What Is MapReduce? Meaning, Working, Features, and Uses

MapReduce is defined as a big data analysis model that processes data sets using a parallel algorithm on computer clusters, typically Apache Hadoop clusters or cloud systems likeÂ Amazon Elastic MapReduce (EMR) clusters. This article explains the meaning of MapReduce, how it works, its features, and its applications.Â

What Is MapReduce?
How Does MapReduce Work?
Key Features of MapReduce
Top 5 Uses of MapReduce

What Is MapReduce?

MapReduce is a big data analysis model that processes data sets using a parallel algorithm on computer clusters, typically Apache Hadoop clusters or cloud systems likeÂ Amazon Elastic MapReduce (EMR) clusters.Â

A software framework and programming model called MapReduce is used to process enormous volumes of data. Map and Reduce are the two stages of the MapReduce program’s operation. Vast volumes of data are generated in today’s data-driven market due to algorithms and applications constantly gathering information about individuals, businesses, systems, and organizations. The tricky part is figuring out how to quickly and effectively digest this vast volume of data without losing insightful conclusions.

It used to be the case that the only way to access data stored in the Hadoop Distributed File SystemÂ (HDFS) was using MapReduce. Other query-based methods are now utilized to obtain data from the HDFS using structured query language (SQL)-like commands, such as Hive and Pig. These, however, typically run alongside tasks created using the MapReduce approach.Â

This is so because MapReduce has unique benefits. To speed up processing, MapReduce executes logic (illustrated above) on the server where the data already sits, rather than transferring the data to the location of the application or logic.

MapReduce first appeared as a tool for Google to analyze its search results. However, it quickly grew in popularity thanks to its capacity to split and process terabytes of data in parallel, producing quicker results.Â

MapReduce is essential to the operation of the Hadoop framework and a core component. While â€œreduce tasksâ€ shuffle and reduce the data, â€œmap tasksâ€ deal with separating and mapping the data. MapReduce makes concurrent processing easier by dividing petabytes of data into smaller chunks and processing them in parallel on Hadoop commodity servers. In the end, it collects all the information from several servers and gives the application a consolidated output.Â

For example, let us consider a Hadoop cluster consisting of 20,000 affordable commodity servers containing 256MB data blocks in each. It will be able to process around five terabytes worth of data simultaneously. Compared to the sequential processing of such a big data set, the usage of MapReduce cuts down the amount of time needed for processing.

To speed up the processing, MapReduce eliminates the need to transport data to the location where the application or logic is housed. Instead, it executes the logic directly on the server home to the data itself. Both the accessing of data and its storing are done using server disks. Further, the input data is typically saved in files that may include organized, semi-structured, or unstructured information. Finally, the output data is similarly saved in the form of files.

The main benefit of MapReduce is that users can scale data processing easily over several computing nodes. The data processing primitives used in the MapReduce model are mappers and reducers. Sometimes it is difficult to divide a data processing application into mappers and reducers. However, scaling an application to run over hundreds, thousands, or tens of thousands of servers in a cluster is just a configuration modification after it has been written in the MapReduce manner.

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How Does MapReduce Work?

MapReduce generally divides input data into pieces and distributes them among other computers. The input data is broken up into key-value pairs. On computers in a cluster, parallel map jobs process the chunked data. The reduction job combines the result into a specific key-value pair output, and the data is then written to the Hadoop Distributed File System (HDFS).

Typically, the MapReduce program operates on the same collection of computers as the Hadoop Distributed File System. The time it takes to accomplish a task dramatically decreases when the framework runs a job on the nodes that store the data. Several component daemons were used in the first iteration of MapReduce, including TaskTrackers and JobTracker.Â

TaskTrackers are agents installed on each machine in the cluster to carry out the map and reduce tasks. JobHistory Server is a component that keeps track of completed jobs and is typically deployed as a separate function or with JobTracker. JobTracker is the master node that manages all the jobs and resources in a cluster.

The JobTracker and TaskTracker daemons from earlier versions of MapReduce and Hadoop are superseded by the ResourceManager and NodeManager components of â€œYet Another Resource Negotiatorâ€œ or YARN, which were introduced with the release of MapReduce and Hadoop version 2. The job submission process and scheduling on the cluster are handled by ResourceManager, which is installed on a master node. It also manages resource allocation and task monitoring.

NodeManager runs on slave nodes in conjunction with Resource Manager to execute activities and monitor resource utilization. NodeManager may use other daemons to aid in task execution on the slave node. MapReduce uses enormous cluster sizes to run parallel operations to spread input data and compile outputs. One can distribute jobs across practically any number of servers because cluster size has little impact on how a processing job turns out.

Consequently, the Hadoop architecture as a whole and MapReduce make program development simpler. Many languages support MapReduce, including C, C++, Java, Ruby, Perl, and Python. To generate tasks without worrying about coordination or communication between nodes, programmers can utilize MapReduce libraries.

Also, fault-tolerant MapReduce regularly reports each node’s status to a master node. The master node redistributes that task to other available cluster nodes if a node doesn’t react as expected. This enables resiliency and makes it viable for MapReduce to run on affordable commodity servers.

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The MapReduce program runs in three phases: the map phase, the shuffle phase, and the reduce phase.

1. The map stage

The task of the map or mapper is to process the input data at this level. In most cases, the input data is stored in the Hadoop file system as a file or directory (HDFS). The mapper function receives the input file line by line. The mapper processes the data and produces several little data chunks.

2. The reduce stage (including shuffle and reduce)

The shuffle and reduce stages are combined to create the reduce stage. Processing the data that arrives from the mapper is the reducer’s responsibility. Following processing, it generates a fresh set of outputs that will be kept in the HDFS.

Hadoop assigns the Map and Reduce tasks to the proper cluster computers during a MapReduce job. The framework controls every aspect of data-passing, including assigning tasks, confirming their completion, and transferring data across nodes within a cluster. Most computing is done on nodes with data stored locally on drives, which lowers network traffic. After the assigned tasks are finished, the cluster gathers and reduces the data to create the necessary results, then delivers it back to the Hadoop server.

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Key Features of MapReduce

The following advanced features characterize MapReduce:

1. Highly scalable

A framework with excellent scalability is Apache Hadoop MapReduce. This is because of its capacity for distributing and storing large amounts of data across numerous servers. These servers can all run simultaneously and are all reasonably priced.Â

By adding servers to the cluster, we can simply grow the amount of storage and computing power. We may improve the capacity of nodes or add any number of nodes (horizontal scalability) to attain high computing power. Organizations may execute applications from massive sets of nodes, potentially using thousands of terabytes of data, thanks to Hadoop MapReduce programming.

2. Versatile

Businesses can use MapReduce programming to access new data sources. It makes it possible for companies to work with many forms of data. Enterprises can access both organized and unstructured data with this method and acquire valuable insights from the various data sources.

Since Hadoop is an open-source project, its source code is freely accessible for review, alterations, and analyses. This enables businesses to alter the code to meet their specific needs. The MapReduce framework supports data from sources including email, social media, and clickstreams in different languages.

3. Secure

The MapReduce programming model uses the HBase and HDFS security approaches, and only authenticated users are permitted to view and manipulate the data. HDFS uses a replication technique in Hadoop 2 to provide fault tolerance. Depending on the replication factor, it makes a clone of each block on the various machines. One can therefore access data from the other devices that house a replica of the same data if any machine in a cluster goes down. Erasure coding has taken the role of this replication technique in Hadoop 3. Erasure coding delivers the same level of fault tolerance with less area. The storage overhead with erasure coding is less than 50%.

4. Affordability

With the help of the MapReduce programming framework and Hadoop’s scalable design, big data volumes may be stored and processed very affordably. Such a system is particularly cost-effective and highly scalable, making it ideal for business models that must store data that is constantly expanding to meet the demands of the present.

In terms of scalability, processing data with older, conventional relational database management systems was not as simple as it is with the Hadoop system. In these situations, the company had to minimize the data and execute classification based on presumptions about how specific data could be relevant to the organization, hence deleting the raw data. The MapReduce programming model in the Hadoop scale-out architecture helps in this situation.

5. Fast-paced

The Hadoop Distributed File System, a distributed storage technique used by MapReduce, is a mapping system for finding data in a cluster. The data processing technologies, such as MapReduce programming, are typically placed on the same servers that enable quicker data processing.

Thanks to Hadoop’s distributed data storage, users may process data in a distributed manner across a cluster of nodes. As a result, it gives the Hadoop architecture the capacity to process data exceptionally quickly. Hadoop MapReduce can process unstructured or semi-structured data in high numbers in a shorter time.

6. Based on a simple programming model

Hadoop MapReduce is built on a straightforward programming model and is one of the technology’s many noteworthy features. This enables programmers to create MapReduce applications that can handle tasks quickly and effectively. Java is a very well-liked and simple-to-learn programming language used to develop the MapReduce programming model.Â

Java programming is simple to learn, and anyone can create a data processing model that works for their company. Hadoop is straightforward to utilize because customers don’t need to worry about computing distribution. The framework itself does the processing.

7. Parallel processing-compatible

The parallel processing involved in MapReduce programming is one of its key components. The tasks are divided in the programming paradigm to enable the simultaneous execution of independent activities. As a result, the program runs faster because of the parallel processing, which makes it simpler for the processes to handle each job. Multiple processors can carry out these broken-down tasks thanks to parallel processing. Consequently, the entire software runs faster.

8. Reliable

The same set of data is transferred to some other nodes in a cluster each time a collection of information is sent to a single node. Therefore, even if one node fails, backup copies are always available on other nodes that may still be retrieved whenever necessary. This ensures high data availability.

The framework offers a way to guarantee data trustworthiness through the use of Block Scanner, Volume Scanner, Disk Checker, and Directory Scanner modules. Your data is safely saved in the cluster and is accessible from another machine that has a copy of the data if your device fails or the data becomes corrupt.

9. Highly available

Hadoop’s fault tolerance feature ensures that even if one of the DataNodes fails, the user may still access the data from other DataNodes that have copies of it. Moreover, the high accessibility Hadoop cluster comprises two or more active and passive NameNodes running on hot standby. The active NameNode is the active node. A passive node is a backup node that applies changes made in active NameNode’s edit logs to its namespace.

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Top 5 Uses of MapReduce

By spreading out processing across numerous nodes and merging or decreasing the results of those nodes, MapReduce has the potential to handle large data volumes. This makes it suitable for the following use cases:

1. Entertainment

Hadoop MapReduce assists end users in finding the most popular movies based on their preferences and previous viewing history. It primarily concentrates on their clicks and logs.Â

Various OTT services, including Netflix, regularly release many web series and movies. It may have happened to you that you couldn’t pick which movie to watch, so you looked at Netflix’s recommendations and decided to watch one of the suggested series or films. Netflix uses Hadoop and MapReduce to indicate to the user some well-known movies based on what they have watched and which movies they enjoy. MapReduce can examine user clicks and logs to learn how they watch movies.

2. E-commerce

Several e-commerce companies, including Flipkart, Amazon, and eBay, employ MapReduce to evaluate consumer buying patterns based on customers’ interests or historical purchasing patterns. For various e-commerce businesses, it provides product suggestion methods by analyzing data, purchase history, and user interaction logs.

Many e-commerce vendors use the MapReduce programming model to identify popular products based on customer preferences or purchasing behavior. Making item proposals for e-commerce inventory is part of it, as is looking at website records, purchase histories, user interaction logs, etc., for product recommendations.

3. Social media

Nearly 500 million tweets, or about 3000 per second, are sent daily on the microblogging platform Twitter. MapReduce processes Twitter data, performing operations such as tokenization, filtering, counting, and aggregating counters.

Tokenization: It creates key-value pairs from the tokenized tweets by mapping the tweets as maps of tokens.
Filtering: The terms that are not wanted are removed from the token maps.
Counting: It creates a token counter for each word in the count.
Aggregate counters: A grouping of comparable counter values is prepared into small, manageable pieces using aggregate counters.

4. Data warehouse

Systems that handle enormous volumes of information are known as data warehouse systems. The star schema, which consists of a fact table and several dimension tables, is the most popular data warehouse model. In a shared-nothing architecture, storing all the necessary data on a single node is impossible, so retrieving data from other nodes is essential.

This results in network congestion and slow query execution speeds. If the dimensions are not too big, users can replicate them over nodes to get around this issue and maximize parallelism. Using MapReduce, we may build specialized business logic for data insights while analyzing enormous data volumes in data warehouses.

5. Fraud detection

Conventional methods of preventing fraud are not always very effective. For instance, data analysts typically manage inaccurate payments by auditing a tiny sample of claims and requesting medical records from specific submitters. Hadoop is a system well suited for handling large volumes of data needed to create fraud detection algorithms. Financial businesses, including banks, insurance companies, and payment locations, use Hadoop and MapReduce for fraud detection, pattern recognition evidence, and business analytics through transaction analysis.

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Takeaway

For years, MapReduce was a prevalent (and the de facto standard) model for processing high-volume datasets. In recent years, it has given way to new systems like Google’s new Cloud Dataflow. However, MapReduce continues to be used across cloud environments, and in June 2022, Amazon Web Services (AWS) made its Amazon Elastic MapReduce (EMR) Serverless offering generally available. As enterprises pursue new business opportunities from big data, knowing how to use MapReduce will be an invaluable skill in building data analysis applications.

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