[bradleyjg]

I read a few of these articles a couple of years ago. Along with a few of the inevitable cuckoo hash filter rejoinders.

I think they are neat algorithms and I'm glad to have come across them. But that said, I have yet to find a problem in my day to day work which required set membership, with space at a premium, and where false positives were acceptable. So I've never used either in anger.

[manish_gill]

I use them at work as a cache during data ingestion phrase (analytics). I have to store a unique URL for each page the user is at, and each page generates a lot of requests. So I store the URLs inside a Bloom Filter, hitting the DB only when the contain() returns False. It's a neat little thing that saves me thousands of unnecessary database hits per second.

[3131s]

I have used them for text segmentation. It's an extremely quick way to test for membership on a set (30+ million tokens in my case) that would otherwise be too large to hold in main memory.

[arielweisberg]

If you are bored with bloom and cuckoo filters then check out quotient filters. Quotienting was one of those mind blown things for me.

[jason_slack]

Thanks for the reading list! :-)

[Jake232]

They're very useful in large scale web crawling/scraping. I use them for a number of things in this field.

[busterarm]

Also in distributed brute-forcing of encryption standards.