🐍 Why Python for DevOps? Python vs Shell Scripting

✅ Why Shell scripting is commonly used by DevOps engineers?

Most of the machines we use we deal with Linux systems because Linux is secure compared to other OS.
Linux comes with by default CLI instead of GUI, so commands are more needed in this case.

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🔁 What is the difference between shell commands and shell scripting?

If we create a file and put the extension as .sh and put multiple shell commands in that file, if we execute that file all commands are gonna run one by one.

Instead of running 5 commands one by one we can simply execute those commands via executing the script by:

bash <script_name>

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💡 Practical Example (Shell Scripting):

As a DevOps Engineer, we need to fetch the utilization of memory, disk, free space remaining, etc.

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🎯 The Primary Purpose of Shell Scripting

To interact with Linux systems and get some information from these Linux systems (get some system-related information from these Linux systems).

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🐍 Why Python?

We as DevOps engineers deal with most Linux systems but not all. So there can be some instances which are Windows as well.

Shell scripting wouldn't work when we need to retrieve info from both Linux and Windows systems. Shell is only restricted to Linux but Python works well with Windows as well.

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Ansible is a templating language built over Python only, so anybody who doesn't know Python can simply do easy tasks via Ansible.

But when we want to perform some complex tasks, we need to interact with APIs. When we want to do more data manipulation—in such use cases, Python wins over shell scripting.

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💡 Practical Example (Python):

As a DevOps Engineer, we need to talk to the Organization's GitHub repo and list out all the issues that are created and who have created these issues. (Talk to API of GitHub & Get List of Author Names who have raised an issue)

When we do an API call, GitHub will provide us a JSON payload and we need to fetch the author name from that.

We can do this via shell scripting too, but the modules of Python are rich and Python is designed to handle these kinds of tasks in a very simple way.

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⚖️ Shell Scripting vs Python: How to Choose?

Certainly! The choice between using shell scripting and Python in DevOps depends on the specific task or problem you're trying to solve. Both have their strengths and are suitable for different scenarios.

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✔️ Use Shell Scripting When:

1. System Administration Tasks: Shell scripting is excellent for automating routine system administration tasks like managing files, directories, and processes. You can use shell scripts for tasks like starting/stopping services, managing users, and basic file manipulation.

2. Command Line Interactions: If your task primarily involves running command line tools and utilities, shell scripting can be more efficient. It's easy to call and control these utilities from a shell script.

3. Rapid Prototyping: If you need to quickly prototype a solution or perform one-off tasks, shell scripting is usually faster to write and execute. It's great for ad-hoc tasks.

4. Text Processing: Shell scripting is well-suited for tasks that involve text manipulation, such as parsing log files, searching and replacing text, or extracting data from text-based sources.

5. Environment Variables and Configuration: Shell scripts are useful for managing environment variables and configuring your system.

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🧠 Use Python When:

1. Complex Logic: Python is a full-fledged programming language and is well-suited for tasks that involve complex logic, data structures, and algorithms. If your task requires extensive data manipulation, Python can be a more powerful choice.

2. Cross-Platform Compatibility: Python is more platform-independent than shell scripting, making it a better choice for tasks that need to run on different operating systems.

3. API Integration: Python has extensive libraries and modules for interacting with APIs, databases, and web services. If your task involves working with APIs, Python may be a better choice.

4. Reusable Code: If you plan to reuse your code or build larger applications, Python's structure and modularity make it easier to manage and maintain.

5. Error Handling: Python provides better error handling and debugging capabilities, which can be valuable in DevOps where reliability is crucial.

6. Advanced Data Processing: If your task involves advanced data processing, data analysis, or machine learning, Python's rich ecosystem of libraries (e.g., Pandas, NumPy, SciPy) makes it a more suitable choice.