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    • A journey towards the most efficient state database for Hyperledger Fabric

    Original Research (Published On: 22-Oct-2023 )
    DOI : https://doi.org/10.54364/AAIML.2023.1188

    Vladimir Gorgadze

    Adv. Artif. Intell. Mach. Learn., 3 (4):1526-1556

    1. Vladimir Gorgadze: Moscow Institute of Physics and Technology

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    DOI: 10.54364/AAIML.2023.1188

    Article History: Received on: 22-May-23, Accepted on: 15-Oct-23, Published on: 22-Oct-23

    Corresponding Author: Vladimir Gorgadze

    Email: vladimir.gorgadze@phystech.edu

    Citation: Ivan Laishevskiy, et al. A journey towards the most efficient state database for Hyperledger Fabric. Advances in Artificial Intelligence and Machine Learning. 2023;3(4):88.


    Abstract

    Hyperledger Fabric is a leading permissioned
    blockchain platform known for its flexibility and customization.
    A crucial yet often overlooked component is its state database,
    which records the current state of blockchain applications. While
    the platform currently supports LevelDB and CouchDB, this
    study argues that there is an unmet need for exploring alternative
    databases to enhance performance and scalability. We evaluate
    RocksDB, Boltdb, and BadgerDB under various workloads,
    focusing on memory and CPU utilization. Our findings reveal
    that each alternative outperforms the existing options: RocksDB
    excels in throughput and latency, Boltdb minimizes CPU usage,
    and BadgerDB is most memory-efficient. This research not only
    provides a roadmap for integrating new state databases into
    Hyperledger Fabric but also offers critical insights for those
    aiming to optimize enterprise blockchain systems. The study
    underscores the significant gains in scalability and performance
    that can be achieved by reconsidering the choice of state database.

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