BlockExplorer: Exploring Blockchain Big Data Via Parallel Processing

Shipeng Li, Jingwei Li, Yuxing Tang, Xiapu Luo, Zheyuan He, Zihao Li, Xi Cheng, Yang Bai, Ting Chen, Yuzhe Tang, Zhe Liu, Xiaosong Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)


Today's blockchain systems store detailed runtime information in the format of transactions and blocks, which are valuable not only to understand the finance of blockchain-based ecosystems but also to audit the security of on-chain applications. However, exploring this blockchain “Big Data” is challenging due to data heterogeneity and the huge amount. Existing blockchain exploration techniques are either incomplete or inefficient, making them inapt in time-sensitive applications. This paper presents BlockExplorer , an efficient and flexible blockchain exploration system for Ethereum. BlockExplorer builds on a master-slave architecture, where the master partitions all blocks into multiple non-overlapped sets and each slave simultaneously processes Ethereum Big Data based on a set of blocks. BlockExplorer implements a transaction-based partitioning approach to address load balance among slaves, and a code instrumentation approach to acquire complete Ethereum Big Data. The evaluation shows that BlockExplorer accelerates the data acquisition performance of the state-of-the-art by 4.1×, while the workload difference among slaves is up to 18%. To demonstrate the application of BlockExplorer , we develop three apps upon BlockExplorer to detect real-life attacks against Ethereum and show that our apps can detect attacks in a large range of blocks (e.g., ten million) within a short time (e.g., multiple hours).
Original languageEnglish
Pages (from-to)2377 - 2389
JournalIEEE Transactions on Computers
Issue number8
Publication statusPublished - 23 Feb 2023


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