A Postgres Gateway (Web UI) with OIDC and ACL

Hacker News

Hey HN, I’ve built a database gateway to provide controlled access to production databases with fine-grained permissions and secure query execution. Key Features: - Role-Based ACLs: Define access rules at the instance, table, and column levels. - OIDC Authentication: Users log in through OpenID Connect. - Multi-Instance Support: Query multiple PostgreSQL instances from a single interface. - Web Interface: Execute approved queries and view results securely. - Query Storage: Save query results for later access or export. How It Works: - Users authenticate via OIDC. - ACLs enforce access to specific instances, tables, and columns. - Approved queries are executed, and results are stored securely. Built For: - Developers who debug production data. - Team Leads and Product Owners who access production data for reporting. - DevOps who run queries on behalf of other roles. - Security Engineers who ensure sensitive production data remains protected. Question: How do your developers access production databases, especially when the data is sensitive and critical to the company?

Visit product View on Hacker NewsNov 24, 2024

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

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

52%52% 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: users, 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 · Strong signals: interface, users · 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

19%19% 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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