The most fair and transparent database benchmarks

Hacker News

Hi HN https://db-benchmarks.com is a platform and a framework for making the most fair, transparent and open source database and search engines benchmarks. No more benchmarketing, because: * anyone can easily reproduce all the results on their hardware * the results you can see on the website are under version control on GitHub and anyone can run the same website locally in just a minute * every byte of the project is 100% open source including test results and detailed test log * the results are highly accurate with low coefficient of variation The first release has Clickhouse, Elasticsearch, Manticore Search and MySQL covered on small / medium and big data realistic data collections. In plans: Percona Server, MongoDB, CocroachDB, FerretDB Links: https://db-benchmarks.com https://github.com/db-benchmarks/db-benchmarks https://github.com/db-benchmarks/ui Disclaimer: I'm a member of Manticore Search team and the framework was initially made to compare Manticoresearch with Elasticsearch, but I did my best to not give any unfair advantage to Manticore since it's important for the team to understand the real situation. However, if you see something is missing or wrong feel free to make a pull request or an issue on Github. Or make your own fork with even more fair results! Ideas/thoughts/comments?

Visit product View on Hacker NewsMay 20, 2022

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

79%79% 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.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, clickhouse · Missing: https docs, excited, just released

73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, efficient · Missing: plus, intuitive, reviews

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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