Explore >> Select a destination
|
You are here 
|
www.mattritter.me | ||
| | | | |
tomaugspurger.net
|
|
| | | | | Last week, I was fortunate to attend Dave Beazley's Rafting Trip course. The pretext of the course is to implement the Raft Consensus Algorithm. I'll post more about Raft, and the journey of implementing, it later. But in brief, Raft is an algorithm that lets a cluster of machines work together to reliably do something. If you had a service that needed to stay up (and stay consistent), even if some of the machines in the cluster went down, then you might want to use Raft. | |
| | | | |
timilearning.com
|
|
| | | | | One common pattern in the previous systems we have discussed like MapReduce, GFS, and VMware FT is that they all rely on a single entity to make the key decisions. While this has the advantage of making it easier for the system to decide, the downside of this approach is that the entity is now a single point of failure. In this post, we'll learn how the Raft consensus algorithm solves this problem. | |
| | | | |
decomposition.al
|
|
| | | | | by Devashish Purandare ยท edited by Sohum Banerjea and Lindsey Kuper | |
| | | | |
chelseatroy.com
|
|
| | | In December, I took a course in which we attempted to implement the Raft distributed consensus algorithm fromthis paper. Parts 1-5 of this series share insights from the course. From then on, I'm guiding you through my continued work implementing Raft "for fun" (I know. I don't understand me, either). Here's where you can see... | ||
