1/ Let's continue exploring how the "Zanzibar" model allows us to solve #fgaatscale 👇

3️⃣ Correctness: no invalid permissions are granted

To provide "correct" answers, an ACL check needs to always read a "valid view" of the system.
2/ "Valid" means: the full state read from storage should have existed at a "logical point in time" and includes all committed records at that point.

The picture provides a counterexample, a request should not read two different values from a namespace at different reads. Image
3/ Similarly, new tuples should not "show up" in a request's query replies while it executes.

This applies to:
- in progress transactions
- records committed in the lifetime of the request

Summarizing: reads should be consistent at a "logical timestamp" within a request.
4/ Additionally, to avoid these issues: writes must maintain causal consistency.

Zanzibar implements:
- Configuration consistency: namespace configuration data is read at a single "logical time" for the entirety of each request.
5/ - Causal consistency and "logical timestamp" reads for tuples through Spanner's TrueTime mechanism (cloud.google.com/spanner/docs/t…)
6/ 4️⃣ Low latency
🌏 Zanzibar runs on multiple geographical regions, maximizing co-location with its clients. This both reduces latency as network RTT is minimized, and also increases the systems availability as issues in a single region do not affect the system's availability.
7/ It also:
- Has an internal indexing system to deal with queries that involve deeply nested group structures
- Uses "hedge requests" to minimize tail latency, i.e. sending requests to multiple servers and responding once the fastest one replies (www2.cs.duke.edu/courses/cps296…)
8/ By storing namespaces configuration as part of the server, Zanzibar avoids the need for potentially slower distribution of logic and data to client side policy execution.
9/ 5️⃣ High availability
👩‍🚒 A consequence of its multi-region setup and write consistency model is that Zanzibar can tolerate more than one region failing and continue to work. Image
10/ 💪 Redundancy at the compute and storage level (through Spanner) exists in each region's Zanzibar cluster, to tolerate issues with single instances.


In the upcoming weeks we'll be sharing more about our decisions implementing a Zanzibar inspired system as a SaaS.

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More from @Auth0Lab

2 Dec 20
1/ Back after last week's break 😴, ready to talk about why we picked the "Zanzibar model" for project #sandcastle: 👇
2/ We've shared the 5 things needed to solve #fgaatscale

Let's explore high-level how "Zanzibar" works and how it meets those needs

3/ 0️⃣ Introduction
Zanzibar is a "Relationship based access control" (ReBAC) authorization system, i.e.: a user has access to an object if it has a particular relation to it.

Zanzibar stores (object, relation, user) "tuples" with data about these relations.
Read 11 tweets
18 Nov 20
1/ We've analyzed the #fgaatscale problem:

We've shared our view on the market:

It's time to tell you what we are planning to build 🥁... 🧵
2/ Project #sandcastle will be a globally distributed, highly reliable service for large scale, fine grained authorization.

It's based on @Google's Zanzibar paper: research.google/pubs/pub48190/, that powers #fgaatscale for @googledrive @googlecloud @YouTube and @Google other products!
3/ You'd:
1. Sign up for a subscription
2. Configure who has access to what
3. Pick an SDK for your favorite language/tool
4. (optionally) Feed your authZ data from existing sources into #sandcastle

That's the ideal future. At this point you'd have AuthZ for your app 🤯
Read 8 tweets
11 Nov 20
1/ Having analyzed the @github and @googledrive #fgaatscale cases, we'll share our view on the authz market.

We'll go over what is currently being addressed and what the gaps are👇
As we've mentioned before, solving #fgaatscale requires:
1️⃣ Permission modelling flexibility
2️⃣ Auditing capabilities
3️⃣ Correctness: no invalid permissions are granted
4️⃣ Low Latency
5️⃣ High availability
3/ Solving #fgaatscale is becoming a need because:
☝️ Users expect collaboration features in most products they used, and that requires FGA
✌️ Increasing privacy and compliance regulations require companies in different verticals to restrict access as much as possible
Read 21 tweets
10 Nov 20
1/ Last week we did a deep dive of @github's authorization model and the problems they solve

In this thread we'll focus on another well known product: @googledrive a great example of a collaboration platform.

📊How is gdrive "authorization at scale"?
2/ Well, in 2018 they:
- hit 1B users
- 2 trillion files


🔐 Review their permission model
🔍Go over their "search" story and how authz fits in it
🎯Analyze examples of why "correctness" () is important
3/ Like github, @googledrive has B2C and B2B models. However, @googledrive's sharing model is the same for B2C and B2B. The difference is who you can share files with.
Read 21 tweets
5 Nov 20
1/ Last time explained why we are exploring fine grained authorization:

In this thread explore we'll the problems of authorization at scale using a real world, well known example
2/ This is a key part of product development, especially when building infrastructure. We want to understand what our customers will eventually build with our service.

Our analysis case is @github.
3/ @github is an interesting example as it’s a *collaboration* platform built on top of git.

We believe collaboration and authorization are two sides of the same coin
Read 19 tweets
30 Oct 20
1/ On Wed, we posted about why we are doing this and what we expect.

We also promised to unveil this week the problem we want to dive into 🥁...
2/ The area we'd like to explore is: *fine-grained authorization*

Why this? And why now?👇
3/ Large scale fine-grained authorization as a building block is an unsolved problem. Just like authentication was ~8 years ago. We implement it in every app we build, over and over. There is no generic, cross-platform, cross-domain solution.
Read 8 tweets

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