A Survey on the Integration of Blockchains and Databases

Introduction

Blockchain technology emerged into public awareness with the release of the Bitcoin white paper in 2008. Since then, its influence has expanded rapidly, powering countless cryptocurrencies and decentralized applications. At its core, a blockchain is a novel data management system maintained collectively by multiple participants—offering unique advantages in data integrity, security, and transparency.

Unlike traditional databases, blockchains operate under decentralized models and provide strong guarantees even when some participants behave maliciously. These properties include:

• Decentralization: With no central authority, every node maintains a full copy of the ledger. This eliminates single points of failure and protects against data loss from centralized outages or attacks.
• Immutability: Once data is recorded and confirmed by consensus, it becomes nearly impossible to alter. Each block is cryptographically linked to the previous one, forming an unbreakable chain.
• Tamper-Proofing: Blocks contain cryptographic proofs that ensure any unauthorized modification can be instantly detected during validation.
• Provenance: Every change to the data is logged as a new transaction, preserving a complete audit trail and enabling full traceability of historical states.

Despite these strengths, blockchains face significant limitations in performance, scalability, and privacy:

• Performance Bottlenecks: Transactions are processed sequentially, limiting throughput. For example, Bitcoin handles only about 7 transactions per second—far below commercial databases that manage tens of thousands.
• High Resource Consumption: The append-only nature of blockchains leads to ever-growing storage demands. Additionally, proof-of-work consensus wastes substantial computational power.
• Privacy Challenges: All nodes store complete data copies for verification, making it difficult to restrict access to sensitive information—a major concern for enterprise use.

These trade-offs highlight a critical insight: neither blockchains nor traditional databases alone can meet all modern data management needs. However, their underlying concepts—such as transaction processing, state changes, and indexing—are surprisingly aligned. Smart contracts resemble stored procedures; both systems support ACID properties; and indexing serves query efficiency in databases and verifiability in blockchains.

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This synergy has sparked growing interest in integrating both technologies into hybrid systems that combine security, speed, and usability. While several surveys have explored blockchain applications in specific domains like storage or sharding, few offer a comprehensive analysis of this integration trend. Our goal is to fill this gap by mapping the full landscape of blockchain-database fusion.

Key Contributions

This article presents:

• A blockchain-database spectrum framework to categorize integration efforts into three models: database-oriented blockchains, blockchain-oriented databases, and hybrid systems.
• In-depth reviews of representative systems within each model.
• Evaluation of core techniques used across these systems.
• Identification of current challenges and future research directions.

The rest of this survey is structured as follows: Section 2 introduces foundational concepts and the proposed spectrum. Sections 3–5 analyze each fusion model in detail. Section 6 compares these systems and discusses open challenges. Finally, Section 7 concludes with insights for researchers and practitioners.

Preliminaries

Blockchain Fundamentals

A blockchain integrates multiple technologies—peer-to-peer networking, cryptography, consensus protocols, and efficient data structures—into a secure, distributed ledger. Inspired by Bitcoin, most blockchains follow a linked-list structure where blocks are chained using cryptographic hashes. Each block contains a header (with metadata like timestamp and previous hash) and a body (containing transactions).

Blockchains are typically layered into five components:

1. Data Layer: Manages data structures, transaction models, indexes, and persistent storage.
2. Network Layer: Uses P2P protocols for node communication.
3. Consensus Layer: Ensures agreement among untrusted nodes via algorithms like PBFT or Proof-of-Stake.
4. Contract Layer: Hosts smart contracts and programmable logic.
5. Application Layer: Provides APIs for building decentralized apps.

There are two main types of blockchains:

• Permissionless (Public): Open to anyone (e.g., Bitcoin, Ethereum). Ethereum enhances functionality with Turing-complete smart contracts via the EVM.
• Permissioned (Private): Requires authorization to join (e.g., Hyperledger Fabric). These often use efficient consensus mechanisms like Raft and support high-level programming languages.

Recent innovations focus on improving scalability:

• Consensus Enhancements: Protocols like SBFT and FastBFT reduce message complexity and improve fault tolerance.
• Concurrency Control: Systems like Fabric++ and XOX Fabric optimize transaction execution to reduce abort rates and boost throughput.

Database Overview

Databases have evolved over decades to deliver high performance, complex querying, and robust transaction support. Major categories include:

• SQL Databases (e.g., MySQL, Oracle): Support structured data with ACID compliance and SQL-based querying.
• NoSQL Databases (e.g., MongoDB, Redis): Offer horizontal scalability for semi-structured or unstructured data across various models (key-value, document, graph).
• NewSQL Databases (e.g., CockroachDB, TiDB): Combine SQL capabilities with NoSQL scalability through distributed architectures.

While databases excel in speed and usability, they lack native immutability and decentralized trust—gaps that blockchain can fill.

The Blockchain-Database Spectrum

To better understand integration efforts, we introduce the blockchain-database spectrum, positioning pure blockchains at the security end and traditional databases at the performance end. Between them lie three types of fusion systems:

1. Database-Oriented Blockchains: Built on blockchain foundations but enhanced with database features (e.g., indexing, sharding).
2. Blockchain-Oriented Databases: Traditional databases augmented with blockchain-like immutability and verifiability.
3. Hybrid Systems: Middleware-based combinations that link separate blockchain and database instances for balanced functionality.

This framework enables systematic comparison of design trade-offs across security, performance, and usability dimensions.

Database-Oriented Blockchains

These systems start from blockchain architecture but integrate database techniques to enhance usability. Early examples include MedRec for medical records and IoT management platforms using Ethereum smart contracts.

Modern approaches aim to improve:

• Throughput via sharding and concurrency
• Query efficiency via indexing
• Developer experience via SQL-like interfaces
• Privacy via access controls

Indexing Innovations

Indexes in blockchain must not only accelerate queries but also verify data integrity. Techniques include:

• SEBDB uses B⁺ trees and bitmap indexes to enable SQL-like operations including joins.
• AuthQX leverages Trusted Execution Environments (TEEs) to securely cache index nodes.
• LineageChain introduces Merkle DAGs and skip lists for efficient historical state tracking.
• vChain/vChain+ employ accumulator-based authenticated data structures (ADS) for verifiable range queries.

Protocol Optimizations

Two key strategies enhance performance:

Sharding

Splitting the network into partitions allows parallel transaction processing:

• Elastico pioneered sharded consensus using PBFT.
• OmniLedger ensures atomicity in cross-shard transactions via 2PC.
• RapidChain reduces communication overhead with erasure coding.
• BrokerChain uses virtual sub-accounts to minimize inter-shard dependencies.

Concurrency

Parallel execution improves utilization:

• Hyperledger Fabric uses execute-order-validate pipelines.
• SChain enables intra- and inter-block parallelism through dependency analysis.
• PEPP introduces deterministic locking for conflict-free parallel updates.

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Data Models & APIs

To improve developer adoption:

• SEBDB and FalconDB implement relational semantics with SQL support.
• BlockchainDB exposes simple key-value APIs (put, get, verify).
• EtherQL offers RESTful endpoints for easy integration.

Ledger Privacy Enhancements

To protect sensitive data:

• Adkins et al.’s encrypted multi-maps allow secure add/update/delete operations.
• LedgerView applies database-style views with revocable encryption keys.
• CAPER uses DAG-based ledgers where applications maintain private views while sharing public hashes.

Blockchain-Oriented Databases

These systems begin with traditional databases and add blockchain features for integrity verification.

Blockchain Middleware

Lightweight layers added atop existing databases:

• TRDB logs hash digests of database entries onto a blockchain for tamper detection.
• Beirami et al.’s approach adds blockchain-style fields (e.g., hash pointers) directly into relational tables for verifiable immutability.

Blockchain Layer Integration

Deeper modifications inside the database engine:

• Blockchain PG extends PostgreSQL with append-only storage and authenticated queries.
• BigchainDB connects RethinkDB instances with a custom consensus algorithm.
• chainifyDB introduces "Whatever-Voting" model to unify heterogeneous databases under a shared consensus layer.

Hybrid Systems

These combine separate blockchain and database instances via middleware:

• GraphChain: Links Neo4j (graph DB) with Exonum (blockchain) for verifiable graph operations.
• Personal Data Manager: Uses blockchain to control access to de-identified data stored in BigchainDB.
• ChainSQL: Logs transactions on-chain while storing actual data off-chain for fast queries.
• MOON: Dynamically routes queries based on whether data should reside on-chain (immutable) or off-chain (frequently updated).

Challenges and Future Directions

Performance

Despite improvements, fusion systems still lag behind commercial databases. Key opportunities:

• Advanced indexing for on-chain data
• High-concurrency consensus protocols
• Conflict-aware transaction scheduling

Privacy

Balancing transparency with confidentiality remains challenging. Promising paths:

• Off-chain storage of sensitive data
• View-based access control
• Cryptographic techniques like zero-knowledge proofs

Data Modeling

Support beyond key-value and relational models is limited. Future work should explore:

• Native integration with graph databases
• Document-centric blockchain designs
• Time-series data handling

Hardware Acceleration

Emerging hardware offers untapped potential:

• TEEs (e.g., Intel SGX) for secure execution
• GPUs for parallel transaction processing
• FPGAs for optimized hashing

Learning-Based Optimization

Machine learning can enhance:

• Sharding strategies based on real data patterns
• Anomaly detection in node behavior
• Smart contract vulnerability analysis

Domain-Specific Applications

Tailored solutions are needed for industries like:

• Finance: High-throughput trading systems
• Healthcare: Secure patient record sharing
• Supply Chain: Verifiable product traceability

Conclusion

The convergence of blockchains and databases represents a transformative shift in data management. By combining immutability with high performance, fusion systems unlock new possibilities across sectors. This survey has mapped the landscape through a unified spectrum model, reviewed key architectures, and identified critical research frontiers—from privacy-preserving designs to AI-driven optimizations.

As technology evolves, the line between blockchains and databases will continue to blur—ushering in a new era of secure, scalable, and intelligent data platforms.

Frequently Asked Questions (FAQ)

Q: What is the main difference between a blockchain and a traditional database?
A: Blockchains are decentralized, immutable ledgers secured by cryptography and consensus, whereas traditional databases are typically centralized, mutable systems optimized for speed and complex queries.

Q: Why integrate blockchains with databases?
A: Integration combines the security and transparency of blockchains with the performance and usability of databases—creating systems that are both trustworthy and efficient.

Q: Are hybrid blockchain-database systems more secure than standalone ones?
A: They offer balanced security: metadata and logs benefit from blockchain immutability, while off-chain data gains from database-level access controls—though careful design is needed to avoid redundancy.

Q: Can I run SQL queries on blockchain data?
A: Yes—systems like SEBDB and SQL-Middleware enable SQL-like querying over blockchain-stored data using specialized indexing and translation layers.

Q: How do fusion systems handle scalability?
A: Through techniques like sharding, parallel execution, off-chain computation, and hardware acceleration—many borrowed from database research.

Q: Where are blockchain-database hybrids being used today?
A: In healthcare (patient records), finance (auditable transactions), supply chains (provenance tracking), and identity management—any domain requiring both trust and performance.

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