How does MapReduce work for Big Data? All you need to know

Dealing with huge amounts of various kinds of data may be challenging, yet it is crucial for many organizations’ success these days. Fortunately, with the right tools, it can become much simpler. Read our article and learn how MapReduce works in a Big Data environment and what the benefits are of using it for business purposes.

Every day, unimaginable amounts of unstructured, semi-structured and structured data are generated and collected by companies. If you work with Big Data, it means you have to handle a massive collection of complex (in many dimensions) and large datasets. They cannot be processed using traditional methods, so you will need advanced solutions and tools in your tech stack. We’ll explain how MapReduce works for Big Data projects.

Why do organizations leverage Big Data?

There are many secrets hiding in business data. If you select the right tools and approach, you can use this information in many ways, for example for:

• creating business insights that will help you improve processes in your company,
• assessing risks and preventing mistakes,
• automating processes and eliminating human error,
• recommending the right products and services to your potential customers,
• improving user experience with personalization, 
• monitoring your systems and preventing fraud and cybercrimes,
• offering services based on real-time data processing.

As you see, Big Data analytics serves many practical purposes and can significantly improve your organization’s operations and make you more competitive. One of the tools you may consider using for Big Data processing is MapReduce – a component of the Apache Hadoop framework. 

What is MapReduce?

So, as you know, MapReduce is not a standalone tool. It is a core part of the Hadoop framework from Apache. This software can be used for distributed processing of huge, unstructured data sets across commodity computer clusters. Each of the nodes within the cluster has its own storage. MapReduce has two main functions – it acts as a:

• mapper – distributing the work to various nodes in the cluster or map,
• reducer – organizing the results from each node and reducing it to a cohesive answer to a query.

That is, of course, a very simple explanation of what MapReduce is responsible for in Hadoop. Are you interested in how MapReduce works in Big Data projects?

How does MapReduce work for Big Data?

The MapReduce algorithm consists of two components:

Map – the Map task converts given datasets into other datasets. It splits jobs into job-parts and maps intermediate data.
Reduce – the Reduce task is performed on the output data from the map task and reduces intermediate data into smaller units.

Of course, the entire process is not so easy. The MapReduce model includes some stages. In fact, even authors disagree on the number of stages. Some mention only three: mapping, shuffling and reducing, and others provide readers with a more detailed description of the process by listing even seven steps of data processing with MapReduce.

1. Splitting

The information that is about to be processed by the MapReduce task is stored in input files in the Hadoop Distributed File System. Its format is arbitrary (for example, binary can be leveraged). MapReduce’s job input-specification is validated, and the input files are split out into logical InputSplit instances that commonly show the record-oriented view of data – each of them is later assigned to the individual Mapper. RecordReader then reads the key-value pairs from the InputSplits in order to make them “record-oriented” for the Mapper and further processing.

To put it simply and explain clearly how MapReduce works in a Big Data project, in this step input data is divided into smaller chunks that can be consumed by a single map.

2. Mapping

After data is divided, each split is processed with the mapping function. The goal is to produce output values from it. The input files are passed to the mapper function, and as a result, several small chunks of data are created. The Mappers output is not stored on the Hadoop Distributed File System, because it is only temporary data. This way, no unnecessary copies are created. Then, the output is passed to the Combiner, which carries out the local aggregation and passes it further to the partitioner for partitioning.

3. Sorting and shuffling

This stage is performed on the mapping phase’s output. The goal is to consolidate accurate records. The shuffling is just the process of transferring data from the mapper to the reducers. As a result of this phase, the input for the reducers is prepared. The shuffling phase can start even before the mapping is completed, which allows you to save some time during data processing. Sorting is carried out automatically by MapReduce by key, before the reducing stage begins. That increases the reducing phase’s efficiency.

4. Reducing

The final phase of processing data in MapReduce is reducing. The output from the shuffling and sorting phase is aggregated and turned into the end result. This stage is all about summarizing the effects of the previous stages and reducing them to a small set of values. The output of that phase is stored in the Hadoop Distributed File System.

Real life MapReduce use cases

Various medium and large companies leverage MapReduce (hence, also Hadoop) in their day-to-day work. Using it significantly improves the efficiency of processing data in organization. MapReduce can be applied in industries like:

• E-commerce – as you already know, MapReduce can process many types of unstructured, structured and semistructured data. It is often leveraged by e-commerce giants for customer purchase behavior (viewed product categories, previous transactions, visited websites) analysis. By processing information about consumers’ activity on the Internet, brands can develop automatic product recommendations, and encourage them to buy more.
• Social media – every day on social media such as Facebook, Twitter, or LinkedIn, millions of users view and react to content. MapReduce processes data, so users can learn how the online community is interacting with their profiles. 
• Healthcare – in the medical sector, Big Data is used for diagnostics, designing treatment, reducing medical costs, predicting and preventing epidemics and assessing the quality of human life. The complexity and volume of data processed by healthcare organizations make Hadoop and MapReduce indispensable for that industry, as they can easily process terabytes of data.

As a part of Apache Hadoop, MapReduce can be applied in any business use case that requires efficient data processing. 

To sum up

MapReduce is a core, crucial component of the Hadoop framework. It enables effective data processing in any type of business organization. Its main strong sides are:

• speed,
• scalability,
• cost-effectiveness. 

MapReduce’s model is suitable for analyzing behavioral patterns, which makes it great for e-commerce platforms, as well as for website traffic evaluation.

MapReduce is one of the most popular algorithms used by brands known over the world for Big Data processing. 

Understanding the complexity of the software for Big Data processing is not easy, and it often requires wide knowledge of data processing. We encourage you to research the topic and learn as much as you can about Big Data. We are here to assist you with your projects. Don’t hesitate to contact us if you need our support.

Check out our blog for more details on Big Data:

• What is big data analytics? Examples, types, definition
• Why is BigQuery replacing Hadoop for enterprise analytics?
• Optimizing Apache Spark