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I wrote a load balancer with XDP and Go

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I wrote a load balancer with XDP and Go

Facebook's Katran looked too scary (and I don't do C++), so I thought I would investigate using XDP/eBPF to redirect packets at high speed and Go to do the control plane work of health-checking backed servers and managing configuration. With luck the resulting binary should be easy to deploy by non-developers. It's still not mature, but seems to work. Code was written in a very exploratory manner, and I'm not a developer so it is quite shocking at the moment, but I aim to improve it. It is intended to be horizontally scalable with ECMP and has been tested on service providing 40Gb/s with a single (hardware) node at ~30% CPU utilisation.

Visit productView on Hacker NewsMar 7, 2022
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: single, using, code · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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