The foundation of blockchain technology lies in the complexities of data structures that play a crucial role in shaping the blockchain’s functionality, ensuring that transactions are secure, verifiable, and tamper-resistant. Among the mass data structures in blockchain, four key players stand out: the Merkle Tree, Merkle Patricia Tree, Merkle Bucket Tree, and the Storage State.
The Merkle Tree
At the heart of blockchain’s data structure arsenal stands the venerable Merkle Tree, a fundamental concept that underpins the very essence of blockchain security. The Merkle Tree derives its name from the renowned computer scientist Ralph Merkle and is sometimes affectionately referred to as the Hash Tree for a good reason.
The Merkle Tree is a hierarchical structure where the non-leaf nodes’ values are calculated based on the values of their child leaf nodes. Typically, the leaf nodes store hash values of data blocks, making the Merkle Tree a guardian of data integrity. Any alteration in the data block would necessitate a recalculation of the hash values in the affected branches, instantly exposing foul play.
Bitcoin, the pioneer in the blockchain world, relies on the Merkle Tree for securing transactions and ensuring the immutability of its blockchain.
Merkle Patricia Tree
Merkle Patricia Tree has a hybrid data structure that marries the best of both Merkle and Prefix Trees. This creation combines the robustness of the Merkle Tree, the speed of the Trie Tree, and the efficiency of the Patricia Tree.
Merkle Patricia Tree, often referred to as “Patricia Trie” or “Patricia Merkle Trie”, integrates the essence of multiple data structures, as it employs Merkle Tree for security, Trie Tree for fast retrieval, and Patricia Tree for space efficiency.
Ethereum, the leading smart contract platform, adopts the Merkle Patricia Tree to store account information and ensure efficient state transitions within the Ethereum Virtual Machine (EVM).
Merkle Bucket Tree
The blockchain world is ever-creative, continually seeking new ways to optimize performance and security, so the Merkle Bucket Tree emerges as a hybrid that capitalizes on the strengths of both Merkle Trees and Hash tables.
The Merkle Bucket Tree aims to minimize the computational cost incurred by recalculating hashes after each change in the tree’s state. By exploiting the inherent characteristics of Merkle Trees, this structure optimizes performance, making it a potent choice for certain blockchain applications.
Merkle Bucket Trees find their place in scenarios where performance optimization is crucial. They strike a balance between security and efficiency, making them an appealing choice for specific blockchain ecosystems.
The trade-offs are common in the blockchain world, and one such trade-off is embodied in the Storage State, as its data structure prioritizes performance over historical data traceability, making it an ideal candidate for federated chains.
Storage State does not support querying historical data states or maintain a recorded history of changes, since it forgoes partial traceability in favor of heightened performance, aligning with the goals of federated chains that prioritize scalability and speed.
Federated chains, driven by a performance-oriented ethos, often adopt Storage State as it aligns with their objectives. It caters to use cases where historical data retention takes a back seat to real-time performance.