Apache Spark is an open-source data processing framework, developed in 2009 in the AMPLab at U.C. Berkeley, that performs functions on large data sets. This framework can also distribute the processing tasks across numerous computing machines, making it a distributed computing system essential in big data and machine learning. Moreover, the users can utilize batch processing, real-time analytics, graph processing, machine learning, and interactive queries for fast analytical and optimized processing. It provides APIs in various languages such as Java, Python, R, and Scala.
The following are the primary components of Apache Spark:
This component performs various functions, such as I/O operations, task dispatching, and scheduling. Moreover, as Apache Spark is user-friendly, it reduces the code complexity for the developers by providing them with easy method calls. These calls encapsulate much of the code complexity and reduce the code size.
Spark Core, also known as Shark, is the interface to process structured data to extract information and allows the developers to query data. Moreover, the developers can use it with other stores, such as Apache Hive, JDBC, Apache ORC, MongoDB, and Apache Cassandra, either directly or by using their connector packages to connect with them.
Apache Spark uses the concept of RDD (Resilient Distributed Dataset) as the base of its architecture. It is a collection of elements split across various clusters that can be run parallelly without any faults. Moreover, the developers can further split the RDD operations and perform parallel batch processing to perform scalable and quick parallel processing.
Spark MLib allows developers to analyze graphs and perform machine learning on extensive data. Moreover, it has in-built classification and clustering functions such as k-means clustering. The developers can efficiently train the ML models using R or Python and then convert or import them to Scala or Java-based pipelines for production. The only drawback is that SprakMLib includes primary ML clustering, classification, and regression techniques. However, it does not provide support for deep neural networks.
This component allows the developers to process live data streams. The developers introduced this component to allow streaming in an extension of batch processing without the user needing to change the code base or framework.
Spark GraphX contains API to allow the users to process graph structures and perform graph analytics on the data. The developers can use this framework to build and transform large graphical data structures.
Apache Hadoop is also an open-source software platform, developed in 2006 as a Yahoo project, to perform data processing and manage the storage of data-related applications for their scalability. This java-based platform breaks down the analytical and big data tasks to run them parallelly by splitting them across various nodes into a computing cluster.
The following are the main components of Apache Hadoop:
Hadoop Distributed File System (HDFS) is a portable and scalable Java-based file system for Apache Hadoop to store large files across various machines and parallelize them across a Hadoop cluster. Moreover, this file system can manage structured and unstructured data fault-tolerantly.
Map Reduce is the algorithm for processing the data parallelly and combining each result to form a final result. Apache Spark and Apache Tez also utilize this execution engine.
YARN stands for “Yet Another Resource Negotiator” and manages the runtimes of the application by planning tasks, scheduling jobs, and managing cluster resources. It efficiently allocates resources to the applications by task tracking and monitoring the resource execution in the application.
Hadoop Common or Hadoop Core provides the users with shared libraries to support other above modules.
Below is the list of the fundamental differences between Apache Hadoop and Apache Spark:
Apache Spark is an RDD-based framework. At the same time, Apache Hadoop uses HDFS to manage data files. Even though Spark does not use HDFS, it supports HDFS for adding a storage layer to the application, depending on the application’s data needs.
Apache Hadoop uses the PageRank algorithm. However, Apache Spark utilizes GraphX.
Apache Spark has MLib to perform various ML-related tasks quickly with in-memory processing, such as regression, pipeline construction, classification, and persistence. However, in Hadoop, data fragments can become too large, making it relatively slower.
Apache Spark and Hadoop are both distributed computing systems. Apache Spark is best for processing unstructured live data and real-time processing with Apache streaming. However, Hadoop is best for linear data processing and batch processing.
Apache Spark uses Directed Acyclic Graph (DAG) to rebuild the data across the nodes where the nodes represent the executable tasks. In contrast, Apache Hadoop, instead of rebuilding it, replicates the data across the nodes to use it when needed, making it a relatively higher fault-tolerant system.
Apache Spark APIs support Java, Scala, Python, Spark SQL, and R, whereas Apache Hadoop supports Python and Java.
Apache Spark uses its built-in tools for resource management, while in Apache Hadoop, YARN (Yet Another Resource Negotiator) is mainly used for resource management and scheduling.
Apache Spark is comparatively faster because it uses random access memory (RAM) for in-memory processing. In contrast, Apache Hadoop’s file system requires it to read and write all data to disks and process it in batches for batch processing.
Although both platforms are open-source, Apache Spark is relatively expensive in terms of money because it uses a lot of random access memory for data processing in real time. However, Apache Hadoop uses disk storage for its read-and-write operations, reducing its cost.
Apache Spark uses shared secret and event logging to provide security to the applications. In contrast, Apache Hadoop uses access control methods and multiple authentication systems for security. Spark users can integrate its API with Hadoop and enhance its security.
Apache Spark is faster but difficult to scale because more data processing requires more RAM for in-memory processing. However, HDFS in Hadoop makes it easier to scale to accommodate processing needs. It can support tens of thousands of nodes, while Spark supports only thousands of nodes in a single cluster. Therefore, Spark also relies on HDFS to meet large data requirements.