Understanding the Scalability of Hyperledger Fabric
This addresses the need for understanding scalability in permissioned blockchain systems, but it is incremental as it updates analysis for a newer version of an existing system.
The paper analyzes the performance of Hyperledger Fabric v1.1 at scale, identifying that scalability bottlenecks are due to communication overhead between execution and ordering phases, and showing that scaling the Kafka cluster does not improve throughput.
The rapid growth of blockchain systems leads to increasing interest in understanding and comparing blockchain performance at scale. In this paper, we focus on analyzing the performance of Hyperledger Fabric v1.1 - one of the most popular permissioned blockchain systems. Prior works have analyzed Hyperledger Fabric v0.6 in depth, but newer versions of the system undergo significant changes that warrant new analysis. Existing works on benchmarking the system are limited in their scope: some consider only small networks, others consider scalability of only parts of the system instead of the whole. We perform a comprehensive performance analysis of Hyperledger Fabric v1.1 at scale. We extend an existing benchmarking tool to conduct experiments over many servers while scaling all important components of the system. Our results demonstrate that Fabric v1.1's scalability bottlenecks lie in the communication overhead between the execution and ordering phase. Furthermore, we show that scaling the Kafka cluster that is used for the ordering phase does not affect the overall throughput.