This library implements the Merkle tree data structure, which can be used for Merkle proofs, block verification, etc.
This project mainly for learning and reference. Note that the code is not optimized for production use, Note that content field of tree node definition should be removed to avoid performance issue. get_proof can be improved by providing leaf index. You need to carefully review the code and test it before using it in production.
- Build and validate Merkle trees
- Node class supports snapshotting and copying
- Verify root hash matches block hash
- Get block location and proof in the tree
- Print tree structure
import merkle
# Build Merkle tree
values = [b'value1', b'value2', ...]
tree = MerkleTree(values, hash_fn)
# Verify block is in tree
root_hash = tree.root.value
block_hash = b'value1'
tree.verify_block(root_hash, block_hash)
# Get block location and proof
location = tree.get_location(block_hash)
proof = tree.get_proof(block_hash)- Initialize tree structure
__init__(self, values: List[bytes], hash_fn: HashFn) -> None - Get root node snapshot
@property root(self) -> Node - Compare tree structures
compare_trees(self, other: "MerkleTree") -> bool - Block verification
verify_block(self, root_hash: bytes, block_hash: bytes) -> bool - Get block location
get_location(self, block_hash: bytes) -> Optional[List[int]] - Get block proof
get_proof(self, block_hash: bytes) -> Optional[List[bytes]] - Print tree structure
print_tree(self, brief: bool = True) -> None
- Define node attributes
__init__(self, left: Optional["Node"], right: Optional["Node"], value: bytes, content: bytes, is_copied: bool = False) - Copy nodes
copy(self) -> "Node"
Contributions welcome! Please check code comments and tests for improvements.
MIT