Slow Grafana Performance? Learn How to Fix It Using Downsampling

Downsampling in Grafana is a technique used to understand data quicker and easier by highlighting trends that otherwise wouldn’t stand out. Grafana, an open-source visualization tool, allows users to create graphs for time-series data with ease. However, problems arise when dealing with extremely large datasets, which can be slow to load and lead to frustrated users or unusable dashboards. To overcome this, two types of downsampling techniques are used: `Largest Triangle Three Buckets` (lttb) and `Automated Smoothing for Attention Prioritization` (ASAP). The lttb method reduces the number of data points while maintaining the visual appearance of a graph, whereas ASAP smooths away noise in the data to reveal underlying trends. Both methods can be implemented using TimescaleDB's hyperfunctions, making it easy to manipulate and analyze time-series data with fewer lines of SQL code.

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