A Rust Merkle tree library with configurable storage backends and hash functions, optimized for fast proof generation.
rs-merkle-tree is a Rust library for implementing Merkle trees, which are cryptographic data structures used to verify the integrity of large datasets efficiently. It solves the problem of generating and verifying Merkle proofs quickly by storing intermediate leaves and offering configurable storage backends and hash functions. The library is designed for fixed-depth, append-only trees, making it suitable for applications where data consistency and proof performance are critical.
Developers building systems that require cryptographic data verification, such as blockchain applications, distributed databases, or zero-knowledge proof systems. It is particularly useful for Rust developers who need a flexible and high-performance Merkle tree implementation.
Developers choose rs-merkle-tree for its fast proof generation, modular design allowing customization of storage and hashing, and its optimized append-only structure. It stands out by providing benchmarks for performance comparisons and supporting multiple storage backends like sled, RocksDB, and SQLite.
Merkle tree implementation in Rust with configurable storage backends and hash functions. Fixed depth and incremental only. Optimized for fast proof generation.
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Fixed depth ensures all proofs have a consistent size equal to the tree depth, simplifying verification and scaling for large datasets, as highlighted in the features.
Stores intermediate leaves, enabling Merkle proof generation in microseconds; benchmarks show proof times as low as 560.990 ns with memory store.
Supports configurable storage backends (sled, RocksDB, SQLite) and hash functions (Keccak256, Poseidon), allowing customization for specific cryptographic needs.
Leaves are added sequentially and cannot be modified, ensuring data integrity for applications like audit logs or blockchain, as per the philosophy.
The tree does not support updating or deleting leaves once added, limiting its use in scenarios requiring data modifications, which the README explicitly states as append-only.
Benchmarks indicate significant disk usage, e.g., 290 MiB for 1 million leaves with sled store, which could be prohibitive for storage-sensitive deployments.
Configuring different storage backends requires enabling specific Cargo features, adding setup complexity and potential dependency conflicts for beginners.