THE VIDEO SHELF · cache replacement algorithms
Bryan Cantrill on ARC: A Self-Tuning, Low Overhead Replacement Cache [PWL SF] 10/2017
Bryan Cantrill presents the ARC (Adaptive Replacement Cache) paper by Megiddo and Modha at Papers We Love. He opens with a survey of caching strategies before digging into the self-tuning LRU/MFU design.
Why HN readers recommended it
Recommended in cache-algorithm threads as a fun, informative walk through caching strategies and ARC.
This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.
Video & recommendation details
- Creator / channel
- PapersWeLove
- Topic
- cache replacement algorithms
- Format
- talk
- Level
- intermediate
- HN recommenders in dataset
- 3
- HN mentions in dataset
- 4
- Recommendation years in dataset
- 2019–2024
These counts and years describe the included Hacker News records; they are not YouTube views or the video’s publication date.
From the original HN conversations
Excerpts from the recorded comments. Follow each source for the full comment and surrounding discussion.
Bryan Cantrill does a pretty good survey of caching strategies in the intro to this Papers We Love talk on the ARC in ZFS. [VIDEO]
greggyb on Hacker News · 2021-04
In The Multi-Generational LRUMight I also recommend: Bryan Cantrill on ARC: A Self-Tuning, Low Overhead Replacement Cache [PWL SF] 10/2017 [VIDEO]
anotherhue on Hacker News · 2023-06
In FIFO-Reinsertion is better than LRU [pdf]> it seemed natural to have a set of generations for items that have only been seen once, and then another generation for things that have been more active Have you looked at ARC? It sounds similar - it is a cache split between LRU …
bminor13 on Hacker News · 2024-01
In Sieve is simpler than LRU
Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.