Hm basically I am just looking for efficient storage and support for date type fields. The numeric values can be floats or integers.
Probably any SQL database will do the job, but I felt there was probably a more domain-specific solution maybe with a good pandas integration? I don’t really want to write my own SQL code and think much about schemas.
I'm a kdb+/q[1] developer. It's a proprietary timeseries database used in finance/trading, but also for IoT/sensor data.
But for your usecase I think TimescaleDB will be the best fit. It's an opensource extension for PostgreSQL, so you can use any PL/framework which can work with Postgres.
And you have all the felxibility of RDBMS and can use standard SQL queries.
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[ 3.1 ms ] story [ 25.4 ms ] threadAlso do you need a custom datapoint schema?
Do you need a generic tags/labels/dimensions?
Do you need flexible datatypes, or numeric/float only?
Do you need roll-ups, aggregations, window functions, stream processing, etc.
Probably any SQL database will do the job, but I felt there was probably a more domain-specific solution maybe with a good pandas integration? I don’t really want to write my own SQL code and think much about schemas.
But for your usecase I think TimescaleDB will be the best fit. It's an opensource extension for PostgreSQL, so you can use any PL/framework which can work with Postgres. And you have all the felxibility of RDBMS and can use standard SQL queries.
[1] https://en.wikipedia.org/wiki/Kdb%2B https://code.kx.com/q/
[2] https://www.timescale.com/
Do you have any experience with QuestDB?