This is my colleague Mike’s write-up about using bloom filters to make our list alerts endpoint much faster for filtering and pagination.
In a past life I’d struggled a lot with a public API that had some really tricky pagination performance problems. It was something we were always fighting, be it awkward edge case data shapes or the Postgres planner having bad statistics, where everything would get difficult past >5TB in the table.
Was really happy to see the team find a solution that feels scalable and can be generically applied to a lot of our endpoints at incident. Quite a great outcome where I think we’ll be safely scalable for the next few years instead of hitting other problems that would crop up had we gone with gin indexes or otherwise.
This is my colleague Mike’s write-up about using bloom filters to make our list alerts endpoint much faster for filtering and pagination.
In a past life I’d struggled a lot with a public API that had some really tricky pagination performance problems. It was something we were always fighting, be it awkward edge case data shapes or the Postgres planner having bad statistics, where everything would get difficult past >5TB in the table.
Was really happy to see the team find a solution that feels scalable and can be generically applied to a lot of our endpoints at incident. Quite a great outcome where I think we’ll be safely scalable for the next few years instead of hitting other problems that would crop up had we gone with gin indexes or otherwise.