The problem of deadlock detection and resolution in database systems has been studied for decades. Although it has long been a mature feature of classical centralized database systems for many years, its use in distributed database systems remains in its infancy. A simple and fully distributed deadlock detection and resolution algorithm was proposed by Don P. Mitchell and Michael J. Merritt ( M&M ), but its assumption that each process waits for only one resource at a time prevents it from being generally applicable. The distributed deadlock detection algorithm based on Lock Chain Length (LCL) surpasses the limitations of the M&M algorithm. However, it is less effective in quickly detecting deadlocks that encompass both distributed and local deadlocks. In this article, we introduce LCL + , an advanced and universally applicable algorithm specifically designed for the detection and resolution of resource deadlocks in distributed environments. This algorithm improves the efficiency of identifying distributed deadlocks by accelerating the detection of hybrid deadlocks. Our extensive experiments demonstrate that LCL + significantly outperforms its predecessor, LCL, in efficiency. In addition, it has been successfully implemented in the OceanBase distributed relational database system. Detailed analyses from multiple perspectives within OceanBase confirm that LCL + significantly improves the system’s scalability and ensures the provision of high-quality service.

In the ongoing evolution of the OceanBase database system, it is essential to enhance its adaptability to small-scale enterprises. The OceanBase database system has demonstrated its stability and effectiveness within the Ant Group and other commercial organizations, besides through the TPC-C and TPC-H tests. In this paper, we have designed a stand-alone and distributed integrated architecture named Paetica to address the overhead caused by the distributed components in the stand-alone mode, with respect to the OceanBase system. Paetica enables adaptive configuration of the database that allows OceanBase to support both serial and parallel executions in stand-alone and distributed scenarios, thus providing efficiency and economy. This design has been implemented in version 4.0 of the OceanBase system, and the experiments show that Paetica exhibits notable scalability and outperforms alternative stand-alone or distributed databases. Furthermore, it enables the transition of OceanBase from primarily serving large enterprises to truly catering to small and medium enterprises, by employing a single OceanBase database for the successive stages of enterprise or business development, without the requirement for migration. Our experiments confirm that Paetica has achieved linear scalability with the increasing CPU core number within the stand-alone mode. It also outperforms MySQL and Greenplum in the Sysbench and TPC-H evaluations.