IdeaWell

Project ideas from Hacker News discussions.

Saving 100 terabytes of memory by optimizing 1.1.1.1's DNS cache

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

Four prevalent themes in the discussion

  1. Premature optimization is often wasteful; optimize after measuring need

    “One of the 'evils' of premature optimization is how much time you spend on the optimization vs. the benefit you get from it. If your goal is correctness and shipping fast and you're not memory constrained then spending time using the least amount of memory is a waste of time specifically because you want to ship fast.” – jgrahamc

  2. Early focus should be on correctness, performance, and handling load before micro‑optimizing memory

    “Having the responses be slow or incorrect is a far more expensive problem. A good engineer would pick a simple data structure that has the right shape but might not be optimal in footprint to focus on correctness and response time.” – sophacles

  3. Engineering decisions are shaped by business constraints: limited engineer time, cost, and familiarity with the codebase

    “Engineers are expensive, especially good system engineers who are trained in your code base. Very possible that this just hadn't gotten to the top of the priority list.” – gbear605

  4. Savings from optimizations become apparent only at large scale; in hindsight they look obvious, but early on the impact was negligible relative to other priorities

    “It only looks super obvious in hindsight and the well explained blog post… making that call at that time would've butchered the product very much similar to Google+, YouTube etc.” – suriyaG

🚀 Project Ideas

Generating project ideas…

StructOpt

Summary

  • Analyzes Rust code to identify memory-wasting patterns such as unused capacity fields in Vec, oversized allocations, and suggests arena-based or slice alternatives.
  • Core value: Reduces memory footprint and allocation overhead with minimal code changes, guided by LLMs for actionable refactor suggestions.

Details

Key Value
Target Audience Rust system programmers, infrastructure engineers (e.g., DNS, networking)
Core Feature Static analysis + LLM-driven refactor suggestions for data structure layout
Tech Stack Rust (rustc analyzer), LLMs (Ollama/OpenAI API), CLI
Difficulty Medium
Monetization Revenue-ready: Subscription $10/dev/month

Notes

  • HN commenters noted LLMs can “write a harness, profile it, build experiments… and give solid advice” (stickfigure) and highlighted the wasted 8 bytes per Vec capacity field (eviks).
  • Provides a concrete way to balance premature optimization vs real gains, sparking discussion on when to invest in memory‑saving refactors.

CacheSlim

Summary

  • Profiles DNS resolver cache memory usage, exposing per-entry overhead such as unused capacity fields and fragmentation.
  • Core value: Guides engineers to apply specific optimizations like contiguous TLV storage or slab allocators, saving gigabytes at scale.

Details

Key Value
Target Audience DNS infrastructure operators, companies running large recursive resolvers (e.g., Cloudflare, ISPs)
Core Feature Memory profiling dashboard with per‑entry breakdown and optimization recommendations
Tech Stack Go/Python agent, eBPF for memory sampling, Grafana/Prometheus, optional Rust low‑overhead collector
Difficulty High
Monetization Revenue-ready: Enterprise licensing per server

Notes

  • Discussion highlighted the 8‑byte capacity field cost and the desire to avoid waste (eviks, micromacrofoot) and the potential savings of “100 TB of RAM” at Cloudflare’s scale.
  • Enables practical utility by showing measurable memory reductions and fosters debate on optimization timing in high‑throughput services.

ContiguousStruct

Summary

  • Provides a Rust derive macro that lays out structs with dynamically sized fields in a single contiguous buffer, akin to database rows or TLV encoding.
  • Core value: Enables zero‑copy, cache‑friendly storage that reduces allocation count and improves locality for heterogeneous data.

Details

Key Value
Target Audience Rust developers building high‑performance services (databases, caches, network stacks)
Core Feature #[contiguous_struct] macro generating safe getters/setters and serialization/deserialization
Tech Stack Rust procedural macro crate, optionally bytemuck/zerocopy
Difficulty Medium
Monetization Hobby

Notes

  • Commenters discussed storing record data right after CacheEntry members (kccqzy, jandresp) and wished for compiler‑managed contiguous fields (jiggawatts).
  • Addresses the ergonomic gap for low‑level data layout and could be adopted in projects like DNS caches to eliminate per‑entry overhead.

PremOpt Guard

Summary

  • CI plugin that measures performance impact of changes and blocks merges unless a predefined benefit threshold is met, discouraging premature optimization.
  • Core value: Encourages data‑driven optimization, ensuring effort is spent only when measurable gains exist.

Details

Key Value
Target Audience Software teams practicing performance‑aware development (backend, infra)
Core Feature Integrates with CI runners, runs benchmarks on PRs, reports delta, enforces policy
Tech Stack Python/bash, criterion.rs or Google Benchmark, GitHub Actions/GitLab CI
Difficulty Low
Monetization Revenue-ready: Freemium (free for OSS, paid for private repos)

Notes

  • HN participants cited Pike’s rules and called out the “evil” of premature optimization (jgrahamc, scott_meyer); this tool operationalizes that wisdom.
  • Promotes discussion on when to optimize and provides practical utility by preventing wasted effort on low‑impact tweaks.

Read Later