I am not sure I see the point in implementing MapReduce when most of the current work seems to be in generalising MapReduce - i.e. Apache Spark and YARN. Is there any reasoning behind this?
Firstly, you can think of map/reduce as the infrastructure for higher level operations (sort of like the assembly language of large scale data processing that higher-level data processing systems compile to). A breakthrough in the quality of the operational engine significantly impacts the experience of doing higher-level work, so if someone finds a better way to run map/reduce jobs, it's a win for everyone. Shipping jars instead of docker containers, and not having snapshots are serious drawbacks in the existing map/reduce infrastructure that significantly impact users in negative ways.
Secondly, an easier way to specify map/reduce jobs (via a simple web server that exposes API endpoints to do data grouping, mapping, and reduction) is a dramatically simpler, more composable way to expose map/reduce jobs. Building higher level infrastructure on top of this abstraction is an order of magnitude easier than doing it on top of Hadoop, so it could be a better underlying platform for the generalization work being done in the community.
So tl;dr we think that a better implementation of MapReduce can be a much more general tool than Hadoop's MapReduce is.