Programming

17394 readers

692 users here now

Welcome to the main community in programming.dev! Feel free to post anything relating to programming here!

Cross posting is strongly encouraged in the instance. If you feel your post or another person's post makes sense in another community cross post into it.

Hope you enjoy the instance!

Rules

Follow the programming.dev instance rules
Keep content related to programming in some way
If you're posting long videos try to add in some form of tldr for those who don't want to watch videos

Wormhole

Follow the wormhole through a path of communities !webdev@programming.dev

founded 1 year ago

MODERATORS

snowe@programming.dev

Ategon@programming.dev

MaungaHikoi@lemmy.nz

Bloom filters: real-world applications (llimllib.github.io)

submitted 1 year ago by Reader9@programming.dev to c/programming@programming.dev

10 comments fedilink hide all child comments

What are your real-world applications of this versatile data structure?

They are useful for optimization in databases like sqlite and query engines like apache spark. Application developers can use them as concise representations of user data for filtering previously seen items.

The linked site gives a short introduction to bloom filters along with some links to further reading:

A Bloom filter is a data structure designed to tell you, rapidly and memory-efficiently, whether an element is present in a set. The price paid for this efficiency is that a Bloom filter is a probabilistic data structure: it tells us that the element either definitely is not in the set or may be in the set.

you are viewing a single comment's thread
view the rest of the comments

[–] Reader9@programming.dev 1 points 1 year ago* (last edited 1 year ago)

I haven’t used them in Spark directly but here’s how they are used for computing sparse joins in a similar data processing framework:

Let’s say you want to join some data “tables” A and B. When B has many more unique keys than are present in A, computing “A inner join B” would require lots of shuffling if B, including those extra keys.

Knowing this, you can add a step before the join to compute a bloom filter of the keys in A, then apply the filter to B. Now the join from A to B-filtered only considers relevant keys from B, hopefully now with much less total computation than the original join.