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// Copyright (c) The Diem Core Contributors
// SPDX-License-Identifier: Apache-2.0
//! This module implements an in-memory Sparse Merkle Tree that is similar to what we use in
//! storage to represent world state. This tree will store only a small portion of the state -- the
//! part of accounts that have been modified by uncommitted transactions. For example, if we
//! execute a transaction T_i on top of committed state and it modified account A, we will end up
//! having the following tree:
//! ```text
//! S_i
//! / \
//! o y
//! / \
//! x A
//! ```
//! where A has the new state of the account, and y and x are the siblings on the path from root to
//! A in the tree.
//!
//! This Sparse Merkle Tree is immutable once constructed. If the next transaction T_{i+1} modified
//! another account B that lives in the subtree at y, a new tree will be constructed and the
//! structure will look like the following:
//! ```text
//! S_i S_{i+1}
//! / \ / \
//! / y / \
//! / _______/ \
//! // \
//! o y'
//! / \ / \
//! x A z B
//! ```
//!
//! Using this structure, we are able to query the global state, taking into account the output of
//! uncommitted transactions. For example, if we want to execute another transaction T_{i+1}', we
//! can use the tree S_i. If we look for account A, we can find its new value in the tree.
//! Otherwise, we know the account does not exist in the tree, and we can fall back to storage. As
//! another example, if we want to execute transaction T_{i+2}, we can use the tree S_{i+1} that
//! has updated values for both account A and B.
//!
//! Each version of the tree holds a strong reference (an Arc<Node>) to its root as well as one to
//! its base tree (S_i is the base tree of S_{i+1} in the above example). The root node in turn,
//! recursively holds all descendant nodes created in the same version, and weak references
//! (a Weak<Node>) to all descendant nodes that was created from previous versions.
//! With this construction:
//! 1. Even if a reference to a specific tree is dropped, the nodes belonging to it won't be
//! dropped as long as trees depending on it still hold strong references to it via the chain of
//! "base trees".
//! 2. Even if a tree is not dropped, when nodes it created are persisted to DB, all of them
//! and those created by its previous versions can be dropped, which we express by calling "prune()"
//! on it which replaces the strong references to its root and its base tree with weak references.
//! 3. We can hold strong references to recently accessed nodes that have already been persisted
//! in an LRU flavor cache for less DB reads.
//!
//! This Sparse Merkle Tree serves a dual purpose. First, to support a leader based consensus
//! algorithm, we need to build a tree of transactions like the following:
//! ```text
//! Committed -> T5 -> T6 -> T7
//! └---> T6' -> T7'
//! └----> T7"
//! ```
//! Once T5 is executed, we will have a tree that stores the modified portion of the state. Later
//! when we execute T6 on top of T5, the output of T5 can be visible to T6.
//!
//! Second, given this tree representation it is straightforward to compute the root hash of S_i
//! once T_i is executed. This allows us to verify the proofs we need when executing T_{i+1}.
// See https://play.rust-lang.org/?version=stable&mode=debug&edition=2018&gist=e9c4c53eb80b30d09112fcfb07d481e7
#![allow(clippy::let_and_return)]
// See https://play.rust-lang.org/?version=stable&mode=debug&edition=2018&gist=795cd4f459f1d4a0005a99650726834b
#![allow(clippy::while_let_loop)]
mod node;
mod updater;
mod utils;
#[cfg(test)]
mod sparse_merkle_test;
#[cfg(any(test, feature = "bench", feature = "fuzzing"))]
pub mod test_utils;
use crate::sparse_merkle::{
node::{Node, SubTree},
updater::SubTreeUpdater,
utils::{partition, swap_if},
};
use diem_crypto::{
hash::{CryptoHash, SPARSE_MERKLE_PLACEHOLDER_HASH},
HashValue,
};
use diem_infallible::Mutex;
use diem_types::{
nibble::{nibble_path::NibblePath, ROOT_NIBBLE_HEIGHT},
proof::{SparseMerkleInternalNode, SparseMerkleLeafNode, SparseMerkleProof},
};
use std::{
borrow::Borrow,
cmp,
collections::{BTreeMap, BTreeSet, HashMap},
sync::Arc,
};
/// `AccountStatus` describes the result of querying an account from this SparseMerkleTree.
#[derive(Debug, Eq, PartialEq)]
pub enum AccountStatus<V> {
/// The account exists in the tree, therefore we can give its value.
ExistsInScratchPad(V),
/// The account does not exist in the tree, but exists in DB. This happens when the search
/// reaches a leaf node that has the requested account, but the node has only the value hash
/// because it was loaded into memory as part of a non-inclusion proof. When we go to DB we
/// don't need to traverse the tree to find the same leaf, instead we can use the value hash to
/// look up the account content directly.
ExistsInDB,
/// The account does not exist in either the tree or DB. This happens when the search reaches
/// an empty node, or a leaf node that has a different account.
DoesNotExist,
/// We do not know if this account exists or not and need to go to DB to find out. This happens
/// when the search reaches a subtree node.
Unknown,
}
/// The inner content of a sparse merkle tree, we have this so that even if a tree is dropped, the
/// INNER of it can still live if referenced by a previous version.
#[derive(Debug)]
struct Inner<V> {
root: SubTree<V>,
children: Mutex<Vec<Arc<Inner<V>>>>,
}
impl<V> Drop for Inner<V> {
fn drop(&mut self) {
let mut q: Vec<_> = self.children.lock().drain(..).collect();
while let Some(descendant) = q.pop() {
if Arc::strong_count(&descendant) == 1 {
// The only ref is the one we are now holding, so the structure will be dropped
// after we free the `Arc`, which results in a chain of such structures being
// dropped recursively and that might trigger a stack overflow. To prevent that we
// follow the chain further to disconnect things beforehand.
q.extend(descendant.children.lock().drain(..));
}
}
}
}
impl<V> Inner<V> {
fn new(root: SubTree<V>) -> Arc<Self> {
Arc::new(Self {
root,
children: Mutex::new(Vec::new()),
})
}
fn spawn(&self, child_root: SubTree<V>) -> Arc<Self> {
let child = Self::new(child_root);
self.children.lock().push(child.clone());
child
}
}
/// The Sparse Merkle Tree implementation.
#[derive(Clone, Debug)]
pub struct SparseMerkleTree<V> {
inner: Arc<Inner<V>>,
}
/// A type for tracking intermediate hashes at sparse merkle tree nodes in between batch
/// updates by transactions. It contains tuple (txn_id, hash_value, single_new_leaf), where
/// hash_value is the value after all the updates by transaction txn_id (txn_id-th batch)
/// and single_new_leaf is a bool that's true if the node subtree contains one new leaf.
/// (this is needed to recursively merge IntermediateHashes).
type IntermediateHashes = Vec<(usize, HashValue, bool)>;
impl<V> SparseMerkleTree<V>
where
V: Clone + CryptoHash + Send + Sync,
{
/// Constructs a Sparse Merkle Tree with a root hash. This is often used when we restart and
/// the scratch pad and the storage have identical state, so we use a single root hash to
/// represent the entire state.
pub fn new(root_hash: HashValue) -> Self {
let root = if root_hash != *SPARSE_MERKLE_PLACEHOLDER_HASH {
SubTree::new_unknown(root_hash)
} else {
SubTree::new_empty()
};
Self {
inner: Inner::new(root),
}
}
fn spawn(&self, child_root: SubTree<V>) -> Self {
Self {
inner: self.inner.spawn(child_root),
}
}
#[cfg(test)]
fn new_with_root(root: SubTree<V>) -> Self {
Self {
inner: Inner::new(root),
}
}
fn root_weak(&self) -> SubTree<V> {
self.inner.root.weak()
}
/// Constructs a new Sparse Merkle Tree as if we are updating the existing tree multiple
/// times with the `batch_update`. The function will return the root hash after each
/// update and a Sparse Merkle Tree of the final state.
///
/// The `serial_update` applies `batch_update' method many times, unlike a more optimized
/// (and parallelizable) `batches_update' implementation below. It takes in a reference of
/// value instead of an owned instance to be consistent with the `batches_update' interface.
pub fn serial_update(
&self,
update_batch: Vec<Vec<(HashValue, &V)>>,
proof_reader: &impl ProofRead<V>,
) -> Result<(Vec<(HashValue, HashMap<NibblePath, HashValue>)>, Self), UpdateError> {
let mut current_state_tree = self.clone();
let mut result = Vec::with_capacity(update_batch.len());
for updates in update_batch {
// sort and dedup the accounts
let accounts = updates
.iter()
.map(|(account, _)| *account)
.collect::<BTreeSet<_>>()
.into_iter()
.collect::<Vec<_>>();
current_state_tree = current_state_tree.batch_update(updates, proof_reader)?;
result.push((
current_state_tree.root_hash(),
current_state_tree.generate_node_hashes(accounts),
));
}
Ok((result, current_state_tree))
}
/// This is a helper function that compares an updated in-memory sparse merkle with the
/// current on-disk jellyfish sparse merkle to get the hashes of newly generated nodes.
pub fn generate_node_hashes(
&self,
// must be sorted
touched_accounts: Vec<HashValue>,
) -> HashMap<NibblePath, HashValue> {
let mut node_hashes = HashMap::new();
let mut nibble_path = NibblePath::new(vec![]);
Self::collect_new_hashes(
touched_accounts.as_slice(),
self.root_weak(),
0, /* depth in nibble */
0, /* level within a nibble*/
&mut nibble_path,
&mut node_hashes,
);
node_hashes
}
/// Recursively generate the partial node update batch of jellyfish merkle
fn collect_new_hashes(
keys: &[HashValue],
subtree: SubTree<V>,
depth_in_nibble: usize,
level_within_nibble: usize,
cur_nibble_path: &mut NibblePath,
node_hashes: &mut HashMap<NibblePath, HashValue>,
) {
assert!(depth_in_nibble <= ROOT_NIBBLE_HEIGHT);
if keys.is_empty() {
return;
}
if level_within_nibble == 0 {
if depth_in_nibble != 0 {
cur_nibble_path
.push(NibblePath::new(keys[0].to_vec()).get_nibble(depth_in_nibble - 1));
}
node_hashes.insert(cur_nibble_path.clone(), subtree.hash());
}
match subtree.get_node_if_in_mem().expect("must exist").borrow() {
Node::Internal(internal_node) => {
let (next_nibble_depth, next_level_within_nibble) = if level_within_nibble == 3 {
(depth_in_nibble + 1, 0)
} else {
(depth_in_nibble, level_within_nibble + 1)
};
let pivot = partition(
&keys.iter().map(|k| (*k, ())).collect::<Vec<_>>()[..],
depth_in_nibble * 4 + level_within_nibble,
);
Self::collect_new_hashes(
&keys[..pivot],
internal_node.left.weak(),
next_nibble_depth,
next_level_within_nibble,
cur_nibble_path,
node_hashes,
);
Self::collect_new_hashes(
&keys[pivot..],
internal_node.right.weak(),
next_nibble_depth,
next_level_within_nibble,
cur_nibble_path,
node_hashes,
);
}
Node::Leaf(leaf_node) => {
assert_eq!(keys.len(), 1);
assert_eq!(keys[0], leaf_node.key);
if level_within_nibble != 0 {
let mut leaf_nibble_path = cur_nibble_path.clone();
leaf_nibble_path
.push(NibblePath::new(keys[0].to_vec()).get_nibble(depth_in_nibble));
node_hashes.insert(leaf_nibble_path, subtree.hash());
}
}
}
if level_within_nibble == 0 && depth_in_nibble != 0 {
cur_nibble_path.pop();
}
}
/// Constructs a new Sparse Merkle Tree, returns the SMT root hash after each update and the
/// final SMT root. Since the tree is immutable, existing tree remains the same and may
/// share parts with the new, returned tree. Unlike `serial_update', intermediate trees aren't
/// constructed, but only root hashes are computed. `batches_update' takes value reference
/// because the algorithm requires a copy per value at the end of tree traversals. Taking
/// in a reference avoids double copy (by the caller and by the implementation).
pub fn batches_update(
&self,
update_batch: Vec<Vec<(HashValue, &V)>>,
proof_reader: &impl ProofRead<V>,
) -> Result<(Vec<HashValue>, Self), UpdateError> {
let num_txns = update_batch.len();
if num_txns == 0 {
// No updates.
return Ok((vec![], self.clone()));
}
// Construct (key, txn_id, value) update vector, where 0 <= txn_id < update_batch.len().
// The entries are sorted and deduplicated, keeping last for each key per batch (txn).
let updates: Vec<(HashValue, (usize, &V))> = update_batch
.into_iter()
.enumerate()
.flat_map(|(txn_id, batch)| {
batch
.into_iter()
.map(move |(hash, value)| ((hash, txn_id), value))
})
.collect::<BTreeMap<_, _>>()
.into_iter()
.map(|((hash, txn_id), value)| (hash, (txn_id, value))) // convert format.
.collect();
let root_weak = self.root_weak();
let mut pre_hash = root_weak.hash();
let (root, txn_hashes) = Self::batches_update_subtree(
root_weak,
/* subtree_depth = */ 0,
&updates[..],
proof_reader,
)?;
// Convert txn_hashes to the output format, i.e. a Vec<HashValue> holding a hash value
// after each of the update_batch.len() many transactions.
// - For transactions with no updates (i.e. root hash unchanged), txn_hashes don't have
// entries. So an updated hash value (txn_hashes.0) that remained the same after some
// transactions should be added to the result multiple times.
// - If the first transactions didn't update, then pre-hash needs to be replicated.
let mut txn_id = 0;
let mut root_hashes = vec![];
for txn_hash in &txn_hashes {
while txn_id < txn_hash.0 {
root_hashes.push(pre_hash);
txn_id += 1;
}
pre_hash = txn_hash.1;
}
while txn_id < num_txns {
root_hashes.push(pre_hash);
txn_id += 1;
}
Ok((root_hashes, self.spawn(root)))
}
/// Given an existing subtree node at a specific depth, recursively apply the updates.
fn batches_update_subtree(
subtree: SubTree<V>,
subtree_depth: usize,
updates: &[(HashValue, (usize, &V))],
proof_reader: &impl ProofRead<V>,
) -> Result<(SubTree<V>, IntermediateHashes), UpdateError> {
if updates.is_empty() {
return Ok((subtree, vec![]));
}
if let SubTree::NonEmpty { root, .. } = &subtree {
match root.get_if_in_mem() {
Some(arc_node) => match arc_node.borrow() {
Node::Internal(internal_node) => {
let pivot = partition(updates, subtree_depth);
let left_weak = internal_node.left.weak();
let left_hash = left_weak.hash();
let right_weak = internal_node.right.weak();
let right_hash = right_weak.hash();
// TODO: parallelize calls up to a certain depth.
let (left_tree, left_hashes) = Self::batches_update_subtree(
left_weak,
subtree_depth + 1,
&updates[..pivot],
proof_reader,
)?;
let (right_tree, right_hashes) = Self::batches_update_subtree(
right_weak,
subtree_depth + 1,
&updates[pivot..],
proof_reader,
)?;
let merged_hashes = Self::merge_txn_hashes(
left_hash,
left_hashes,
right_hash,
right_hashes,
);
Ok((SubTree::new_internal(left_tree, right_tree), merged_hashes))
}
Node::Leaf(leaf_node) => Self::batch_create_subtree(
subtree.weak(), // 'root' is upgraded: OK to pass weak ptr.
/* target_key = */ leaf_node.key,
/* siblings = */ vec![],
subtree_depth,
updates,
proof_reader,
),
},
// Subtree with hash only, need to use proofs.
None => {
let (subtree, hashes, _) = Self::batch_create_subtree_by_proof(
updates,
proof_reader,
subtree.hash(),
subtree_depth,
*SPARSE_MERKLE_PLACEHOLDER_HASH,
)?;
Ok((subtree, hashes))
}
}
} else {
// Subtree was empty.
Self::batch_create_subtree(
subtree.weak(), // 'root' is upgraded: OK to pass weak ptr.
/* target_key = */ updates[0].0,
/* siblings = */ vec![],
subtree_depth,
updates,
proof_reader,
)
}
}
/// Generate a proof based on the first update and call 'batch_create_subtree' based
/// on the proof's siblings and possibly a leaf. Additionally return the sibling hash of
/// the subtree based on the proof (caller needs this information to merge hashes).
fn batch_create_subtree_by_proof(
updates: &[(HashValue, (usize, &V))],
proof_reader: &impl ProofRead<V>,
subtree_hash: HashValue,
subtree_depth: usize,
default_sibling_hash: HashValue,
) -> Result<(SubTree<V>, IntermediateHashes, HashValue), UpdateError> {
if updates.is_empty() {
return Ok((
SubTree::new_unknown(subtree_hash),
vec![],
default_sibling_hash,
));
}
let update_key = updates[0].0;
let proof = proof_reader
.get_proof(update_key)
.ok_or(UpdateError::MissingProof)?;
let siblings: Vec<HashValue> = proof.siblings().iter().rev().copied().collect();
let sibling_hash = if subtree_depth > 0 {
*siblings
.get(subtree_depth - 1)
.unwrap_or(&SPARSE_MERKLE_PLACEHOLDER_HASH)
} else {
default_sibling_hash
};
let (subtree, hashes) = match proof.leaf() {
Some(existing_leaf) => Self::batch_create_subtree(
SubTree::new_leaf_with_value_hash(existing_leaf.key(), existing_leaf.value_hash()),
/* target_key = */ existing_leaf.key(),
siblings,
subtree_depth,
updates,
proof_reader,
)?,
None => Self::batch_create_subtree(
SubTree::new_empty(),
/* target_key = */ update_key,
siblings,
subtree_depth,
updates,
proof_reader,
)?,
};
Ok((subtree, hashes, sibling_hash))
}
/// Creates a new subtree. Important parameters are:
/// - 'bottom_subtree' will be added at the bottom of the construction. It is either empty
/// or a leaf, containing either (a weak pointer to) a node from the previous version
/// that's being re-used, or (a strong pointer to) a leaf from a proof.
/// - 'target_key' is the key of the bottom_subtree when bottom_subtree is a leaf, o.w. it
/// is the key of the first (leftmost) update.
/// - 'siblings' are the siblings if bottom_subtree is a proof leaf, otherwise empty.
fn batch_create_subtree(
bottom_subtree: SubTree<V>,
target_key: HashValue,
siblings: Vec<HashValue>,
subtree_depth: usize,
updates: &[(HashValue, (usize, &V))],
proof_reader: &impl ProofRead<V>,
) -> Result<(SubTree<V>, IntermediateHashes), UpdateError> {
if updates.is_empty() {
return Ok((bottom_subtree, vec![]));
}
if siblings.len() <= subtree_depth {
if let Some(res) = Self::leaf_from_updates(target_key, updates) {
return Ok(res);
}
}
let pivot = partition(updates, subtree_depth);
let child_is_right = target_key.bit(subtree_depth);
let (child_updates, sibling_updates) =
swap_if(&updates[..pivot], &updates[pivot..], child_is_right);
let mut child_pre_hash = bottom_subtree.hash();
let sibling_pre_hash = *siblings
.get(subtree_depth)
.unwrap_or(&SPARSE_MERKLE_PLACEHOLDER_HASH);
// TODO: parallelize up to certain depth.
let (sibling_tree, sibling_hashes) = if siblings.len() <= subtree_depth {
// Implies sibling_pre_hash is empty.
if sibling_updates.is_empty() {
(SubTree::new_empty(), vec![])
} else {
Self::batch_create_subtree(
SubTree::new_empty(),
/* target_key = */ sibling_updates[0].0,
/* siblings = */ vec![],
subtree_depth + 1,
sibling_updates,
proof_reader,
)?
}
} else {
// Only have the sibling hash, need to use proofs.
let (subtree, hashes, child_hash) = Self::batch_create_subtree_by_proof(
sibling_updates,
proof_reader,
sibling_pre_hash,
subtree_depth + 1,
child_pre_hash,
)?;
child_pre_hash = child_hash;
(subtree, hashes)
};
let (child_tree, child_hashes) = Self::batch_create_subtree(
bottom_subtree,
target_key,
siblings,
subtree_depth + 1,
child_updates,
proof_reader,
)?;
let (left_tree, right_tree) = swap_if(child_tree, sibling_tree, child_is_right);
let (left_hashes, right_hashes) = swap_if(child_hashes, sibling_hashes, child_is_right);
let (left_pre_hash, right_pre_hash) =
swap_if(child_pre_hash, sibling_pre_hash, child_is_right);
let merged_hashes =
Self::merge_txn_hashes(left_pre_hash, left_hashes, right_pre_hash, right_hashes);
Ok((SubTree::new_internal(left_tree, right_tree), merged_hashes))
}
/// Given a key and updates, checks if all updates are to this key. If so, generates
/// a SubTree for a final leaf, and IntermediateHashes. Each intermediate update is by
/// a different transaction as (key, txn_id) pairs are deduplicated.
fn leaf_from_updates(
leaf_key: HashValue,
updates: &[(HashValue, (usize, &V))],
) -> Option<(SubTree<V>, IntermediateHashes)> {
let first_update = updates.first().unwrap();
let last_update = updates.last().unwrap();
// Updates sorted by key: check that all keys are equal to leaf_key.
if first_update.0 != leaf_key || last_update.0 != leaf_key {
return None;
};
// Updates are to the same key and thus sorted by txn_id.
let mut hashes: IntermediateHashes = updates
.iter()
.take(updates.len() - 1)
.map(|&(_, (txn_id, value_ref))| {
let value_hash = value_ref.hash();
let leaf_hash = SparseMerkleLeafNode::new(leaf_key, value_hash).hash();
(txn_id, leaf_hash, /* single_new_leaf = */ true)
})
.collect();
let final_leaf =
SubTree::new_leaf_with_value(leaf_key, last_update.1 .1.clone() /* value */);
hashes.push((
last_update.1 .0, /* txn_id */
final_leaf.hash(),
/* single_new_leaf = */ true,
));
Some((final_leaf, hashes))
}
/// Given the hashes before updates, and IntermediateHashes for left and right Subtrees,
/// compute IntermediateHashes for the parent node.
fn merge_txn_hashes(
left_pre_hash: HashValue,
left_txn_hashes: IntermediateHashes,
right_pre_hash: HashValue,
right_txn_hashes: IntermediateHashes,
) -> IntermediateHashes {
let (mut li, mut ri) = (0, 0);
// Some lambda expressions for convenience.
let next_txn_num = |i: usize, txn_hashes: &Vec<(usize, HashValue, bool)>| {
if i < txn_hashes.len() {
txn_hashes[i].0
} else {
usize::MAX
}
};
let left_prev_txn_hash = |i: usize| {
if i > 0 {
left_txn_hashes[i - 1].1
} else {
left_pre_hash
}
};
let right_prev_txn_hash = |i: usize| {
if i > 0 {
right_txn_hashes[i - 1].1
} else {
right_pre_hash
}
};
let mut to_hash = vec![];
while li < left_txn_hashes.len() || ri < right_txn_hashes.len() {
let left_txn_num = next_txn_num(li, &left_txn_hashes);
let right_txn_num = next_txn_num(ri, &right_txn_hashes);
if left_txn_num <= right_txn_num {
li += 1;
}
if right_txn_num <= left_txn_num {
ri += 1;
}
// If one child was empty (based on previous hash) while the other child was
// a single new leaf node, then the parent hash mustn't be combined. Instead,
// it should be the single leaf hash (the leaf would have been added aerlier).
let override_hash = if li > 0
&& left_txn_hashes[li - 1].2
&& ri == 0
&& right_pre_hash == *SPARSE_MERKLE_PLACEHOLDER_HASH
{
Some(left_prev_txn_hash(li))
} else if ri > 0
&& right_txn_hashes[ri - 1].2
&& li == 0
&& left_pre_hash == *SPARSE_MERKLE_PLACEHOLDER_HASH
{
Some(right_prev_txn_hash(ri))
} else {
None
};
to_hash.push((
cmp::min(left_txn_num, right_txn_num),
left_prev_txn_hash(li),
right_prev_txn_hash(ri),
override_hash,
));
}
// TODO: parallelize w. par_iter.
to_hash
.iter()
.map(|&(txn_num, left_hash, right_hash, override_hash)| {
(
txn_num,
match override_hash {
Some(hash) => hash,
None => SparseMerkleInternalNode::new(left_hash, right_hash).hash(),
},
override_hash.is_some(),
)
})
.collect()
}
/// Queries a `key` in this `SparseMerkleTree`.
pub fn get(&self, key: HashValue) -> AccountStatus<V> {
let mut cur = self.root_weak();
let mut bits = key.iter_bits();
loop {
if let Some(node) = cur.get_node_if_in_mem() {
if let Node::Internal(internal_node) = node.borrow() {
match bits.next() {
Some(bit) => {
cur = if bit {
internal_node.right.weak()
} else {
internal_node.left.weak()
};
continue;
}
None => panic!("Tree is deeper than {} levels.", HashValue::LENGTH_IN_BITS),
}
}
}
break;
}
let ret = match cur {
SubTree::Empty => AccountStatus::DoesNotExist,
SubTree::NonEmpty { root, .. } => match root.get_if_in_mem() {
None => AccountStatus::Unknown,
Some(node) => match node.borrow() {
Node::Internal(_) => {
unreachable!("There is an internal node at the bottom of the tree.")
}
Node::Leaf(leaf_node) => {
if leaf_node.key == key {
match &leaf_node.value.data.get_if_in_mem() {
Some(value) => {
AccountStatus::ExistsInScratchPad(value.as_ref().clone())
}
None => AccountStatus::ExistsInDB,
}
} else {
AccountStatus::DoesNotExist
}
}
},
},
};
ret
}
/// Constructs a new Sparse Merkle Tree by applying `updates`, which are considered to happen
/// all at once. See `serial_update` and `batches_update` which take in multiple batches
/// of updates and yields intermediate results.
/// Since the tree is immutable, existing tree remains the same and may share parts with the
/// new, returned tree.
pub fn batch_update(
&self,
updates: Vec<(HashValue, &V)>,
proof_reader: &impl ProofRead<V>,
) -> Result<Self, UpdateError> {
// Flatten, dedup and sort the updates with a btree map since the updates between different
// versions may overlap on the same address in which case the latter always overwrites.
let kvs = updates
.into_iter()
.collect::<BTreeMap<_, _>>()
.into_iter()
.collect::<Vec<_>>();
let current_root = self.root_weak();
if kvs.is_empty() {
Ok(self.clone())
} else {
let root = SubTreeUpdater::update(current_root, &kvs[..], proof_reader)?;
Ok(self.spawn(root))
}
}
/// Returns the root hash of this tree.
pub fn root_hash(&self) -> HashValue {
self.inner.root.hash()
}
}
impl<V> Default for SparseMerkleTree<V>
where
V: Clone + CryptoHash + Send + Sync,
{
fn default() -> Self {
SparseMerkleTree::new(*SPARSE_MERKLE_PLACEHOLDER_HASH)
}
}
/// A type that implements `ProofRead` can provide proof for keys in persistent storage.
pub trait ProofRead<V>: Sync {
/// Gets verified proof for this key in persistent storage.
fn get_proof(&self, key: HashValue) -> Option<&SparseMerkleProof<V>>;
}
/// All errors `update` can possibly return.
#[derive(Debug, Eq, PartialEq)]
pub enum UpdateError {
/// The update intends to insert a key that does not exist in the tree, so the operation needs
/// proof to get more information about the tree, but no proof is provided.
MissingProof,
/// At `depth` a persisted subtree was encountered and a proof was requested to assist finding
/// details about the subtree, but the result proof indicates the subtree is empty.
ShortProof {
key: HashValue,
num_siblings: usize,
depth: usize,
},
}