Document e2e logging performance for time series data

[nikolausWest]

We want to benchmark logging scalars, including setting a timeline value for each logged scalar, i.e. something like

for frame_nr in range(0, 1_000_000) {
    rr. set_time_sequence("frame", frame_nr)
    rr.log("scalar", rr.TimeSeriesScalar(sin(frame_nr / 1000.0)))
}

We have the tool for it:

just rs-plot-dashboard --num-plots 10 --num-series-per-plot 5 --num-points-per-series 5000 --freq 1000

For each language (C++, Python, Rust), measure the max throughputs (scalars per second), end-to-end (logging -> visualization) for single-threaded/single-plot and multi-threaded logging (so 3 x 2 throughput figures).

We also want to check the memory use in the viewer when we have logged 100M scalars or so, to measure the RAM overhead.

---

manually document this somewhere in our docs, i.e.:

On a 2023 MacBook M1:

Language	Single-threaded	Multi-threaded
C++	? kHz	? kHz
Python	? kHz	? kHz
Rust	? kHz	? kHz

Viewing 100M scalars use up ?GB of RAM in the native viewer.

Very rough numbers is fine, e.g. "~10 M scalars / second"

[emilk]

We should link to #4423 too

[emilk]

I know there was some decision to punt on this (and it was moved to Triage), so I'm moving this down in urgency.

It would be nice with a short comment explaining why we are punting on this though.