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FaultWall – Which tenant is killing your Postgres? Now you can find out

Hacker News

FaultWall – Which tenant is killing your Postgres? Now you can find out

Slow database pages kept waking me up. Every time, same story: dig through pg_stat_activity, find a runaway query, trace it back to one tenant doing something dumb, kill the connection, go back to sleep. Next week, different tenant, same thing. Datadog never told me the tenant. So I built FaultWall. Single Go binary, polls pg_stat_statements every 10s, maps queries to tenants via schema detection. Gives you per-tenant QPS, P50/P95/P99 latencies, cost attribution ("acme_corp is 94% of your DB bill"), and an auto-throttler that pg_terminate_backend() before things get bad. Anomaly detection with self-tuning thresholds (genetic algorithm). MCP server for LLM agent control. I'm building eBPF kernel-level per-query tracing, for better and accurate detection. DATABASE_URL=postgres://... ./faultwall If you run multi-tenant Postgres, try it and tell me what's missing. MIT, ~2500 lines Go. https://github.com/shreyasXV/faultwall

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, mcp, single · Missing: mac, agents, macos
80%80% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
39%39% 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
30%30% 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
22%22% 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.

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