It's the main reason we decided to use influx in our small team with simple enough timeseries needs
It's the main reason we decided to use influx in our small team with simple enough timeseries needs
"Why is it difficult, because you're self managed?"
I eventually spent a while reading about Timescale 1 vs 2, and testing the pg_prometheus[1] adapter and started thinking through integrating its schema to our other needs then realizing it's "sunsetted" and then reading about the new timescale-prometheus[2] adapter and reading through its ongoing design doc[3] with updated schema that I'm less a fan of.
I finally wound up mostly-settling on Timescale although I've put the Prometheus extension question on hold, just pulling in metrics data and outputting with ChartJS and some basic queries got me a lot closer to done for now. Our use case may be a little odd regardless, but I think a timescale-prometheus extension with a some ability to customize how the data is persisted would be quite useful.
[1] https://github.com/timescale/pg_prometheus
[2] https://github.com/timescale/timescale-prometheus
[3] https://docs.google.com/document/d/1e3mAN3eHUpQ2JHDvnmkmn_9r...
Because of that and the small scale required, the choice of table-per-metric would be a tough fit and I think a single table with JSONB and maybe some partial indexes is going to work a lot better for us. It would just be nice if we could somehow code in our schema mapping and use the supported extension, but I get it may be too baked-into the implementation.
Anyway, overall we're quite happy with TimescaleDB!
For serious applications, it doesn't cut it. Trying to do more than a few TB a day is a waste of time outside of enterprise, which ain't cheap.
I plopped VictoriaMetrics in place of Influx for my cases and haven't even had a single hiccup.
[1] https://medium.com/@valyala/insert-benchmarks-with-inch-infl...
[1] https://victoriametrics.github.io/#how-to-send-data-from-inf...
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