# Untangling Blockchain: A Data Processing View of Blockchain Systems

arXiv: https://arxiv.org/abs/1708.05665 · HTML: https://mlsanity.com/paper/1708.05665/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2017.

## Built on by (most cited first, 8 of 8)

- [Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2107.11598/) (2021)
- [Detecting Various DeFi Price Manipulations with LLM Reasoning](https://mlsanity.com/paper/2502.11521/) (2025)
- [Reinforcement Learning-Based Cooperative P2P Power Trading between DC Nanogrid Clusters with Wind and PV Energy Resources](https://mlsanity.com/paper/2209.07744/) (2022)
- [Global Public Sentiment on Decentralized Finance: A Spatiotemporal Analysis of Geo-tagged Tweets from 150 Countries](https://mlsanity.com/paper/2409.00843/) (2024)
- [Graph Neural Networks Enhanced Smart Contract Vulnerability Detection of Educational Blockchain](https://mlsanity.com/paper/2303.04477/) (2023)
- [Building a Knowledge Graph of Distributed Ledger Technologies](https://mlsanity.com/paper/2303.16528/) (2023)
- [Improving the Accuracy of Transaction-Based Ponzi Detection on Ethereum](https://mlsanity.com/paper/2308.16391/) (2023)
- [A Semantic Link Network Model for Supporting Traceability of Logistics on Blockchain](https://mlsanity.com/paper/2503.08717/) (2025)

## Abstract (arXiv)

Blockchain technologies are gaining massive momentum in the last few years. Blockchains are distributed ledgers that enable parties who do not fully trust each other to maintain a set of global states. The parties agree on the existence, values and histories of the states. As the technology landscape is expanding rapidly, it is both important and challenging to have a firm grasp of what the core technologies have to offer, especially with respect to their data processing capabilities. In this paper, we first survey the state of the art, focusing on private blockchains (in which parties are authenticated). We analyze both in-production and research systems in four dimensions: distributed ledger, cryptography, consensus protocol and smart contract. We then present BLOCKBENCH, a benchmarking framework for understanding performance of private blockchains against data processing workloads. We conduct a comprehensive evaluation of three major blockchain systems based on BLOCKBENCH, namely Ethereum, Parity and Hyperledger Fabric. The results demonstrate several trade-offs in the design space, as well as big performance gaps between blockchain and database systems. Drawing from design principles of database systems, we discuss several research directions for bringing blockchain performance closer to the realm of databases.