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import functools | |
from django.conf import settings | |
from django.db import transaction, utils | |
def durable(func): | |
""" | |
Decorator to ensure that a function is not being called within an atomic block. |
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// load call | |
$(window).on('load', function() { | |
//active loading | |
//loadout | |
$('#preloader').fadeOut(); | |
}); |
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name: CI | |
on: [push] | |
jobs: | |
test: | |
runs-on: ubuntu-latest | |
services: |
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[ | |
{ | |
"value": "001", | |
"label": "Banco do Brasil S.A." | |
}, | |
{ | |
"value": "003", | |
"label": "Banco da Amazônia S.A." | |
}, | |
{ |
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#!/usr/bin/python3 | |
import argparse | |
import logging | |
import subprocess | |
import os | |
import tempfile | |
from tempfile import mkstemp | |
import configparser | |
import gzip |
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class Errors { | |
/** | |
* Create a new Errors instance. | |
*/ | |
constructor() { | |
this.errors = {}; | |
} | |
/** |
The following are examples of the four types rate limiters discussed in the accompanying blog post. In the examples below I've used pseudocode-like Ruby, so if you're unfamiliar with Ruby you should be able to easily translate this approach to other languages. Complete examples in Ruby are also provided later in this gist.
In most cases you'll want all these examples to be classes, but I've used simple functions here to keep the code samples brief.
This uses a basic token bucket algorithm and relies on the fact that Redis scripts execute atomically. No other operations can run between fetching the count and writing the new count.
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import functools | |
def force_async(fn): | |
''' | |
turns a sync function to async function using threads | |
''' | |
from concurrent.futures import ThreadPoolExecutor | |
import asyncio | |
pool = ThreadPoolExecutor() |
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