MapReduce with Python

MapReduce is a programming model that enables large volumes of data to be processed and generated by dividing work into independent tasks and executing the tasks in parallel across a cluster of machines. The MapReduce programming style was inspired by the functional programming constructs map and reduce, which are commonly used to process lists of data. At a high level, every Map-Reduce program transforms a list of input data elements into a list of output data elements twice, once in the map phase and once in the reduce phase.

This chapter begins by introducing the MapReduce programming model and describing how data flows through the different phases of the model. Examples then show how MapReduce jobs can be written in Python.

Data Flow

The MapReduce framework is composed of three major phases: map, shuffle and sort, and reduce. This section describes each phase in detail.


The first phase of a MapReduce application is the map phase. Within the map phase, a function (called the mapper) processes a series of key-value pairs. The mapper sequentially processes each key-value pair individually, producing zero or more output keyvalue pairs

As an example, consider a mapper whose purpose is to transform sentences into words. The input to this mapper would be strings that contain sentences, and the mapper’s function would be to split the sentences into words and output the words

Shuffle and Sort

The second phase of MapReduce is the shuffle and sort. As the mappers begin completing, the intermediate outputs from the map phase are moved to the reducers. This process of moving output from the mappers to the reducers is known as shuffling. Shuffling is handled by a partition function, known as the partitioner. The partitioner is used to control the flow of key-value pairs from mappers to reducers. The partitioner is given the mapper’s output key and the number of reducers, and returns the index of the intended reducer. The partitioner ensures that all of the values for the same key are sent to the same reducer. The default partitioner is hash-based. It computes a hash value of the mapper’s output key and assigns a partition based on this result.

The final stage before the reducers start processing data is the sorting process. The intermediate keys and values for each partition are sorted by the Hadoop framework before being presented to the reducer.


The third phase of MapReduce is the reduce phase. Within the reducer phase, an iterator of values is provided to a function known as the reducer. The iterator of values is a nonunique set of values for each unique key from the output of the map phase. The reducer aggregates the values for each unique key and produces zero or more output key-value pairs

As an example, consider a reducer whose purpose is to sum all of the values for a key. The input to this reducer is an iterator of all of the values for a key, and the reducer sums all of the values. The reducer then outputs a key-value pair that contains the input key and the sum of the input key values

The next section describes a simple MapReduce application and its implementation in Python.

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