TimescaleDB users have seen 98% (ie over 50x) compression rates in some real-world cases (e.g., for some IT monitoring datasets), but compression ratio will definitely vary by dataset. (For example, a dataset of just 0s will compress even better! But that's probably not a realistic dataset :-) )
The reality is that Citus and TimescaleDB [0][1] take very different approaches to columnar compression, which result in different usability and performance trade-offs. In reality one should choose the right tool for their workload.
(As an aside, if you have time-series data, no one has spent more time developing an awesome time-series experience on Postgres than the TimescaleDB team has :-) )
Kudos to the Citus team for this launch! I love seeing how different members of the Postgres community keep pushing the state-of-the art.
