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There are several different types of timeseries workloads: financial, IoT/sensor data, monitoring metrics, timestamped events, etc. Also do you need a custom d
by nivertech 5y ago
There are several different types of timeseries workloads: financial, IoT/sensor data, monitoring metrics, timestamped events, etc.
Also 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.
- 0x008 5y agoHm 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.
- nivertech 5y agoI'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. [1] https://en.wikipedia.org/wiki/Kdb%2B https://en.wikipedia.org/wiki/Kdb%2B https://code.kx.com/q/ https://code.kx.com/q/ [2] https://www.timescale.com/ https://www.timescale.com/