permify VS Mongoose

However, in this piece we're focusing on the PBAC model also known as Policy-Based Access Control and how it differentiates itself these from traditional access control models in terms of scalability, flexibility and security.

Hi I'm Karan, one of the maintainers of Permify (https://github.com/Permify/permify), an open source authorization service to build scalable authorization systems.

I want to share with you that we've built an AI assistant to help modeling your desired authorization logic! You can basically describe your authorization logic in Permify AI and it will generate the respective model and semantics accordingly. Think of it like ChatGPT for authorization modeling/policy generation.

Here's the project if you would like to play with it: https://ai.permify.co/.

Brief backstory:

Since authorization is generally a domain specific issue use cases vary widely - roles, relationships, attributes, hierarchies between business units, contextual permissions, etc.

To address this, we're offering a domain specific language that we built purely using golang to help model authorization logic programmatically. You can see what it looks like with sample examples in our playground: https://play.permify.co/

Although our domain specific language helps our users significantly, the general idea of policy generation is hard challenging if you have complex authorization logic and versatile permission requirements. Additionally, the flexibility of our modeling language allows for achieving the same policy/permissions through various approaches. But creating the best possible policy is crucial for several reasons including the performance of access checks, the readability of the authorization logic, visibility, and achieving least privilege, etc.

When we tallied up all those reasons, it hit us: using AI could really smooth out the policy generation process. It could not only reduce the engineering effort but also yield the best possible results. That's why we integrated Groq to make to create Permify AI!

Would love to get your feedback on this!

At that point consider exploring our solution, Permify. It's a Google Zanzibar-based open-source authorization service that helps to build scalable authorization systems.

Additional to shifts from large tech companies, ReBAC based solutions increased over the time. We're also one of them, building an open source authorization service that builds its core on top of ReBAC and Google Zanzibar.

Permify is an open-source service for creating and managing complex permissions in applications, inspired by Google Zanzibar. It offers a flexible authorization language compatible with various models like RBAC, ReBAC, and ABAC, and allows for efficient authorization data management in preferred databases. Permify's API facilitates access checks, resource filtering, and bulk permission analyses. It also includes comprehensive testing tools for authorization logic, including scenario-based testing and policy coverage analysis. Additionally, Permify supports multi-tenancy, enabling distinct authorization models for different applications within a single instance.

As Permify we're building open source authorization infrastructure to build fine grained authorization systems at scale. Would love to learn your use case and help you to build robust authorization systems. Don't hesitate to reach us from our Discord Channel!

I’m one of the maintainers of the OSS project Permify (https://github.com/Permify/permify), an open-source authorization service inspired by Google Zanzibar, which is the global authorization system used at Google to handle authorization for hundreds of its services and products, including YouTube, Drive, Calendar, Cloud, and Maps.

Hi folks,

I’m one of the maintainers and co-founder of the Go OSS project Permify (https://github.com/Permify/permify), an open-source authorization infrastructure inspired by Google Zanzibar.

I would like to share a post where we aimed to provide a brief overview of how we integrated WASM with Golang to provide a richer user experience in our open-source playground. You can use the playground to create and test any authorization logic in a browser.

The playground has three main sections:

Schema: to model your authorization logic through our domanin specific language we built.

Data: to create sample authorization data according to the model for use in tests.

Enforcement: to test your authorization structure bt access check scenarios.

Here's the post if you're interested: https://www.permify.co/post/wasm-go/

And here's the playground: https://play.permify.co/

Appreciate your time!

To manage user auth we need to create a user account - identification. So, let's install mongoose and jsonwebtoken to handle JWT authentication - an alternative to creating a user session when a user logs in

If your application uses MongoDB's Node.js driver or Mongoose ODM, occasionally you may observe errors such as querySrv ECONNREFUSED _mongodb._tcp.cluster0.abcde.mongodb.net or Error: querySrv ETIMEOUT _mongodb._tcp.cluster0.abcde.mongodb.net being thrown. The MongoDB Atlas documentation outlines several methods to troubleshoot connection issues, including how to handle "Connection Refused using SRV Connection String" scenarios, but why does this happen in the first place?

If you use Sequalize, TypeORM or for MongoDB, we have Mongoose these types of ORM tools, then you are safe by default because these help us against the SQL query injection attacks by default.

Mongoose

Now that we have created a new MongoDB deployment and saved the connection string in .env file, we would enter the code below to connect to MongoDB database through mongoose. Then, open db.js and enter this code:

Mongoose is a popular JavaScript library used with MongoDB, a NoSQL database. It provides an elegant and user-friendly way to interact with MongoDB from Node.js applications. Mongoose offers several key benefits:

To simplify this process, Cerbos developers have come up with adapters for popular Object-Relational Mapping (ORM) frameworks. You can check out for more details on the query plan repo - which also contains adapters for Prisma and SQLAlchemy - as well as a fully functioning application using Mongoose as its ORM.

After importing the mongoose module, use the [mongoose.connect()](https://mongoosejs.com/docs/api/mongoose.html#mongoose_Mongoose-connect) function to connect to the database. The first argument is the connection string, and the second argument is an object that contains the options, which are used to configure the connection. The above code logs the message, MongoDB connection is established successfully! 🎉, once the connection is successful.

Helmet helps “sanitise” the input, which might not have come from the UI directly. Mongoose is what is known as an Object Document Modelling (ODM), which defines a structure (schema) for the stored data, making it easier to manage in Express. These additions have been omitted from our example stack purely to simplify the tutorial and focus on the fundamental tiers and interfaces.