Improving High Contention OLTP Performance via Transaction Scheduling

TL;DR

This paper introduces Strife, a transaction scheduling protocol that clusters conflict-free transactions to improve high-contention OLTP performance, achieving up to twice the throughput of existing protocols.

Contribution

The paper proposes a novel batching and clustering approach for transaction execution that significantly enhances throughput under high contention workloads.

Findings

Strife doubles throughput compared to optimistic concurrency control.

Clustering reduces conflicts and improves parallel execution.

Performance gains are validated through micro-benchmark analysis.

Abstract

Research in transaction processing has made significant progress in improving the performance of multi-core in-memory transactional systems. However, the focus has mainly been on low-contention workloads. Modern transactional systems perform poorly on workloads with transactions accessing a few highly contended data items. We observe that most transactional workloads, including those with high contention, can be divided into clusters of data conflict-free transactions and a small set of residuals. In this paper, we introduce a new concurrency control protocol called Strife that leverages the above observation. Strife executes transactions in batches, where each batch is partitioned into clusters of conflict-free transactions and a small set of residual transactions. The conflict-free clusters are executed in parallel without any concurrency control, followed by executing the residual cluster either serially or with concurrency control. We present a low-overhead algorithm that partitions a batch of transactions into clusters that do not have cross-cluster conflicts and a small residual cluster. We evaluate Strife against the optimistic concurrency control protocol and several variants of two-phase locking, where the latter is known to perform better than other concurrency protocols under high contention, and show that Strife can improve transactional throughput by up to 2x. We also perform an in-depth micro-benchmark analysis to empirically characterize the performance and quality of our clustering algorithm