Challenges and pitfalls of partitioning blockchains

TL;DR

This paper investigates the challenges of sharding in Ethereum by modeling it as a graph and evaluating five partitioning methods to understand impacts on scalability, transaction complexity, and data movement.

Contribution

It provides a systematic analysis of sharding techniques applied to Ethereum, highlighting potential pitfalls and considerations for improving blockchain scalability.

Findings

Sharding can improve scalability but introduces transaction complexity.

Partitioning methods vary in balance and data movement.

Repartitioning impacts data relocation and system performance.

Abstract

Blockchain has received much attention in recent years. This immense popularity has raised a number of concerns, scalability of blockchain systems being a common one. In this paper, we seek to understand how Ethereum, a well-established blockchain system, would respond to sharding. Sharding is a prevalent technique to increase the scalability of distributed systems. To understand how sharding would affect Ethereum, we model Ethereum blockchain as a graph and evaluate five methods to partition the graph. We analyze the results using three metrics: the balance among shards, the number of transactions that would involve multiple shards, and the amount of data that would be relocated across shards upon a repartitioning of the system.