Struct fuel_merkle::sparse::MerkleTree

pub struct MerkleTree<TableType, StorageType> { /* private fields */ }

Implementations

impl<TableType, StorageType> MerkleTree<TableType, StorageType>

pub const fn empty_root() -> &'static Bytes32

pub fn root(&self) -> Bytes32

pub fn into_storage(self) -> StorageType

impl<TableType, StorageType, StorageError> MerkleTree<TableType, StorageType>where TableType: Mappable<Key = Bytes32, Value = Primitive, OwnedValue = Primitive>, StorageType: StorageInspect<TableType, Error = StorageError>,

pub fn new(storage: StorageType) -> Self

pub fn load( storage: StorageType, root: &Bytes32 ) -> Result<Self, MerkleTreeError<StorageError>>

impl<TableType, StorageType, StorageError> MerkleTree<TableType, StorageType>where TableType: Mappable<Key = Bytes32, Value = Primitive, OwnedValue = Primitive>, StorageType: StorageMutate<TableType, Error = StorageError>,

pub fn from_set<B, I, D>( storage: StorageType, set: I ) -> Result<Self, StorageError>where I: Iterator<Item = (B, D)>, B: Into<Bytes32>, D: AsRef<[u8]>,

Build a sparse Merkle tree from a set of key-value pairs. This is equivalent to creating an empty sparse Merkle tree and sequentially calling update for each key-value pair. This constructor is more performant than calling individual sequential updates and is the preferred approach when the key-values are known upfront. Leaves can be appended to the returned tree using update to further accumulate leaf data.

pub fn update( &mut self, key: MerkleTreeKey, data: &[u8] ) -> Result<(), MerkleTreeError<StorageError>>

pub fn delete( &mut self, key: MerkleTreeKey ) -> Result<(), MerkleTreeError<StorageError>>

Trait Implementations

impl<TableType: Debug, StorageType: Debug> Debug for MerkleTree<TableType, StorageType>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.