When writing programs, we often encounter program exceptions. At this time, we have to deal with these exceptions to ensure the robustness of the program.
There are several versions of exception handling, which one do you usually do?
irresponsible version
In this case, do not do anything, let the program report an error, resulting in program interruption.
For simple programs, it's fine to do this, the big deal is that I'll fix the problem and run it again. But if it is a complex system, it will be very troublesome. Maybe you block the operation of the system with an exception, which will bring catastrophic consequences.
Simple processing version
The simple processing version is to add exception capture, record the log when an exception occurs, and locate the exception through the log.
def do_something():
pass
def log_error(xxx):
pass
try:
do_something()
except:
log_error(xxxx)Improved handling version
Improvements have been made to the simple processing version to increase the number of retries. This is more common in crawler programs. The first request times out, and the request may be successful after a while, so retrying a few times may eliminate the exception.
attempts = 0
success = False
while attempts < 3 and not success:
try:
do_something()
success = True
except:
attempts += 1
if attempts == 3:
breakBut this is still not elegant, you may have to hard code this kind of retry in many places, and the program will look messy.
Today I will introduce a third-party module - retrying. It is an elegant solution to exception retries in a program.
installation and use
Install
The install command is still pretty bland:
pip install retrying
use
Let me introduce to you the parameters that can be used in this decorator function.
life is long so add oil
retrying provides a decorator function retry, the decorated function will be re-executed if it fails to run. By default, it will always retry when an error is reported.
import random
from retrying import retry
@retry
def do_something_unreliable():
if random.randint(0, 10) > 1:
print("just have a test")
raise IOError("raise exception!")
else:
return "good job!"
print(do_something_unreliable())Running this program, you can see that the number of times the sentence "just have a test" is printed is different each time. This is because our program will print and throw an exception whenever the random integer is greater than 1. But since we have the decorator function retry, the method will be re-executed when an exception occurs, until the random integer is greater than 1, and "good job!" will be printed.
Don't be too stubborn
This endless retry is a waste of life and resources. We want to build green homes, so let's add some restrictions:
# 最大重试次数
@retry(stop_max_attempt_number=5)
def do_something_limited():
print("do something several times")
raise Exception("raise exception")
do_something_limited()Cherish the limited time
An inch of time is worth an inch of gold, and an inch of gold cannot buy an inch of time. We want to cherish limited time, so let's add a time limit to our retry:
# 限制最长重试时间（从执行方法开始计算）
@retry(stop_max_delay=5000)
def do_something_in_time():
print("do something in time")
raise Exception("raise exception")
do_something_in_time()Stop and admire the scenery along the way
Life has been in a hurry for decades. Don't run all the way and forget to appreciate the beautiful scenery along the road. Sometimes we need to take a moment to appreciate the beautiful scenery along the way:
# 设置固定重试时间
@retry(wait_fixed=2000)
def wait_fixed_time():
print("wait")
raise Exception("raise exception")
wait_fixed_time()set a limit on failure
Although we need the resilience of repeated defeats and battles, there must be a limit to failure, and we cannot spend a lifetime in failure:
# 设置重试时间的随机范围
@retry(wait_random_min=1000,wait_random_max=2000)
def wait_random_time():
print("wait")
raise Exception("raise exception")
wait_random_time()some people are worth the wait
In the vast sea of people, I just want to wait for the right person:
# 根据异常重试
def retry_if_io_error(exception):
return isinstance(exception, IOError)
# 设置特定异常类型重试
@retry(retry_on_exception=retry_if_io_error)
def retry_special_error():
print("retry io error")
raise IOError("raise exception")
retry_special_error()We define a function ourselves, judge the exception type, and then pass the function as a parameter to the decorated function retry. If the exception type matches, it will retry.
Some results are what we want to see
Life is not smooth sailing, and sometimes we encounter setbacks that may be what we want in the first place:
# 通过返回值判断是否重试
def retry_if_result_none(result):
"""Return True if we should retry (in this case when result is None), False otherwise"""
# return result is None
if result =="111":
return True
@retry(retry_on_result=retry_if_result_none)
def might_return_none():
print("Retry forever ignoring Exceptions with no wait if return value is None")
return "111"
might_return_none()Here we define a function that determines the return value, and then pass this function as a parameter to the retry decorator. When the result returned is "111", the might_return_nonefunction .
Life is colorful, not monotonous
Our lives are colorful and never monotonous. Therefore, the above parameters can be used in any combination, and it is not limited to use only one at a time. For example, you can limit the retry IOErrorwhen , and the number of retries is up to 5 times.
Summarize
Life can't be repeated, but Python can try again!
I have retryingdedicated the use of this decorator function to you without reservation, so hurry up and use it!
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