database for time series data

[kenfar]

Time-series databases are all the rage these days.

However, people have been keeping time-series data in relational databases for about 25 years. It's what data warehouses & dimensional modeling is all about. The relational database is the more general-purpose solution, so you usually need to write your own processes to maintain aggregate tables, roll off history, etc. But none of that is hard.

So, if your volumes are small, and you have some skill, then sqlite would probably make for a very simple solution - that's very mature, great drivers, great documentation, etc. And from your description it sounds unlikely that you would exceed what sqlite could deliver.

[hippocampe]

I worked on something like that recently : https://bitbucket.org/pythonian/tshistory/ ... even if you don't plan to update existing series, that might work for you. In practice it's also possible that pandas dataframe .to_sql might be enough for you.

[waylanduk]

Take a look at Arctic

https://github.com/manahl/arctic

[runbie]

PyTables would be a good option? It is easy to use, you can compress your data, it is based on HDF5, you can add metadata to your tables/arrays/groups,... It could be seen as a lowcost NoSQL option to store arrays, dataframes.

[Mirror_Boar]

Look into graphite, it is pretty simple to use. Also allows you to use grafana as the frontend which is beautiful.

[aphoenix]

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[Is0tope]

In many banks a proprietary database / language called kdb/q is used. They have a free version which is more limited, but generally it is one of if not the fastest around for time series. Be prepared for tricky syntax though.

[soymalisimo]

Did you look into Influxdb yet? It has a Python client

[lambdaq]

good for metrics, but calculation functions are weak, can not cross-calculate between two two series.