Database plays an important role in transactional information systems. One significant performance impacting factor is data lock contention in transaction processing. In order to guide betterdatabase design, we propose a novel solution to identify contention hotspots displayed inDBMS transaction logs. To analyze the large volume of data collected in the transaction log, our solution employees bigdata engine Spark for better computation performance and scalability. A novel algorithm is also introduced to optimize computation for analyzing hotspots in the distributed cluster. The experimental results from a benchmark OLTP workload demonstrate the effectiveness and high scalability of our solution.