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.
Yeah I'm at a point where I have a bookmark folder of status pages mostly for very large orgs because I've been using those pages relatively regularly. This is not something I felt the need for historically.
I’ve worked in systems which at worse have had three nines for years, but I’ve also worked in systems where five nines is a failure.
This attitude of modern tech claiming 98% is good just doesn’t work in the old tech acceptance. We had individual components fail all the time. We’re still looking at a 230ms outage to a branch office last week caused by a power failure combined with a badly plumbed power distribution.
Modern software people don’t consider 230ms to be an outage. Glad they don’t work in electricity.
(The failure we had was only on the services we guarentee at 99.1%, our lowest sla. After that there’s 99.95 and 99.999.
(In reality we reach five nines year after year on even the lowest levels, but there are major concerns like “large bomb in data centre” which could cause some of our less critical units to drop way more than 5 minutes a year.
That's not really an old versus new thing. The water company isn't going to consider 230ms an outage either.
With tech, like, it's crazily difficult to make sure every http request succeeds, so you build in retry, and look at that 230ms doesn't cause real disruption now. Not to excuse 98%, that's awful.
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.
Exactly. If my build and test CI takes several hours and it gets interrupted, it really doesn’t matter how long the interruption was. It impacts me all the added time of realizing it stopped, investigating and confirming why it stopped, triggering another run, and continued monitoring.
> 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.
One thing I would like to see is how many of those hours are during normal business hours in my country.
Not all hours are created equal when it comes to downtime and my intuition is that most of these 12 landed within my working hours.
In terms of impact that then might mean they were down for 7.5% of the time I needed them, or had business hours uptime of 92.5% which is… both not very good and very disruptive.
On the other hand, downtime at 4AM would be much less impactful even if it happened every day and added up to more overall downtime.
> they were down for 7.5% of the time I needed them, or had business hours uptime of 92.5%
You can obviously compute this for a particular customer, but being a global service, it's pretty much guaranteed that someone somewhere experienced the worse of those numbers
You can compute them for the average. In other words, the total customer impact is the number of business-hours of downtime across all customers divided by the total number of business hours of all customers.
This is important because it's quite possible that the downtime is biased toward the times they have the most active users.
Big systems worth their salt already do this as weighted uptime, considering request successes / total requests rather than wall clock uptime as internal SLOs. But these numbers aren't really ever published because it gives away information about your customer base.
Then again if you’re a business relying on GitHub enterprise you have an SLA and you can and WILL charge GitHub for failing their SLA. Usually there’s a real measurable dollar value tied to that SLA per dollar and it’s not cheap.
What surprises me in particular is that the global API and the GitHub EE API are the same which is a big no no. This is even more surprising given the fact that paying GitHub customers are clearly the minority both in numbers and code velocity.
My assumption would be that GitHub is keeping EE up and the rest of free or pro users just have to suck it up. If that’s not even the case then it’s only a matter of very short time until GitHub will see businesses leave to more reliable competitors
> We shouldn't be trying to spin downtime as "just a few hours a month." Those hours cost real dollars.
They're also completely irrelevant, you as a customer of a service that is down can lose the same amount of money in a 5 minutes outage or 30 days outage, if you were only relying on this service for one operation that took 1 second and had to happen during the time where the outage happened.
Depending on the service in question, no amount of downtime is acceptable, however unrealistic this is.
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.
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.
We've stopped with our caring about quality and understanding so I don't see why we'd keep caring about reliability and availability. How are you going to pinpoint performance issues when you have no idea how your app works?
Point the AI at your logs, tell it to fix things, rinse and repeat. It's faster than debugging, and fast is good.
I think this idea is objectively good, but practically hazardous. "Four nines" is a meme at this point, and it's a helpful one: a commonly understood gold standard. Few people know that translates to roughly 4 minutes and 19 seconds of downtime per month. Since service quality is degrading, I fear that changing the way we express those uptime might also unintentionally provide convenient cover to reset expectations, e.g. 10 minutes of downtime is the new norm for reliability. Or 30 minutes. I don't think we should yield the Overton Window, so to speak, to this.
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.
One thing that is I feel missed about uptime percentage when compared to on premise uptime is when the downtime occurs. Its far more impactful if its in the middle of the working day or during the busy period of shopping. A store that goes offline in the middle of black friday or in the run up to Christmas is harmed a lot more than some down time on a Sunday night/Monday morning at 3am.
One thing I have noted over time is a lot of these AWS, Azure et el downtimes is they occur in the middle of everyones day, millions of people are impacted by them. Same with github its getting in the way of work. Whereas when we hosted services on our own equipment the downtime was usually out of main hours. The percentages are in many ways the wrong measure of downtime because hours aren't equal in impact to businesses.
These service status pages have so many other problems, I can't really get excited about this article's point. As a user of a service, I don't really care that an additional 0.09% of uptime is any more or less difficult to achieve than an additional 0.9%, even if you describe it in terms of fractions of time. I only care about two things: what the service status is right now and your service reliability's impact to me over the long term (get out of here with your 30-day crap).
In the electric utility world we have a few IEEE standardized metrics (with appropriately IEEE'd acronyms) for tracking service reliability that I like much better and always wish for when I'm looking at a status page. Pie in the sky stuff for sure, nobody wants to do this analysis and publish the results without a regulator telling them have to, but c'est la vie.
SAIDI - System Average Interruption Duration Index. How many minutes an average customer experienced service interruption in a year. This is the big one I'd want to see on your service status page IMHO. For the power grid, we consider any outage longer than five minutes to be an interruption ("non-momentary outage").
SAIFI - System Average Interruption Frequency Index. How many total periods of interruption occurred for the average customer in a year.
CAIDI - Customer Average Interruption Duration Index. How long it takes service to be restored for the average customer when there is an interruption.
For the "right now" aspect you have probably visited your utility's outage map, but here I would say we do much better than most utilities. The level of detail on the investigation and resolution is often more detailed, and we usually know better than to bother providing much in the way of a concrete estimate for restoration time of a current outage (though this is getting better in the utility space).
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.
One 12 hour outage is different to 12 1 hour outages at 3am which is different to 24 30 minute outages at 4:30pm when you're trying to commit something at the end of the day. Percentage and time are both flawed ways of looking at downtime.
Downtime really matters if it's at a time you need something to be up, and Github is big enough to have users for that to be all the time. That moves the conversation from 'It's down for a few hours a month' to 'Github is failing a significant number of it's users'.
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.
Well, we don't know the application. Six nines is appropriate for some applications. If you're doing PSIP lookups to route 911 calls, that's reasonable. If you're sending out paper mailers, it's not.
But six nines gives you 7.9 seconds a quarter. If you run a multihost system, that translates to ~1s dead host detection and switch and 3-4 switches per quarter. It's acheivable with reliable hardware and reasonable software. Otoh, it's very hard to hit if you need to move traffic to a different location to respond to a no notice location failure (failed automatic transfer switch, all fiber paths severed by construction because the redundant paths were in the same bundle, etc). If you have an out for 'cloud provider failure', that probably covers location failures.
Often times a tight uptime promise like that also comes with maintenance windows. Depending on the application, degraded service or no service may be acceptable within the maintenance window.
If it wasn't prorated anyone who approved the contract needs training and/or firing. If it is prorated, that is generally not a problem. Small outages aren't even worth the effort of trying to get the money back, and if you have a large enough one to make it worthwhile it is likely the prorated refund is still going to be laughably small.
Well, someone on the business side of the house is free to negotiate. Whether engineering learns about the contract before sales has inked a 6-nines availability guarantee varies wildly by the company
I worked in realtime trading. No. Not at all outrageous. Quite reasonable actually. If that’s what we agreed and I need you to be reliable I will charge you back for being unreliable. I’m happy to pay top and extra dollar for the SLA but that means it needs to be acted on.
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.
Its not for you, its advertisement, to preemptively avoid both consumer and securities fraud accusations, and for their enterprise client’s IT security review and SLAs
I don't think this is true - people struggle with exponentials. Most people can't intuitively grasp the staggering differences between 2 nines, 4 nines, and 6 nines of availability.
> 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.
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.
Right. And there's nothing wrong with having regular scheduled downtime. That's obviously not appropriate for most massive scale global cloud services, but it's great for services that know their usage patterns well.
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?
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.
Yeah I'm at a point where I have a bookmark folder of status pages mostly for very large orgs because I've been using those pages relatively regularly. This is not something I felt the need for historically.
Just two weeks ago protonmail had a massive outage related to data center cooling failure.
I’ve worked in systems which at worse have had three nines for years, but I’ve also worked in systems where five nines is a failure.
This attitude of modern tech claiming 98% is good just doesn’t work in the old tech acceptance. We had individual components fail all the time. We’re still looking at a 230ms outage to a branch office last week caused by a power failure combined with a badly plumbed power distribution.
Modern software people don’t consider 230ms to be an outage. Glad they don’t work in electricity.
(The failure we had was only on the services we guarentee at 99.1%, our lowest sla. After that there’s 99.95 and 99.999.
(In reality we reach five nines year after year on even the lowest levels, but there are major concerns like “large bomb in data centre” which could cause some of our less critical units to drop way more than 5 minutes a year.
That's not really an old versus new thing. The water company isn't going to consider 230ms an outage either.
With tech, like, it's crazily difficult to make sure every http request succeeds, so you build in retry, and look at that 230ms doesn't cause real disruption now. Not to excuse 98%, that's awful.
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
Exactly. If my build and test CI takes several hours and it gets interrupted, it really doesn’t matter how long the interruption was. It impacts me all the added time of realizing it stopped, investigating and confirming why it stopped, triggering another run, and continued monitoring.
> 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.
I don’t see why you think “12 hours affected in the last 30 days (98.31% uptime)”
Is trying to spin anything. It’s making easier to see the impact over the last 30 days. I agree with the article
One thing I would like to see is how many of those hours are during normal business hours in my country.
Not all hours are created equal when it comes to downtime and my intuition is that most of these 12 landed within my working hours.
In terms of impact that then might mean they were down for 7.5% of the time I needed them, or had business hours uptime of 92.5% which is… both not very good and very disruptive.
On the other hand, downtime at 4AM would be much less impactful even if it happened every day and added up to more overall downtime.
> they were down for 7.5% of the time I needed them, or had business hours uptime of 92.5%
You can obviously compute this for a particular customer, but being a global service, it's pretty much guaranteed that someone somewhere experienced the worse of those numbers
You can compute them for the average. In other words, the total customer impact is the number of business-hours of downtime across all customers divided by the total number of business hours of all customers.
This is important because it's quite possible that the downtime is biased toward the times they have the most active users.
Big systems worth their salt already do this as weighted uptime, considering request successes / total requests rather than wall clock uptime as internal SLOs. But these numbers aren't really ever published because it gives away information about your customer base.
https://cloud.google.com/blog/products/gcp/available-or-not-...
Then again if you’re a business relying on GitHub enterprise you have an SLA and you can and WILL charge GitHub for failing their SLA. Usually there’s a real measurable dollar value tied to that SLA per dollar and it’s not cheap. What surprises me in particular is that the global API and the GitHub EE API are the same which is a big no no. This is even more surprising given the fact that paying GitHub customers are clearly the minority both in numbers and code velocity. My assumption would be that GitHub is keeping EE up and the rest of free or pro users just have to suck it up. If that’s not even the case then it’s only a matter of very short time until GitHub will see businesses leave to more reliable competitors
Favorable spin? The article's point is that "12 hours a month" makes the cost very obvious, and I agree with the article.
> We shouldn't be trying to spin downtime as "just a few hours a month." Those hours cost real dollars.
They're also completely irrelevant, you as a customer of a service that is down can lose the same amount of money in a 5 minutes outage or 30 days outage, if you were only relying on this service for one operation that took 1 second and had to happen during the time where the outage happened.
Depending on the service in question, no amount of downtime is acceptable, however unrealistic this is.
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.
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.
We've stopped with our caring about quality and understanding so I don't see why we'd keep caring about reliability and availability. How are you going to pinpoint performance issues when you have no idea how your app works?
Point the AI at your logs, tell it to fix things, rinse and repeat. It's faster than debugging, and fast is good.
I think this idea is objectively good, but practically hazardous. "Four nines" is a meme at this point, and it's a helpful one: a commonly understood gold standard. Few people know that translates to roughly 4 minutes and 19 seconds of downtime per month. Since service quality is degrading, I fear that changing the way we express those uptime might also unintentionally provide convenient cover to reset expectations, e.g. 10 minutes of downtime is the new norm for reliability. Or 30 minutes. I don't think we should yield the Overton Window, so to speak, to this.
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.
I'm not sure that having your expensive software engineers not work 2.4 days a year is very palatable
One thing that is I feel missed about uptime percentage when compared to on premise uptime is when the downtime occurs. Its far more impactful if its in the middle of the working day or during the busy period of shopping. A store that goes offline in the middle of black friday or in the run up to Christmas is harmed a lot more than some down time on a Sunday night/Monday morning at 3am.
One thing I have noted over time is a lot of these AWS, Azure et el downtimes is they occur in the middle of everyones day, millions of people are impacted by them. Same with github its getting in the way of work. Whereas when we hosted services on our own equipment the downtime was usually out of main hours. The percentages are in many ways the wrong measure of downtime because hours aren't equal in impact to businesses.
It's fairly safe to assume that most outages occur at the worst time possible. They tend to occur at times of high load.
These service status pages have so many other problems, I can't really get excited about this article's point. As a user of a service, I don't really care that an additional 0.09% of uptime is any more or less difficult to achieve than an additional 0.9%, even if you describe it in terms of fractions of time. I only care about two things: what the service status is right now and your service reliability's impact to me over the long term (get out of here with your 30-day crap).
In the electric utility world we have a few IEEE standardized metrics (with appropriately IEEE'd acronyms) for tracking service reliability that I like much better and always wish for when I'm looking at a status page. Pie in the sky stuff for sure, nobody wants to do this analysis and publish the results without a regulator telling them have to, but c'est la vie.
SAIDI - System Average Interruption Duration Index. How many minutes an average customer experienced service interruption in a year. This is the big one I'd want to see on your service status page IMHO. For the power grid, we consider any outage longer than five minutes to be an interruption ("non-momentary outage").
SAIFI - System Average Interruption Frequency Index. How many total periods of interruption occurred for the average customer in a year.
CAIDI - Customer Average Interruption Duration Index. How long it takes service to be restored for the average customer when there is an interruption.
For the US, here is what these numbers look like: https://www.eia.gov/electricity/annual/html/epa_11_03.html. If you're outside the US look up yours and have a good laugh at us. :)
For the "right now" aspect you have probably visited your utility's outage map, but here I would say we do much better than most utilities. The level of detail on the investigation and resolution is often more detailed, and we usually know better than to bother providing much in the way of a concrete estimate for restoration time of a current outage (though this is getting better in the utility space).
> If you're outside the US look up yours and have a good laugh at us. :)
If you want to feel better about that, check out South Africa. You know you have a problem when "load shedding" is a household word.
(I should note that things have improved recently: it was a big milestone when they went a full year without load shedding, as of May 16.)
You can use -log10(1-p). On the "nines" it is exactly the number of nines you have:
(Modulo floating point issues of course.)Which then smoothly covers the entire space:
But good luck getting that standardized.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.
The 12 hours out of 30 days seems like sugarcoating the issue.
Keep the percentages, and regardless of that - GitHub fix your uptime
One 12 hour outage is different to 12 1 hour outages at 3am which is different to 24 30 minute outages at 4:30pm when you're trying to commit something at the end of the day. Percentage and time are both flawed ways of looking at downtime.
Downtime really matters if it's at a time you need something to be up, and Github is big enough to have users for that to be all the time. That moves the conversation from 'It's down for a few hours a month' to 'Github is failing a significant number of it's users'.
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.
Mentally convert to downtime: 99.8 is clearly twice as bad as 99.9
Similar for LLM measures from an ideal 1.0 mark.
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.
Well, we don't know the application. Six nines is appropriate for some applications. If you're doing PSIP lookups to route 911 calls, that's reasonable. If you're sending out paper mailers, it's not.
But six nines gives you 7.9 seconds a quarter. If you run a multihost system, that translates to ~1s dead host detection and switch and 3-4 switches per quarter. It's acheivable with reliable hardware and reasonable software. Otoh, it's very hard to hit if you need to move traffic to a different location to respond to a no notice location failure (failed automatic transfer switch, all fiber paths severed by construction because the redundant paths were in the same bundle, etc). If you have an out for 'cloud provider failure', that probably covers location failures.
Often times a tight uptime promise like that also comes with maintenance windows. Depending on the application, degraded service or no service may be acceptable within the maintenance window.
Are you sure it wasn't prorated?
If it wasn't prorated anyone who approved the contract needs training and/or firing. If it is prorated, that is generally not a problem. Small outages aren't even worth the effort of trying to get the money back, and if you have a large enough one to make it worthwhile it is likely the prorated refund is still going to be laughably small.
I think it was prorated but am not 100% sure.
It's a contract, you're free to negotiate?
If you agree to those terms knowing it's unrealistic, you're agreeing to give away your service for free.
> It's a contract, you're free to negotiate?
Well, someone on the business side of the house is free to negotiate. Whether engineering learns about the contract before sales has inked a 6-nines availability guarantee varies wildly by the company
I worked in realtime trading. No. Not at all outrageous. Quite reasonable actually. If that’s what we agreed and I need you to be reliable I will charge you back for being unreliable. I’m happy to pay top and extra dollar for the SLA but that means it needs to be acted on.
I think in your case it’s reasonable. Also a $1.5M contract is likely chump change in that realm.
This particular case was in cybersecurity- specifically static analysis of assets, for the purpose of providing a SBOM.
Until there is an industry wide definition of outage, degraded performance, etc then it's all moot
There are regulatory definitions for some industries, like banking or telecommunication (in my country).
Yeah, this is always fun. Logarithmic graphs of downtime, people.
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.
`-log10(1 - uptime)`
Its not for you, its advertisement, to preemptively avoid both consumer and securities fraud accusations, and for their enterprise client’s IT security review and SLAs
My b
Uptime % has the benefit that it's easy to understand.
I don't think this is true - people struggle with exponentials. Most people can't intuitively grasp the staggering differences between 2 nines, 4 nines, and 6 nines of availability.
They want to move goal posts of integrity because it makes their slop factory value proposition work better
> 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.
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.
Right. And there's nothing wrong with having regular scheduled downtime. That's obviously not appropriate for most massive scale global cloud services, but it's great for services that know their usage patterns well.
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.
No since kernel live patching is not universal it promotes bad security practices to maximize the uptime of a single server.
Not to mention being completely useless in the face of FaaS or just autoscaling.
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?
> Also, is anyone else getting the bitter taste of AI writing from this page?
Nah. Jim is just a decent writer (and historically has been fairly suspicious of AI)
> What's the point here? That everyone should use the n-nines notation? Sure. However, companies have no inte
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