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Python Requests实现API请求重试与超时配置全解析

作者:站大爷IP

API请求的稳定性直接决定系统可靠性,本文通过真实案例拆解,Python如何用Requests库实现防抖动+抗异常的健壮请求方案,需要的可以了解下

​在电商物流追踪、金融数据监控等场景中,API请求的稳定性直接决定系统可靠性。当顺丰API因网络抖动返回503错误,或因跨地域调用出现10秒延迟时,如何确保程序不崩溃且数据不丢失?本文通过真实案例拆解,用Requests库实现"防抖动+抗异常"的健壮请求方案。

一、血泪教训:那些年踩过的API坑

某跨境电商系统在"黑色星期五"大促期间突发故障:调用顺丰国际件接口时,30%的请求因超时失败,导致2000+包裹状态同步延迟。事后分析发现三大元凶:

这些场景揭示核心问题:API请求需要"有智慧的等待"和"有策略的坚持"

二、超时配置:给请求装上"安全阀"

1. 连接超时 vs 读取超时

import requests

try:
    # 连接超时3秒(TCP握手阶段)
    # 读取超时10秒(服务器处理阶段)
    response = requests.get(
        'https://api.sf-express.com/track',
        params={'trackingNumber': 'SF123456789'},
        timeout=(3, 10)  # 元组形式分别设置
    )
    print(response.json())
except requests.exceptions.ConnectTimeout:
    print("连接服务器失败,请检查网络")
except requests.exceptions.ReadTimeout:
    print("服务器处理超时,请稍后重试")

关键决策点

2. 动态超时策略

某物流监控系统采用分级超时机制:

def get_dynamic_timeout(retry_count):
    base_timeout = 3  # 基础超时
    if retry_count > 0:
        return min(base_timeout * (2 ** retry_count), 30)  # 指数退避,最大30秒
    return base_timeout

# 使用示例
for i in range(3):
    try:
        timeout = get_dynamic_timeout(i)
        response = requests.get(url, timeout=timeout)
        break
    except Exception as e:
        print(f"第{i+1}次尝试失败,超时时间调整为{timeout}秒")

三、重试机制:让请求学会"坚持"

1. 指数退避重试(推荐方案)

from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry

def create_retry_session(retries=3, backoff_factor=1, status_forcelist=(500, 502, 503, 504)):
    session = requests.Session()
    retry = Retry(
        total=retries,
        read=True,  # 允许读取超时重试
        connect=True,  # 允许连接超时重试
        backoff_factor=backoff_factor,
        status_forcelist=status_forcelist,
        allowed_methods=["GET", "POST"]  # 支持POST请求重试
    )
    adapter = HTTPAdapter(max_retries=retry)
    session.mount("http://", adapter)
    session.mount("https://", adapter)
    return session

# 使用示例
session = create_retry_session()
response = session.get('https://api.sf-express.com/track', params={'trackingNumber': 'SF123456789'})

参数深解:

2. 熔断机制实现(避免雪崩)

from collections import deque
import time

class CircuitBreaker:
    def __init__(self, max_failures=3, reset_timeout=60):
        self.failures = deque(maxlen=max_failures)
        self.reset_timeout = reset_timeout

    def is_open(self):
        if len(self.failures) < self.failures.maxlen:
            return False
        # 如果最近max_failures次请求都失败,且最后一次失败在reset_timeout秒内
        return (time.time() - self.failures[-1]) < self.reset_timeout

    def record_failure(self):
        self.failures.append(time.time())

    def record_success(self):
        self.failures.clear()

# 结合重试使用
breaker = CircuitBreaker(max_failures=3, reset_timeout=30)

def safe_request():
    if breaker.is_open():
        raise Exception("Service unavailable, circuit breaker open")
    
    try:
        response = create_retry_session().get(url)
        if response.status_code == 200:
            breaker.record_success()
            return response
        else:
            breaker.record_failure()
            raise Exception("API request failed")
    except Exception as e:
        breaker.record_failure()
        raise e

四、代理配置:突破封禁的"隐身术"

1. 代理池实战方案

import random
from requests.adapters import HTTPAdapter

class ProxyPool:
    def __init__(self):
        self.proxies = [
            {"http": "http://1.1.1.1:8080", "https": "http://1.1.1.1:8080"},
            {"http": "http://2.2.2.2:8080", "https": "http://2.2.2.2:8080"},
            # 更多代理...
        ]
        self.failed_proxies = set()

    def get_proxy(self):
        available_proxies = [p for p in self.proxies if p not in self.failed_proxies]
        if not available_proxies:
            raise Exception("No available proxies")
        return random.choice(available_proxies)

    def mark_failed(self, proxy):
        self.failed_proxies.add(proxy)

# 使用示例
proxy_pool = ProxyPool()
session = requests.Session()

for _ in range(3):  # 尝试3个不同代理
    try:
        proxy = proxy_pool.get_proxy()
        response = session.get(
            'https://api.sf-express.com/track',
            proxies=proxy,
            timeout=(3, 10)
        )
        if response.status_code == 200:
            print("Success with proxy:", proxy)
            break
        else:
            proxy_pool.mark_failed(proxy)
    except Exception:
        proxy_pool.mark_failed(proxy)
else:
    print("All proxies failed")

2. Tor代理配置(高匿名场景)

import requests

def make_tor_request(url):
    proxies = {
        'http': 'socks5h://127.0.0.1:9050',
        'https': 'socks5h://127.0.0.1:9050'
    }
    try:
        response = requests.get(url, proxies=proxies, timeout=(5, 15))
        print("Tor出口IP:", response.json()['origin'])
        return response
    except Exception as e:
        print("Tor请求失败:", e)

# 需提前安装Tor服务并启动
# sudo apt install tor  # Ubuntu系统
# sudo service tor start

五、完整实战案例

import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import logging
from datetime import datetime

# 日志配置
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)

class SFExpressTracker:
    def __init__(self, app_key, app_secret):
        self.app_key = app_key
        self.app_secret = app_secret
        self.session = self._create_session()

    def _create_session(self):
        """创建带重试和超时的会话"""
        retry_strategy = Retry(
            total=3,
            backoff_factor=1,
            status_forcelist=[429, 500, 502, 503, 504],
            allowed_methods=["GET", "POST"]
        )
        adapter = HTTPAdapter(max_retries=retry_strategy)
        session = requests.Session()
        session.mount("http://", adapter)
        session.mount("https://", adapter)
        return session

    def _generate_sign(self, params):
        """生成API签名(简化版)"""
        import hashlib
        sorted_params = sorted(params.items(), key=lambda x: x[0])
        raw_str = self.app_secret + ''.join([f"{k}{v}" for k, v in sorted_params]) + self.app_secret
        return hashlib.md5(raw_str.encode()).hexdigest().upper()

    def query_track(self, tracking_number):
        """查询物流轨迹"""
        url = "https://bsp-ois.sf-express.com/bsp-ois/express/service/queryTrack"
        params = {
            "appKey": self.app_key,
            "trackNumber": tracking_number,
            "timestamp": str(int(datetime.now().timestamp()))
        }
        params["sign"] = self._generate_sign(params)

        try:
            response = self.session.get(
                url,
                params=params,
                timeout=(3, 10)  # 连接3秒,读取10秒
            )
            response.raise_for_status()
            data = response.json()
            
            if data.get('success'):
                return data['data']['tracks']
            else:
                logging.warning(f"API返回错误: {data.get('errorMsg', '未知错误')}")
                return None
                
        except requests.exceptions.RequestException as e:
            logging.error(f"请求失败: {str(e)}")
            return None

# 使用示例
if __name__ == "__main__":
    tracker = SFExpressTracker(app_key="YOUR_APP_KEY", app_secret="YOUR_APP_SECRET")
    tracks = tracker.query_track("SF123456789")
    if tracks:
        for step in tracks:
            print(f"{step['acceptTime']} {step['acceptAddress']} - {step['remark']}")

六、常见问题Q&A

Q1:被网站封IP怎么办?

A:立即启用备用代理池,建议使用住宅代理(如站大爷IP代理),配合每请求更换IP策略。对于大规模爬取,可采用Tor网络或IP轮换中间件。

Q2:如何选择重试次数?

A:遵循"3次黄金法则":

Q3:代理请求速度慢怎么解决?

A:

Q4:如何记录重试日志?

A:扩展Retry类实现自定义日志:

from urllib3.util.retry import Retry
import logging

class LoggingRetry(Retry):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.logger = logging.getLogger(__name__)

    def new(self, **kw):
        self.logger.debug(f"Creating new retry adapter with params: {kw}")
        return super().new(**kw)

    def increment(self, method, url, response=None, error=None, **kwargs):
        self.logger.warning(f"Retry attempt {self.total - self._remaining + 1} for {method} {url}")
        return super().increment(method, url, response, error, **kwargs)

Q5:POST请求重试需要注意什么?

A:

通过合理组合超时配置、智能重试和代理策略,可构建出应对各种异常场景的健壮API请求系统。实际开发中建议结合Prometheus监控重试率、失败率等指标,持续优化请求策略。

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