Docker Swarm Multi Tenant Proxy

Hacker News

You might know this issue with docker swarm setups: Either have many people access the same swarm and possibly step on each others feet or have resources underutilized with separate Swarms. This project aims to alleviate this. We implement a Docker Socket Proxy which is intended to give a tenant specific view onto a Docker Swarm. It exposes all necessary operations to deploy stacks with all features to Docker Swarm as well as management endpoints for volumes, secrets, configs, networks. Every proxy can be configured with a unique label to give a tenant specific view onto the swarm. This way you can have multiple teams of people collaborate on the same cluster. This is done by filtering all requests on resource labels to check whether the resources are "owned" by the proxy/tenant. This projects uses Node.js and Express for the server, along with the dockerode (and docker-modem) library to interact with Docker.

Visit product View on Hacker NewsNov 12, 2023

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Indie HackersIH features products with proven revenue · Strong signals: para · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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Product HuntUnlikely to reach the leaderboard · Strong signals: dock · Missing: mac, agents, macos

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Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal

29%29% 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

27%27% 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.

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