I mean, their viz is free and straight forward, not hidden behind a paywall or a demo page. I also appreciate not putting any comment based signal indicators as that is often noise.
There is still a tendency within some parts of aviation (safety auditing) to look for root causes and use tools like "fish bone diagrams" despite the more holistic approach used after an actual crash or incident.
A bunch of different services on a single status page doesn’t make it a complex system. Most of these have no relation to each other other than the high level services on the cloud providers.
> A bunch of different services on a single status page doesn’t make it a complex system.
you're it does not.
> Most of these have no relation to each other other than the high level services on the cloud providers.
so, some of them are related to each other? some of them even share underlying infrastructure? perhaps multiple of these are considered infrastructure for some teams?
No, they exist on the internet but calling them part of the same system is a bit torturous.
My toaster and the dam 1000 miles away are on the same electrical grid. Calling my toaster part of the electrical generation system because it consumes from it doesn’t make sense.
Coming back to the dashboard example, almost none of those work together to provide some kind of combined outcome you would expect from complex systems analysis (e.g. electrical generation, healthcare, etc).
If all of the boxes were ISPs instead, it would be a great example. Because they all work together to provide IP connectivity to the world and many can be down while the overall internet continues to function.
Services like Cloudflare and Twilio have so many POPs globally that one or more always have an outage going on. Then there's the question of whether it's a major outage or a minor outage. Even though major status page providers like Atlassian and Incident.io have public status APIs (Cloudflare uses Atlassian), it takes more than just parsing them to determine what is "down" and at what granularity.
I run an outage detection service - and some of these issues, like parsing hundreds of - sometimes undocumented - status APIs, make for an interesting engineering problem.
With these guys you get into a weird world of "is it them, us, or upstream of both of us" all the time. I had been using Twilio's telco partner maintenance notifications as a way of figuring out if someone like Orange was responsible for a bunch of French end points independent of Twilio had network degradation.
This app looks to be incorrectly parsing Slack and Auth0 official status page and showing incidents as ongoing that are not
And those are just the 2 that I checked.
To be fair, accurately scraping and normalizing data from status pages is really hard to to do consistently (my company has a team of 5 engineers to do it and it's a lot of work).
Correlated downtime and this is a place I wouldn't actually mind a guess from AI on whether their is a common underlying cause between some of the things. I say AI because I don't really think anyone is going to keep all of the possible common dependencies of different privately hosted systems up to date, but AI could at least take an initial guess + try to find if anyone else is posting root cause theories elsewhere at the time and link to those (and a guess is fine enough).
Well if you count every minor service outage which maybe 0.1% of the users are non-critically affected by, you quickly get to 0.6%. So, this doesn't really tell you anything.
Where does this draw data from? It's a similar visual concept to what we're doing at ThousandEyes within Internet Insights (see https://www.thousandeyes.com/outages/) however we make it fairly clear how we are making these determinations. Our data comes from billions of daily pseudonymous metrics from within synthetic tests running across thousands of agents around the world.
If you're drawing the data from a public resource like downdetector or using the sites status pages, then you may not be reflecting reality, but it should be clear what the provenance of the data is.
Something must be wrong, it's showing github as up!
GitHub does not report their outages. If you see GitHub.com, does not mean GH actions are working.
So is reddit.
But then, the home page can be cached, and bots can be batched and nobody would ever notice the difference.
Pretty cool visualization.
I've been building something like this for 12 years now.
One major difference is mine does not only rely on the "official" status page but also receive millions of reports from users about outages.
So your single pane of glass can show not just known outages but emerging ones that haven't been acknowledged yet by providers.
Also supports more than 8,000 services.
Where do you source these "millions of reports" from?
users of product and visitors to our site
I mean, their viz is free and straight forward, not hidden behind a paywall or a demo page. I also appreciate not putting any comment based signal indicators as that is often noise.
beautiful visualization of "complex systems run in degraded mode"
https://how.complexsystems.fail/#5
What a great capsule of wisdom!
There is still a tendency within some parts of aviation (safety auditing) to look for root causes and use tools like "fish bone diagrams" despite the more holistic approach used after an actual crash or incident.
A bunch of different services on a single status page doesn’t make it a complex system. Most of these have no relation to each other other than the high level services on the cloud providers.
> A bunch of different services on a single status page doesn’t make it a complex system.
you're it does not.
> Most of these have no relation to each other other than the high level services on the cloud providers.
so, some of them are related to each other? some of them even share underlying infrastructure? perhaps multiple of these are considered infrastructure for some teams?
what is the point you're trying to make?
They're all part of the internet, which is one of the most complex systems ever built.
No, they exist on the internet but calling them part of the same system is a bit torturous.
My toaster and the dam 1000 miles away are on the same electrical grid. Calling my toaster part of the electrical generation system because it consumes from it doesn’t make sense.
Coming back to the dashboard example, almost none of those work together to provide some kind of combined outcome you would expect from complex systems analysis (e.g. electrical generation, healthcare, etc).
If all of the boxes were ISPs instead, it would be a great example. Because they all work together to provide IP connectivity to the world and many can be down while the overall internet continues to function.
Probably unfair to class Cloudflare as "degraded" they have over 300 PoPs theres always going to be some in maintenance mode and re-routed
Auth0 and Slack appear degraded here, but not on their status pages
Cloudflare as well
Services like Cloudflare and Twilio have so many POPs globally that one or more always have an outage going on. Then there's the question of whether it's a major outage or a minor outage. Even though major status page providers like Atlassian and Incident.io have public status APIs (Cloudflare uses Atlassian), it takes more than just parsing them to determine what is "down" and at what granularity.
I run an outage detection service - and some of these issues, like parsing hundreds of - sometimes undocumented - status APIs, make for an interesting engineering problem.
With these guys you get into a weird world of "is it them, us, or upstream of both of us" all the time. I had been using Twilio's telco partner maintenance notifications as a way of figuring out if someone like Orange was responsible for a bunch of French end points independent of Twilio had network degradation.
Yea I was wondering where that data/info was coming from?
And what does it mean exactly?
This app looks to be incorrectly parsing Slack and Auth0 official status page and showing incidents as ongoing that are not
And those are just the 2 that I checked.
To be fair, accurately scraping and normalizing data from status pages is really hard to to do consistently (my company has a team of 5 engineers to do it and it's a lot of work).
I notice that the site 'boxes' are different sizes.
Does the size indicate anything?
Maybe try using <wbr> for example Cloud<wbr>flare or mongo<wbr>db for more natural break on small screens.
Would be interesting if sites could be grouped based on what services they rely on, or just grouped based on which have correlated downtime.
Correlated downtime and this is a place I wouldn't actually mind a guess from AI on whether their is a common underlying cause between some of the things. I say AI because I don't really think anyone is going to keep all of the possible common dependencies of different privately hosted systems up to date, but AI could at least take an initial guess + try to find if anyone else is posting root cause theories elsewhere at the time and link to those (and a guess is fine enough).
https://finviz.com/map says Hi :)
Suggestion: The area of each rectangle should be proportional to the UPTIME capitalization
Maybe this is the idea, but how come github uptime is 100%!?
No love for mindgeek assets?
Are those ever down?
Facebook, Twitter (X), Instagram is no longer a thing?
They don't have straightforward status pages or APIs to detect outages - I think that's the reason they are not listed.
No Apple services listed? Where's iCloud?
Playstation is in the list but not Xbox? Weird
Interesting.. Ms Teams blocks the entire url..
Ouch, Azure isn't even present
They said major sites
Yeah, highly inaccurate data. Shows Auth0 with an uptime of 0.6% over 24h. Smells like a slop project.
Well if you count every minor service outage which maybe 0.1% of the users are non-critically affected by, you quickly get to 0.6%. So, this doesn't really tell you anything.
But 55 of them is unknown (edit: fixed now)
And github has 100% uptime while cloudflare has 20%. Yeah, right.
What a godsend this is! Thanks a lot! I hope the data is accurate! Keep improving it.
I'm assuming there's an optimisation in the source of this:
``` if(github) return false ```
Kids those days. What happened to netcraft ?
over half are unknown
Where does this draw data from? It's a similar visual concept to what we're doing at ThousandEyes within Internet Insights (see https://www.thousandeyes.com/outages/) however we make it fairly clear how we are making these determinations. Our data comes from billions of daily pseudonymous metrics from within synthetic tests running across thousands of agents around the world.
If you're drawing the data from a public resource like downdetector or using the sites status pages, then you may not be reflecting reality, but it should be clear what the provenance of the data is.
Wtf, no porn category ?