Hadoop vs Spark: A Comparative Study

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In an increasingly connected world, real-time information has become critical to all businesses. To extract, store and analyse heaps of information efficiently, data has to be stored in a scalable manner that allows the business to respond in real-time. Apache Spark and Hadoop were built for precisely this purpose.

Apache Hadoop is an open-source software library that enables reliable, scalable, distributed computing. Apache Spark is a popular open-source cluster computing framework within the Hadoop ecosystem. Both entities are useful in big data processing. They both run-in distributed mode on a cluster.

Hadoop and Spark are two popular open-source technologies making our lives simpler today. They have always been close competitors with their own fan base in the world of data analytics. 

Interestingly, both are Apache projects with a unique set of use cases. Though both have a wide array of advantages and disadvantages, they are still pretty easily comparable to decide which one is better for your business.

Let us dive deep and try to understand what these two technologies stand for, through a thorough analysis of their benefits and use cases.

What is Hadoop? 

The Apache Hadoop project has been around for a while, but its origins lie in the late 1990s. Doug Cutting and Mike Cafarella created it at Yahoo. Ever since then, it’s become one of the most widely used distributed file systems in the world.

The Hadoop framework is written in Java and enables scalable processing of large data sets across clusters of commodity hardware, leading to high-performance computing. In simple terms, Hadoop is a way to store and process data in an easy, cost-effective way. Hadoop uses a distributed processing model that allows users to access the information they need without storing it on a single machine. 

It is used for distributed storage and database management by businesses, governments, and individuals. Hadoop can also be considered a cluster computing platform that uses the MapReduce programming model to process big data sets.  

Hadoop was created mainly to address the limitations of traditional relational databases and provide faster processing of large data sets, particularly in the context of web services and internet-scale applications.

The four major modules of Hadoop are:

  1. Hadoop Distributed File System (HDFS): This system stores and manages large data sets across the clusters. HDFS handles both unstructured and structured data. The storage hardware that is being used can be anything from consumer-grade HDDs to enterprise drives.
  2. MapReduce: MapReduce is the processing component in the Hadoop ecosystem. The data fragments in the HDFS are assigned to separate map tasks in the cluster. MapReduce processes teh chunks to combine the pieces into the desired result.
  3. Yet Another Resource Negotiator: YARN is responsible for managing job scheduling and computing resources.
  4. Hadoop Common: This module is also called Hadoop Core. It consists of all common utilities and libraries that other modules depend on. It acts as a support system for other modules.

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What is Spark? 

Apache Spark is an open-source project by Databricks and supports the processing of fast data sets in real-time. Databricks provides Spark as a service and now offers more than 100 pre-built applications in different domains. It’s used for interactive queries, machine learning, big data analytics and streaming analytics.

Spark is a fast and easy-to-use in-memory data processing framework. It was developed at UC Berkeley as an extension of the big data ecosystem that has been supported by Hadoop, Apache HBase, Hive, Pig, Presto, Tez, and other components since its inception. Spark engine was created to boost the efficiency of MapReduce without compromising its benefits. Spark uses Resilient Distributed Dataset (RDD), which is the primary user-facing API.

It provides an optimised distributed programming model in which computations are carried out in a distributed manner on clusters of machines connected by high-speed networks. Its technology is specially devised for large-scale data processing. It reduces the task of handling huge amounts of data by breaking it into smaller tasks that can be processed independently. 

It also offers a distributed computing framework based on Java for big data processing. Spark uses Scala and Python programming languages, and it is open source. 

The Five Major Components of Apache Spark – 

  1. Apache Spark Core: This component is responsible for all the key functions like task dispatching, scheduling, fault recovery, input and output operations, and much more. Apache spark core acts as a base for the whole project, and all functionalities are built on it. 
  2. Spark streaming: As the name suggests, this component enables the processing of live data streams. The live stream data can originate from any of the sources like Kinesis, Kafka, Flume, etc.,
  3. Spark SQL: In this component, Spark gathers all information about the structured data and processing information of the data structures.
  4. Machine Learning Library (MLLib): This component consists of a vast library of machine learning algorithms. The goal of a machine learning library is to make it scalable and make machine learning more accessible.
  5. GraphX: It consists of a set of APIs that can be used for facilitating graph analytics tasks.

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Hadoop vs Spark: Key Differences

Hadoop is a mature enterprise-grade platform that has been around for quite some time. It provides a complete distributed file system for storing and managing data across clusters of machines. Spark is a relatively newer technology with the primary goal to make working with machine learning models easier.

Apache Hadoop and Apache Spark are the two giants in the big data world. While many of us cannot tell the exact difference between them, understanding them is pretty important. Both have their pros and cons; it all depends upon what you are looking for and what your needs are.

Both are distributed computing solutions and each has value in the right circumstances. Choosing between Hadoop and Spark can be a difficult task, as there is no easy “winner” or black-and-white answer to the question. The best approach for your business will likely depend on what you are currently working with, your team’s skill sets, and your long-term strategy.

Let’s now look into the differences between Hadoop and Spark on different parameters – 

Performance

Performance is the most important metric that drives the success of any data analytics software and platform. The performance of Hadoop and Spark has been a major topic of debate since the release of Apache Spark. But how different is the performance one from the other? Is one better than the other? Is it even possible to compare Hadoop and Spark?

Performance comparison between Hadoop and Spark is inevitable. Unfortunately, comparing them based on performance is not as easy as we would like to believe. Several factors contribute to performance in a big data environment, including software choice, hardware capabilities, number of nodes used, storage availability etc.

Hadoop Boosts the overall performance when it comes to accessing the locally stored data on HDFS. But when it comes to in-memory processing, Hadoop can never match with Spark. Apache claims that when using adequate RAM for computing, Spark is 100 times faster than Hadoop through MapReduce. 

In 2014, Spark set a new world record in sorting the data on disk. Spark was able to dominate Hadoop by being three times faster and using 10 times fewer nodes to process 100TB data on HDFS.

The main reason for Spark’s high performance is that it doesn’t write or read the intermediate data to the storage disks. It instead uses RAM to store the data. On the other hand, Hadoop stores the data on many different levels. After this, the data is processed in batches using M