Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform
Open accessAbstract
The rise in popularity of permissioned blockchain platforms in recent time is significant. Hyperledger Fabric is one such permissioned blockchain platform and one of the Hyperledger projects hosted by the Linux Foundation. The Fabric comprises various components such as smart-contracts, endorsers, committers, validators, and orderers. As the performance of blockchain platform is a major concern for enterprise applications, in this work, we perform a comprehensive empirical study to characterize the performance of Hyperledger Fabric and identify potential performance bottlenecks to gain a better understanding of the system. We follow a two-phased approach. In the first phase, our goal is to understand the impact of various configuration parameters such as block size, endorsement policy, channels, resource allocation, state database choice on the transaction throughput & latency to provide various guidelines on configuring these parameters. In addition, we also aim to identify performance bottlenecks and hotspots. We observed that (1) endorsement policy verification, (2) sequential policy validation of transactions in a block, and (3) state validation and commit (with CouchDB) were the three major bottlenecks. In the second phase, we focus on optimizing Hyperledger Fabric v1.0 based on our observations. We introduced and studied various simple optimizations such as aggressive caching for endorsement policy verification in the cryptography component (3x improvement in the performance) and parallelizing endorsement policy verification (7x improvement). Further, we enhanced and measured the effect of an existing bulk read/write optimization for CouchDB during state validation & commit phase (2.5x improvement). By combining all three optimizations1, we improved the overall throughput by 16x (i.e., from 140 tps to 2250 tps).
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In 2016, blockchain platform Hyperledger Fabric introduced multi-ledger based architecture for private network.
“In 2016, blockchain platform Hyperledger Fabric [50,51] introduced multi-ledger based architecture for private network, as shown in Figure 7.”
From A Review of Blockchain Architecture and Consensus Protocols: Use Cases, Challenges, and Solutions (2019) · cites this paper as [51]
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State of Public and Private Blockchains
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Blockchain-based automated and robust cyber security management
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- Authors
- Thakkar, P., Nathan, S., & Viswanathan, B.
- Affiliations
- National Institute of Technology Tiruchirappalli (IN)
- IBM Research - India (IN)
- Venue
- 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)
- Publisher
- IEEE
- Published
- 2018-09
- Pages
- 264-276
- ISSN
- 1526-7539 (ISSN-L)
- OpenAlex
- W2806281946
- Cited by
- 632
- References
- 15
- Citations by year
- 2026: 45 · 2025: 49 · 2024: 49 · 2023: 79 · 2022: 97 · 2021: 118 · 2020: 107 · 2019: 84 · 2018: 4
- FWCI
- 61.41
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Keywords
- Hyperledger Fabric, permissioned blockchain, blockchain performance, performance bottlenecks, transaction throughput, latency, CouchDB, caching
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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