It wasn't obsolete 15 years ago. There were production mapreduces making double-digit improvements in key metrics (watch time, apps purchased) much more recently than that. The system I worked on, Sibyl, isn't well known outside of Google, but used MR as an engine to do extremely large-scale machine learning. we added a number of features and used MR in ways that would have been extremely challenging to reimplement while maintaining performance targets.

I'm not even sure the mapreduce code has been deleted from google3 yet.

To be fair, MR was definitely dated by the time I joined- 2007- and I'm surprised it lasted as long as it did. But it was really well-tuned and reliable.

Also the MR paper was never intended to stake Google's position as a data processing provider (that came far, far later). The MR, Bigtable, and GFS papers were written to attract software engineers to work on infra at google, to share some useful ideas with the world (specifically, the distributed shuffle in mapreduce, the bloom filter in bigtable, and the single-master-index-in-ram of GFS), and finally, to "show off".