What is MapReduce?
MapReduce is a programming model for enormous data processing. We can write MapReduce programs in various programming languages such as C++, Ruby, Java, Python, and other languages. Parallel to the MapReduce programs, they are very useful in large-scale data analysis using several cluster machines. MapReduce’s biggest advantage is that data processing is easy to scale over multiple computer nodes. The primitive processing of the data is called mappers and reducers under the MapReduce model. It is sometimes nontrivial to break down an application for data processing into mappers and reducers.
Top 3 Stages of MapReduce
There are namely three stages in the program:
- Map Stage
- Shuffle Stage
- Reduce Stage
Example:
Following is an example mentioned:
Wordcount problem:
Suppose below is the input data:
- Mike Jon Jake
- Paul Paul Jake
- Mike Paul Jon
1. The above data is divided into three input splits.
- Mike Jon Jake
- Paul Paul Jake
- Mike Paul Jon
2. Then, this data is fed into the next phase, called the mapping phase.
So, for the first line (Mike Jon Jake), we have 3 key-value pairs – Mike, 1; Jon, 1; Jake, 1.
Below is the result in the mapping phase:
- Mike,1
Jon,1
Jake,1 - Paul,1
Paul,1
Jake,1 - Mike,1
Paul,1
Jon,1
3. The above data is then fed into the next phase, called the sorting and shuffling phase.
In this phase, the data is grouped into unique keys and is sorted. Below is the result of the sorting and shuffling phase:
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- Jake,(1,1)
- Jon,(1,1)
- Mike,(1,1)
- Paul,(1,1,1)
4. The above data is then fed into the next phase, called the reduce phase.
Here all the key values are aggregated, and the number of 1s is counted.
Below is the result in reduce phase:
- Jake,2
- Jon,2
- Mike,2
- Paul,3
Advantages of MapReduce
Given below are the advantages mentioned:
1. Scalability
Hadoop is a highly scalable platform and is largely because of its ability that it stores and distributes large data sets across lots of servers. The servers used here are quite inexpensive and can operate in parallel. The processing power of the system can be improved with the addition of more servers. The traditional relational database management systems or RDBMS were not able to scale to process huge data sets.
2. Flexibility
Hadoop MapReduce programming model offers flexibility to process structure or unstructured data by various business organizations who can use the data and operate on different types of data. Thus, they can generate a business value out of those meaningful and useful data for the business organizations for analysis. Irrespective of the data source, whether it be social media, clickstream, email, etc. Hadoop offers support for a lot of languages used for data processing. Along with all this, Hadoop MapReduce programming allows many applications such as marketing analysis, recommendation system, data warehouse, and fraud detection.
3. Security and Authentication
If any outsider person gets access to all the data of the organization and can manipulate multiple petabytes of the data, it can do much harm in terms of business dealing in operation to the business organization. The MapReduce programming model addresses this risk by working with hdfs and HBase that allows high security allowing only the approved user to operate on the stored data in the system.
4. Cost-effective Solution
Such a system is highly scalable and is a very cost-effective solution for a business model that needs to store data growing exponentially in line with current-day requirements. In the case of old traditional relational database management systems, it was not so easy to process the data as with the Hadoop system in terms of scalability. In such cases, the business was forced to downsize the data and further implement classification based on assumptions of how certain data could be valuable to the organization and hence removing the raw data. Here the Hadoop scaleout architecture with MapReduce programming comes to the rescue.
5. Fast
Hadoop distributed file system HDFS is a key feature used in Hadoop, which is basically implementing a mapping system to locate data in a cluster. MapReduce programming is the tool used for data processing, and it is also located in the same server allowing faster processing of data. Hadoop MapReduce processes large volumes of data that is unstructured or semi-structured in less time.
6. Simple Model of Programming
MapReduce programming is based on a very simple programming model, which basically allows the programmers to develop a MapReduce program that can handle many more tasks with more ease and efficiency. MapReduce programming model is written using Java language is very popular and very easy to learn. It is easy for people to learn Java programming and design a data processing model that meets their business needs.
7. Parallel Processing
The programming model divides the tasks to allow the execution of the independent task in parallel. Hence this parallel processing makes it easier for the processes to take on each of the tasks, which helps to run the program in much less time.
8. Availability and Resilient Nature
Hadoop MapReduce programming model processes the data by sending the data to an individual node as well as forward the same set of data to the other nodes residing in the network. As a result, in case of failure in a particular node, the same data copy is still available on the other nodes, which can be used whenever it is required ensuring the availability of data.
In this way, Hadoop is fault-tolerant. This is a unique functionality offered in Hadoop MapReduce that it is able to quickly recognize the fault and apply a quick fix for an automatic recovery solution.
There are many companies across the globe using map-reduce like Facebook, Yahoo, etc.
Conclusion
Map-reduce has a large capability when it comes to large data processing compared to traditional RDBMS systems. Many organizations have already realized its potential and are moving to this new technology. Clearly, map-reduce has a very long to go in a big data processing platform.
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