Subscribe Now
Trending News

Big Data Write For Us Guest Blog Submission

Big Data Write For Us – The programming model called MapReduce was initially used by Google to analyze the results of its searches. Therefore, it gained great admiration due to its skill to split and process terabytes of data in parallel, achieving faster results.

MapReduce is a software design model or pattern within the Hadoop framework. This model is used to access Big Data, which is stored in the Hadoop Distributed File System, also called HDFS. Thus, it is an integral component of the functioning of this framework.

Thus, this model facilitates data processing, dividing petabytes into smaller portions and processing them in parallel. All this is on Hadoop servers. In the end, it masses all the data from various servers to return a consolidated result to the application.

For example, a Hadoop cluster with 20,000 low-cost servers and a 256 MB data block on each can process around 5 TB of data simultaneously. This action reduces data processing time compared to the sequential processing of a massive data set.

Similarly, instead of sending the data to where the application or logic is installed, the reason runs on the server where the data is already stored to speed up processing.

That is why data access and storage are disk-based: input is stored in files containing structured, semi-structured, or unstructured data. Moreover, the output is also stored in files.

Similarly, MapReduce was once the only method by which data stored in the Hadoop Distributed File System could retrieve. The thing has changed.

Changes in the structures and operation of MapReduce

To this day, there are other query-based schemes such as Hive and Pig. Which are used to recover data from the Hadoop Distributed File System through SQL-type statements. However, these often run in conjunction with tasks are written using the MapReduce model. This is because this programming model has critical advantages.

How does MapReduce work?

To begin with, at the core of MapReduce, there are two separate functions: Map and Reduce. These functions are sequenced one after the extra, to obtain consistent results. So the “Map” function takes the input from the disk as pairs, processes them, and crops another set of intermediate pairs, such as a key and a value for the output.

While the Map is a required step to filter and sort the initial data, the Decrease function is optional. Likewise, the “Reduce” function takes the inputs as pairs, keys, values, and produces pairs of the same input value as a logical output. The types of keys and values ​​differ depending on the use case given to them. Therefore, all input and output are stored in the Hadoop File System.

In this way, the so-called “Mappers” and “Reducers” are the Hadoop servers that execute the Map and Reduce functions, respectively and separately. So it doesn’t matter if they are the same or different servers.

map function

First, the input data is divided into smaller blocks. Thus, each block is assigned to a “mapper” (Mapper) for processing. This is how, for example, if a file has 200 records of processing, 200 mappers can be executed together to process one form each. Maybe 50 mappers can work together to process two documents each.

Therefore, the Hadoop outline resolves how many mappers to use, built on the size of the data to be treated and the memory block available on each mapper server.

Reduce function

Now, once all the whole mappers processed, the framework shuffles and sorts the results earlier, passing them to the “reducers”. An existing rule is that a reducer cannot be started while a mapper is still in progress. This way, all mapper output morals ​​with the same key are allotted to a single reducer, which then adds the values ​​for that key.

Other steps: combine and divide

The Merge function is an optional process. So the Combiner is a reducer that runs individually on each mapping server. This way, it compresses the data from each mapper to a simplified form before passing it to the next level. In this sense, there are 2 intermediate steps between Map and Reduce.

This makes mixing and sorting more accessible, since there is less data to work with. Frequently the combiner class set to the reducer class itself, this due to the cumulative and associative functions of the reduce function. However, if necessary, the Combiner can be a separate class if that’s the case.

Now, partitioning is the process that translates the resulting key/value pairs from the mappers into extra set of key/value pairs to feed into the reducer. In this way, you decide how the data should presented to the reducer and also assign it to a specific reducer.

The default partition determines the hash value of the key that results from the mapper. Also, it allocates a partition based on this hash value. There are as many partitions as reducers. For this reason, once the partition complete, the data for each partition  sent to a specific reducer.

Advantages of the MapReduce model

In this way, programming in MapReduce offers several advantages that will greatly help you obtain valuable information from the Big Data analysis of your organization or your company.

  • scalability; organizations can process petabytes of data stored in the Hadoop Spread File System (HDFS).
  • Flexibility; the Hadoop environment makes it easy to access multiple data sources and various types of figures.
  • Speed; With parallel processing and minimal data movement, this model offers fast processing of massive amounts of data .
  • Ease; developers can write code in multiple languages, including Java, C++, and Python.

What is the architecture of MapReduce like?

First, MapReduce ‘s architecture consists of several components, all of which work independently, but together work to improve the processing of the data itself.

  • Worked; it the actual work that needs to executed or processed.
  • Task; it a part of the actual work that needs to executed or processed. A MapReducejob comprises several small tasks that need to executed.
  • Job Tracker or Job Tracker; This tracker plays the role of scheduling jobs and tracking all those that assigned to the task tracker.
  • Task tracker; said tracker plays the role of tracking the tasks and reporting the status of the tasks to the job tracker.
  • Input data; the data used for processing in the mapping phase.
  • Output data; it is the result of the work of mapping (Map) and reduction (reduce).
  • Client; often, it is a program or application programming interface (API) that submits jobs to MapReduce. This model can accept jobs from multiple clients.
  • Hadoop MapReduceMaster; which has the function of separating the jobs into job-parts.
  • Job-parts; They are sub-jobs that result from the main division of labor.

In the MapReduce architecture , clients submit jobs to the MapReduce Master. This master will subdivide the work in equal parts. Now, the parts of the job will used for the 2 main tasks of MapReduce : mapping and reducing.

In this way, the developer will write the logic that will satisfy the business requirements. Thus, the input data will split and mapped. In turn, the intermediate data will  sorted and merged. Finally, the reducer that will generate a final output stored in the Hadoop Distributed File System will process the resulting output.

How do job and task trackers work?

Each job consists of 2 main components: the first one is the mapping task and the second one is the reduction task. On the one hand, the mapping task has the role of dividing the jobs into job parts and mapping the intermediate data. In this sense, the reduction task plays the role of mixing and reducing the intermediate data into smaller units.

Similarly, the job tracker acts as master. That , it makes sure that all jobs executed. Similarly, the job tracker schedules jobs that have been submitted by clients. In turn, it will assign the jobs to task trackers. Thus, each task tracker consists of a map task and a reduce task. Task trackers report the status of each job assigned to the job tracker.

What are the stages of MapReduce?

The MapReduce programming model executes in three main stages: mapping, shuffling, and reducing. There is also an optional stage, known as the combining phase.

mapping phase

This is the first stage of the programming model and consists of 2 steps: divide and Map. Therefore, a data set divided into equal units called chunks. Which input slices in the split step. The Hadoop environment consists of a RecordReader that uses its own input text format to transform the input slices into key and value pairs.

Now, the key and value pairs used as inputs in the mapping step. This is the only data format that a mapper can read or understand. Thus, the mapping step contains encoding logic that applied to these data blocks. In this course, the mapper processes the key and value pairs, to produce an output of the same shape. That , produce a set of key and value pairs.

shuffling stage

This is the second stage that takes place after the mapping stage ends. Also, it consists of 2 main steps: classification and fusion. In the sort phase, the key and value pairs sorted using the same keys. In this sense, the merger guarantees the combination of the key and value pairs.

Similarly, the shuffle stage makes it easy to remove duplicate values ​​and group values. In turn, different values ​​with similar keys grouped together in the same place, so to speak. The output of this stage will be separate keys and values, just like in the mapping phase.

reduction phase

In the reduce or reduce stage, the output of the previous stage (shuffled) used as input. In this way, the reducer processes this input to reduce intermediate values ​​to smaller values. In fact, it provides a synthesis of the entire data pool, and the output of this stage stored in the Hadoop Distributed File System.

Combination stage

This an optional stage that used to optimize the MapReduce process . This stage used to reduce the outputs at the node level. At this stage, the duplicate outputs on the Map can combined into one. In this way, the combining stage increases the speed in the shuffling stage. Depending on the improvement of the performance of the works.

What applications are there for MapReduce today?

You may not have heard of MapReduce, but the truth is that it has been round Big Data environments for some time, applying to various fields of technology and business decision making, as its main objective.

MapReduce in e-Commerce

E-Commerce companies like Walmart, E-Bay, and Amazon use MapReduce to analyze purchasing behavior. This model provides relevant information that used as the basis for making product recommendations. Some of the information includes site logs, e-Commerce catalogs, purchase history, and interaction logs.

Applications in social networks

The MapReduce programming tool can evaluate information concerning common networking platforms such as Facebook, Twitter and LinkedIn. In addition, it evaluates important information such as who has liked your status and who has viewed your profile.

streaming content

Streaming giant Netflix uses MapReduce to analyze online customer clicks and registrations. This information helps the company to suggest movies based on customers’ interests and behavior. Do you want to know everything about the tools used to process data? With the Master in Big Data and Business Analytics , you will be able to create projects using these tools and thus improve the profitability of your business.

Likewise, You can submit your articles at

How to Submit Your Big Data Articles (Big Data Write For Us)?

That is to say, To submit your article at, mail us at

Why Write for Technostag – Big Data Write For Us

Why to Write for Technostag - Big Data Write For UsBig Data Write For Us

That is to say, here at Technostag, we publish well-researched, informative, and unique articles. In addition, we also cover reports related to:

data sets


application software.

statistical power,

false discovery rate

capturing data,

data storage,

data analysis





Guidelines of the Article – Big Data Write For Us

Guidelines of the Article - Big Data Write For Us Search Terms Related to [Big Data Write For Us]

artificial intelligence + “write for us”

write for us + business

write for us technology

business intelligence write for us

write for us – science

data science central

machine learning + “write for us”

write for us sites

towards data science


data science central blog

data science central uk limited

statisticshowto data science central

data science blogs

what is data science blog

data science news

Related Pages

Gadgets Write For Us
Machine Learning Write For Us
Digital Marketing Write For Us
Anti Virus Write For Us
Gaming Write For Us
Hacking Write For Us
Smartphone Write For Us
Web Design Write For Us