Har-GAO: Harmonic Giant Armadillo Optimization based Merkle Tree for Hybrid Query Processing in Block Chain based HTAP System

Modern data-intensive applications demand the integration of analytical and transactional processing to support real-time business operations. Hybrid Transactional and Analytical Processing (HTAP) systems have emerged as a response, combining Online Transactional Processing (OLTP) and Online Analytical Processing (OLAP) engines to efficiently handle interleaved workloads. However, most current distributed HTAP solutions rely on asynchronous data replication between specialized stores, limiting their utility for realtime operational analytics and lacking support for polyglot persistence. To address diverse and dynamically changing workloads, there is a need for a flexible HTAP-aware polystore system that combines transactional guarantees with support for heterogeneous data sources and query interfaces. Additionally, ensuring data integrity and provenance has become crucial. Blockchains, with their immutable, append-only structure, offer resistance to tampering and denial-of-service attacks, enabling historical data queries and provenance analysis. However, they often require external storage and local query processing. Distributed Hash Tables (DHTs) have gained popularity in decentralized service discovery, yet their basic exact-match functionality falls short for complex range queries. Layered DHT-based range query schemes attempt to overcome these limitations but face scalability and performance challenges. To further ensure data integrity and enable efficient verification, Merkle hash trees provide cryptographic commitments to dynamic data structures and are increasingly applied in distributed systems.