The widespread adoption of database middleware for supporting distributed transaction processing is prevalent in numerous applications, with heterogeneous data sources deployed across national and international boundaries. However, transaction processing performance significantly drops due to the high network latency between the middleware and data sources and the long lock contention span, where transactions may be blocked while waiting for the locks held by concurrent transactions. In this paper, we propose GeoTP, a latency-aware geo-distributed transaction processing approach in database middleware. GeoTP incorporates three key techniques to enhance performance in geo-distributed scenarios. First, we propose a decentralized prepare mechanism to reduce network round-trips for distributed transactions. Second, we design a latency-aware scheduler to minimize the lock contention span by strategically delaying the lock acquisition. Third, heuristic optimizations are proposed for the scheduler to reduce the lock contention span further. We implemented GeoTP on Apache Shardingsphere, a state-of-the-art middleware, and extended it into Apache ScalarDB. Experimental results on YCSB and TPC-C demonstrate that GeoTP achieves up to 17.7x performance improvement.

Achieving the serializable isolation level is costly. Recent studies have revealed that adjusting specific query patterns within the workload can still achieve serializability, even at lower isolation levels. Nevertheless, these studies typically overlook the trade-off between the performance advantages of lower isolation levels and the overhead required to maintain serializability, potentially leading to suboptimal isolation level choices that fail to maximize performance. In this paper, we present TxnSails, a middle-tier solution designed to achieve serializable scheduling with self-adaptive isolation level selection. First, TxnSails incorporates a unified concurrency control algorithm that achieves serializability at lower isolation levels with minimal overhead. Second, TxnSails employs a deep learning method to characterize the trade-off between the performance benefits and overhead associated with lower isolation levels, thus predicting the optimal isolation level. Finally, TxnSails implements a cross-isolation validation mechanism to ensure serializability during real-time isolation level transitions. Extensive experiments demonstrate that TxnSails outperforms existing solutions by up to 26.7× and PostgreSQL 's serializable isolation level by up to 4.8×.

In decentralized database systems, it is reported that serializability could still produce unexpected transaction orderings, leading to the stale read anomaly. To eliminate this anomaly, strict serializability imposes an additional ordering constraint, called the real-time order, which is required to be preserved among serializable transactions. Yet, preserving the real-time order in strict serializability often causes the performance to drop significantly. Because a weaker data consistency often yields better performance, in this paper, we model serializability from different consistency perspectives to properly leverage the performance and consistency. To do this, we first define a group of orderings, based on which we formulate multi-level serializability by preserving a certain set of ordering constraints among transactions. We then propose a bidirectional timestamp adjustment algorithm (abbreviated as BDTA) to support multi-level serializability with various optimizations. Our special design makes ordering constraints among transactions be preserved simply by adjusting timestamp intervals. Finally, we conduct extensive experiments to show the necessity of introducing multi-level serializability and confirm that BDTA achieves up to 1.19 × better performance than the state-of-the-art concurrency control algorithms.