How to store weather forecast data for fast time-series APIs
a day ago
- #time-series
- #weather-data
- #api-optimization
- Weather forecast data is stored in multi-dimensional grids with columns and rows representing geographical locations.
- Each grid-cell corresponds to a specific area (e.g., 40x40 km for a global grid).
- High-resolution local grids are also common for smaller areas.
- A single weather-model run can generate large amounts of data (e.g., 87.5 GB for 35 variables over 168 hours).
- Weather models are updated every 3 hours, referred to as 'model-runs' (e.g., 0z, 3z, 6z).
- Gridded files are efficient for weather maps but inefficient for time-series data due to multiple file reads.
- Relational databases can store weather data but have drawbacks like storage overhead and slow updates.
- Time-series gridded files organize data sequentially by grid-cell and time, enabling fast API access.
- Open-Meteo uses memory-mapped files and Linux page cache to optimize read performance.
- API response times can be as low as 2 milliseconds for 10 weather variables.