Published Paper PDF: https://ijrmeet.org/wp-content/uploads/2025/07/IJRMEET0725270034_Architecting%20Distributed%20Transactional%20Systems%20Using%20DB%20Sharding%20and%20Kafka.pdf
DOI: https://doi.org/10.63345/ijrmeet.org.v13.i7.4
Dr S P Singh
Ex-Dean, Gurukul Kangri Vishwavidyalaya
Haridwar, Uttarakhand 249404 India
Abstract
Distributed transactional systems are critical for modern applications requiring high availability, scalability, and consistency across geographically dispersed nodes. This manuscript investigates the architectural integration of database (DB) sharding and Apache Kafka as a distributed commit-log to enable efficient, fault-tolerant transaction processing. We first outline the challenges inherent to distributed transactions—network latency, partial failures, and consistency guarantees—then propose a hybrid approach combining horizontal data partitioning with an event-driven messaging backbone. Our methodology encompasses simulated workloads reflecting typical OLTP scenarios, with performance metrics captured through throughput, latency, and failure-recovery experiments. A statistical analysis presents comparative results across three sharding strategies: range-based, hash-based, and consistent-hash. The findings demonstrate that Kafka-enabled distributed two-phase commit offers substantial improvements in throughput (up to 45% under peak load) and recovery time (down by 60%) compared to traditional two-phase commit over TCP. We conclude by discussing implementation considerations, trade-offs, and the future scope for adaptive shard rebalancing and cross-cluster replication. This study provides a blueprint for practitioners seeking to architect robust, scalable, and resilient transactional platforms in microservices and cloud-native environments.
Keywords
horizontal partitioning; event streaming; two-phase commit; fault tolerance; scalability; consistency
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