-managed support services. 

Else, want to try fully hosted TimescaleDB in the cloud? Get it for free (no credit card required) for 30 days on AWS, Azure, or GCP.