Runtime Configuration

Configure backoff behavior dynamically at runtime.

Overview

Decorator parameters can accept callables that are evaluated at runtime, allowing dynamic configuration based on application state, environment variables, or configuration files.

Basic Pattern

class Config:
    MAX_RETRIES = 5
    MAX_TIME = 60

@backoff.on_exception(
    backoff.expo,
    Exception,
    max_tries=lambda: Config.MAX_RETRIES,
    max_time=lambda: Config.MAX_TIME
)
def configurable_function():
    pass

# Change configuration at runtime
Config.MAX_RETRIES = 10

Environment Variables

import os

@backoff.on_exception(
    backoff.expo,
    Exception,
    max_tries=lambda: int(os.getenv('RETRY_MAX_TRIES', '5')),
    max_time=lambda: int(os.getenv('RETRY_MAX_TIME', '60'))
)
def env_configured():
    pass

Configuration Files

import json

def load_config():
    with open('config.json') as f:
        return json.load(f)

@backoff.on_exception(
    backoff.expo,
    Exception,
    max_tries=lambda: load_config()['retry']['max_tries'],
    max_time=lambda: load_config()['retry']['max_time']
)
def file_configured():
    pass

Dynamic Wait Strategies

def get_wait_gen():
    if app.config.get('fast_retry'):
        return backoff.constant
    return backoff.expo

@backoff.on_exception(
    lambda: get_wait_gen(),
    Exception
)
def dynamic_wait():
    pass

Application State

class RateLimiter:
    def __init__(self):
        self.rate_limited = False

    def get_interval(self):
        return 10 if self.rate_limited else 1

rate_limiter = RateLimiter()

@backoff.on_predicate(
    backoff.constant,
    interval=lambda: rate_limiter.get_interval()
)
def adaptive_poll():
    return check_resource()