Map Reduce in Big Data Processing
MapReduce is a powerful technique for processing large data sets. It enables the execution of Map and Reduce tasks on different machines in parallel. The final result is obtained only after all Reduce tasks are completed.
For example, the e-commerce company can leverage this technology for customer purchase behavior analysis. The e-commerce company can then use this information to improve their business.
What is Map Reduce?
The MapReduce system (also known as the framework) splits an input data-set into independent chunks. These chunks are processed by map tasks in a completely parallel manner. The outputs of the map tasks are sorted by key, and the results are sent to reduce tasks.
The result sets from the reduce tasks are merged to produce a final output set. The MapReduce system also saves all of the intermediate results in files on server disks. This allows for fast access to the data and helps protect it against hardware failures.
MapReduce makes it easy for programmers to use advanced mathematical and statistical algorithms. They can find patterns, uncover correlations, make predictions and much more. In the past, these calculations required Ph.D. scientists and supercomputers. Now they can be run across a cluster of low-cost commodity servers. The parallel processing offered by MapReduce also speeds up the calculations and offers some protection against failures in the system.
Advantages of Map Reduce
Map Reduce allows businesses to process petabytes of data stored in Hadoop Distributed File System (HDFS) using parallel processing and minimal data movement. The technology is highly scalable and supports multiple programming languages to cater to different software needs.
MapReduce works on the principle of pushing code to the data, reducing the need for transfer across slow connections. This enables it to process large amounts of data faster than non-distributed solutions.
It also provides high reliability and fault tolerance by periodically reporting completed work and status updates to a master server. The master server records these results in a log and resends tasks to another node in case of failure. It also uses atomic operations to name output files and checks for duplicates. This ensures that the final output is accurate and complete. The framework also automatically reruns failed Map and Reduce tasks. This makes it more reliable than other distributed computing systems. This scalability and flexibility has made MapReduce an important tool for enterprises and other organizations around the world.
Requirements of Map Reduce
MapReduce is a powerful programming paradigm that has transformed the data science world. Its relative simplicity makes it a natural choice for organizations managing large sets of data. It can be used to perform tasks such as logistic regression, tall skinny QR factorization and other machine learning algorithms.
The MapReduce framework shards the input data-set across worker nodes and processes them in parallel. It then combines the output of each Map task into a single result set and writes that to stable storage. The framework also provides tools for storing backup copies of the data on other nodes in case any one node fails.
A hidden step in the map and reduce operation is a shuffle that redistributes data to reduce processors based on the output keys (produced by the Map function). This helps speed up the Reduce operation by eliminating the need for grouping the results by key. This is done by applying a partition function to the output of the map function.
Conclusions of Map Reduce
The ability of MapReduce to process data across a large number of machines (or nodes) is what makes it useful for big data processing. This is because it enables parallelism by distributing workloads over multiple computers and servers, which can be located in the same network and use similar hardware.
For example, e-commerce retailers use the framework to evaluate customer buying patterns and provide product recommendations. The system also enables companies to perform fraud detection by analyzing large volumes of online transaction data and other information.
A key feature of the MapReduce framework is that it supports a variety of storage formats and allows users to process structured and unstructured data at the same time. Moreover, it is less susceptible to hardware failures that can bring down databases. Using this framework, ordinary IT departments can handle business problems that would require Ph.D. scientists and supercomputers to solve in the past. Hence, it helps to streamline processes and deliver a greater level of efficiency.