![Slow Grafana Performance? Learn How to Fix It Using Downsampling](/blog/content/images/size/w300/2022/06/Grafana-Downsampling-post--1-.png)
![Slow Grafana Performance? Learn How to Fix It Using Downsampling](/blog/content/images/size/w300/2022/06/Grafana-Downsampling-post--1-.png)
![Introducing Hyperfunctions: New SQL Functions to Simplify Working With Time-Series Data in PostgreSQL](/blog/content/images/size/w300/2021/07/ryan-stone-OlxJVn9fxz4-unsplash.jpg)
Introducing Hyperfunctions: New SQL Functions to Simplify Working With Time-Series Data in PostgreSQL
TimescaleDB hyperfunctions are pre-built functions for the most common and difficult queries that developers write today in TimescaleDB and PostgreSQL. Hyperfunctions help developers measure what matters in time-series data, which generates massive, ever-growing streams of information.
![Time-Series Analytics for PostgreSQL: Introducing the Timescale Analytics Project](/blog/content/images/size/w300/2021/01/alexander-andrews-4JdvOwrVzfY-unsplash.jpg)
Time-Series Analytics for PostgreSQL: Introducing the Timescale Analytics Project
We're excited to announce Timescale Analytics, a new project focused on combining all of the capabilities SQL needs to perform time-series analytics into one Postgres extension. Learn about our plans, why we're sharing it now, and ways to contribute your feedback and ideas.
![Time-Series Compression Algorithms, Explained](/blog/content/images/size/w300/2020/04/miniature-town-1465321573Cc4.jpg)
![Continuous aggregates: faster queries with automatically maintained materialized views](/blog/content/images/size/w300/2019/05/20190508_Timescale_Blog_ContinuousAggregates.jpg)
![What is high cardinality, and how do time-series databases like InfluxDB and TimescaleDB compare?](/blog/content/images/size/w300/2019/04/cardinality1.jpg)