A Hybrid SQL Query Execution Model for JSON Data: Balancing Resource Efficiency and Analytical Performance

The rapid growth of unstructured and semi- structured data has necessitated the development of efficient query execution frameworks capable of handling complex data formats such as JSON. This paper presents a hybrid framework that intelligently leverages Apache Drill and DuckDB to optimize SQL query execution on JSON data. Our framework dynamically selects the appropriate query engine based on dataset size, available memory, and the nature of the SQL workload, effectively balancing memory efficiency with analytical performance. We explore the advantages of using Apache Drill for complex joins and nested JSON data, while DuckDB is employed for analytical queries when sufficient memory resources are available. Experimental results demonstrate the framework’s capability to reduce execution time and enhance resource utilization across various workloads. By providing insights into hybrid query processing techniques, this work aims to contribute to the efficient management of JSON data in modern data-driven applications.