IJBTT

Performance Tuning and Optimization of Apache Spark Applications
	© 2023 by IJCTT Journal
	Volume-71 Issue-5
	Year of Publication : 2023
	Authors : Anish Ninan
	DOI :  10.14445/22312803/IJCTT-V71I5P103

How to Cite?

Anish Ninan, "Performance Tuning and Optimization of Apache Spark Applications," International Journal of Computer Trends and Technology, vol. 71, no. 5, pp. 10-14, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I5P103

Abstract
Apache Spark has emerged as a powerful and widely used distributed data processing engine for big data analytics. However, achieving optimal performance in Spark applications can be challenging due to the complex nature of distributed computing and the myriad of configuration parameters involved. This paper presents a comprehensive study of performance tuning and optimization techniques for Apache Spark applications, with the goal of enabling users to maximize resource utilization, minimize execution time, and improve overall application efficiency.
We begin by providing an overview of Apache Spark’s architecture, including its data structures, core components, and execution model. This foundation allows us to explore the impact of various factors on Spark application performance, such as data partitioning, data serialization, and caching strategies. We then discuss critical performance-related parameters, including executor configuration, memory management, and garbage collection settings.
Next, we delve into advanced optimization techniques, such as adaptive query execution, dynamic allocation, and data locality. We demonstrate the effectiveness of these techniques through a series of experiments and benchmarks using real-world datasets and workloads. Additionally, we introduce tools and best practices for monitoring and profiling Spark applications, allowing users to identify and address performance bottlenecks.
By providing a comprehensive understanding of performance tuning and optimization for Apache Spark applications, this paper aims to empower users to harness the full potential of this powerful data processing engine, unlock new insights from their big data workloads and most importantly, save on costs!

Keywords
Apache spark, Big data, AI, ML, Data Engineering, Performance tuning.

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