2.8. Combining Map and Reduce¶
2.8.1. The MapReduce Paradigm¶
In 2004, Jeffrey Dean and Sanjay Ghemawat of Google published a paper describing a paradigm for distributed computation that has come to be called MapReduce. It illustrated the influence of functional programming on the way in which Google organized computational work that could be parallelized on distributed clusters of computers.
The essence of Dean and Ghemawat’s idea was to define a mapping function that would perform a specified task in parallel on multiple data sets distributed across many computers. The results of each mapping function were then returned to a reducing function that accumulated the results into the “answer” being sought.
To illustrate, suppose we had a distributed database, called db2, of salesperson records with the sales records of “Smith” on one computer, the sales records of “Jones” on a second computer, and the sales records of “Green” on a third computer.
var db2 = [ ["Jones", 9, 2, 8, 6, 4], ["Smith", 4, 1, 8, 32, 45], ["Green", 4, 4, 6, 1, 12, 8] ];
Given this database, we want a computation (the mapping function) done on each computer that returns the name of the salesperson along with the sum of all the sales records for that person. The results of those three computations are then returned to a reducing function that picks out the salesperson who sold the most.
> bestSalesPerson(db2) [ 'Smith', 90 ]
The following bestSalesPerson function achieves this computation by defining two functions (the mapper and the reducer) and then appropriately calling on fp.reduce. Read through the following slide show for more details and then attempt the review problem that follows.
The following randomized problem is about the MapReduce model. You must solve it correctly three times in a row to earn credit for it.