0X0x54MUR41·Oct 22, 2024Submission
Distributed Consensus Reading List
1 points · Open discussionheidihoward
distributed-consensus-reading-list
Systems & Infrastructure2020–2024
heidihoward / distributed-consensus-reading-list
distributed-consensus-reading-list
A list of papers about distributed consensus.
4Distinct people
4Mentions
0Explicit recommenders
2.7KGitHub stars
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What it is
A list of papers about distributed consensus.
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Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:03:16 PM UTC.
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HN mentions by year4 dated mentions
Observed mentions: 2020-12-18 16:49:37 UTC to 2024-10-22 02:42:10 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 3 | 1 | 2 |
What Hacker News says
4 sourcesThis list looks a bit dated, I'd recommend Heidi Howard's Distributed Consensus Reading List
https://github.com/heidihoward/distributed-consensus-reading...
In A Distributed Systems Reading List (2014)The papers and talks[1] by Heidi Howard and Richard Mortier, in particular “Paxos vs Raft: Have we reached consensus on distributed consensus?”[2,3] and “Flexible Paxos”[4,5], are what finally made things click for me. A real implementation also needs other stuff[6], though, such as dynamic membership and state machine replication, which I still don’t know how to do.
[1] https://fpaxos.github.io/
[2] https://dl.acm.org/doi/abs/10.1145/3380787.3393681 or https://arxiv.org/abs/2004.05074
[3] https://www.youtube.com/watch?v=0K6kt39wyH0
[4] https://doi.org/10.4230/LIPIcs.OPODIS.2016.25 or https://arxiv.org/abs/1608.06696
[5] https://www.youtube.com/watch?v=r6NG_1HM0lA
[6] https://github.com/heidihoward/distributed-consensus-reading...
In Raft: Understandable Distributed Consensus (2014)A few ideas:
1. Add "abstract" to your search query to surface papers.
2. Search for "... reading list". For example, Heidi Howard maintains https://github.com/heidihoward/distributed-consensus-reading...
3. Read blogs like "The Morning Paper" (https://blog.acolyer.org) but skip fields that are outside your scope. You don't have time to follow more than one or two (or three) major fields.
4. Use Google Scholar to find the most cited papers, or to find papers that build on papers you think are good.
5. Keep an eye out for the conferences where these papers were presented. Then read the other papers that were also presented.
6. When you come across an amazing paper, read other papers by the same authors or supervisors.
7. If you're lucky you might also find good "survey" papers that cover and reference the state of the art.
8. Lecture notes
Read the rest
from Stanford or MIT or another university can also be a great way to get a big picture of the evolution of techniques for a given data structure or problem. For example, these lecture notes are just brilliant for getting started with stuff around memory hierarchies: https://www.eidos.ic.i.u-tokyo.ac.jp/~tau/lecture/parallel_d...
These are a few tricks that I find useful. What else?