Me

Metrics, Logs, Traces and Events in One Place. This Is Arc

Hacker News

Metrics, Logs, Traces and Events in One Place. This Is Arc

Hey HN, I’ve posted here before about Arc, and wanted to share a quick update. We’ve been heads down the last few weeks, and things are moving fast, performance is up, features are landing, and the community response has been awesome. Arc uses DuckDB as the SQL engine, with MessagePack, Arrow, and Parquet under the hood. Right now we’re seeing around 2.45M RPS for metrics, and about 1M RPS for logs, events, and traces. It now supports retention policies, deletes, and continuous queries that aggregate data as it comes in. We also built a VS Code extension to explore and query Arc, added Apache Superset integration, and are working on Telegraf and a Grafana datasource next. Feedback is always welcome. good, bad, or brutal. It all helps. Happy Thursday!

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Actual performance

3points
1comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
48%48% 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 · Strong signals: way · 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
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
16%16% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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