384 lines
15 KiB
Python
384 lines
15 KiB
Python
# TODO(mwhittaker): We can define a set of read quorums that are not minimal.
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# Does this mess things up?
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from . import distribution
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from .distribution import Distribution
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from .expr import Expr, Node
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from .strategy import Strategy
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from typing import (Callable, Dict, Iterator, Generic, List, Optional, Set,
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TypeVar)
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import collections
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import datetime
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import itertools
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import pulp
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T = TypeVar('T')
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LOAD = 'load'
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NETWORK = 'network'
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LATENCY = 'latency'
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# TODO(mwhittaker): Add some other non-optimal strategies.
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# TODO(mwhittaker): Make it easy to make arbitrary strategies.
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class QuorumSystem(Generic[T]):
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def __init__(self, reads: Optional[Expr[T]] = None,
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writes: Optional[Expr[T]] = None) -> None:
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if reads is not None and writes is not None:
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optimal_writes = reads.dual()
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if not all(optimal_writes.is_quorum(write_quorum)
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for write_quorum in writes.quorums()):
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raise ValueError(
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'Not all read quorums intersect all write quorums')
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self.reads = reads
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self.writes = writes
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elif reads is not None and writes is None:
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self.reads = reads
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self.writes = reads.dual()
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elif reads is None and writes is not None:
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self.reads = writes.dual()
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self.writes = writes
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else:
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raise ValueError('A QuorumSystem must be instantiated with a set '
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'of read quorums or a set of write quorums')
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def __repr__(self) -> str:
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return f'QuorumSystem(reads={self.reads}, writes={self.writes})'
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def read_quorums(self) -> Iterator[Set[T]]:
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return self.reads.quorums()
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def write_quorums(self) -> Iterator[Set[T]]:
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return self.writes.quorums()
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def is_read_quorum(self, xs: Set[T]) -> bool:
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return self.reads.is_quorum(xs)
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def is_write_quorum(self, xs: Set[T]) -> bool:
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return self.writes.is_quorum(xs)
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def nodes(self) -> Set[Node[T]]:
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return self.reads.nodes() | self.writes.nodes()
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def resilience(self) -> int:
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return min(self.read_resilience(), self.write_resilience())
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def read_resilience(self) -> int:
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return self.reads.resilience()
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def write_resilience(self) -> int:
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return self.writes.resilience()
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def strategy(self,
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optimize: str = LOAD,
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load_limit: Optional[float] = None,
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network_limit: Optional[float] = None,
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latency_limit: Optional[datetime.timedelta] = None,
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read_fraction: Optional[Distribution] = None,
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write_fraction: Optional[Distribution] = None,
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f: int = 0) \
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-> 'Strategy[T]':
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if f < 0:
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raise ValueError('f must be >= 0')
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if optimize == LOAD and load_limit is not None:
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raise ValueError(
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'a load limit cannot be set when optimizing for load')
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if optimize == NETWORK and network_limit is not None:
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raise ValueError(
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'a network limit cannot be set when optimizing for network')
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if optimize == LATENCY and latency_limit is not None:
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raise ValueError(
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'a latency limit cannot be set when optimizing for latency')
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d = distribution.canonicalize_rw(read_fraction, write_fraction)
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if f == 0:
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return self._load_optimal_strategy(
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list(self.read_quorums()),
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list(self.write_quorums()),
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d,
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optimize=optimize,
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load_limit=load_limit,
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network_limit=network_limit,
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latency_limit=latency_limit)
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else:
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xs = [node.x for node in self.nodes()]
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read_quorums = list(self._f_resilient_quorums(f, xs, self.reads))
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write_quorums = list(self._f_resilient_quorums(f, xs, self.reads))
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if len(read_quorums) == 0:
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raise ValueError(f'There are no {f}-resilient read quorums')
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if len(write_quorums) == 0:
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raise ValueError(f'There are no {f}-resilient write quorums')
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return self._load_optimal_strategy(
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read_quorums,
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write_quorums,
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d,
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optimize=optimize,
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load_limit=load_limit,
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network_limit=network_limit,
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latency_limit=latency_limit)
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def dup_free(self) -> bool:
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return self.reads.dup_free() and self.writes.dup_free()
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def _f_resilient_quorums(self,
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f: int,
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xs: List[T],
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e: Expr) -> Iterator[Set[T]]:
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assert f >= 1
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def helper(s: Set[T], i: int) -> Iterator[Set[T]]:
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if all(e.is_quorum(s - set(failure))
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for failure in itertools.combinations(s, min(f, len(s)))):
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yield set(s)
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return
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for j in range(i, len(xs)):
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s.add(xs[j])
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yield from helper(s, j + 1)
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s.remove(xs[j])
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return helper(set(), 0)
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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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f: int = 0) \
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-> float:
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return 0
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# TODO(mwhittaker): Remove.
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# sigma = self.strategy(read_fraction, write_fraction, f)
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# return sigma.load(read_fraction, write_fraction)
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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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f: int = 0) \
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-> float:
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return 0
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# TODO(mwhittaker): Remove.
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# return 1 / self.load(read_fraction, write_fraction, f)
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def _read_quorum_latency(self, quorum: Set[Node[T]]) -> datetime.timedelta:
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return self._quorum_latency(quorum, self.is_read_quorum)
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def _write_quorum_latency(self, quorum: Set[Node[T]]) -> datetime.timedelta:
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return self._quorum_latency(quorum, self.is_write_quorum)
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def _quorum_latency(self,
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quorum: Set[Node[T]],
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is_quorum: Callable[[Set[T]], bool]) \
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-> datetime.timedelta:
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nodes = list(quorum)
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nodes.sort(key=lambda node: node.latency)
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for i in range(len(quorum)):
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if is_quorum({node.x for node in nodes[:i+1]}):
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return nodes[i].latency
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raise ValueError('_quorum_latency called on a non-quorum')
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def _load_optimal_strategy(
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self,
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read_quorums: List[Set[T]],
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write_quorums: List[Set[T]],
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read_fraction: Dict[float, float],
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optimize: str = LOAD,
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load_limit: Optional[float] = None,
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network_limit: Optional[float] = None,
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latency_limit: Optional[datetime.timedelta] = None) -> 'Strategy[T]':
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"""
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Consider the following 2x2 grid quorum system.
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a b
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c d
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with
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read_quorums = [{a, b}, {c, d}]
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write_quorums = [{a, c}, {a, d}, {b, c}, {b, d}]
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We can form a linear program to compute the optimal load of this quorum
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system for some fixed read fraction fr as follows. First, we create a
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variable ri for every read quorum i and a variable wi for every write
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quorum i. ri represents the probabilty of selecting the ith read
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quorum, and wi represents the probabilty of selecting the ith write
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quorum. We introduce an additional variable l that represents the load
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and solve the following linear program.
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min L subject to
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r0 + r1 + r2 = 1
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w0 + w1 = 1
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fr (r0) + (1 - fr) (w0 + w1) <= L # a's load
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fr (r0) + (1 - fr) (w2 + w3) <= L # b's load
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fr (r1) + (1 - fr) (w0 + w2) <= L # c's load
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fr (r1) + (1 - fr) (w1 + w3) <= L # d's load
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If we assume every element x has read capacity rcap_x and write
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capacity wcap_x, then we adjust the linear program like this.
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min L subject to
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r0 + r1 + r2 = 1
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w0 + w1 = 1
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fr/rcap_a (r0) + (1 - fr)/wcap_a (w0 + w1) <= L # a's load
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fr/rcap_b (r0) + (1 - fr)/wcap_b (w2 + w3) <= L # b's load
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fr/rcap_c (r1) + (1 - fr)/wcap_c (w0 + w2) <= L # c's load
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fr/rcap_d (r1) + (1 - fr)/wcap_d (w1 + w3) <= L # d's load
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Assume we have fr = 0.9 with 80% probabilty and fr = 0.5 with 20%. Then
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we adjust the linear program as follows to find the strategy that
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minimzes the average load.
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min 0.8 * L_0.9 + 0.2 * L_0.5 subject to
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r0 + r1 + r2 = 1
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w0 + w1 = 1
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0.9/rcap_a (r0) + 0.1/wcap_a (w0 + w1) <= L_0.9 # a's load
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0.9/rcap_b (r0) + 0.1/wcap_b (w2 + w3) <= L_0.9 # b's load
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0.9/rcap_c (r1) + 0.1/wcap_c (w0 + w2) <= L_0.9 # c's load
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0.9/rcap_d (r1) + 0.1/wcap_d (w1 + w3) <= L_0.9 # d's load
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0.5/rcap_a (r0) + 0.5/wcap_a (w0 + w1) <= L_0.5 # a's load
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0.5/rcap_b (r0) + 0.5/wcap_b (w2 + w3) <= L_0.5 # b's load
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0.5/rcap_c (r1) + 0.5/wcap_c (w0 + w2) <= L_0.5 # c's load
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0.5/rcap_d (r1) + 0.5/wcap_d (w1 + w3) <= L_0.5 # d's load
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"""
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nodes = self.nodes()
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x_to_node = {node.x: node for node in nodes}
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read_capacity = {node.x: node.read_capacity for node in nodes}
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write_capacity = {node.x: node.write_capacity for node in nodes}
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# Create a variable for every read quorum and every write quorum. While
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# we do this, map each element x to the read and write quorums that
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# it's in. For example, image we have the following read and write
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# quorums:
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#
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# read_quorums = [{a}, {a, b}, {a, c}]
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# write_quorums = [{a, b}, {a, b, c}]
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#
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# Then, we'd have
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#
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# read_quorum_vars = [r0, r1, 2]
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# write_quorum_vars = [w0, w1]
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# x_to_read_quorum_vars = {a: [r1, r2, r3], b: [r1], c: [r2]}
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# x_to_write_quorum_vars = {a: [w1, w2], b: [w2, w2], c: [w2]}
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read_quorum_vars: List[pulp.LpVariable] = []
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x_to_read_quorum_vars: Dict[T, List[pulp.LpVariable]] = \
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collections.defaultdict(list)
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for (i, read_quorum) in enumerate(read_quorums):
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v = pulp.LpVariable(f'r{i}', 0, 1)
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read_quorum_vars.append(v)
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for x in read_quorum:
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x_to_read_quorum_vars[x].append(v)
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write_quorum_vars: List[pulp.LpVariable] = []
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x_to_write_quorum_vars: Dict[T, List[pulp.LpVariable]] = \
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collections.defaultdict(list)
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for (i, write_quorum) in enumerate(write_quorums):
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v = pulp.LpVariable(f'w{i}', 0, 1)
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write_quorum_vars.append(v)
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for x in write_quorum:
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x_to_write_quorum_vars[x].append(v)
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fr = sum(weight * f for (f, weight) in read_fraction.items())
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def network() -> pulp.LpAffineExpression:
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read_network = fr * sum(
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v * len(rq)
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for (rq, v) in zip(read_quorums, read_quorum_vars)
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)
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write_network = (1 - fr) * sum(
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v * len(wq)
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for (wq, v) in zip(write_quorums, write_quorum_vars)
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)
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return read_network + write_network
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def latency() -> pulp.LpAffineExpression:
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read_latency = fr * sum(
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v * self._read_quorum_latency(quorum).total_seconds()
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for (rq, v) in zip(read_quorums, read_quorum_vars)
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for quorum in [{x_to_node[x] for x in rq}]
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)
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write_latency = (1 - fr) * sum(
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v * self._write_quorum_latency(quorum).total_seconds()
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for (wq, v) in zip(write_quorums, write_quorum_vars)
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for quorum in [{x_to_node[x] for x in wq}]
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)
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return read_latency + write_latency
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def fr_load(problem: pulp.LpProblem, fr: float) -> pulp.LpAffineExpression:
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l = pulp.LpVariable(f'l_{fr}', 0, 1)
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for node in nodes:
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x = node.x
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x_load: pulp.LpAffineExpression = 0
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if x in x_to_read_quorum_vars:
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vs = x_to_read_quorum_vars[x]
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x_load += fr * sum(vs) / read_capacity[x]
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if x in x_to_write_quorum_vars:
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vs = x_to_write_quorum_vars[x]
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x_load += (1 - fr) * sum(vs) / write_capacity[x]
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problem += (x_load <= l, f'{x}{fr}')
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return l
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def load(problem: pulp.LpProblem,
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read_fraction: Dict[float, float]) -> pulp.LpAffineExpression:
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return sum(weight * fr_load(problem, fr)
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for (fr, weight) in read_fraction.items())
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# Form the linear program to find the load.
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problem = pulp.LpProblem("load", pulp.LpMinimize)
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# We add these constraints to make sure that the probabilities we
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# select form valid probabilty distributions.
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problem += (sum(read_quorum_vars) == 1, 'valid read strategy')
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problem += (sum(write_quorum_vars) == 1, 'valid write strategy')
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# Add the objective.
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if optimize == LOAD:
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problem += load(problem, read_fraction)
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elif optimize == NETWORK:
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problem += network()
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else:
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assert optimize == LATENCY
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problem += latency()
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# Add any constraints.
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if load_limit is not None:
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problem += (load(problem, read_fraction) <= load_limit,
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'load limit')
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if network_limit is not None:
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problem += (network() <= network_limit, 'network limit')
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if latency_limit is not None:
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problem += (latency() <= latency_limit.total_seconds(),
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'latency limit')
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# Solve the linear program.
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print(problem)
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problem.solve(pulp.apis.PULP_CBC_CMD(msg=False))
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if problem.status != pulp.LpStatusOptimal:
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raise ValueError('no strategy satisfies the given constraints')
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# Prune out any quorums with 0 probability.
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non_zero_read_quorums = [
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(rq, v.varValue)
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for (rq, v) in zip(read_quorums, read_quorum_vars)
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if v.varValue != 0]
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non_zero_write_quorums = [
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(wq, v.varValue)
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for (wq, v) in zip(write_quorums, write_quorum_vars)
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if v.varValue != 0]
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return Strategy(nodes,
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[rq for (rq, _) in non_zero_read_quorums],
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[weight for (_, weight) in non_zero_read_quorums],
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[wq for (wq, _) in non_zero_write_quorums],
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[weight for (_, weight) in non_zero_write_quorums])
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