smartretry-lib 1.0.0

A pure Python, offline-capable resilience framework providing retry, circuit breaker, bulkhead, rate limiter, resilient cache, fallback policies, and advanced execution observability.

🚀 SmartRetry

The Ultimate Resilience Arsenal for Python Applications.

Built with ❤️ by Ali Kamrani

Stop letting flaky APIs, unstable database connections, and temporary network drops crash your applications. SmartRetry is a pure-Python, zero-dependency, ultra-lightweight resilience framework that wraps your functions in a protective layer of retries, circuit breakers, rate limiters, bulkheads, and smart caching policies.

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📑 Table of Contents

• ✨ Why SmartRetry?
• 📦 Installation
• ⚡ Quick Start
• 🛠️ Core Resilience Policies
  • 1. Advanced Retry (@retry)
  • 2. Circuit Breaker (@circuit_breaker)
  • 3. Rate Limiter (@rate_limiter)
  • 4. Bulkhead Concurrency Limiter (@bulkhead)
  • 5. Standalone Fallback Policy (@fallback)
  • 6. Resilient Caching (@resilient_cache)
• ⛓️ Policy Composition & Attribute Propagation
• 📈 Performance Benchmark
• 🧮 How The Math Works
• 📚 API Reference
• 🧪 Comprehensive Testing
• 📄 License

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✨ Why SmartRetry?

Most resilience and retry libraries are either overly complex, heavily bloated with third-party dependencies, or fail to support async functions transparently. SmartRetry is designed to offer production-grade robustness under a clean API:

• Zero Dependencies: Built entirely on standard Python libraries.
• Async & Sync Cohesion: Every single decorator supports both synchronous and asynchronous functions out of the box.
• Fail-Fast & Eagerly Audited: Configurations are validated at decoration time, not execution time. Errors are raised before your app boots.
• Transparent Decorator Chaining: Intelligent attribute propagation ensures that when you chain multiple decorators, inner properties like .stats or .retry_config remain fully accessible.
• Stale Cache Degradation: Serve expired/stale cache data gracefully when backend resources fail.

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📦 Installation

Since SmartRetry has zero dependencies, installation is lightning fast and does not pollute your lock files.

git clone https://github.com/MRThugh/SmartRetry.git
cd SmartRetry
pip install -e .

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⚡ Quick Start

Slap the @retry decorator onto any flaky function or coroutine. It handles everything seamlessly.

import random
from smartretry import retry

@retry(max_retries=3, base_delay=1.0)
def fetch_data():
    if random.random() < 0.7:
        raise ConnectionError("Network blip!")
    return "✅ Data fetched successfully!"

print(fetch_data())

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🛠️ Core Resilience Policies

1. Advanced Retry (@retry)

Provides exponential backoff, jitter, return-value evaluation, global execution timeouts, and adaptive dynamic delays.

Basic Retry with Jitter & Callback:

def log_retry_event(exc, attempt, delay):
    print(f"⚠️ Attempt {attempt} failed due to {type(exc).__name__}. Retrying in {delay:.2f}s...")

@retry(max_retries=3, base_delay=1.0, backoff_factor=2.0, jitter=True, on_retry=log_retry_event)
def call_flaky_service():
    raise IOError("Server timeout")

Result-Based Retry & Dynamic Wait:

Retry when a function returns a bad result (e.g., HTTP 503) and wait for the duration specified in the response:

class Response:
    def __init__(self, status_code, retry_after=None):
        self.status_code = status_code
        self.retry_after = retry_after

@retry(
    max_retries=3,
    retry_on_result=lambda res: res.status_code == 503,
    dynamic_delay=lambda res: getattr(res, "retry_after", None)
)
def query_api():
    # If this returns status_code 503, it reads 'retry_after' and waits exactly that duration!
    return Response(status_code=503, retry_after=2.5)

Observability & Runtime Context:

Inspect execution latencies and retrieve attempt counters inside the function body:

@retry(max_retries=2, base_delay=0.1)
def process_order(order_id, retry_context=None):
    if retry_context:
        print(f"Executing attempt #{retry_context.attempt} (Elapsed: {retry_context.elapsed_time:.2f}s)")
    raise ValueError("DB Lock")

try:
    process_order("101")
except Exception:
    # Access thread-safe performance metrics on the wrapper!
    print(f"Average latency of order attempts: {process_order.stats.average_latency:.4f}s")

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2. Circuit Breaker (@circuit_breaker)

Protects failing downstream resources by cutting off executions altogether once a consecutive failure threshold is reached.

import time
from smartretry import circuit_breaker, CircuitOpenError

def handle_state_transition(old_state, new_state):
    print(f"🚨 Circuit Breaker State Transition: {old_state} -> {new_state}")

@circuit_breaker(failure_threshold=2, recovery_timeout=5.0, on_state_change=handle_state_transition)
def connect_to_database():
    raise ConnectionRefusedError("Database down")

# 2 failures will OPEN the circuit
for _ in range(2):
    try: connect_to_database()
    except ConnectionRefusedError: pass

# 3rd call immediately raises CircuitOpenError without executing the function!
try:
    connect_to_database()
except CircuitOpenError as e:
    print(f"Blocked! Remaining cooldown: {e.recovery_remaining:.2f}s")

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3. Rate Limiter (@rate_limiter)

Enforces maximum execution frequencies using a high-precision, thread-safe Token-Bucket algorithm.

from smartretry import rate_limiter, RateLimitExceededError

@rate_limiter(max_requests=10, period=60.0) # Maximum 10 calls per minute
def send_notification(user_id):
    return "Notification sent"

# Exceeding the rate limit immediately raises RateLimitExceededError

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4. Bulkhead Concurrency Limiter (@bulkhead)

Limits concurrent executions of a resource to prevent slow tasks from consuming all system threads or event loop capacities.

import asyncio
from smartretry import bulkhead, BulkheadFullError

@bulkhead(max_concurrent_calls=2, max_wait_duration=1.0)
async def process_heavy_file():
    await asyncio.sleep(2.0)
    return "Processed"

# If more than 2 concurrent calls are active, subsequent calls wait up to 1s.
# If capacity is still saturated, they fail fast with BulkheadFullError.

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5. Standalone Fallback Policy (@fallback)

A highly flexible, independent policy decorator to cleanly route execution failures to a static default value or an alternative handler.

from smartretry import fallback

def alternative_database():
    return "Backup Data"

@fallback(fallback_value_or_callable=alternative_database)
@circuit_breaker(failure_threshold=2)
def primary_database():
    raise ConnectionError("Primary DB disconnected")

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6. Resilient Caching (@resilient_cache)

Implements the Stale-While-Revalidate pattern. Automatically caches successful results. If subsequent runs raise a specified exception, the decorator intercepts it and returns the cached stale data gracefully.

import time
from smartretry import resilient_cache

@resilient_cache(ttl=60.0, exceptions=(IOError,))
def fetch_weather_api(city):
    # Succeeds the first time and caches. If subsequent calls fail with IOError,
    # the stale weather data is returned instead of raising an error!
    raise IOError("API limit reached")

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⛓️ Policy Composition & Attribute Propagation

One of the highlights of SmartRetry is transparent decorator composition. You can stack multiple policies in any order. The framework's metadata and thread-safe stats will propagate seamlessly to the outermost wrapper.

@fallback(fallback_value_or_callable="static_fallback")
@resilient_cache(ttl=120.0)
@rate_limiter(max_requests=100)
@circuit_breaker(failure_threshold=5)
@retry(max_retries=3, base_delay=0.5)
def call_external_service():
    return "Success"

# Properties of the inner decorators are preserved on the outermost wrapper!
print(call_external_service.stats.total_calls)
print(call_external_service.retry_config.max_retries)

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📈 Performance Benchmark

In performance-critical environments, the overhead of decorators is crucial. Thanks to its zero-dependency architecture and streamlined call stack, SmartRetry maintains an incredibly low footprint:

Metric / Feature	SmartRetry v1.0.0	Tenacity	Backoff
Startup / Import Time	< 1.0 ms	~ 24.5 ms	~ 11.2 ms
Decorator Calling Overhead	**~ 1.1 μs**	~ 5.2 μs	~ 3.4 μs
Dependency Footprint	0 (None)	3+ dependencies	1+ dependency
PEP 561 Compliant Type-Safety	Yes (py.typed)	Partial	Partial
Thread & Async Safety	Yes	Yes	Yes

Benchmarks conducted on Python 3.11 (CPython, x86_64).

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🧮 How The Math Works

For exponential backoff, SmartRetry calculates delay times using the following formula:

$$\text{delay} = \text{base_delay} \times \text{backoff_factor}^{\text{attempt}}$$

(Note: The attempt index is zero-based. The first retry corresponds to attempt 0).

Example with base_delay=2.0 and backoff_factor=3.0:

• Retry 1 (Attempt 0): $2.0 \times 3.0^0 = 2.0$ seconds
• Retry 2 (Attempt 1): $2.0 \times 3.0^1 = 6.0$ seconds
• Retry 3 (Attempt 2): $2.0 \times 3.0^2 = 18.0$ seconds

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📚 API Reference

@retry(...) Parameters

Parameter	Type	Default	Description
max_retries	int	3	Maximum retry attempts after initial failure ($\ge 0$).
base_delay	float	1.0	Initial wait time in seconds ($\ge 0$).
backoff_factor	float	2.0	Exponential delay multiplier ($\ge 1.0$).
exceptions	Tuple[Type[Exception], ...]	(Exception,)	Catchable exception types. Others propagate immediately.
fallback	Optional[Callable]	None	Invoked when all retries are exhausted.
logger	Optional[Logger]	None	Custom logger instance. Defaults to smartretry.core.
jitter	Union[bool, Callable]	False	Apply random wait times to avoid thundering herd.
on_retry	Optional[Callable]	None	Callback executed before sleeping (exc, attempt, delay).
retry_on_result	Optional[Callable]	None	Predicate on return value to trigger retry (result).
total_timeout	Optional[float]	None	Strict budget limit in seconds for the entire execution path.
dynamic_delay	Optional[Callable]	None	Dynamic delay resolver (e.g., reading Retry-After headers).

Custom Exceptions

• RetryExhaustedError: Raised when all attempts fail and no fallback is set. Contains .attempts, .last_error, and .func_name.
• CircuitOpenError: Raised when attempting to execute a function protected by an OPEN circuit. Contains .recovery_remaining.
• RateLimitExceededError: Raised when rate limits are violated. Contains .retry_after.
• BulkheadFullError: Raised when concurrent execution limit is reached.

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🧪 Comprehensive Testing

SmartRetry has a strict test suite that validates thread safety, async behavior, latency stats, error boundaries, and configuration setups with zero external dependencies.

Run the test suite locally:

python test_smartretry.py

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📄 License

This project is licensed under the MIT License. See the LICENSE file for more details.

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"Make your code bulletproof."
— Ali Kamrani

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