Survey: sharding in blockchains
Open accessAbstract
The Blockchain technology, featured with its decentralized tamper-resistance based on a Peer-to-Peer network, has been widely applied in financial applications, and even further been extended to industrial applications. However, the weak scalability of traditional Blockchain technology severely affects the wide adoption due to the well-known trillema of decentralization-security-scalability in Blockchains. In regards to this issue, a number of solutions have been proposed, targeting to boost the scalability while preserving the decentralization and security. They range from modifying the on-chain data structure and consensus algorithms to adding the off-chain technologies. Therein, one of the most practical methods to achieve horizontal scalability along with the increasing network size is sharding, by partitioning network into multiple shards so that the overhead of duplicating communication, storage, and computation in each full node can be avoided. This paper presents a survey focusing on sharding in Blockchains in a systematic and comprehensive way. We provide detailed comparison and quantitative evaluation of major sharding mechanisms, along with our insights analyzing the features and restrictions of the existing solutions. We also provide theoretical upper-bound of the throughput for each considered sharding mechanism. The remaining challenges and future research directions are also reviewed.
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Sharding partitions a P2P network to improve blockchain's performance.
“Sharding [75] that partitions a P2P network is introduced to improve blockchain's performance.”
From A survey on the adoption of blockchain in IoT: challenges and solutions (2021) · cites this paper as [75]
Researchers are working on innovative solutions, like sharding and layer-2 scalability solutions, to improve transaction throughput and make blockchain scalable.
“To improve transaction throughput and make blockchain scalable, researchers are working on innovative solutions, like sharding [23] and layer-2 scalability solutions [24].”
From The Role of Blockchain to Fight Against COVID-19 (2020) · cites this paper as [24]
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- Authors
- Yu, G., Wang, X., Yu, K., Ni, W., Zhang, J. A., & Liu, R. P.
- Affiliations
- University of Technology Sydney (AU)
- La Trobe University (AU)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 14155-14181
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2998762116
- Cited by
- 267
- References
- 93
- Citations by year
- 2026: 21 · 2025: 43 · 2024: 50 · 2023: 59 · 2022: 44 · 2021: 33 · 2020: 16 · 2019: 1
- FWCI
- 35.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UTS ePRESS (University of Technology Sydney) (submittedVersion, other-oa)
- Funders
- Food Agility CRC Ltd., through the Commonwealth Government CRC Program
- UCOT Australia Pty Ltd
- 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
- blockchain sharding, blockchain scalability, horizontal scaling, throughput upper bound, consensus algorithms, peer-to-peer networks
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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