Apache Spark 数据帧在弹性分布式数据集 (RDD) 上的比较分析

Ashima Sahni
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摘要

Apache Spark 凭借其灵活性、可扩展性、健壮性、速度以及与 Java、Scala、Python 等多种编程语言的集成,如今已成为处理应用程序中庞大数据集的一种广泛使用的技术。它为这些编程语言提供了多种实现方法,如数据帧、RDD。本文深入概述了如何使用 Apache Spark 数据框架来提高 Apache Spark 弹性分布式数据集(RDD)和基于 SQL 的数据处理性能。Spark 数据框架广泛用于管理和处理大型数据集,这些数据集可以是结构化、非结构化或半结构化的。本文通过实际示例和用例,介绍了如何利用 Spark 数据帧来提高大型数据处理的性能,以取代传统的 Spark RDDs。它强调了在需要将大型数据集处理为有意义的输出的任何应用程序中,为什么要使用数据帧来实现更好的性能的关键点。
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A Comparative Analysis of Apache Spark Dataframes over Resilient Distributed Datasets (RDDs)
Apache Spark is a widely used technology now a days for handling huge datasets in applications due to its flexibility, scalability, robustness, speed and integration with multiple programming languages like Java, Scala, Python. It provides multiple methodologies for implementation like dataframes, RDDs with these programming languages. This paper provides a deep overview of Apache spark dataframes usage for performance enhancement over Apache Spark Resilient Distributed Datasets (RDDs) and SQL based data processing. Spark dataframes are widely used in managing and processing large datasets which can be structured, non-structured or semi-structured. This paper describes the approach towards Spark dataframes for performance enhancement for large data processing in place of traditional usage of Spark RDDs with practical examples and use cases. It highlights the key points on why to use dataframes for a better performance achievement for any application where large dataset needs to be processed into a meaningful output.
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