Overview

Continuing from our last article, we’ll delve deeper into implementing a cache with Time-to-Live (TTL).
Let’s quickly recap what we covered previously.

Summary of Part 1

1. We learned how to equip functions with cache capabilities using lru_cache.
2. We implemented the get_ttl_hash function, which updates return values at specified intervals.
3. We were able to give functions TTL caching by:
   1. Enhancing them with the caching functionality of lru_cache.
   2. Adding a dummy argument.
   3. Inputting the return value of get_ttl_hash to the dummy argument.

Our goal in this article is to create a decorator that automates this process.

Overview

In this article, we’ll discuss how to implement a caching mechanism in Python that has a Time-To-Live (TTL).
This cache can be useful in scenarios like:

• Functions that connect to the internet to fetch data, but the data remains constant over a period.
  (e.g., weather forecast data)
• Functions that are executed frequently but don’t require real-time data.

Adding Caching to a Function

First, let’s see how to add caching to a Python function.

Have you ever been running a time-consuming code in Jupyter Notebook, like training a machine learning model, only to realize later that it had stopped due to an unexpected error?

In this article, I’ll introduce a method to automatically send notifications to platforms like Slack or Discord when such exceptions occur.

Creating a Notification Function

First, let’s prepare a function for sending notifications. If you’re using Slack or Discord, you can notify with the following functions:

In this article, I’ll introduce how to save the code execution history from Jupyter Notebook (JupyterLab) as a Python script.

First, I’ll go over the commonly used %history magic command and then share a custom function I’ve written.

1. %history Magic Command

• By running the following command in a notebook cell, you can display the code execution history: