[Paper Review] Database Perspectives on Blockchains

Sara Cohen, Aviv Zohar|arXiv (Cornell University)|Mar 15, 2018

Data Management and Algorithms8 references3 citations

TL;DR

This paper establishes formal theoretical foundations for blockchains as a data storage layer by analyzing two core challenges: (1) determining whether denial constraints can be violated given pending transactions and blockchain state, and (2) generating transactions that are consistent with or separable from subsets of pending transactions. The key contribution is a complexity-theoretic framework for ensuring data consistency and user intent in decentralized, consensus-driven environments.

ABSTRACT

Modern blockchain systems are a fresh look at the paradigm of distributed computing, applied under assumptions of large-scale public networks. They can be used to store and share information without a trusted central party. There has been much effort to develop blockchain systems for a myriad of uses, ranging from cryptocurrencies to identity control, supply chain management, etc. None of this work has directly studied the fundamental database issues that arise when using blockchains as the underlying infrastructure to manage data. The key difference between using blockchains to store data and centrally controlled databases is that transactions are accepted to a blockchain via a consensus mechanism. Hence, once a user has issued a transaction, she cannot be certain if it will be accepted. Moreover, a yet unaccepted transaction cannot be retracted by the user, and may be appended to the blockchain in the future. This causes difficulties as the user may wish to reissue a transaction, if it was not accepted. Yet this data may then become appended twice to the blockchain. In this paper we present a database perspective on blockchains by introducing formal foundations for blockchains as a storage layer that underlies a database. The main issue that we tackle is uncertainty in transaction appending that is a result of the consensus mechanism. We study two flavors of transaction appending problems: (1) the complexity of determining whether it is possible for a denial constraint to be contradicted, given the state of the blockchain, pending transactions, and integrity constraints and (2) the complexity of generating transactions that are mutually (in)consistent with given subsets of pending transactions. Solving these problems is critical to ensure that users can issue transactions consistent with their intentions. Finally, we chart important directions for future work.

Motivation & Objective

• To address the fundamental challenge of transaction uncertainty in blockchains, where users cannot guarantee acceptance or retraction of pending transactions.
• To formalize the data consistency layer of blockchains as a foundation for database systems, independent of specific consensus protocols.
• To develop theoretical tools for ensuring that issued transactions do not violate integrity constraints or conflict with pending ones.
• To enable safe, automated transaction generation that respects user intent and avoids unintended data states.
• To identify open research problems in blockchain-based database management, especially around consistency, scalability, and user-level control.

Proposed method

• Formalizing a blockchain database as a system with a consensus layer and a data consistency layer, where the latter governs legal data states and evolution.
• Introducing the problem of denial constraint satisfaction: determining whether a constraint can be violated given current blockchain state and pending transactions.
• Proposing the generation of $({ m T}_{ m in}, { m T}_{ m out})$-separating transactions to prevent conflicts with existing pending transactions.
• Analyzing the computational complexity of both problems—denial constraint satisfaction and transaction separation—under various database constraints.
• Extending the framework to consider user-defined constraints on generated transactions, such as query-based or domain-specific requirements.
• Applying the model to real-world scenarios like off-chain channels (e.g., Lightning Network) and commutative transaction sets to assess scalability and consistency.

Experimental results

Research questions

• RQ1 What is the computational complexity of determining whether a denial constraint can be violated by any future state of the blockchain, given current data and pending transactions?
• RQ2 How can we algorithmically generate transactions that are mutually consistent with a given subset of pending transactions while avoiding conflicts with others?
• RQ3 What is the complexity of generating transactions under user-defined constraints, such as query conditions or domain-specific rules?
• RQ4 How can the proposed framework be extended to support advanced protocols like the Lightning Network, where off-chain transactions must be secured against malicious intermediate states?
• RQ5 What are the theoretical foundations for identifying commutative transaction sets that can be processed in any order, improving consensus efficiency?

Key findings

• The problem of denial constraint satisfaction in blockchains is shown to be NP-complete, indicating inherent computational difficulty in ensuring data integrity under uncertainty.
• The generation of $({ m T}_{ m in}, { m T}_{ m out})$-separating transactions is also computationally hard, placing it in the NP-hard complexity class, highlighting the challenge of safe transaction composition.
• The framework enables formal reasoning about possible future states of the blockchain, allowing users to verify whether a transaction is safe to issue without violating constraints.
• The model provides a theoretical basis for building tools that automatically generate transactions that avoid conflicts with pending ones, preserving user intent.
• The analysis reveals that off-chain protocols like the Lightning Network rely on similar consistency reasoning, suggesting shared theoretical foundations with on-chain database problems.
• The paper identifies key open problems in blockchain database management, including constrained transaction generation, commutative transaction detection, and advanced unspent output management in Bitcoin wallets.