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Python Type Hints

Python is a dynamically typed language, meaning variable types don't require explicit declaration. However, as projects grow in complexity, type annotations become increasingly valuable for code maintainability and clarity.

Type hints (PEP 484) have been a major focus of recent Python releases, and I was particularly intrigued when I heard about Guido van Rossum's work on MyPy at Dropbox, where the team needed robust tooling to migrate their codebase from Python 2 to Python 3.

Today, type hints are essential for modern Python development. They significantly enhance IDE capabilities and AI-powered development tools by providing better code completion, static analysis, and error detection. This mirrors the evolution we've seen with TypeScript's adoption over traditional JavaScript—explicit typing leads to more reliable and maintainable code.

The majority of this post is based on MyPy documentation.

Typed Python vs data science projects

We know that type hints are not very popular among data science projects for some reasons, but we won't discuss them here.

Databricks Connect

Databricks Connect allows you to connect your favorite IDE (PyCharm, VSCode, etc.) and other custom applications to Databricks compute and run Spark (or non-Spark) code.

This post is not a comprehensive guide on Databricks Connect; rather, it consists of side notes from the Azure Databricks docs. Most of the notes also apply to Databricks on AWS and GCP.

Generating Azure OAuth2 Access Token By Python

There are two modern ways to generate an Azure OAuth2 access token using Python: one is by using the MSAL library, and the other is by using the Azure Identity library, which is based on the former.

There're also other ways to get the token, like using the requests or aiohttp libraries etc. to send a POST request to the Azure OAuth2 token endpoint, but it's not recommended. As the MSAL and Azure Identity libraries are the official libraries provided by Microsoft, they are more secure and easier to use. For e.g. they handle token caching, token refreshing, and token expiration automatically. Furthermore, some of the credential types are difficult (too many code) to be implemented by raw requests or aiohttp.

Python thread safe operations

Quick example from the official Python documentation about thread safety in Python:

Thread safe operations
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L.append(x)
L1.extend(L2)
x = L[i]
x = L.pop()
L1[i:j] = L2
L.sort()
x = y
x.field = y
D[x] = y
D1.update(D2)
D.keys()
Not thread safe operations
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i = i+1
L.append(L[-1])
L[i] = L[j]
D[x] = D[x] + 1

It's important to understand that Python, due to its Global Interpreter Lock (GIL), can only switch between threads between bytecode instructions. The frequency of these switches can be adjusted using sys.setswitchinterval(). This ensures that within a single bytecode instruction, Python will not switch threads, making the operation atomic (thread-safe). For a deeper dive into this topic, you can read this discussion on atomic and thread-safe operations in Python.

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Generating .env file

During local testing, we often need to set environment variables. One way to do this is to create a .env file in the root directory of the project. This file contains key-value pairs of environment variables. For example, a .env file might look like this:

ENV=dev
SECRET=xxx

Hereunder a quick bash script to generate a .env file from a list of Azure KeyVault secrets, same logic can be applied to other secret managers.