Performance Optimization Techniques for Java-Based Big Data Applications
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I3P114Keywords:
Java-Based Big Data Applications, Performance Optimization, JVM Tuning, Memory Management, Apache Spark, Hadoop Ecosystem, Data Serialization, Parquet and ORC, Distributed Computing, Scalability and Throughput, Resource Utilization, Performance Profiling, MonitoringAbstract
Java-based big data applications are widely used to process and analyze massive datasets in distributed environments, powering critical systems in finance, healthcare, e-commerce, and cloud computing. However, achieving optimal performance in these applications remains a persistent challenge due to JVM overhead, memory constraints, data skew, network latency, and inefficient resource utilization. This study investigates performance optimization techniques specifically tailored for Java-driven big data platforms such as Apache Hadoop, Spark, and Flink. The research aims to identify architectural, algorithmic, and runtime-level strategies that improve execution efficiency and scalability. The methodology combines a systematic review of existing literature with experimental evaluation of optimization techniques, including memory tuning, garbage collection optimization, parallel processing strategies, data partitioning, caching mechanisms, and JVM configuration enhancements. Additionally, workload-aware scheduling and adaptive resource management approaches are analyzed to understand their impact on distributed performance. The findings demonstrate that performance gains are best achieved through a multi-layer optimization strategy that integrates code-level improvements with system-level tuning and intelligent workload management. The study concludes that effective performance optimization in Java-based big data applications requires a holistic approach that balances computation, memory, and I/O efficiency while maintaining system reliability. These insights provide practical guidance for developers and system architects seeking to design high-performance, scalable big data solutions.
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