Bryan Cantrill on ARC: A Self-Tuning, Low Overhead Replacement Cache [PWL SF] 10/2017

PapersWeLove · 13.3K views · 2017-10-17 · 1:47:35

Open on YouTube ↗

If playback is restricted in your region, try opening the original video. Use Mark watched to add it to your marked videos.

Overview

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.

On Hacker News

Recommended in cache-algorithm threads as a fun, informative walk through caching strategies and ARC.

Recommended by 3 HN people across 3 months · HN mentions 2019–2024

Topics

Algorithms & data structures Databases & storage Software performance & optimization

What people said

bminor13 · 2024-01 · Recommendation
> 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 and MFU areas, where the split point changes dynamically depending on the workload. [VIDEO] is a fun watch on the topic.
Read the original HN comment ↗

anotherhue · 2023-06 · Recommendation
Might I also recommend: Bryan Cantrill on ARC: A Self-Tuning, Low Overhead Replacement Cache [PWL SF] 10/2017 [VIDEO]
Read the original HN comment ↗

greggyb · 2021-04 · Recommendation
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]
Read the original HN comment ↗

Sources

Video by PapersWeLove, played through YouTube.

HN Library · Source license

Data and selection notes

HN summaries and labels may contain AI errors. HN dates refer to discussions, not video publication.

HN Library: HackerTube export (2026-10-31) and open-index/hacker-news. Upstream summaries and classifications may contain AI errors.

Views, duration and playback status refresh separately from the published catalog. When an update fails, existing data remains visible.

Selection checks

SaaS & AI candidates: Topic keywords do not match
Editor-approved picks: Editorial review is required

Rule matches identify candidates. They do not guarantee video quality or financial results.

About the library and its sources