Repairing Serializability Bugs in Distributed Database Programs via Automated Schema Refactoring

TL;DR

This paper introduces an automated schema refactoring method that transforms database data layouts to eliminate serializability bugs, improving performance and reducing concurrency issues without changing concurrency control logic.

Contribution

It presents a novel automated schema refactoring technique that removes serializability bugs in distributed databases by modifying data layout, not concurrency control, enhancing performance and correctness.

Findings

120% higher throughput on average

45% lower latency compared to baselines

Effective in eliminating concurrency bugs

Abstract

Serializability is a well-understood concurrency control mechanism that eases reasoning about highly-concurrent database programs. Unfortunately, enforcing serializability has a high-performance cost, especially on geographically distributed database clusters. Consequently, many databases allow programmers to choose when a transaction must be executed under serializability, with the expectation that transactions would only be so marked when necessary to avoid serious concurrency bugs. However, this is a significant burden to impose on developers, requiring them to (a) reason about subtle concurrent interactions among potentially interfering transactions, (b) determine when such interactions would violate desired invariants, and (c) then identify the minimum number of transactions whose executions should be serialized to prevent these violations. To mitigate this burden, in this paper we present a sound and fully automated schema refactoring procedure that transforms a program's data layout -- rather than its concurrency control logic -- to eliminate statically identified concurrency bugs, allowing more transactions to be safely executed under weaker and more performant database guarantees. Experimental results over a range of database benchmarks indicate that our approach is highly effective in eliminating concurrency bugs, with safe refactored programs showing an average of 120% higher throughput and 45% lower latency compared to the baselines.