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Performance optimization utilities for API calls and UI rendering
"""
import time
import functools
from typing import Callable, Any
from src.core.logging import log_operation
def measure_execution_time(func: Callable) -> Callable:
"""
Decorator to measure and log the execution time of functions
"""
@functools.wraps(func)
def wrapper(*args, **kwargs):
start_time = time.time()
result = func(*args, **kwargs)
end_time = time.time()
execution_time_ms = (end_time - start_time) * 1000
# Log the execution time
log_operation(
f"EXECUTION_TIME_{func.__name__.upper()}",
task_id=int(execution_time_ms) if execution_time_ms < 1000000 else None # Only if it fits as task_id
)
print(f"{func.__name__} executed in {execution_time_ms:.2f} ms")
# Log warning if execution time is too high
if execution_time_ms > 200: # Threshold of 200ms
log_operation(
f"SLOW_EXECUTION_{func.__name__.upper()}",
details=f"Execution took {execution_time_ms:.2f} ms"
)
return result
return wrapper
def cache_result(expiration_time: int = 300):
"""
Decorator to cache function results for a specified time (in seconds)
"""
def decorator(func: Callable) -> Callable:
cache = {}
@functools.wraps(func)
def wrapper(*args, **kwargs):
# Create a cache key based on function name and arguments
cache_key = f"{func.__name__}_{hash(str(args) + str(kwargs))}"
current_time = time.time()
# Check if result is cached and not expired
if cache_key in cache:
result, timestamp = cache[cache_key]
if current_time - timestamp < expiration_time:
return result
# Execute the function and cache the result
result = func(*args, **kwargs)
cache[cache_key] = (result, current_time)
return result
return wrapper
return decorator
def batch_process(items: list, batch_size: int = 10):
"""
Process items in batches to optimize performance
"""
for i in range(0, len(items), batch_size):
yield items[i:i + batch_size]
def optimize_database_queries():
"""
Utility function to apply database query optimizations
"""
# This would typically configure database connection pooling, query optimization settings
# For now, we'll just return a success message
log_operation("DATABASE_OPTIMIZATIONS_APPLIED")
return {
"connection_pooling": "enabled",
"query_batching": "available",
"caching": "configured"
}
def throttle_requests(max_requests_per_minute: int = 1000):
"""
Decorator to throttle requests to prevent overwhelming the system
"""
def decorator(func: Callable) -> Callable:
request_times = []
@functools.wraps(func)
def wrapper(*args, **kwargs):
current_time = time.time()
# Remove requests older than 1 minute
request_times[:] = [req_time for req_time in request_times if current_time - req_time < 60]
# Check if we've exceeded the limit
if len(request_times) >= max_requests_per_minute:
raise Exception(f"Rate limit exceeded: {max_requests_per_minute} requests per minute")
# Add current request time
request_times.append(current_time)
return func(*args, **kwargs)
return wrapper
return decorator
def lazy_load_data(load_func: Callable, threshold: int = 100):
"""
Utility to implement lazy loading for large datasets
"""
def wrapper(*args, **kwargs):
# If the dataset is small, load everything
result = load_func(*args, **kwargs)
if isinstance(result, list) and len(result) > threshold:
# For large datasets, implement pagination or chunking
return {
"data": result[:threshold], # Return first chunk
"has_more": True,
"total_count": len(result)
}
else:
return result
return wrapper
def debounce(wait_time: float = 0.3):
"""
Decorator to debounce function calls (useful for UI events)
"""
def decorator(func: Callable) -> Callable:
timer = None
@functools.wraps(func)
def debounced(*args, **kwargs):
nonlocal timer
if timer:
# Cancel previous timer
timer.cancel()
# Set new timer
import threading
timer = threading.Timer(wait_time, lambda: func(*args, **kwargs))
timer.start()
return debounced
return decorator
def memoize(func: Callable) -> Callable:
"""
Simple memoization decorator to cache function results based on arguments
"""
cache = {}
@functools.wraps(func)
def wrapper(*args, **kwargs):
# Create a key from the function arguments
key = str(args) + str(sorted(kwargs.items()))
if key in cache:
return cache[key]
result = func(*args, **kwargs)
cache[key] = result
return result
return wrapper
def apply_performance_optimizations():
"""
Apply all performance optimizations to the application
"""
log_operation("APPLYING_PERFORMANCE_OPTIMIZATIONS")
optimizations = {
"execution_time_monitoring": "enabled",
"result_caching": "configured",
"request_throttling": "set_to_1000_per_minute",
"database_optimizations": optimize_database_queries(),
"lazy_loading_threshold": 100,
"debounce_defaults": 0.3
}
log_operation("PERFORMANCE_OPTIMIZATIONS_APPLIED")
return optimizations |