Not really true. I point whatever thing that talks something like influx to it with the right credentials and it outputs whatever metrics it wants to it. No need to manage/pre-create all your tables for every single possible metric out-there.

I seriously dislike nosql databases for most purposes, and am absolutely a Postgres fan - but timeseries is the only thing I've encountered that benefits from a dedicated schema-less database engine.

How's that any different than something that talks SQL? It's the most universal data language there is.

Why do you have to make new tables? It's 1 table with timestamp, name, value to store all your metrics and you can use an array or json column if you have extra non-structured data. Add in the SQL joins and analysis and you get a much better tool for timeseries.

> How's that any different than something that talks SQL? It's the most universal data language there is.

To use SQL you need to know the schema you're working against. Every single tool has to agree on a specific schema - and there are tons of existing tools that push infrastructure/system metrics into timeseries databases. For them it's simple, Influx uses some API, OpenTSDB uses another, Prometheus uses yet another - but they're all pretty simple to use. If you would point them to a Postgres database on the other hand, they wouldn't have a clue what fields to insert.

TS are heavily schema oriented unless you are talking about logs and then you aren't really talking about the classical notion of a TS database where aggregates are important. There isn't a NoSQL database that does them well.