[Fripplebubby]

> This is partly a tooling problem. Many of the tools we use do not do a good job of capturing and representing this data. For example, the majority of latency graphs produced by Grafana, such as the one below, are basically worthless. We like to look at pretty charts, and by plotting what’s convenient we get a nice colorful graph which is quite readable. Only looking at the 95th percentile is what you do when you want to hide all the bad stuff. As Gil describes, it’s a “marketing system.” Whether it’s the CTO, potential customers, or engineers—someone’s getting duped. Furthermore, averaging percentiles is mathematically absurd. To conserve space, we often keep the summaries and throw away the data, but the “average of the 95th percentile” is a meaningless statement. You cannot average percentiles, yet note the labels in most of your Grafana charts. Unfortunately, it only gets worse from here.

I think this is getting a bit carried away. I don't have any argument against the observation that that average of a p95 is not something that mathematically makes sense, but if you actually understand what it is, it is absolutely still meaningful. With time series data, there is always some time denominator, so it really means (say) "the p95 per minute averaged over the last hour", which is or can be meaningful (and useful at a glance).

Also, the claim that "[o]nly looking at the 95th percentile is what you do when you want to hide all the bad stuff" is very context dependent. As long as you understand what it actually means, I don't see the harm in it. The author makes this point that, because a load of a single webpage will result in 40 requests or so, you are much more likely to hit a p99 and so you should really care about p99 and up - more power to you, if that's the contextually appropriate, then that is absolutely right, but that really only applies to a webserver serving webpage assets which is only one kind of software that you might be writing. I think it is definitely important to know, for one given "eyeball" waiting on your service to respond, what the actual flow is - whether it's just one request, or multiple concurrent requests, or some kind of dependency graph of calls to your service all needed in sequence - but I don't really think that challenges the commonsense notion of latency, does it?

[camel_gopher]

Nearly all time series databases store single value aggregations (think p95) over a time period. A select few store actual serialized distributions (Atlas from Netflix, Apica IronDB, some bespoke implementations). Latency tooling is sorely overlooked mostly because the good tooling is complex, and requires corresponding visualization tooling. Most of the vendors have some implementation of heat map or histogram visualization but either the math is wrong or the UI can’t handle a non trivial volume of samples. Unfortunately it’s been a race to the bottom for latency measurement tooling, with the users losing.

Source: I’ve done this a lot

[Fripplebubby]

I take it as a given that what is stored and graphed is an information-destroying aggregate, but I think that aggregate is actually still useful + meaningful