I am a heavy user of Brave and I would love for you to expand on what you mean.
For those curious, here is the open-code repo of all Chromium changes Brave applies. I have not read every commit myself, so any flagging would be appreciated: https://github.com/brave/brave-core
I worked on LiveGraph for a long time at Figma. We went through our own evolution:
1. first we would use the WAL records to invalidate queries that could be affected (with optimizations for fast matching) and requery the data
2. then we used the info from the WAL record to update the query in-memory without asking the DB for the new result, it worked for majority of the queries that can be reliably modeled outside of the DB
3. I believe after I left the team reverted to the re-query approach, as managing a system that replicates the DB behavior was not something they were excited to maintain, and as the DB layer got scaled out, extra DB queries were less of a downside
Thoroughly enjoyed the article since I have heard about Neon but never understood what it offers on the technical level over other PG-compatible projects.
The article mentions that a consequence of separating storage from compute is that compute nodes cache hot pages in memory and load cold pages from object storage (like S3?) when needed. Does anyone know what are the consequences of this decision. In case of a query that touches multiple rarely used pages, would that incur high latency and ingress? How does that penalty compare to a vanilla postgres running on AWS and storing pages on EBS?
For others looking for more details on how Figma's sync engines differ and why 2 sync engines emerged, I had a long thread about it here:
https://x.com/imslavko/status/1890482196697186309