Can we stop with the uptime percentages?

runjake

I don't really care about percentages, either. But for some industries, the difference between "two 9s" and "five 9s" can be millions of dollars, so that's why they're published that way to the customer.

yuye

What's the point here? That everyone should use the n-nines notation? Sure. However, companies have no interest in doing anything that makes them look worse. Also, is anyone else getting the bitter taste of AI writing from this page?

wang_li

Services can have a 50% uptime (or a 50% downtime if you prefer) as long as it's the time when I need it to be up (or don't need it.) Which is to say that the significance of downtime depends on the user. Talking about nines only makes sense internally when you are evaluating your infrastructure and operations. It doesn't tell you squat about impact to your customer.

doublerabbit

Can we post uptime stats instead? I just axed two of my servers from colocation a couple of days ago. Sad to see them go, six years of FreeBSD. root@vixen:/fountain/crystals # *** FINAL System shutdown message from dblrabbit@ *** System going down IMMEDIATELY System shutdown time has arrived root@vixen:/fountain/crystals # uptime 3:05PM up 1931 days, 18:13, 0 users, load averages: 1.01, 1.03, 1.41 root@cookie:/srv/users/dblrabbit # uptime 3:07PM up 1931 days, 16:59, 1 user, load averages: 1.76, 1.17, 1.06 root@cookie:/srv/users/dblrabbit # poweroff Shutdown NOW! poweroff: [pid 47177]

hx8

> We say something like: > GitHub Actions: 12 hours affected in the last 30 days (98.31% uptime). This is trying to shine the most favorable possible light onto a deteriorating situation. It doesn't take away from the fact that most businesses have measurable missed revenue in downtime. Customers that shop somewhere else, ads that were never severed, leads that grew a little colder. 12 hours of downed GitHub results in millions of dollars of lost developer productivity that was externalized by Microsoft to other companies. We shouldn't be trying to spin downtime as "just a few hours a month." Those hours cost real dollars.

swiftcoder

Yeah, this is always fun. Logarithmic graphs of downtime, people.

denysvitali

The 12 hours out of 30 days seems like sugarcoating the issue. Keep the percentages, and regardless of that - GitHub fix your uptime

lucfranken

I'm also not sure that all downtime is really properly measured now as more and more services are connected and intertwined. Some measure quite detailled but some just don't summarize the downtime from all providers up and below their own platforms.

tyho

`-log10(1 - uptime)`

stairlane

These metrics tend to be bullshit in contracts. For example we had a 6 9 (99.9999%) requirement from a customer for any given 3-6 month period. If we violated that, we owed them their money back (baring the outage wasn’t caused by us - I.e our cloud provider shit the bed). That’s something like 7.5 seconds. For a contract over $1.5M. Am I the only one who thinks that’s outrageous expectations? EDIT: The web app was for generating SBOMs of static assets.

micromacrofoot

The job of the uptime numbers are to look good (and sometimes to meet contractual obligations), more context doesn't make them sound better. Not being understood in layman's terms is a feature. These companies are happy that you don't know the difference between 99%, 99.9%, and 99.99% and that you think they all sound pretty good.

cdkmoose

As more and more things we might consider "platform" move to the cloud, I think it also matters what the service provider means by saying it's up. Just because the servers are alive and responding doesn't mean the platform is really functional. One vendor in particular we deal with has a powerful feature which we use to a large extent. Unfortunately, that particular feature is all too often not working. The servers are up and the rest of the platform is working, but we need that feature, so if it's down, it doesn't help much that the rest of the platform is up.

jakevoytko

These numbers are useful proxies for how likely you are to have your work disrupted outside of your own control. If you do something 100 times a day against a four-nines service, you can reasonably expect that everything will succeed. If you do something 10,000 times a day against a two-nines service, you can expect to hit a substantial number of errors during that day, or even have long periods where your work cannot happen at all. People aren't frustrated with Github because Github has 98% uptime or whatever the specific number is. They're frustrated because it regularly interferes with their ability to work. The 98% number is just a concise way to say it.

port3000

Until there is an industry wide definition of outage, degraded performance, etc then it's all moot

teraflop

Separately from how you present the number, the very concept of "uptime" as a single number is a bit muddy in the context of a distributed system, where different components can be differently available for different users. Also, 0.1% downtime in the form of a 45-minute outage per month is very different from 0.1% of requests failing in brief bursts. You often see downtime reported as "increased error rates" which is so vague as to be meaningless. Google's "windowed user-uptime" attempts to deal with this a bit better, by exposing different views of the data instead of trying to condense uptime into a single number: https://www.usenix.org/system/files/nsdi20-paper-hauer.pdf

jerf

You can use -log10(1-p). On the "nines" it is exactly the number of nines you have: $ python3 Python 3.12.3 (main, Aug 31 2026, 10:18:26) [GCC 13.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import math >>> def nines(num): ... return -math.log10(1-num) ... >>> nines(.9) 1.0 >>> nines(.99) 1.9999999999999996 >>> nines(.999) 2.9999999999999996 (Modulo floating point issues of course.) Which then smoothly covers the entire space: >>> nines(.9321) 1.1681302257194985 >>> nines(.2) 0.09691001300805639 But good luck getting that standardized.

iLoveOncall

> So how about, and I’ll just throw this out there, instead of: > > GitHub Actions: 98.31% uptime. > > We say something like: > > GitHub Actions: 12 hours affected in the last 30 days (98.31% uptime). The suggested format is equally unhelpful. You can get 12 hours of downtime by being down once for 12 hours, or 144 times for 5 minutes. The user experience is VERY different in those two cases. Ultimately the graphs are the most useful format.

charcircuit

Uptime % has the benefit that it's easy to understand.

proxysna

Wider audience started to use status pages because the service unreliability became so much more noticeable than before and not the other way around. I never had to use a status page for bear blog or protonmail because i never had and issue with it or just i never noticed. I am now _required_ to consult status page of github, circleci or MS services etc because i need to know why a build is not passing, why i cannot open a repo, why is my work stalling. Percentages matter, it is just so much more obvious why they matter when it comes down to important pieces of the internet like github. And i highly doubt the number of 12 hours in the last month. MS has been downplaying the issues they have with GH performance for a while now and i don't think it is time to start to believe them yet. Maintaining these pieces of infrastructure is responsibility and a burden. Overall i would be careful with "nonlinear significance of numbers near 100%" we are talking gh being well into the 90's this year and one number that infra people are also often being reminded about is that "1% is 3.5 days". Things are tough for gh people and i feel for them but they are not a startup or a underdog of some sort to receive sympathy in that case.

dmurray

I usually tell people you don't need as much reliability as you think. Three nines reliability is great for most purposes. 8 hours downtime a year. If your system produces money at a constant rate, it captures 99.9% of the available money. Even two nines or one nine might be pretty good on that basis, when the alternative is spending 2x or 10x as much - let's build another unreliable system with that money that captures some other independent market opportunity. Poor reliability is a problem where you need to chain many systems together, or where the cost of a single failure is very large compared to a success. Or - as happens commonly because of load - if your periods of unreliability are correlated with periods of maximum opportunity, like an e-commerce site failing on Black Friday or a trading system failing when the market is most busy. But if you don't have one of those cases, evaluate whether investing in reliability is actually worth it to you. GitHub is an example where two nines of reliability ought to be OK. The argument against it is that it's bad marketing to have an unreliable service, especially one aimed at software engineers. And if GitHub is largely a marketing play by Microsoft anyway (do they really make back its cost in enterprise subscriptions?) then marketing considerations need to drive its reliability.