Xor Filters: Faster and Smaller Than Bloom Filters (lemire.me)

Article summary

The article discusses Xor Filters, a data structure that aims to be faster and smaller than Bloom filters. Xor Filters use a combination of hash functions and bitwise operations to determine membership in a set. The filters are designed to be immutable and require a complicated insertion procedure. The article claims that Xor Filters have a lower false positive rate than Bloom filters.

Main themes

• Xor Filters
• Bloom Filters
• Data Structures
• Hash Functions
• Immutable Data

What commenters say

• Xor Filters are more complex than Bloom filters but offer better performance in certain scenarios.
• The immutability of Xor Filters is a significant limitation, making them less suitable for applications that require frequent updates.
• The comparison between Xor Filters and other data structures, such as Cuckoo filters and Golomb-coded sequences, is not entirely fair due to differences in their design goals and use cases.
• The use of Xor Filters may be justified in situations where the data set is static and the filter can be rebuilt periodically.
• The implementation of Xor Filters in code is not necessarily simple or efficient, and may require careful optimization to achieve good performance.
• The choice of hash function and other parameters can significantly impact the performance of Xor Filters.
• Some commenters argue that the benefits of Xor Filters are not significant enough to justify their added complexity, while others see potential use cases for this data structure.