Exploiting Symbolic Execution to Accelerate Deterministic Databases

Abstract

Deterministic databases (DDs) are a promising approach for replicating data across different replicas. A fundamental component of DDs is a deterministic concurrency control algorithm that, given a set of transactions in a specific order, guarantees that their execution always results in the same serial order. State-of-the-art approaches either rely on single threaded execution or on the knowledge of read- and write-sets of transactions to achieve this goal. The former yields poor performance in multi-core machines while the latter requires either manual inputs from the user — a time-consuming and error prone task — or a reconnaissance phase that increases both the latency and abort rates of transactions.In this paper, we present Prognosticator, a novel deterministic database system. Rather than relying on manual transaction classification or an expert programmer, Prognosticator employs Symbolic Execution to build fine-grained transaction profiles (at the key-level). These profiles are then used by Prognosticator’s novel deterministic concurrency control algorithm to execute transactions with a high degree of parallelism.Our experimental evaluation, based on both TPC-C and RUBiS benchmarks, shows that Prognosticator can achieve up to 5× higher throughput with respect to state-of-the-art solutions.