nomos-specs/deprecated/carnot/tree_overlay.py

206 lines
8.7 KiB
Python

import itertools
from hashlib import blake2b
from typing import List, Dict, Tuple, Set, Optional, Self
from carnot.carnot import Id, Committee
from carnot.overlay import EntropyOverlay
import random
def blake2b_hash(committee: Committee) -> bytes:
hasher = blake2b(digest_size=32)
for member in sorted(committee):
hasher.update(member)
return hasher.digest()
def fisher_yates_shuffle(nodes: List[Id], entropy: bytes):
"""
Fisher-yates shuffling algorithm
It is the one used by python by default
https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle
https://softwareengineering.stackexchange.com/a/215780
:param nodes:
:param entropy:
:return:
"""
random.seed(a=entropy, version=2)
random.shuffle(nodes)
class CarnotTree:
"""
This balanced binary tree implementation uses a combination of indexes and keys to easily calculate parenting
committee relationships. It also has caching on different kind of access to conveniently retrieve the committees
based on:
* Member of a committee
* Committee id (hash)
It is composed of `inner_committees`, an array that matches a binary tree node distribution:
0, 1, 2, 3..
[c0, c1, c2, c3 ]
where `cX` is the committee id (hash of the set with the committee members ids)
The number of leafs in the committee is calculated with:
total_leafs = (len(inner_committees) + 1) // 2
Parenting relation can be calculated for a committee index (idx) with:
parent_committee_idx = committee_idx // 2 - 1
Children relation is calculated with those indexes (idx) as well:
left_child, right_child = (committee_idx*2 + 1, committee_idx*2 + 2)
Then we have some dictionaries/maps that matches different information to those indexes:
* `membership_committees`: matches committee idx to the actual committee set of participants
* `committee_id_to_index`: matches committee id (hash) to committee index (idx) in `inner_committees`
* `committee_by_member`: matches member id to the committee id that is a member from
"""
def __init__(self, nodes: List[Id], number_of_committees: int):
# useless to build an overlay with no committees
assert number_of_committees > 0
# inner_committees: list of tree nodes (int index) matching hashed external committee id
self.inner_committees: List[Id]
# membership committees: matching committee idx to the set of members of a committee
self.membership_committees: Dict[int, Committee]
self.inner_committees, self.membership_committees = (
CarnotTree.build_committee_from_nodes_with_size(
nodes, number_of_committees
)
)
# committee match between tree nodes and external hashed ids
self.committee_id_to_index: Dict[Id, int] = {c: i for i, c in enumerate(self.inner_committees)}
# id (int index) of committee membership by member id
self.committees_by_member: Dict[Id, int] = {
member: committee
for committee, v in self.membership_committees.items()
for member in v
}
@staticmethod
def build_committee_from_nodes_with_size(
nodes: List[Id],
number_of_committees: int,
) -> Tuple[List[Id], Dict[int, Committee]]:
committee_size, remainder = divmod(len(nodes), number_of_committees)
committees = [
set(nodes[n*committee_size:(n+1)*committee_size])
for n in range(0, number_of_committees)
]
# refill committees with extra nodes,
if remainder != 0:
cycling_committees = itertools.cycle(committees)
for node in nodes[-remainder:]:
next(cycling_committees).add(node)
hashes = [blake2b_hash(s) for s in committees]
committees = [frozenset(s) for s in committees]
return hashes, dict(enumerate(committees))
def parent_committee(self, committee_id: Id) -> Optional[Id]:
# root committee doesnt have a parent
if committee_id == self.inner_committees[0]:
return None
return self.inner_committees[max(self.committee_id_to_index[committee_id] // 2 - 1, 0)]
def child_committees(self, committee_id: Id) -> Tuple[Optional[Id], Optional[Id]]:
base = self.committee_id_to_index[committee_id] * 2
committees_size = len(self.inner_committees)
first_child = base + 1
second_child = base + 2
first_child = self.inner_committees[first_child] if first_child < committees_size else None
second_child = self.inner_committees[second_child] if second_child < committees_size else None
return first_child, second_child
def leaf_committees(self) -> Dict[Id, Committee]:
total_leafs = (len(self.inner_committees) + 1) // 2
return {
self.inner_committees[i]: self.membership_committees[i]
for i in range(len(self.inner_committees) - total_leafs, len(self.inner_committees))
}
def root_committee(self) -> Committee:
return self.membership_committees[0]
def committee_by_committee_idx(self, committee_idx: int) -> Optional[Committee]:
return self.membership_committees.get(committee_idx)
def committee_idx_by_member_id(self, member_id: Id) -> Optional[int]:
return self.committees_by_member.get(member_id)
def committee_id_by_member_id(self, member_id: Id) -> Id:
return self.inner_committees[self.committees_by_member.get(member_id)]
def committee_by_member_id(self, member_id: Id) -> Optional[Committee]:
if (committee_idx := self.committee_idx_by_member_id(member_id)) is not None:
return self.committee_by_committee_idx(committee_idx)
def committee_by_committee_id(self, committee_id: Id) -> Optional[Committee]:
if (committee_idx := self.committee_id_to_index.get(committee_id)) is not None:
return self.committee_by_committee_idx(committee_idx)
def parent_committee_from_member_id(self, _id):
if (parent_id := self.parent_committee(
self.committee_id_by_member_id(_id)
)) is not None:
return self.committee_by_committee_idx(self.committee_id_to_index[parent_id])
class CarnotOverlay(EntropyOverlay):
def __init__(self, nodes: List[Id], current_leader: Id, entropy: bytes, number_of_committees: int):
self.entropy = entropy
self.number_of_committees = number_of_committees
self.nodes = nodes.copy()
self.current_leader = current_leader
fisher_yates_shuffle(self.nodes, self.entropy)
self.carnot_tree = CarnotTree(nodes, number_of_committees)
def advance(self, entropy: bytes) -> Self:
return CarnotOverlay(self.nodes, self.next_leader(), entropy, self.number_of_committees)
def is_leader(self, _id: Id):
return _id == self.leader()
def leader(self) -> Id:
return self.current_leader
def next_leader(self) -> Id:
random.seed(a=self.entropy, version=2)
return random.choice(self.nodes)
def is_member_of_leaf_committee(self, _id: Id) -> bool:
return _id in set(itertools.chain.from_iterable(self.carnot_tree.leaf_committees().values()))
def is_member_of_root_committee(self, _id: Id) -> bool:
return _id in self.carnot_tree.root_committee()
def is_member_of_child_committee(self, parent: Id, child: Id) -> bool:
child_parent = self.parent_committee(child)
parent = self.carnot_tree.committee_by_member_id(parent)
return child_parent == parent
def parent_committee(self, _id: Id) -> Optional[Committee]:
self.carnot_tree.parent_committee_from_member_id(_id)
def leaf_committees(self) -> Set[Committee]:
return set(self.carnot_tree.leaf_committees().values())
def root_committee(self) -> Committee:
return self.carnot_tree.root_committee()
def is_child_of_root_committee(self, _id: Id) -> bool:
return self.parent_committee(_id) == self.root_committee()
def leader_super_majority_threshold(self, _id: Id) -> int:
root_committee = self.carnot_tree.inner_committees[0]
childs = self.carnot_tree.child_committees(root_committee)
childs_size = sum(
len(committee) for c in childs
if (committee := self.carnot_tree.committee_by_committee_id(c)) is not None
)
root_committee_size = len(self.root_committee())
committee_size = root_committee_size + childs_size
return (committee_size * 2 // 3) + 1
def super_majority_threshold(self, _id: Id) -> int:
if self.is_member_of_leaf_committee(_id):
return 0
committee_size = len(self.carnot_tree.committee_by_member_id(_id))
return (committee_size * 2 // 3) + 1