Hadoop vs Spark - Detailed Comparison

Introduction

Big Data has become a buzzword in the technology industry over the past decade. With vast amounts of data being generated every second, it's essential to manage and process it efficiently.

That’s where Hadoop and Spark come into play. Both are powerful big data processing frameworks that can handle large datasets at scale.

Hadoop Overview

History and Development

Hadoop was created by Doug Cutting and Mike Cafarella in 2005 while they were working at Yahoo. The project was named after a toy elephant that belonged to Cutting's son. Initially designed to handle large amounts of unstructured data, Hadoop has grown into a powerful distributed computing platform used for Big Data processing.

Architecture

The Hadoop architecture consists of two main components: HDFS (Hadoop Distributed File System) and YARN (Yet Another Resource Negotiator). HDFS is responsible for storing large amounts of data across distributed clusters, while YARN manages the resources in the cluster and schedules tasks to be executed.

Components

Hadoop has several major components that work together to form a complete Big Data processing ecosystem. These include −

• HDFS − A distributed file system that can handle petabytes of data

• YARN − A resource management system that can allocate resources among different applications running on the same cluster

• MapReduce − A distributed computing framework used for batch processing

• Hive − A data warehouse infrastructure that provides SQL-like interface for querying data stored in HDFS.

• Pig − A high-level scripting language used for creating MapReduce jobs.

• ZooKeeper − A service used for maintaining configuration information across multiple nodes

Features

Hadoop provides several features that make it popular with Big Data developers and analysts. These include −

• Scalability − Hadoop can scale horizontally to handle petabytes of data

• Fault-tolerance − The system can detect failures at the application layer and provide automatic recovery mechanisms

• Cost-effectiveness − Hadoop runs on commodity hardware and open-source software, making it a cost-effective solution for Big Data processing.

• Flexibility − Hadoop can process both structured and unstructured data using various data processing techniques such as batch processing, real-time processing, and machine learning.

Spark Overview

Apache Spark is an open-source distributed computing system designed for large-scale data processing. Spark provides a unified framework for batch, streaming, and interactive queries through its high-level APIs in Java, Scala, Python, and R.

History and Development

Spark was first introduced in 2009 as a research project at the University of California, Berkeley's AMPLab. It was designed to improve upon Hadoop's MapReduce by providing a faster and more flexible data processing engine that can handle both batch and real-time workloads. In 2010, it was released as an open-source project under the Apache Software Foundation (ASF), which greatly contributed to its popularity and wider adoption.

Architecture

The architecture of Spark is based on the concept of Resilient Distributed Datasets (RDDs) - immutable distributed collections of objects that can be processed in parallel across multiple nodes in a cluster. RDDs are created through transformations on other RDDs or external datasets such as Hadoop Distributed File System (HDFS), Amazon S3, Cassandra, etc., and can be cached in memory for faster access.

The core components of Spark's architecture include −

• Spark Core − the underlying computation engine responsible for scheduling tasks across a cluster.

• Spark SQL − a module for working with structured data using sql-like queries.

• Spark Streaming − a real-time processing module that allows users to process live streams of data.

• Mllib (Machine Learning Library) − a library with various machine learning algorithms such as classification models or regression models

Components

Spark has various components that make it a powerful and flexible data processing engine. These include −

• Spark Core − the foundation layer of spark, responsible for scheduling and executing tasks across a cluster.

• Spark SQL − a module for working with structured data using sql-like queries.

• Spark Streaming − a real-time processing module that allows users to process live streams of data.

• Mllib (Machine Learning Library) − a library with various machine learning algorithms such as classification models or regression models

• DataFrames API:a distributed collection of data organized into named columns that offers operations on both structured and semi-structured data.

Features

Spark provides the following features that make it a popular choice for big data processing −

• Faster Processing Speeds − Spark's in-memory computing capabilities allow it to operate up to 100 times faster than Hadoop MapReduce when running certain applications.

• Flexible Processing Models − Spark supports batch processing, interactive queries, real-time stream processing, and machine learning workloads all within one platform.

• Ease of Use − The high-level APIs provided by Spark make it easy for developers to build complex analysis workflows without needing in-depth knowledge about the underlying systems.

Spark is designed to provide an efficient and flexible way to process large-scale datasets. Its architecture is based on RDDs which are immutable distributed collections of objects processed in parallel across multiple nodes in a cluster.

Comparison of Hadoop and Spark

Below is a Table of Differences Between Hadoop and Spark

Basis	Hadoop	Spark
Processing Speed & Performance	Hadoop’s MapReduce model reads and writes from a disk, thus slowing down the processing speed.	Spark reduces the number of read/write cycles to disk and stores intermediate data in memory, hence faster-processing speed.
Usage	Hadoop is designed to handle batch processing efficiently.	Spark is designed to handle real-time data efficiently.
Latency	Hadoop is a high latency computing framework, which does not have an interactive mode.	Spark is a low latency computing and can process data interactively.
Data	With Hadoop MapReduce, a developer can only process data in batch mode only.	Spark can process real-time data, from real-time events like Twitter, and Facebook.
Cost	Hadoop is a cheaper option available while comparing it in terms of cost	Spark requires a lot of RAM to run in-memory, thus increasing the cluster and hence cost.
Algorithm Used	The PageRank algorithm is used in Hadoop.	Graph computation library called GraphX is used by Spark.
Fault Tolerance	Hadoop is a highly fault-tolerant system where Fault-tolerance achieved by replicating blocks of data.  If a node goes down, the data can be found on another node	Fault-tolerance achieved by storing chain of transformations If data is lost, the chain of transformations can be recomputed on the original data
Security	Hadoop supports LDAP, ACLs, SLAs, etc and hence it is extremely secure.	Spark is not secure, it relies on the integration with Hadoop to achieve the necessary security level.
Machine Learning	Data fragments in Hadoop can be too large and can create bottlenecks. Thus, it is slower than Spark.	Spark is much faster as it uses MLib for computations and has in-memory processing.
Scalability	Hadoop is easily scalable by adding nodes and disk for storage. It supports tens of thousands of nodes.	It is quite difficult to scale as it relies on RAM for computations. It supports thousands of nodes in a cluster.
Language support	It uses Java or Python for MapReduce apps.	It uses Java, R, Scala, Python, or Spark SQL for the APIs.
User-friendliness	It is more difficult to use.	It is more user-friendly.
Resource Management	YARN is the most common option for resource management.	It has built-in tools for resource management.

Conclusion

Both Hadoop and Spark have their pros and cons when it comes to big data processing. However, what works best for your organization ultimately depends on your specific needs.

While Hadoop excels at large-scale storage and versatility with community support but has some limitations like latency issue during batch processing while Spark overcomes these bottleneck through speedier in-memory computations but falls short when dealing with larger storage systems than what fits into RAM which makes them complementary technologies rather than competitors in most cases.