I'm curious how this can both avoid the average-of-averages problem (presumably by using the original full-rate data to compute multiple aggregates) and also supports backfilling. Is there a danger of the full-rate data expiring and having a different behavior for backfills past that horizon? Or am I wholly misunderstading both these features?
Just one more note. Timescale is hiring, including for roles working on Promscale.
https://www.timescale.com/careers
Promscale roles are listed in the "Observability" section.
Congratz timescale on being #1 on the frontpage 3 days in a row !
This sounds awesome! But is it the right approach if I am just running a simple Prometheus instance on my home NAS? I've wondered for a while how I can persist my Prometheus timeseries, I guess I could use promscale for this, but maybe it's overkill for something this simple. Advice appreciated :)
Years ago I had a Graphite installation where I configured retention policies, and the same for InfluxDB if my memory doesn't fail me.
The downsampling feature at first glance seems to serve a different use case than Prometheus was built for, which I think is observability and alerting for a relatively short time period. For systems that need to work with years of data it totally makes sense, but I don't think Prometheus is used in those cases.
Since this feature has been built for a reason however, I could be wrong
At some point somebody "invents" the circular buffers that have the multiple data resolutions that was RRDtool and maybe we get compact and fast time series storage and reporting again.