func

from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import relationship, sessionmaker from sqlalchemy.sql import func Base = declarative_base() class User(Base): __tablename__ = 'users' id = Column(Integer,…

- Using a context manager can make your code cleaner and easier to read. Here's an improved version of your code with these suggestions: ```python import time import logging # Configure logging logging.basicConfig(level=logging.INFO) …

start_time = time.perf_counter() result = func(*args, **kwargs) end_time = time.perf_counter() latency = end_time - start_time logging.info(f"Function {func.__name__} took {latency:.6f} seconds") …

from typing import List, Optional class SearchQuery(BaseModel): query: str limit: int class SearchResult(BaseModel): id: int title: str content: str class SearchResponse(BaseModel): results: List[SearchResult] …

To implement a multi-stage caching system, you need to ensure that the decorators are applied in the correct order and that the caching logic is properly nested. Here's a revised version of your implementation: ### Revised Implementation …

- **Optimization**: Use the timing information to identify bottlenecks and optimize the query rewriting logic. ### Example with Profiling You can use `cProfile` to profile the entire process: ```python import cProfile import pstats def …

from functools import wraps def timer_decorator(func): @wraps(func) def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() print(f"Function {func…

def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() print(f"Function {func.__name__} took {end_time - start_time} seconds to execute") retu…

def profile_function(func, *args, **kwargs): pr = cProfile.Profile() pr.enable() result = func(*args, **kwargs) pr.disable() s = io.StringIO() ps = Stats(pr, stream=s).sort_stats('cumtime') ps.print_stats() p…