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Untangling Blockchain: A Data Processing View of Blockchain Systems

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TLDR
This paper conducts a comprehensive evaluation of three major blockchain systems based on BLOCKBENCH, namely Ethereum, Parity, and Hyperledger Fabric, and discusses several research directions for bringing blockchain performance closer to the realm of databases.
Abstract
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.

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Citations
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Journal ArticleDOI

Blockchain: a literature review

TL;DR: A blockchain consists of an ordered list with nodes and links where the nodes store information and are connected through links called chains, which supports the availability of a public ledger.
Journal ArticleDOI

Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection

TL;DR: Wang et al. as discussed by the authors explored using graph neural networks and expert knowledge for smart contract vulnerability detection, which cast the rich control-and data-flow semantics of the source code into a contract graph, and designed a node elimination phase to normalize the graph.
Journal ArticleDOI

Analysis of Using Blockchain to Protect the Privacy of Drone Big Data

TL;DR: Wang et al. as discussed by the authors proposed a privacy protection scheme for UAV big data based on blockchain technology, which uses a number theory research unit cryptosystem for encryption of blockchain data.
Journal ArticleDOI

Smart contracts on the blockchain – A bibliometric analysis and review

TL;DR: This paper analyzes 468 articles on the topic of smart contracts, providing a summary and analysis of the current state of research on smart contracts and identifying intellectual structures and emerging trends, and derives starting points for future research.
Journal ArticleDOI

A bibliometric analysis and visualization of blockchain

TL;DR: A cluster analysis from the keyword perspective was carried out to obtain emerging trends and frontiers of blockchain research and showed that future research should concentrate on management, blockchain technology, energy, machine learning, and smart home.
References
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Book ChapterDOI

Short Signatures from the Weil Pairing

TL;DR: A short signature scheme based on the Computational Diffie-Hellman assumption on certain elliptic and hyperelliptic curves is introduced, designed for systems where signatures are typed in by a human or signatures are sent over a low-bandwidth channel.
Proceedings ArticleDOI

Practical Byzantine fault tolerance

TL;DR: A new replication algorithm that is able to tolerate Byzantine faults that works in asynchronous environments like the Internet and incorporates several important optimizations that improve the response time of previous algorithms by more than an order of magnitude.
Proceedings ArticleDOI

Benchmarking cloud serving systems with YCSB

TL;DR: This work presents the "Yahoo! Cloud Serving Benchmark" (YCSB) framework, with the goal of facilitating performance comparisons of the new generation of cloud data serving systems, and defines a core set of benchmarks and reports results for four widely used systems.
Proceedings Article

In search of an understandable consensus algorithm

TL;DR: Raft is a consensus algorithm for managing a replicated log that separates the key elements of consensus, such as leader election, log replication, and safety, and it enforces a stronger degree of coherency to reduce the number of states that must be considered.
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