Bloomfilter

Learn about Bloom filters: memory-efficient data structures using hashing for fast set membership queries. 

Bloom Filter Basics in Go

In this article, I am trying to implement and test the efficiency of a cuckoo filter over a bloom filter. (Read previous post on <a href="https://blog.goodaudience.com/chord-building-a-dht-distributed-hash-table-in-golang-67c3ce17417b">Chord DHT</a>, implementing a distributed hash table in&nbsp;Golang)

CUCKOO vs BLOOM filter, from a Gopher’s perspective

If you have a glass-protected bookshelf, that will protect your books from dust and insects, but it will cost you more time to access the books when you need them. Because you first need to slide or open the glass and then can get the books. On the other hand, if it’s an open bookshelf, that will give you quicker access but you will lose the protection. Similarly, if you organize your books in lexicographic order of their name, you can easily search for a book if you know it’s name. But if your bookshelf has cases of different size and you organize your books based on their size, it will look nice, but can you find a book in a hurry? I don’t think so.

Probabilistic Data structures: Bloom filter

I recently ran across <a href="http://blog.michaelschmatz.com/2016/04/11/how-to-write-a-bloom-filter-cpp/" target="_blank">this bloom filter post by Michael Schmatz</a> and it inspired me to write about a neat variation on <a href="https://en.wikipedia.org/wiki/Bloom_filter" target="_blank">the bloom filter</a> that I’ve found useful in my work. Quick refresher: a bloom filter is a probabilistic <a href="https://hackernoon.com/tagged/data-structure" target="_blank">data structure</a> that tests if an element is <a href="https://hackernoon.com/tagged/potentially" target="_blank">potentially</a> in a set. False positives are possible when testing if an element is in the set, but negatives mean an element is definitely not in the set. Got it? Cool.

Counting Bloom Filter in C++

Learn about bloom filters, pros/cons and their applications.

Understanding Bloom Filters: An Efficient Probabilistic Data Structure

Apache Doris provides a new data type: Variant, for semi-structured data analysis, which enables 8 times faster query performance than JSON with 1/3 storage.

This New Data Type Is 8 Times Faster Than JSON: Improve Your Semi-Structured Data Analysis

This post is a deep dive into the inverted index and NGram BloomFilter index, providing a hands-on guide to applying them for various queries.

A Hands-On Guide to Inverted Indexes: Accelerate Text Searches by 40

Dive into the intricacies of databases, starting from the simplest key-value store using bash functions to the complexities of LSM trees and B-trees. 

Decoding Database Complexity: A Journey from Text Files to LSM Trees and B-Trees

According to McKinsey, 78% of software projects are late, over budget, or don't ship. At Bloomfilter, we're on a mission to reverse this statistic by getting product teams and business stakeholders on the same page. Bloomfilter is process mining for the software development lifecycle, because in 2023, product teams shouldn't still need to guess when new features will ship and how much they'll cost to build. We aren't a developer productivity tool, we know software engineering is both an art and a science. Instead, we're focused on using objective analysis to help you improve your processes, eliminate guesswork, and ship more software.

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Bloomfilter

thebloomfilter.com

employees

Since 2022

COMPANY RANKING

BLOOMFILTER

EVERGREEN INDEX #6218

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