(Timescale engineer here) We are an extension on top of Postgres and not a whole new DBMS. That means that we inherit a lot of the reliability / stability of Postgres (critically we do not change the WAL/data on disk format which leads to the fact that even as a young database we don't loose data). What we provide on top of Postgres are significant optimization aimed at time-series data. Postgres is optimized for relational data, and it requires significant work to optimize it for time-series data workloads on both the insert and query side.

TimescaleDB provides automatic, adaptive partitioning of time-series tables to make management of time-series tables both easier and more efficient. We add new time-based analytical functions as well as improve query planning and optimization.

For new databases that I'm interested in, I generally check if there's a Jepsen analysis for it (https://jepsen.io/analyses) or whether it's being open-sourced by a company that's been using it in production for a while like Foundation DB (https://news.ycombinator.com/item?id=16877395). In either of these two cases I would say it's well-tested. Otherwise, it's often hit or miss. Thankfully the number of new db products I've seen is fewer than the halcyon days of the MongoDB wave (2011-2014) and tend to be a bit more focused on specific problems that the db solves.

It’s just a plugin to Postgres, you just convert the tables you want to it’s type and otherwise it’s all the same.

No need to learn new databases all the time, Postgres does it all, regular tables, “documents” (JSON), timeseries, spatial/GIS, you name it.

TimescaleDB is merely a PostgreSQL extension, not a complete DBMS all by itself.

If I recall correctly, this is just Postgres with extra handling around time series data isn’t it?