Storing time-series data, relational or non?

Question

I am creating a system which polls devices for data on varying metrics such as CPU utilisation, disk utilisation, temperature etc. at (probably) 5 minute intervals using SNM

Answer 1

You table has data in single table. So relational vs non relational is not the question. Basically you need to read a lot of sequential data. Now if you have enough RAM to store a years worth data then nothing like using Redis/MongoDB etc.

Mostly NoSQL databases will store your data on same location on disk and in compressed form to avoid multiple disk access.

NoSQL does the same thing as creating the index on device id and metric id, but in its own way. With database even if you do this the index and data may be at different places and there would be a lot of disk IO.

Tools like Splunk are using NoSQL backends to store time series data and then using map reduce to create aggregates (which might be what you want later). So in my opinion to use NoSQL is an option as people have already tried it for similar use cases. But will a million rows bring the database to crawl (maybe not , with decent hardware and proper configurations).