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Towards Scalable Blockchain Analysis

Abstract : Analysing the blockchain is becoming more and more relevant for detecting attacks and frauds on cryptocurrency exchanges and smart contract activations. However, this is a challenging task due to the continuous growth of the blockchain. For example, in early 2017 Ethereum was estimated to contain approximately 300GB of data [4], a number that keeps growing day after day. In order to analyse such ever-growing amount of data, this paper argues that blockchain analysis should be treated as a novel type of application for Big Data platforms. We also explore the application of parallelization techniques from the Big Data domain, in particular Map/Reduce, to extract and analyse information from the blockchain. We show that our approach significantly improves the index generation by 7.77 times, with a setup of 20 worker nodes, 1 Ethereum node and 1 Database node. We also share our findings of our massively parallel setup for querying Ethereum in terms of architecture and the bottlenecks. This should help researchers setup similar infrastructures for analysing the blockchain in the future.
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https://hal.inria.fr/hal-02058008
Contributor : Santiago Bragagnolo <>
Submitted on : Thursday, October 15, 2020 - 2:25:26 PM
Last modification on : Saturday, October 17, 2020 - 3:05:11 AM

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  • HAL Id : hal-02058008, version 1

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Santiago Bragagnolo, Matteo Marra, Guillermo Polito, Elisa Gonzalez Boix. Towards Scalable Blockchain Analysis. WETSEB 2019 - 2nd International Workshop on Emerging Trends in Software Engineering for Blockchain, May 2019, Montréal, Canada. ⟨hal-02058008⟩

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