Performance Tuning and Optimization of Apache Spark Applications
|© 2023 by IJCTT Journal|
|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
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!
Apache spark, Big data, AI, ML, Data Engineering, Performance tuning.
 IBM, The First Multi-Core, 1GHz Processor. [Online]. Available: https://www.ibm.com/ibm/history/ibm100/us/en/icons/power4
 Lawrence Livermore National Laboratory, Introduction to Parallel Computing Tutorial, 2022. [Online]. Available: https://hpc.llnl.gov/documentation/tutorials/introduction-parallel-computing-tutorial
 B. Chambers, and M. Zaharia, Spark: The Definitive Guide, 1005 Gravenstein Highway North, Sebastopol, CA: O’Reilly Media, Inc., 2018.
 Karthikeyan Rajendran, Speed Dialer: How AT&T Rings Up New Opportunities with Data Science, Nvidia, 2022. [Online]. Available: https://blogs.nvidia.com/blog/2022/03/22/att-data-science-rapids /
 AWS, What is Apache Spark?, AWS, 2022. [Online]. Available:
 IBM Cloud Education, Hadoop vs Spark: What's the Difference?, IBM, 2021. [Online]. Available: https://www.ibm.com/cloud/blog/hadoop-vs-spark
 Holden Karau, and Rachel Warren, How Spark Works, High Performance Spark, 2017, pp. 7-22.
 Apache Team, RDD Programming Guide, Apache, 2022. [Online]. Available: https://spark.apache.org/docs/latest/rdd-programming-guide.html
 Jules S. Damji et al., Learning Spark, 2nd Edition, O'Reilly Media, Inc., 2020. [Google Scholar] [Publisher Link]
 Institute of Computer Science, University of Tartu, Parallel Computing, Databricks. [Online]. Available: https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/6908168003362015/2853703854010541/462026168 4428706/latest.html
 Oracle Team, Interface Serializable, Oracle. [Online]. Available: https://docs.oracle.com/javase/8/docs/api/java/io/Serializable.html Aoache Team, Spark Configuration. [Online]. Available: https://spark.apache.org/docs/latest/configuration.html#spark-properties
 Azure Team, How to Improve Performance with Bucketing, Microsoft, 2022. [Online]. Available: https://learn.microsoft.com/en-us/azure/databricks/kb/data/bucketing
 Azure Team, Bucketing Example in Slack, Microsoft, 2022. [Online]. Available: https://learn.microsoft.com/en-us/azure/databricks/_static/notebooks/bucketing-example.html
 Vithal S., Apache Spark SQL Bucketing Support – Explanation, DW Geek, 2020. [Online]. Available: https://dwgeek.com/apache-spark-sql-bucketing-support-explanation.html
 Jun Guo, Bucketing 2.0: Improve Spark SQL Performance by Removing Shuffle, Databricks & Bytedance, 2020. [Online]. Available: https://www.databricks.com/session_na20/bucketing-2-0-improve-spark-sql-performance-by-removing-shuffle
 Siddharth Ghosh, Partitioning vs Bucketing — In Apache Spark, A Medium Corporation, 2022. [Online]. Available: https://medium.com/@ghoshsiddharth25/partitioning-vs-bucketing-in-apache-spark-a37b342082e4
 Holden Karau, and Rachel Warren, Resource Allocation Across Applications, High Performance Spark, 1005 Gravenstein Highway North, Sebastopol, CA 95472, O’Reilly Media, Inc., p. 20, 2017. [Publisher Link]
 Apache Team, Performance Tuning, Apache, 2022.[Online]. Available: https://spark.apache.org/docs/latest/sql-performance-tuning.html
 Apache Team, Tuning Spark, Apache, 2022. [Online]. Available: https://spark.apache.org/docs/latest/tuning.html