With the advancement of technology, every business wants to store and process their online data. This requirement brings on a new demand to gather big data for enterprises from their online and offline activities. The data which is collected needs to be stored and processed effectively.
Hadoop is one of the most popular frameworks to process big data, and one of the best-supporting blocks of Hadoop is MapReduce. If you are seeking a career as a data analyst in the data science field, then you must be aware of this rising and popular programming language.
Importance of MapReduce in Data analysis
MapReduce processes extensive scale data, while Hadoop accomplishes consecutive MapReduce programs inscribed in multiple programming dialects, including C++, Python, Ruby on Rails, Java, and many others. The nature of MapReduce is parallel, which makes it very much useful in programming a massive amount of data that can be used by multiple machines in the form of clusters.
What is MapReduce programming?
MapReduce is a program module for distributed computing. It works on Java in two phases namely,
- Map Phase
- Reduce Phase
For understanding MapReduce, every coder and programmer has to understand these two phases and their functions.
1. Map Phase
In Map Phase, the information of the data will split be into two main parts, namely Value and Key. The value is recorded just in the dealing out stage, while the key is written in the processing stage. Whenever the client succumbs the involvement of data to the Hadoop framework, the job tracker assigns jobs, and the information of data gets divided into many parts.
The information is then divided on the basis of its nature. The record reader transports the divided information in key-value pairs, which is commonly known as a (KV) pair. KV is the original input data form for the Map Phase, which again processes the data inside the job tracker. The information on the form will be different for different applications. So, you need to optimize the input data to encrypt accordingly.
When you take information in the text format, you will find the key, which is the byte offset. This map phase also uses the combiner and partition module to code a program such that it performs unusual data operations. You will find that the data localization will happen only in the mapper unit of data.
- Combiner module in Map phase
In the Map stage, the combiner modules are also known as mini reducers. A combiner is necessary to conquer high bandwidth when the mapper processes a massive amount of data. In order to address the top bandwidth problem, you need to use the combiner logic in the map phase to get an excellent output result.
- Partition module in Map phase
Just like in the combiner module, the partition segment offers a vital aspect to the MapReduce programming language, which ultimately affects the Hadoop framework. The partition segment decreases the pressure that is created during the reducing process, giving excellent output. You can even customize the partition in accordance with your data, depending on different circumstances.
You can even use default partition during the process. Besides, there are static and dynamic partitions that help a computer operator to divide data into multiple figures using the reduce and map phase methods. You can design and customize these partitions as per the business requirements. This partition module will be valuable to transfer the data between the above two processes of MapReduce architecture.
2. Reduce Phase
After the map phase processes, the organized and scuffled data will become the input for the Reduce phase. During this phase, all the sorted data will be combined, and the actual Key-Value pair will be considered in the HDFS framework. The record writers note statistics from the Reducer phase to the HDFS framework. Although this phase is optional for searching and mapping, it plays a vital part in the betterment of performance.
This phase initiates the actual process on the provided data by the Map phase. The Map phase offers reducer results, such as part-r-0001. You also need to provide a set of numbers for each task that your users want to track. You can also set many priorities that will be enabled to place the names of specific situations.
In this phase, theoretical execution is essential for running the data. If multiple reducers are processing the same data and the first reducer is processing slow, then the task tracker can assign the processing to the next available reducer to accelerate the process. This kind of job of allocation to an available reducer is called FIFO, i.e. First In First Out.
Understanding the Process of MapReduce Architecture
Here are the points that you should keep in mind while working with the MapReduce architecture in the Hadoop framework.
Map phase job creation: In the MapReduce architecture, the first Map phase job is created to divide data and execute map modules to record the data.
Division of data: The combiner and partition module help data to process many separations. The time required to process the entire input of data is higher when equated to the time required to process the divisions. Smaller separations provide better processing and balancing of data in a parallel way.
Also read: Features and Applications of Hadoop
Accurate splitting: Separations that are too small in size are not ideal in the Map phase, as they increase the load of handling the divisions and Map task creation beings to govern the time of performing the entire task.
Considering the average size of splitting: Ideally, the division size should be 64 MB, and you should set it as default to create a uniform size of splits. The divided size should be equivalent to HDFS blocks.
Implementing the HDFS module: The Map phase output proceeds the writing production to the local disk on the individual unit of data and not on the HDFS module. In order to avoid repetition, which is common in HDFS, you need to choose a local drive other than HDFS.
Preventing duplication: The Map phase is the central part that processes the data to feed to the Reduce phase and provide the outcomes. Once the job is done, the map output can be deleted, preventing the replication of data.
Results offered to reduce phase job: The results from the Map phase are consecutively offered to the Reduce phase. In the order, the production is combined and processed to the user-defined reduce functions.
Local storage: Apart from the Map method, the data from the Reduce part is kept in HDFS, which is also the first copy to save in your local unit of data.
The MapReduce framework simplifies the complex process of processing massive data that is available in the Hadoop structure. There have been many significant changes in the MapReduce programming language in Hadoop 2.0 when compared to Hadoop 1.0.
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