Generative AI

Efficient Blockchain Region Management with Quick Merkle Database (QMDB)

Blockchain systems face significant challenges in managing and updating state storage due to increased write overhead (WA) and extensive I/O operations. In traditional structures, such as Merkle Patricia Tries (MPT), the common and expensive disk interaction causes inefficiencies that prevent throughput and scaling. Such issues are one of the major problems of decentralized applications that require high transaction rates and low infrastructure costs. Overcoming these limitations is critical to supporting broad adoption and unlocking the full potential of blockchain technology.

Existing blockchain state management methods, such as MPT, AVL Trees, and NOMT, suffer from inherent inefficiencies that limit their robustness and performance. MPTs are widely used because they can efficiently generate proofs, but they add overhead and require large DRAM to reduce SSD read overhead. AVL trees offer little improvement by relying on the structures they measure themselves, but rely on path-based operations, making them resource-intensive and unsuitable for real-time demands. NOMT, a new development, also improves the performance of flash storage in the Merkle tree operation but suffers from the problem of high write throughput and poorly distributed key holding. These limitations present significant obstacles to achieving a balance between scalability, efficiency, and effectiveness.

QMDB introduces a flexible way to manage state across the blockchain by combining key-value storage and Merkle tree functionality within a single architecture. This design addresses the inefficiencies of previous systems with a set of novel features. The twig-based compression mechanism significantly reduces memory requirements by compressing 2048 entries into a single hash and bitmap, achieving a 99.9% reduction in DRAM footprint. By using Merkleization of memory, QMDB eliminates the need for disk I/O during state updates, allowing the system to run smoothly even on consumer-grade hardware. Its append-only structure for state updates minimizes scripting while allowing efficient state changes. In addition, including the power of historical evidence facilitates the efficient verification and reconstruction of blockchain regions at any block height, thus improving the transparency and performance of established applications. This development, therefore, puts QMDB at the forefront as a highly efficient and potentially harmful alternative to traditional methods.

The system uses a binary Merkle tree design with fixed size branches and a modular index optimized for large datasets. An index that can handle only 2.3 bytes of DRAM per entry allows QMDB to scale to millions of entries with the highest throughput possible. Shading and a three-stage pipeline for prefetching, updating, and flushing have been implemented to make the most of available hardware resources and parallel processing. It also provides CRUD functionality with minimal interaction with the SSD to manage the state correctly across all hardware configuration variations. Technological advancements support QMDB to handle data sets much larger than Ethereum's projected world size of 2024 while being highly efficient on enterprise-grade and consumer hardware.

QMDB delivers dramatic improvements in blockchain state management, reaching up to 2.28 million updates per second and handling datasets with billions of entries. It consistently outperforms existing systems, delivering six times the performance of RocksDB and eight times that of NOMT, even when tested under extreme conditions. It is flexible and efficient: it can receive 150,000 updates per second in a low-cost consumer setup while reaching 280 billion entries on high-capacity servers. These results show the ability of QMDB to reduce the hardware barriers of blockchain participation, maintain the exclusive scaling and output, and hence it has been a great success in the creation of a decentralized system.

By addressing key inefficiencies in blockchain architecture, QMDB introduces a robust and scalable solution for managing updates and state maintenance. Its innovative approach, which includes twig-based compression, Merkleization of memory, and append-only status updates, redefines the limits of performance and efficiency in blockchain systems. These innovations reduce hardware requirements and allow wider participation in decentralized networks, paving the way for advanced applications that require high transaction rates and efficient state management. Its unique capabilities make QMDB a landmark, pushing all other benchmarks for scale and performance in the government management system on the blockchain.


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Aswin AK is a consultant at MarkTechPost. He is pursuing his Dual Degree at the Indian Institute of Technology, Kharagpur. He is passionate about data science and machine learning, which brings a strong academic background and practical experience in solving real-life domain challenges.

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