Blockchains vs. Distributed Databases: Dichotomy and Fusion

TL;DR

This paper compares blockchains and distributed databases across key design dimensions, analyzing how their core goals influence their architecture and performance, and proposes a framework for hybrid system forecasting.

Contribution

It introduces a taxonomy of blockchain and database systems based on four dimensions and provides an analytical framework for understanding their differences and potential fusion.

Findings

Design choices are driven by security in blockchains and performance in databases.

Performance analysis of Quorum, Hyperledger Fabric, TiDB, and etcd reveals key differences.

A framework for predicting hybrid blockchain-database system performance is proposed.

Abstract

Blockchain has come a long way: a system that was initially proposed specifically for cryptocurrencies is now being adapted and adopted as a general-purpose transactional system. As blockchain evolves into another data management system, the natural question is how it compares against distributed database systems. Existing works on this comparison focus on high-level properties, such as security and throughput. They stop short of showing how the underlying design choices contribute to the overall differences. Our work fills this important gap and provides a principled framework for analyzing the emerging trend of blockchain-database fusion. We perform a twin study of blockchains and distributed database systems as two types of transactional systems. We propose a taxonomy that illustrates the dichotomy across four dimensions, namely replication, concurrency, storage, and sharding. Within each dimension, we discuss how the design choices are driven by two goals: security for blockchains, and performance for distributed databases. To expose the impact of different design choices on the overall performance, we conduct an in-depth performance analysis of two blockchains, namely Quorum and Hyperledger Fabric, and two distributed databases, namely TiDB, and etcd. Lastly, we propose a framework for back-of-the-envelope performance forecast of blockchain-database hybrids.