Solution to retry when an exception occurs

This article mainly introduces you to the solution to abnormal retry in Python. The introduction in the article is very detailed. I believe it has certain reference learning value for everyone to learn or use python. Friends who need it will follow the editor below. Let’s take a look.

Preface

When everyone is doing data capture, they often encounter program saving due to network problems. Previously, only errors were recorded. content, and post-process the error content.

Original process:

def crawl_page(url):
 pass
 
def log_error(url):
 pass
 
url = ""
try:
 crawl_page(url)
except:
 log_error(url)

Improved process:

attempts = 0
success = False
while attempts < 3 and not success:
 try:
  crawl_page(url)
  success = True
 except:
  attempts += 1
  if attempts == 3:
   break

Recently discovered new solution: retrying

retrying is a Python retry package , can be used to automatically retry some program segments that may fail. retrying Provide a decorator function retry. The decorated function will be re-executed if it fails. By default, it will continue to retry as long as an error is reported. .

import random
from retrying import retry
 
@retry
def do_something_unreliable():
 if random.randint(0, 10) > 1:
  raise IOError("Broken sauce, everything is hosed!!!111one")
 else:
  return "Awesome sauce!"
 
print do_something_unreliable()

If we run the have_a_try function, it will not end until random.randint returns 5, otherwise it will Always re-execute.

retry can also accept some parameters. You can see the optional parameters from the initialization function of the Retrying class in the source code:

• stop_max_attempt_number: Used to set the maximum number of attempts. If the number is exceeded, retry will stop.

• # #stop_max_delay: For example, set to 10000, then from the time when the decorated function starts executing to the time when the function ends successfully or fails with an error, as long as this period exceeds 10 seconds, the function will no longer Executed

• wait_fixed: Set the dwell time between two retrying

• wait_random_min and wait_random_max: Randomly generate the residence time between two retrying

• ##wait_exponential_multiplier and wait_exponential_max

  : Generate the residence time between two retrying in exponential form, the generated value is 2^previous_attempt_number * wait_exponential_multiplier, previous_attempt_number is the previous The number of retry, if the generated value exceeds the size of wait_exponential_max, then the stay value between the two retryings will be wait_exponential_max. This design caters to the exponential backoff algorithm, which can alleviate blocking situations.

We can specify which exceptions we want to retry when an exception occurs. This requires using
• retry_on_

  exception to pass in a function Object :

def retry_if_io_error(exception):
 return isinstance(exception, IOError)
 
@retry(retry_on_exception=retry_if_io_error)
def read_a_file():
 with open("file", "r") as f:
  return f.read()

During the execution of the

read_a_file

function, if an exception is reported, then this exception will Pass the formal parameter exception into the retry_if_io_error function. If exception is IOError, then retry will be performed. If Otherwise, it stops running and throws an exception.
We can also specify which results to

retry

when we get them. This requires using retry_on_result to pass in a function object:

def retry_if_result_none(result):
 return result is None
 
@retry(retry_on_result=retry_if_result_none)
def get_result():
 return None

After the execution of

get_result

is successful, the return value of the function will be passed in in the form of formal parameter resultretry_if_result_noneIn the function, if the return value is None, then perform retry, otherwise it will end and return the function value.

Summary

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