The current implementation of InfluxDB is excellent at handling timeseries data provided it’s consistently moving forward. Yes you can write in the past, but the point here is that you query for data in a specific time range to visualize what happens over that specific range.
In the case of regression tests, you may want to overlay the results from a variety of iterations over the same time range. The problem with this currently is that each run would have to have the exact same time slice for each iteration to overlay properly.
The workaround for this at the moment is to artificially start your time at a known zero endpoint (say timestamp == 0000000000000000000) and then overlays would work, in theory. But this isn’t necessarily a clean or effective method of overlaying information. The question then becomes:
- Can we look to add functionality in the future that would provide relative timestamping, or shifting, similar to what tools like splunk would do? This would allow any set of runs to be graphed on top of each other to show quick diffs
- Is writing at an artificial timestamp of zero the best way to do this with current toolset?
This conversation starter (hopefully) is intended to discuss the tools within influxdb itself. We can definitely do this by pulling the data into pandas with influxdb-python, as an example, but is there a way we co do this natively?