Explore >> Select a destination
|
You are here 
|
backdrifting.net | ||
| | | | |
ketanvijayvargiya.com
|
|
| | | | | Bloom filters. Set membership. 1D array with k hash functions. False positives possible but no false negatives. Applications: One hit wonders can take up to 75% of cache space. BF can help identify so that you can skip caching such items and save cache space. Check for weak passwords, malicious URLs, username etc. If you want 100% accuracy, check in BF first and fallback to the real data store as required. In a NoSQL type database, check BF on whether an item exists before going to the disk. Therefore, decrease disk access. Count min sketch. Estimate frequency of all elements in a set. 2D array. Each row is for a given hash function, so R hash functions. These functions give a number between 0 and C-1, where C is the number of columns. Once all elements are ... | |
| | | | |
zmievski.org
|
|
| | | | | Life, technology, and other good things | |
| | | | |
www.jamesridgway.co.uk
|
|
| | | | | Querying large datasets can often be challenging, especially when performance is a key concern. Achieving performance at scale often comes with an element of trade-off in how a system is designed to achieve the desired functionality and performance at scale. A bloom filter is one example of a probabilistic data | |
| | | | |
ouroboros.rocks
|
|
| | | |||
