What kind of problems can MapReduce solve?

MapReduce works on any problem that is made up of exactly 2 functions at some level of abstraction. The first function is is applied to each of the items in the input set, and the second function aggregates the results.

Why did Google stop using MapReduce?

The technology is unable to handle the amounts of data Google wants to analyze these days, however. Urs Hölzle, senior vice president of technical infrastructure at the Mountain View, California-based giant, said it got too cumbersome once the size of the data reached a few petabytes.

Why would a developer create a MapReduce without the reduce step?

Why would a developer create a map-reduce without the reduce step? A. Developers should design Map-Reduce jobs without reducers only if no reduce slots are available on the cluster.

Why is MapReduce bad?

For fault tolerance, MapReduce keeps writing to disk all the time, which drags down your application performance significantly. A more severe problem is that MapReduce provides only a very LIMITED parallel computing paradigm. Not all problems fit in MapReduce.

Why was MapReduce created?

MapReduce was designed as a generic programming model. Google engineers designed MapReduce to solve a specific practical problem. Therefore, it was designed as a programming model combined with the implementation of that model — in essence, a reference implementation.

What is the main problem faced while reading and writing data in parallel from multiple disks?

Q 4 – What is the main problem faced while reading and writing data in parallel from multiple disks? A – Processing high volume of data faster.

Can a custom data type be created and implemented in MapReduce?

A custom hadoop writable data type that can be used as key field in Mapreduce programs must implement WritableComparable interface which intern extends Writable (org. So, i.e. a data type created by implementing WritableComparable Interface can be used as either key or value field data type.