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472
hcfs-python/hcfs/sdk/decorators.py
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472
hcfs-python/hcfs/sdk/decorators.py
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"""
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HCFS SDK Decorators
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Decorators for caching, retry logic, rate limiting, and context management.
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"""
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import asyncio
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import time
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import random
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from functools import wraps
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from typing import Optional, Dict, Any, Callable, List
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from datetime import datetime, timedelta
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from .models import RetryConfig, RetryStrategy, CacheConfig
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from .exceptions import HCFSError, HCFSRateLimitError, HCFSTimeoutError
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from .utils import MemoryCache, cache_key
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def cached_context(cache_config: Optional[CacheConfig] = None, key_func: Optional[Callable] = None):
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"""
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Decorator to cache context-related operations.
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Args:
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cache_config: Cache configuration
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key_func: Custom function to generate cache keys
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"""
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config = cache_config or CacheConfig()
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cache = MemoryCache(
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max_size=config.max_size,
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strategy=config.strategy,
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ttl_seconds=config.ttl_seconds
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)
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def decorator(func):
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@wraps(func)
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async def async_wrapper(*args, **kwargs):
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if not config.enabled:
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return await func(*args, **kwargs)
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# Generate cache 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 = cache_key(func.__name__, *args, **kwargs)
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# Try to get from cache
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cached_result = cache.get(key)
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if cached_result is not None:
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return cached_result
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# Execute function and cache result
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result = await func(*args, **kwargs)
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cache.put(key, result)
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return result
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@wraps(func)
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def sync_wrapper(*args, **kwargs):
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if not config.enabled:
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return func(*args, **kwargs)
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# Generate cache 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 = cache_key(func.__name__, *args, **kwargs)
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# Try to get from cache
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cached_result = cache.get(key)
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if cached_result is not None:
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return cached_result
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# Execute function and cache result
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result = func(*args, **kwargs)
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cache.put(key, result)
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return result
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# Attach cache management methods
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if asyncio.iscoroutinefunction(func):
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async_wrapper.cache = cache
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async_wrapper.clear_cache = cache.clear
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async_wrapper.cache_stats = cache.stats
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return async_wrapper
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else:
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sync_wrapper.cache = cache
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sync_wrapper.clear_cache = cache.clear
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sync_wrapper.cache_stats = cache.stats
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return sync_wrapper
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return decorator
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def retry_on_failure(retry_config: Optional[RetryConfig] = None):
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"""
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Decorator to retry failed operations with configurable strategies.
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Args:
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retry_config: Retry configuration
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"""
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config = retry_config or RetryConfig()
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def decorator(func):
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@wraps(func)
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async def async_wrapper(*args, **kwargs):
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if not config.enabled:
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return await func(*args, **kwargs)
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last_exception = None
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for attempt in range(config.max_attempts):
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try:
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return await func(*args, **kwargs)
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except Exception as e:
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last_exception = e
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# Check if we should retry this exception
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if not _should_retry_exception(e, config):
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raise e
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# Don't delay on the last attempt
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if attempt < config.max_attempts - 1:
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delay = _calculate_delay(attempt, config)
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await asyncio.sleep(delay)
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# All attempts failed, raise the last exception
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raise last_exception
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@wraps(func)
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def sync_wrapper(*args, **kwargs):
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if not config.enabled:
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return func(*args, **kwargs)
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last_exception = None
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for attempt in range(config.max_attempts):
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try:
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return func(*args, **kwargs)
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except Exception as e:
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last_exception = e
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# Check if we should retry this exception
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if not _should_retry_exception(e, config):
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raise e
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# Don't delay on the last attempt
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if attempt < config.max_attempts - 1:
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delay = _calculate_delay(attempt, config)
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time.sleep(delay)
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# All attempts failed, raise the last exception
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raise last_exception
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if asyncio.iscoroutinefunction(func):
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return async_wrapper
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else:
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return sync_wrapper
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return decorator
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def _should_retry_exception(exception: Exception, config: RetryConfig) -> bool:
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"""Check if an exception should trigger a retry."""
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# Check for timeout errors
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if isinstance(exception, HCFSTimeoutError) and config.retry_on_timeout:
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return True
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# Check for rate limit errors
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if isinstance(exception, HCFSRateLimitError):
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return True
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# Check for HTTP status codes (if it's an HTTP-related error)
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if hasattr(exception, 'status_code'):
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return exception.status_code in config.retry_on_status
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return False
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def _calculate_delay(attempt: int, config: RetryConfig) -> float:
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"""Calculate delay for retry attempt."""
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if config.strategy == RetryStrategy.EXPONENTIAL_BACKOFF:
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delay = config.base_delay * (config.backoff_multiplier ** attempt)
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elif config.strategy == RetryStrategy.LINEAR_BACKOFF:
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delay = config.base_delay + (config.base_delay * attempt)
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elif config.strategy == RetryStrategy.FIBONACCI:
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delay = config.base_delay * _fibonacci(attempt + 1)
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else: # CONSTANT_DELAY
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delay = config.base_delay
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# Apply maximum delay limit
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delay = min(delay, config.max_delay)
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# Add jitter if enabled
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if config.jitter:
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jitter_range = delay * 0.1 # 10% jitter
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delay += random.uniform(-jitter_range, jitter_range)
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return max(0, delay)
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def _fibonacci(n: int) -> int:
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"""Calculate nth Fibonacci number."""
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if n <= 1:
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return n
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a, b = 0, 1
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for _ in range(2, n + 1):
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a, b = b, a + b
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return b
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class RateLimiter:
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"""Token bucket rate limiter."""
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def __init__(self, rate: float, burst: int = 1):
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self.rate = rate # tokens per second
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self.burst = burst # maximum tokens in bucket
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self.tokens = burst
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self.last_update = time.time()
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def acquire(self, tokens: int = 1) -> bool:
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"""Try to acquire tokens from the bucket."""
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now = time.time()
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# Add tokens based on elapsed time
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elapsed = now - self.last_update
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self.tokens = min(self.burst, self.tokens + elapsed * self.rate)
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self.last_update = now
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# Check if we have enough tokens
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if self.tokens >= tokens:
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self.tokens -= tokens
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return True
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return False
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def time_until_tokens(self, tokens: int = 1) -> float:
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"""Calculate time until enough tokens are available."""
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if self.tokens >= tokens:
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return 0.0
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needed_tokens = tokens - self.tokens
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return needed_tokens / self.rate
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def rate_limited(requests_per_second: float, burst: int = 1):
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"""
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Decorator to rate limit function calls.
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Args:
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requests_per_second: Rate limit (requests per second)
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burst: Maximum burst size
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"""
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limiter = RateLimiter(requests_per_second, burst)
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def decorator(func):
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@wraps(func)
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async def async_wrapper(*args, **kwargs):
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if not limiter.acquire():
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wait_time = limiter.time_until_tokens()
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await asyncio.sleep(wait_time)
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if not limiter.acquire():
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raise HCFSRateLimitError()
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return await func(*args, **kwargs)
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@wraps(func)
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def sync_wrapper(*args, **kwargs):
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if not limiter.acquire():
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wait_time = limiter.time_until_tokens()
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time.sleep(wait_time)
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if not limiter.acquire():
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raise HCFSRateLimitError()
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return func(*args, **kwargs)
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if asyncio.iscoroutinefunction(func):
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return async_wrapper
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else:
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return sync_wrapper
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return decorator
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class ContextManager:
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"""Context manager for HCFS operations with automatic cleanup."""
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def __init__(self, client, auto_cleanup: bool = True):
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self.client = client
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self.auto_cleanup = auto_cleanup
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self.created_contexts: List[int] = []
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self.temp_files: List[str] = []
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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if self.auto_cleanup:
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self.cleanup()
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async def __aenter__(self):
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return self
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async def __aexit__(self, exc_type, exc_val, exc_tb):
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if self.auto_cleanup:
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await self.cleanup_async()
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def track_context(self, context_id: int):
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"""Track a created context for cleanup."""
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self.created_contexts.append(context_id)
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def track_file(self, file_path: str):
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"""Track a temporary file for cleanup."""
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self.temp_files.append(file_path)
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def cleanup(self):
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"""Cleanup tracked resources synchronously."""
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# Cleanup contexts
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for context_id in self.created_contexts:
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try:
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self.client.delete_context(context_id)
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except Exception:
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pass # Ignore cleanup errors
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# Cleanup files
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import os
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for file_path in self.temp_files:
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try:
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if os.path.exists(file_path):
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os.remove(file_path)
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except Exception:
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pass # Ignore cleanup errors
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self.created_contexts.clear()
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self.temp_files.clear()
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async def cleanup_async(self):
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"""Cleanup tracked resources asynchronously."""
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# Cleanup contexts
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for context_id in self.created_contexts:
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try:
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await self.client.delete_context(context_id)
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except Exception:
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pass # Ignore cleanup errors
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# Cleanup files
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import os
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for file_path in self.temp_files:
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try:
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if os.path.exists(file_path):
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os.remove(file_path)
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except Exception:
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pass # Ignore cleanup errors
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self.created_contexts.clear()
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self.temp_files.clear()
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def context_manager(auto_cleanup: bool = True):
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"""
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Decorator to automatically manage context lifecycle.
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Args:
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auto_cleanup: Whether to automatically cleanup contexts on exit
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"""
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def decorator(func):
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@wraps(func)
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async def async_wrapper(*args, **kwargs):
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# Assume first argument is the client
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client = args[0] if args else None
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if not client:
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return await func(*args, **kwargs)
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async with ContextManager(client, auto_cleanup) as ctx_mgr:
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# Inject context manager into kwargs
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kwargs['_context_manager'] = ctx_mgr
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return await func(*args, **kwargs)
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@wraps(func)
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def sync_wrapper(*args, **kwargs):
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# Assume first argument is the client
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client = args[0] if args else None
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if not client:
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return func(*args, **kwargs)
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with ContextManager(client, auto_cleanup) as ctx_mgr:
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# Inject context manager into kwargs
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kwargs['_context_manager'] = ctx_mgr
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return func(*args, **kwargs)
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if asyncio.iscoroutinefunction(func):
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return async_wrapper
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else:
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return sync_wrapper
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return decorator
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def performance_monitor(track_timing: bool = True, track_memory: bool = False):
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"""
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Decorator to monitor function performance.
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Args:
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track_timing: Whether to track execution timing
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track_memory: Whether to track memory usage
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"""
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def decorator(func):
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@wraps(func)
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async def async_wrapper(*args, **kwargs):
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start_time = time.time() if track_timing else None
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start_memory = None
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if track_memory:
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import psutil
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process = psutil.Process()
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start_memory = process.memory_info().rss
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try:
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result = await func(*args, **kwargs)
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# Record performance metrics
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if track_timing:
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execution_time = time.time() - start_time
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# Could store or log timing data here
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if track_memory and start_memory:
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end_memory = process.memory_info().rss
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memory_delta = end_memory - start_memory
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# Could store or log memory usage here
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return result
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except Exception as e:
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# Record error metrics
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raise e
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@wraps(func)
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def sync_wrapper(*args, **kwargs):
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start_time = time.time() if track_timing else None
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start_memory = None
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if track_memory:
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import psutil
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process = psutil.Process()
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start_memory = process.memory_info().rss
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try:
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result = func(*args, **kwargs)
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# Record performance metrics
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if track_timing:
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execution_time = time.time() - start_time
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# Could store or log timing data here
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if track_memory and start_memory:
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end_memory = process.memory_info().rss
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memory_delta = end_memory - start_memory
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# Could store or log memory usage here
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return result
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except Exception as e:
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# Record error metrics
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raise e
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if asyncio.iscoroutinefunction(func):
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return async_wrapper
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else:
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return sync_wrapper
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return decorator
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