> I honestly don't know what time-series databases do that's particularly unique. I understand the "spirit" of your comment and I agree in general. However so

> I honestly don't know what time-series databases do that's particularly unique. I understand the "spirit" of your comment and I agree in general. However something unique that comes to my mind about some TSDBs is automated aggregation of data points (using min/max/avg/whatever functions) into something less granular after that a metric's specific time becomes older than X. By using a TSDB for example you'll be able to look at datapoints with a max resolution of 10 seconds for metrics collected during the past 7 days, but after that their max resolution will be (aggregated into) e.g. 60-seconds intervals, and so on. I think that the theory behind it is that you're probably interested at the details for recent stuff, but the older the stuff gets the more you just want to look at the general trend without caring about the details. I can agree with that. In the end by doing these kinds of aggregations in theory everything should be faster & should use less storage (as for older data there are fewer data points). This is how "Graphite/Carbon" ( https://graphite.readthedocs.io/en/latest/faq.html https://graphite.readthedocs.io/en/latest/faq.html ) works. I did use Graphite/Carbon for some years, but I didn't like a lot its architecture and had some performance problems => I've replaced it with Clickhouse (I'm not doing any kind of data aggregation) and that's using less space and is quicker (respectively it uses a lot less CPU & I/O) :)