MapReduce is a parallel programming model that follows a simple divide-and-conquer strategy to tackle big datasets in distributed computing. This chapter begins with a discussion of the key distinguishing features and differences of MapReduce with respect to similar distributing computing tools like Message Passing Interface (MPI). Then, we introduce its two main functions, map and reduce, based on functional programming. After that, the notation of how MapReduce works is presented using the classical WordCount example as the Hello World of big data, discussing different ways to parallelize it and their main advantages and disadvantages. Next, we delve into MapReduce a bit more formally, and its functions in terms of key–value pairs, as well as the key properties of the map, shuffle, and reduce operations. At the end of the chapter we cover some important details as to how to achieve fault tolerance, how to exploit MapReduce to preserve data locality, how it can reduce data transfer across computers using combiners, and additional information about its internal working.

The short story is not just a story that is short: the short story generally differs significantly from the novel in terms of scope, timeframe, number of characters and locations. How details acquire priority in short fiction. The relationships between the short story, flash fiction and poetry. The challenges and pleasures of short and very short fiction. The usefulness of short form writing to the developing novelist in the scope it offers for experimentation with narrative voice, characterisation and dialogue, as well as its value in its own right.

‘It is a complexity of afterthought, a psychological or emotional residue, that we seek to leave with the reader following the intense experience of consuming a short story.’