Overview of Apache Spark

Apache Spark helps people study big information and teach machines how to learn. Some smart people at a university called UC Berkeley made it in 2009. Companies like yahoo! and Intel help make it better. Apache Spark is a big project that lots of people work on to help process data really fast and keep it in the computer's memory.

In this article, we will discuss Apache Spark and its history, components and various features.

History of Apache Spark

Spark is a distributed computing framework. It was first developed in 2009 at UC Berkeley R&D Lab, formerly known as AMPLab. Initially, it was designed to overcome the inefficiencies of Hadoop MapReduce for iterative and interactive computing jobs. In 2010, Spark was released as an open-source software under a BSD license. Subsequently, in June 2013, Spark was transferred to the Apache Software Foundation (ASF).

The researchers at UC Berkeley R&D Lab observed that Hadoop MapReduce was not efficient for iterative and interactive computing jobs, which prompted them to develop Spark. The primary design goal of Spark was to provide efficient support for in-memory storage and fault recovery to achieve high-speed interactive queries and iterative algorithms.

Components of Apache Spark

Apache Spark has several components. These make it efficient and powerful big data processing engine.

These components include

Spark Core

It provides basic functionality like distributed task scheduling, memory management, and fault recovery.

Spark SQL

It is used to process structured data using SQL-like syntax. It allows users to run SQL queries on Spark data.

Spark Streaming

It is used for real-time processing of streaming data. It can process data from various sources such as Kafka, Flume, and HDFS.

MLlib

This is ML library of Apache Spark. It provides wide range of machine learning algorithms.

GraphX

This component is used for graph processing and analytics

How does Apache Spark work?

Apache Spark uses master-client architecture. Master node coordinates the distribution of tasks to client nodes. The client nodes execute the tasks assigned to them. Client node returns the results to the master node. Apache Spark can run in standalone mode. It can run on a single machine. It can also run on multiple machines but in distributed mode.

Apache Spark works by creating RDDs. These are distributed collections of data that can be processed in parallel. RDDs can be created from data stored in HDFS, HBase, Cassandra, Amazon S3, and other storage systems. Once an RDD is created, it can be transformed using various operations like map, filter, join, and reduce. These transformations are executed in parallel across the worker nodes.

Features of Spark

Spark has various features for data processing. Some of these features include −

Batch Processing

Spark can be utilized for performing batch processing on large volumes of data.

Stream Processing

Spark can also be used for performing stream processing. Previously, Apache Storm / S4 were used for stream processing.

Interactive Processing

Spark is useful for interactive processing as well. Previously, Apache Impala or Apache Tez were used for interactive processing.

Graph Processing

Spark is also capable of performing graph processing. Previously, Neo4j / Apache Graph were used for graph processing.

Real-Time and Batch Mode Processing

Spark is capable of processing data in both real-time and batch mode, making it an ideal tool for handling data in various use cases.

So, Spark is powerful engine that offers a range of capabilities for data processing. It is valuable tool in database management systems.

Conclusion

Apache Spark is fast cluster computing technology. It is designed to efficiently process large-scale data: SQL, streaming, machine learning, and graph processing. It was developed in 2009 by researchers at UC Berkeley R&D Lab to overcome the inefficiencies of Hadoop MapReduce for iterative and interactive computing jobs. Spark has various components, for example Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX. These components are efficient and versatile tool for data processing. Spark works by creating RDDs, distributed collections of data that can be processed in parallel.

Spark has various capabilities, i.e., batch processing, stream processing, interactive processing, graph processing, and real-time and batch mode processing, These are valuable tool for handling data in various use cases. Companies like Yahoo! and Intel continue to work on Spark. Spark is an open-source software under a BSD license. Anyone can use it and contribute to its development. In short, Apache Spark is a valuable tool in database management systems that helps people study big information and teach machines how to learn.