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Facebook messenger chatbot with django heroku and IBM watson

on June 17, 2022

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how to approach fixing memory leaks in Python & Celery

General tips and guidance on how to approach fixing memory leaks in Python, which can be applied to the Celery project. 1. Identify the leak source: Use memory profiling tools like memory_profiler or objgraph to identify the objects that are causing the memory leak. This will help you pinpoint the part of the code that needs fixing. from memory_profiler import profile ``` @profile def your_function(): # Your code here ``` 2. Use weak references: If the memory leak is caused by circular references between objects, you can use Python's weakref module to create weak references that don't prevent garbage collection. ``` import weakref class MyClass: def __init__(self, other_instance=None): self.other_instance = weakref.ref(other_instance) if other_instance else None instance1 = MyClass() instance2 = MyClass(instance1) instance1.other_instance = weakref.ref(instance2) ``` Another example: ``` import weakref from celery import Celery app = Celery('tasks', bro

Why I am using Emberjs instead of reactjs in 2022?

React is a cool UI rendering library & undoubtedly the most popular library in the JS world now. But as a Django developer, I found ember to be very productive out of the box. Moreover, the ember workflows and concepts are very identical to any Django/rails/sails developers & if you have JSON-API 1.0 standard Restful API ember-data work just out of the box with any JSON-API 1.0 standard API! react lack this and react ecosystem especially mobx lack this.

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Facebook messenger chatbot with django heroku and IBM watson

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