Improving transaction processing performance by consensus reduction

Transaction and high availability are both important to applications. While data partition, distribution and replication are the three key mechanisms to guarantee high availability, a coordination to reach consensuses on replica state transitions, transaction operation orders and commit decisions is required for transaction processing. This coordination impairs transaction processing performance. In classic transactional approaches, lock granularity is exploited to trade off consistency for performance under weaker isolation levels. We question whether transaction processing performance can be similarly improved under weaker isolation levels by consensus reduction. To carry out consensus reduction, we categorize transactions based on their scopes of consensus, as well as their requirements on consensus. A transaction in one category can be reduced to multiple transactions in other categories with smaller consensus scope and weaker consensus requirement. We theoretically analyze what anomalies the reduction can lead to. We thus find and define eight isolation levels by anomaly sets. We experiment to find out how these weaker isolation levels can improve transaction processing performance. Interesting results show that three weak isolation levels improve performance, while the weakest isolation level has the worst performance. Results in this paper enable users to actively choose the appropriate isolation levels for their applications.