1. MapReduce

2. Google and MapReduce
Google searches billions of web pages very, very quickly
How?
It uses a technique called “MapReduce” to distribute the work across a large number of computers, then combine the results
This has made MapReduce a very popular approach
Hadoop is an open source implementation of MapReduce
Unless you work for Google, you will probably use Hadoop

3. How it works
List(a, b, c, …).map(x => f(x)) gives List(f(a), f(b), f(c),…)
List(a, b, c, …).reduce((x, y) => x  y) gives a  b  c … where  is some binary operator

4. Another view
(in Japanese)

5. ForkJoin How does ForkJoin differ from MapReduce?
Answers from stackoverflow:
ForkJoin recursively partitions a task into several subtasks, on a single machine. Takes advantage of multiple cores
MapReduce only does one big split, with no communication between the parts until the reduce step. Massively scalable.
Java fork/join starts quickly and scales well for small inputs (<5MB), but it cannot process larger inputs due to the size restrictions of shared-memory, single node architectures.
MapReduce takes tens of seconds to start up, but scales well for much larger inputs (>100MB) on a compute cluster.

6. The End