in other words, "I used a chainsaw to cut an apple and it SUCKED at it."
If you're processing an amount of data that comfortably fits in memory on a single machine, then obviously Hadoop is going to perform poorly in comparison. The costs of scheduling a job onto N mappers/reducers, transferring code to each node, waiting for the slowest mapper/reducer to finish, transferring data from mappers -> reducers, replicating output on HDFS, etc. are well-understood. It's true that many people try to use Hadoop when they'd be better served with simpler solutions, but that does not justify the amount of shade that the author throws at it.
If you're processing an amount of data that comfortably fits in memory on a single machine, then obviously Hadoop is going to perform poorly in comparison. The costs of scheduling a job onto N mappers/reducers, transferring code to each node, waiting for the slowest mapper/reducer to finish, transferring data from mappers -> reducers, replicating output on HDFS, etc. are well-understood. It's true that many people try to use Hadoop when they'd be better served with simpler solutions, but that does not justify the amount of shade that the author throws at it.