99 lines
3.8 KiB
Python
99 lines
3.8 KiB
Python
from . import distribution
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from . import geometry
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from .distribution import Distribution
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from .expr import Node
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from .geometry import Point, Segment
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from typing import Dict, Generic, List, Optional, Set, Tuple, TypeVar
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import collections
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import itertools
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import numpy as np
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T = TypeVar('T')
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class Strategy(Generic[T]):
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def __init__(self,
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nodes: Set[Node[T]],
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reads: List[Set[T]],
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read_weights: List[float],
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writes: List[Set[T]],
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write_weights: List[float]) -> None:
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self.nodes = nodes
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self.read_capacity = {node.x: node.read_capacity for node in nodes}
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self.write_capacity = {node.x: node.write_capacity for node in nodes}
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self.reads = reads
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self.read_weights = read_weights
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self.writes = writes
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self.write_weights = write_weights
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self.unweighted_read_load: Dict[T, float] = \
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collections.defaultdict(float)
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for (read_quorum, weight) in zip(self.reads, self.read_weights):
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for x in read_quorum:
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self.unweighted_read_load[x] += weight
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self.unweighted_write_load: Dict[T, float] = \
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collections.defaultdict(float)
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for (write_quorum, weight) in zip(self.writes, self.write_weights):
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for x in write_quorum:
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self.unweighted_write_load[x] += weight
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def __str__(self) -> str:
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non_zero_reads = {tuple(r): p
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for (r, p) in zip(self.reads, self.read_weights)
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if p > 0}
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non_zero_writes = {tuple(w): p
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for (w, p) in zip(self.writes, self.write_weights)
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if p > 0}
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return f'Strategy(reads={non_zero_reads}, writes={non_zero_writes})'
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def __repr__(self) -> str:
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return (f'Strategy(nodes={self.nodes}, '+
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f'reads={self.reads}, ' +
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f'read_weights={self.read_weights},' +
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f'writes={self.writes}, ' +
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f'write_weights={self.write_weights})')
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def get_read_quorum(self) -> Set[T]:
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return np.random.choice(self.reads, p=self.read_weights)
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def get_write_quorum(self) -> Set[T]:
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return np.random.choice(self.writes, p=self.write_weights)
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def load(self,
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read_fraction: Optional[Distribution] = None,
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write_fraction: Optional[Distribution] = None) \
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-> float:
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d = distribution.canonicalize_rw(read_fraction, write_fraction)
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return sum(weight * self._load(fr)
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for (fr, weight) in d.items())
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def node_load(self,
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node: Node[T],
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read_fraction: Optional[Distribution] = None,
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write_fraction: Optional[Distribution] = None) \
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-> float:
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d = distribution.canonicalize_rw(read_fraction, write_fraction)
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return sum(weight * self._node_load(node.x, fr)
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for (fr, weight) in d.items())
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def capacity(self,
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read_fraction: Optional[Distribution] = None,
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write_fraction: Optional[Distribution] = None) \
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-> float:
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return 1 / self.load(read_fraction, write_fraction)
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def _node_load(self, x: T, fr: float) -> float:
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"""
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_node_load returns the load on x given a fixed read fraction fr.
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"""
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fw = 1 - fr
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return (fr * self.unweighted_read_load[x] / self.read_capacity[x] +
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fw * self.unweighted_write_load[x] / self.write_capacity[x])
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def _load(self, fr: float) -> float:
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"""
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_load returns the load given a fixed read fraction fr.
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"""
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return max(self._node_load(node.x, fr) for node in self.nodes)
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