this is one of those things that when we’re old and trying to tell our grandkids about what it was like to live in this era, nobody is going to believe. “Grandpa said Republican conventions and events used to bring gay hookup sites offline, as if hookup web sites were city-specific domains, each with their own separate database and each running on a potato, as late as 2025”
To be fair, this is not actually a graph of outages - it’s a graph of the number of users reporting outages. What’s more likely happening is that the service itself is working fine, but there is an outsized number of people having problems reaching the service, due to any number of unrelated factors (network congestion, individual device issues, temporary ISP outages or other internet hiccups)
This could happen to any service if the number of people trying to access it multiplies. If 1% of the time someone tries to access a service, there is an issue (even temporary), and the number of people trying to access that service goes from 1000 people (10 issues) to 10,000 people (100 issues) then it looks like there is a huge jump in problems accessing a service, when really the service is working just as well as it was before.
this is one of those things that when we’re old and trying to tell our grandkids about what it was like to live in this era, nobody is going to believe. “Grandpa said Republican conventions and events used to bring gay hookup sites offline, as if hookup web sites were city-specific domains, each with their own separate database and each running on a potato, as late as 2025”
To be fair, this is not actually a graph of outages - it’s a graph of the number of users reporting outages. What’s more likely happening is that the service itself is working fine, but there is an outsized number of people having problems reaching the service, due to any number of unrelated factors (network congestion, individual device issues, temporary ISP outages or other internet hiccups)
This could happen to any service if the number of people trying to access it multiplies. If 1% of the time someone tries to access a service, there is an issue (even temporary), and the number of people trying to access that service goes from 1000 people (10 issues) to 10,000 people (100 issues) then it looks like there is a huge jump in problems accessing a service, when really the service is working just as well as it was before.