Spark vs. MapReduce: What’s the Difference?

Apache Spark (Spark) and Apache Hadoop (MapReduce) are applications that allow programmers to make use of parallel computing, which are groups of computers that enable faster data processing. As the use of big data grew in the 2010s, the older data processing framework Hadoop, using MapReduce, became a limitation for data processing. 

Developed through research at UC Berkley, Apache Spark’s in-memory data processing makes it significantly faster than MapReduce, which stores data on disks at every step. In 2014, Spark became a top project at the Apache Software Foundation, completing certain data processing tasks up to 100 times faster than MapReduce. 

Explore how Spark compares to Hadoop’s data processing component, MapReduce. Learn about each framework's use cases and advantages and disadvantages before choosing which one is right for your needs. 

Spark

Apache Spark is an engineer for data analytics, data science, data engineering, and machine learning. It integrates the use of multiple programming languages over an array of distributed computers. Spark went beyond the limitations of using just disk read and write speeds for complex data operations by storing data in memory. This allows for the continual reuse of functions called many times by an operation. 

Spark works by having a primary node, called the Spark Driver, that controls the secondary nodes in the cluster. These secondary nodes process data and send it back to the client. Spark delivers its speeds through the use of resilient distributed datasets (RDDs) that work in parallel among multiple nodes in the cluster. Spark caches data in the RDDs memory to perform two processes:

• Transformations: Create new RDDs.

• Actions: Perform the requested computations by the client and send back the results.

Spark handles the distribution of the entire parallel computing system, so you only need to focus on your work. 

What is Spark used for?

Apache Spark has various uses in processing data, performing SQL analytics, exploratory data analysis (EDA), and training machine learning algorithms. One of Spark’s most common uses is its ability to process big data in industries like finance, health care, manufacturing, and retail. Spark uses dataframes; application programming interfaces (APIs) that allow for the usage of many programming languages like Python, R, Scala, and Java. With its popular machine learning library (MLlib) and APIs, Spark helps data scientists, developers, data engineers, and statisticians analyze big data. 

Advantages of Spark

With Spark’s fast memory-based processing and programming language APIs, it comes with many advantages for anyone who works with large quantities of data. Some of its advantages include the following:

• Spark has many libraries, such as MLlib for machine learning, Graphx for visualization, and SQL streaming to make developing applications faster and more efficient. 

• Spark makes programming in different languages easy as it supports Python, Java, Scala, and R without any work needed by the user. 

• Spark can perform some data processing tasks up to 100 times faster than Hadoop and MapReduce, using its memory caching engine.

• Spark can use GPU data processing, like NVIDIA RAPIDS. This accelerates machine learning processing speed even further using GPU acceleration instead of CPU-based processing. 

Disadvantages of Spark

Apache Spark has many advantages when it comes to big data processing, however, it does have some limitations to consider, including:

• Spark uses memory, specifically random access memory (RAM), an expensive hardware component compared to that of a spinning disk drive.

• Spark does not have real-time data analytics processing. That said, it comes close to real-time with Spark Streaming, which processes data in small batches over predetermined times. 

• Spark has a steep learning curve for beginners looking to use distributed computing for data processing. However, it has many capabilities once you understand it. 

• File management in Spark can be problematic for users since it relies on third-party systems like the Hadoop Distributed File System (HDFS). This dependency can cause issues, particularly when dealing with numerous small files. 

MapReduce

Apache MapReduce is the main processing component in Apache Hadoop. It distributes data processing tasks across hundreds of thousands of computers and servers into a Hadoop cluster. MapReduce receives its name from the two operations intrinsic to the process:

• Map: This aspect breaks down the data input into tuples; another set of data that contains the key and value pairs (<key, value>) of the data input. 

• Reduce: This aspect takes the map output, now a tuple, and breaks that dataset into a smaller set of tuples.

After the MapReduce process, the data writes into the Hadoop Distributed File System (HDFS). This framework then controls the processing, tasks, and confirmation of the data passing from node to node. Local drives store the MapReduce data. After completion of the process, the results are sent back to the client using the Hadoop server. 

What is MapReduce used for?

MapReduce and Spark do similar processes, especially since Spark was created to go beyond some speed limitations inherent in the MapReduce framework in Hadoop. MapReduce can handle lots of data and store it in HDFS. Businesses can use MapReduce to create algorithms for recommendations like Netflix by storing data in HDFS and then having MapReduce examine that data. MapReduce can also speed up the process of retrieving data from a data warehouse by adding in the component of parallel computing. Other tasks that MapReduce can do include:

• Data mining

• Predictive analysis

• Machine learning

Advantages of MapReduce

MapReduce is a highly scalable framework within the Apache Hadoop architecture. It can increase the storage capacity by incorporating additional servers and boost computing power by adding more nodes. Other advantages of MapReduce include:

• MapReduce uses HBase and HDFS to create security, authenticating users and fault tolerance. By making a copy of the data, it ensures accessibility if a machine fails. 

• MapReduce and Hadoop give businesses an affordable way to store and use data while creating scalability if needed. 

• Parallel processing in MapReduce ensures programs run quickly and efficiently by breaking down tasks and sending them to each machine. 

Disadvantages of MapReduce

With all of the advantages that MapReduce provides, it does come with a few limitations, namely in how it uses hard disks to store information as opposed to Spark, which uses memory. Other disadvantages of MapReduce include:

• Java is a core component of using MapReduce and requires you to compile the code separately, before sending it into the cluster. 

• MapReduce requires all data to be read and written in HDFS with little storage in memory. This creates speed limitations when processing large amounts of data. 

• Iterative logic, like that in machine learning algorithms, struggles in performance since data is not kept in memory like in Spark. 

Other things to consider with Spark vs. MapReduce

Spark and MapReduce both excel in processing big data in distributed parallel computing clusters, but differ in how they process that data. They both create workflows for businesses to analyze their data and make informed decisions. Additionally, both frameworks build fault tolerance so that if one node fails, data remains safe and can still be processed. 

Some situations you may want to use Spark include:

• When you need the speed of processing data in memory instead of on disk

• If you require the use of iterative processing a library of algorithms for machine learning applications

• When you need to see data analysis and computations in near real-time

Some situations where you may find MapReduce and Hadoop more useful include:

• You need to start analyzing data but have a limited budget and hardware resources to do so.

• You need the built-in data security protocols available with Hadoop that have encryption and authorization policies. 

• You need the cheaper scalability of MapReduce to add processing does or more storage space when the amount of data is larger than you can process with memory. 

To bypass some cost and computation limitations, you can run Spark on the Hadoop infrastructure to shift processing that is not time-sensitive to Hadoop clusters. 

Getting started with Spark and MapReduce on Coursera

When it comes to Spark versus MapReduce, both have advantages and limitations when you are considering an architecture for big data analysis and machine learning. To gain in-demand skills in big data analysis and machine learning using Spark and Hadoop, explore the Machine Learning with Apache Spark course from IBM, which is part of the IBM Data Engineering Professional Certificate on Coursera.