Hi, co-founder of Tigris here. There is a key difference between Tigris and a CDN. Tigris doesn't simply provide read caching like CloudFront. It allows what I call dynamic caching, i.e., you can literally update the object anywhere in the world, and any cached versions of the objects will be updated. So, I think of Tigris simply as a global object storage service, and the fact that it has caching built-in is more to provide fast performance and low latency than simply to mimic CDN behavior. But if you want to build a CDN, Tigris makes it trivial for a single developer to build a CDN.
> It allows what I call dynamic caching, i.e., you can literally update the object anywhere in the world, and any cached versions of the objects will be updated.
how much it typically takes to propagate the changes? Also what is the consistency model of the storage?
We have posted a few straightforward articles comparing Tigris to Mongo. As you can see given the popularity of this post, we wrote it to show how Tigris would fit in an *ERN stack.
A commentary on the WSJ post featuring Dara driving and delivering for the first time in his tenure.
This was a common practice during Travis' time but was abandoned with the arrival of a new CEO. Seeing him dogfooding the product is nice, but that shouldn't have taken six years.
Having many point solutions is problematic from a cost and complexity standpoint. General purpose solutions that would work for 80% of your use cases would be better long term. Having said that, I don’t think Postgres can be used as a general purpose solution to cover vector search, full text search and NoSQL use cases. The best general purpose solution would expose a unified API but under the hood use different storage engines to support these diverse vector search and full text search use cases.
I think, in the long run, AI will have a positive impact on education for kids. But I agree that we have to start by being skeptical about it because it is still being determined whether the effect will be positive or negative with the current state of AI tools.
In this post, we explore modeling one-to-many relations. We'll look at two ways to implement one-to-many relations. The first uses an embedded pattern, and the second uses a separate collection with a relation field. We will also look at when using either of these designs is best.
Continuous backups and quickly restoring data are fundamentally important for any production-grade database platform. As we are using FoundationDB as the persistence layer for Tigris, this means having continuous backups in place for it. This post describes how backups work in FoundationDB and how to setup it up reliably.
