I have a huge time series (about 30 million) of network paths with the following format:
timestamp, path, latency
The path is a sequence of IP address, so it can be represented either as a string or an array of integers. Currently the data are stored in text files which makes it very slow the analysis and querying of paths. It was suggested to me to use a timeseries database (TSDB), such as InfluxDB or OpenTSDB, to store them efficiently, but some background reading I did suggests that TSDBs are appropriate for numerical values. For instance OpenTSDB mentions:
OpenTSDB is a time series database. A time series is a series of numeric data points of some particular metric over time.
Is there any optimization I'll gain from using a TSDB instead of a relational DB in my case, and generally for timeseries that include non-numerical values?
The main queries I plan to do is basically to get all the paths between two timestamps, check if there are path changes, and how this changes affect the lattency. Additionally I may need to search for path with specific hops (e.g. select all records where the path includes the IP hop
184.108.40.206), or all the paths with latency over a certain threshold.