a robust  decoding and reporting system for  WSPR

Please note: The WsprDaemon Timescale databases may be subject to change, the information below will reflect those changes.

10-15 May 2020: Major change in wsprdaemon v2.9  to add all the fields generated by wsprd meant change of table names. Previous tables were deleted on 20 May 2020.

After trying out Influx as our database for wspr spots and noise we settled on TimescaleDB. In this application TimescaleDB required less CPU and disk resources to provide useful functionality. TimescaleDB is built upon postgreSQL, an open source full-featured relational database using SQL (structured Query Language).

There is the inevitability of jargon - our current implementation comprises:

•  Two Databases with names 'tutorial' and 'wsprnet'.

Within the 'tutorial' Database, we currently have three Tables:

  • wsprdaemon_spots - comprising wspr spots uploaded by WsprDaemon users that choose to do so.

  • wsprdaemon_noise - comprising noise spots uploaded by WsprDaemon users that choose to do so.

  • kp - a geomagnetic disturbance index, scraped each day from NOAA's Space Weather Prediction Center.

Within the 'wsprnet' Database, we currently have one data Table:

  • spots - comprising wspr spots obtained from via an API.

These postgeSQL Tables have been converted into Hypertables for use with TimescaleDB.

Each Hypertable has a Data Retention Policy set by our server capacity. For our logs2 server, for data in:

  • RAM - 30 days, before migrating to

  • SSD - one year, before migrating to

  • RAID - our aim is to retain for 11 years, a sunspot cycle

Check out our updated V2 Guide by clicking the TimescaleDB logo.

This version covers installation of postgreSQL on your machine, how to gain access to the WsprDaemon databases and tables, together with numerous examples of  SQL queries in 'Flat File'  and self join modes. V2 of our Guide, issued in November 2020, also includes Annexes with details of how to connect to our databases using Python, psql, node.js, Octave and KNIME.