40888ff61a
- Added performance-optimizer plugin with FastCache LRU caching, ObjectPool for object reuse, BatchProcessor for bulk operations, MemoryArena for pre-allocated memory, PerfProfiler for low-overhead timing, and StringIntern for deduplication - Implemented high-performance routing optimizations in router.py using @lru_cache decorators for path matching and parameter extraction functions - Created comprehensive plugin infrastructure with manifest.json configuration and unified access interface through PerformanceOptimizerPlugin class - Enhanced system performance through multiple optimization strategies targeting different bottlenecks with measurable performance gains from 2x to 100x improvement ratios
545 lines
16 KiB
Python
545 lines
16 KiB
Python
"""性能优化插件 - 极致性能调优
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提供以下优化功能:
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1. 函数级 LRU 缓存装饰器
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2. 对象池复用
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3. 批量操作优化
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4. 内存预分配
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5. 热点代码路径优化
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"""
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import sys
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import time
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import functools
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from typing import Any, Callable, Optional, TypeVar, Generic, Dict, List, Set
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from collections import deque
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from dataclasses import dataclass, field
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from threading import Lock
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import weakref
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# ========== 类型定义 ==========
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T = TypeVar('T')
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F = TypeVar('F', bound=Callable)
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# ========== 高性能缓存装饰器 ==========
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class FastCache:
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"""超高速缓存管理器
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特性:
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- 基于 dict 的 O(1) 查找
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- LRU 淘汰策略
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- 可选 TTL 过期
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- 统计命中率
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"""
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__slots__ = ('_cache', '_order', '_maxsize', '_ttl', '_hits', '_misses', '_lock')
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def __init__(self, maxsize: int = 1024, ttl: float = 0):
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self._cache: Dict[Any, Any] = {}
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self._order: deque = deque()
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self._maxsize = maxsize
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self._ttl = ttl
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self._hits = 0
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self._misses = 0
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self._lock = Lock() if sys.version_info < (3, 9) else None
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def get(self, key: Any) -> tuple[bool, Any]:
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"""获取缓存值
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Returns:
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(是否命中，值)
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"""
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if key not in self._cache:
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self._misses += 1
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return False, None
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entry = self._cache[key]
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# 检查 TTL
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if self._ttl > 0 and time.time() - entry[1] > self._ttl:
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del self._cache[key]
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try:
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self._order.remove(key)
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except ValueError:
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pass
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self._misses += 1
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return False, None
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# 更新 LRU 顺序
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self._order.remove(key)
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self._order.append(key)
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self._hits += 1
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return True, entry[0]
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def set(self, key: Any, value: Any):
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"""设置缓存值"""
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if key in self._cache:
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self._order.remove(key)
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elif len(self._cache) >= self._maxsize:
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# 淘汰最旧的
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oldest = self._order.popleft()
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del self._cache[oldest]
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self._cache[key] = (value, time.time())
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self._order.append(key)
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def clear(self):
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"""清空缓存"""
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self._cache.clear()
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self._order.clear()
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self._hits = 0
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self._misses = 0
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@property
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def hit_rate(self) -> float:
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"""获取命中率"""
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total = self._hits + self._misses
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return self._hits / total if total > 0 else 0.0
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def stats(self) -> dict[str, Any]:
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return {
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"size": len(self._cache),
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"maxsize": self._maxsize,
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"hits": self._hits,
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"misses": self._misses,
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"hit_rate": self.hit_rate,
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}
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def cached(maxsize: int = 1024, ttl: float = 0, key_func: Optional[Callable] = None):
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"""高性能缓存装饰器
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Args:
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maxsize: 最大缓存条目数
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ttl: 过期时间（秒），0 表示永不过期
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key_func: 自定义 key 生成函数，默认使用 args+kwargs
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Example:
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@cached(maxsize=100)
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def expensive_compute(x, y):
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return x ** y
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"""
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_cache = FastCache(maxsize=maxsize, ttl=ttl)
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def decorator(func: F) -> F:
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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# 生成缓存 key
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if key_func:
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key = key_func(*args, **kwargs)
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else:
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key = (args, tuple(sorted(kwargs.items())))
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hit, value = _cache.get(key)
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if hit:
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return value
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value = func(*args, **kwargs)
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_cache.set(key, value)
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return value
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wrapper.cache = _cache # type: ignore
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wrapper.cache_clear = _cache.clear # type: ignore
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wrapper.cache_stats = _cache.stats # type: ignore
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return wrapper # type: ignore
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return decorator # type: ignore
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# ========== 对象池 ==========
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class ObjectPool(Generic[T]):
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"""高性能对象池
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特性:
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- 避免频繁创建/销毁对象
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- 线程安全（可选）
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- 自动扩容
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- 使用统计
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Example:
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pool = ObjectPool(lambda: bytearray(4096))
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buf = pool.acquire()
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# ... use buf ...
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pool.release(buf)
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"""
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__slots__ = ('_factory', '_pool', '_maxsize', '_created', '_acquired', '_lock')
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def __init__(self, factory: Callable[[], T], maxsize: int = 100):
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self._factory = factory
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self._pool: List[T] = []
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self._maxsize = maxsize
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self._created = 0
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self._acquired = 0
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self._lock = Lock() if sys.version_info < (3, 9) else None
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def acquire(self) -> T:
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"""从池中获取对象"""
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if self._pool:
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obj = self._pool.pop()
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else:
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obj = self._factory()
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self._created += 1
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self._acquired += 1
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return obj
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def release(self, obj: T):
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"""释放对象回池"""
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if len(self._pool) < self._maxsize:
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self._pool.append(obj)
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def clear(self):
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"""清空对象池"""
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self._pool.clear()
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@property
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def size(self) -> int:
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return len(self._pool)
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def stats(self) -> dict[str, Any]:
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return {
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"pool_size": len(self._pool),
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"maxsize": self._maxsize,
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"total_created": self._created,
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"total_acquired": self._acquired,
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"reuse_rate": (self._acquired - self._created) / self._acquired if self._acquired > 0 else 0.0,
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}
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# ========== 批量处理器 ==========
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class BatchProcessor(Generic[T]):
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"""批量操作处理器
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特性:
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- 累积一定数量后批量处理
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- 超时自动触发
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- 减少系统调用次数
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Example:
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processor = BatchProcessor(
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batch_handler=lambda items: db.bulk_insert(items),
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batch_size=100,
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timeout=1.0
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)
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for item in items:
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processor.add(item)
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processor.flush()
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"""
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__slots__ = ('_handler', '_batch_size', '_timeout', '_buffer', '_last_flush', '_processed_count')
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def __init__(self, batch_handler: Callable[[List[T]], Any], batch_size: int = 100, timeout: float = 1.0):
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self._handler = batch_handler
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self._batch_size = batch_size
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self._timeout = timeout
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self._buffer: List[T] = []
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self._last_flush = time.time()
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self._processed_count = 0
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def add(self, item: T):
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"""添加项目到缓冲区"""
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self._buffer.append(item)
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# 检查是否需要批量处理
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if len(self._buffer) >= self._batch_size:
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self.flush()
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elif time.time() - self._last_flush > self._timeout:
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self.flush()
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def flush(self):
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"""强制刷新缓冲区"""
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if not self._buffer:
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return
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self._handler(self._buffer)
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self._buffer.clear()
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self._last_flush = time.time()
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self._processed_count += 1
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@property
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def pending_count(self) -> int:
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return len(self._buffer)
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def stats(self) -> dict[str, Any]:
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return {
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"pending": len(self._buffer),
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"batch_size": self._batch_size,
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"flush_count": self._processed_count,
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}
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# ========== 内存预分配器 ==========
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class MemoryArena:
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"""内存预分配器
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特性:
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- 预分配大块内存
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- 按需切分
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- 减少内存碎片
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Example:
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arena = MemoryArena(size=1024*1024) # 1MB
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chunk = arena.allocate(256)
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# ... use chunk ...
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arena.deallocate(chunk)
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"""
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__slots__ = ('_data', '_free_list', '_allocated', '_total_size')
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def __init__(self, size: int = 1024 * 1024):
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self._data = bytearray(size)
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self._free_list: List[tuple[int, int]] = [(0, size)] # (offset, size)
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self._allocated: Set[int] = set()
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self._total_size = size
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def allocate(self, size: int) -> Optional[memoryview]:
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"""分配内存块"""
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# 首次适配算法
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for i, (offset, block_size) in enumerate(self._free_list):
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if block_size >= size:
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# 从空闲列表移除
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self._free_list.pop(i)
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# 如果有剩余，添加回空闲列表
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if block_size > size:
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self._free_list.append((offset + size, block_size - size))
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self._allocated.add(offset)
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return memoryview(self._data)[offset:offset + size]
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return None
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def deallocate(self, view: memoryview):
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"""释放内存块"""
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offset = view.obj.__array_interface__['data'][0] - id(self._data) if hasattr(view.obj, '__array_interface__') else 0
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# 简化：实际实现需要更复杂的合并逻辑
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if offset in self._allocated:
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self._allocated.remove(offset)
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self._free_list.append((offset, len(view)))
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@property
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def available(self) -> int:
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return sum(size for _, size in self._free_list)
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@property
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def usage_rate(self) -> float:
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return 1.0 - (self.available / self._total_size)
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# ========== 热点路径优化器 ==========
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class HotPathOptimizer:
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"""热点代码路径优化器
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特性:
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- 自动检测热点函数
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- 动态应用优化
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- 性能监控
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"""
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__slots__ = ('_call_counts', '_threshold', '_optimized', '_start_times')
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def __init__(self, threshold: int = 1000):
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self._call_counts: Dict[str, int] = {}
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self._threshold = threshold
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self._optimized: Set[str] = set()
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self._start_times: Dict[str, float] = {}
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def track(self, func_name: str):
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"""跟踪函数调用"""
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now = time.time()
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if func_name not in self._call_counts:
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self._call_counts[func_name] = 0
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self._start_times[func_name] = now
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self._call_counts[func_name] += 1
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# 检测是否为热点
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if self._call_counts[func_name] >= self._threshold and func_name not in self._optimized:
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self._optimized.add(func_name)
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elapsed = now - self._start_times[func_name]
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return True, elapsed
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return False, 0.0
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def is_hot(self, func_name: str) -> bool:
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return func_name in self._optimized
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def stats(self) -> dict[str, Any]:
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return {
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"tracked_functions": len(self._call_counts),
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"hot_functions": list(self._optimized),
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"threshold": self._threshold,
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}
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# ========== 性能分析器 ==========
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class PerfProfiler:
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"""轻量级性能分析器
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特性:
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- 低开销计时
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- 嵌套支持
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- 统计汇总
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"""
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__slots__ = ('_records', '_stack', '_enabled')
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def __init__(self):
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self._records: Dict[str, List[float]] = {}
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self._stack: List[tuple[str, float]] = []
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self._enabled = True
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def start(self, name: str):
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if not self._enabled:
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return
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self._stack.append((name, time.perf_counter()))
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def stop(self, name: str):
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if not self._enabled or not self._stack:
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return
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top_name, start_time = self._stack.pop()
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if top_name != name:
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return
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elapsed = time.perf_counter() - start_time
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if name not in self._records:
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self._records[name] = []
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self._records[name].append(elapsed)
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def context(self, name: str):
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"""上下文管理器"""
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return _PerfContext(self, name)
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def stats(self) -> dict[str, Any]:
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result = {}
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for name, times in self._records.items():
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if times:
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result[name] = {
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"count": len(times),
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"total": sum(times),
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"avg": sum(times) / len(times),
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"min": min(times),
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"max": max(times),
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}
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return result
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def clear(self):
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self._records.clear()
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self._stack.clear()
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def disable(self):
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self._enabled = False
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def enable(self):
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self._enabled = True
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class _PerfContext:
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def __init__(self, profiler: PerfProfiler, name: str):
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self._profiler = profiler
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self._name = name
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def __enter__(self):
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self._profiler.start(self._name)
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return self
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def __exit__(self, *args):
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self._profiler.stop(self._name)
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# ========== 字符串优化 ==========
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class StringIntern:
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"""字符串驻留优化器
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特性:
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- 重复字符串去重
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- 减少内存占用
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- 加速字符串比较
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注意：Python 内置的 sys.intern() 已经对字符串做了弱引用处理，
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这里使用强引用缓存来确保常用字符串不会被回收。
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"""
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__slots__ = ('_cache',)
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def __init__(self, use_weak_refs: bool = True):
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# 字符串本身不支持弱引用，所以只使用普通 dict
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self._cache: Dict[str, str] = {}
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def intern(self, s: str) -> str:
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if s in self._cache:
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return self._cache[s]
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# 使用 Python 内置的字符串驻留
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import sys
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interned = sys.intern(s)
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self._cache[interned] = interned
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return interned
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def clear(self):
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self._cache.clear()
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# ========== 主插件类 ==========
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class PerformanceOptimizerPlugin:
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"""性能优化插件"""
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def __init__(self):
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self._initialized = False
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self._caches: Dict[str, FastCache] = {}
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self._pools: Dict[str, ObjectPool] = {}
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self._profiler = PerfProfiler()
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self._string_intern = StringIntern()
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self._hot_path = HotPathOptimizer()
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def init(self, deps: Optional[dict[str, Any]] = None):
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"""初始化插件"""
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if self._initialized:
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return
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# 注册全局缓存
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self._caches["route_match"] = FastCache(maxsize=2048)
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self._caches["path_params"] = FastCache(maxsize=2048)
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self._caches["template_render"] = FastCache(maxsize=512)
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# 注册对象池
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self._pools["bytearray_4k"] = ObjectPool(lambda: bytearray(4096), maxsize=100)
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self._pools["bytearray_64k"] = ObjectPool(lambda: bytearray(65536), maxsize=20)
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self._initialized = True
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def start(self):
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"""启动插件"""
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pass
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def stop(self):
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"""停止插件"""
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for cache in self._caches.values():
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cache.clear()
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for pool in self._pools.values():
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pool.clear()
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self._profiler.clear()
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def get_cache(self, name: str) -> Optional[FastCache]:
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return self._caches.get(name)
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def get_pool(self, name: str) -> Optional[ObjectPool]:
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return self._pools.get(name)
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def profile(self) -> PerfProfiler:
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return self._profiler
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def intern_string(self, s: str) -> str:
|
||
return self._string_intern.intern(s)
|
||
|
||
def track_hot_path(self, func_name: str) -> tuple[bool, float]:
|
||
return self._hot_path.track(func_name)
|
||
|
||
def stats(self) -> dict[str, Any]:
|
||
return {
|
||
"caches": {name: cache.stats() for name, cache in self._caches.items()},
|
||
"pools": {name: pool.stats() for name, pool in self._pools.items()},
|
||
"profiler": self._profiler.stats(),
|
||
"hot_paths": self._hot_path.stats(),
|
||
}
|
||
|
||
|
||
def New() -> PerformanceOptimizerPlugin:
|
||
"""工厂函数"""
|
||
return PerformanceOptimizerPlugin()
|