Full Code of techiescamp/python-for-devops for AI

Repository: techiescamp/python-for-devops
Branch: main
Commit: 7e1742bf1d23
Files: 20
Total size: 30.1 KB

Directory structure:
gitextract_872x2lis/

├── README.md
├── awsrdsmysqlp.py
├── azure-openapi/
│   └── basic-api/
│       ├── main.py
│       └── requirements.txt
├── boto3/
│   ├── dynamodb/
│   │   ├── README.md
│   │   ├── createTable.py
│   │   ├── data.json
│   │   ├── loadData.py
│   │   └── query.py
│   └── ec2/
│       └── create-instance.py
├── database/
│   └── postgresql.py
├── generic/
│   ├── command-line-argument.py
│   └── exception-handling.py
├── json/
│   ├── files/
│   │   └── example.json
│   └── parse-json.py
└── operating-system/
    ├── README.md
    ├── command-execution.py
    ├── files/
    │   ├── param.sh
    │   └── script.sh
    └── shell-execution.py

================================================
FILE CONTENTS
================================================

================================================
FILE: README.md
================================================

# Python for DevOps: 8-Week Learning Roadmap

If you want to understand the importance of python for DevOps, please read the [detailed python for DevOps guide](https://devopscube.com/python-for-devops/).

## 📌 Overview

This roadmap is designed to help DevOps Engineers and Platform Engineers master Python for automation, cloud operations, infrastructure as code, and Kubernetes. The curriculum includes hands-on projects that mimic real-world DevOps tasks using industry tools like Docker, Terraform, Kubernetes, and CI/CD pipelines.

By the end of this roadmap, you'll:
- Automate repetitive tasks with Python
- Work with cloud APIs and infrastructure automation
- Build Python-based DevOps tools and microservices
- Deploy containerized apps with Docker and Kubernetes
- Use AI-powered tools like LlamaIndex & GenAI for DevOps
- Be fully prepared for Python-based DevOps interviews

## 👨‍💻 Python Learning Resources

If you are looking for a guided way to learn Python from scratch., I recommend the following resources.

1. [learnpython.org](https://www.learnpython.org/)
2. [Learn Python 3 from Scratch](https://www.educative.io/courses/learn-python-3-from-scratch?aff=KNLz)
3. [Python for Beginners – Full Video Course](https://www.youtube.com/watch?v=eWRfhZUzrAc)

## 🚀 Week 1: Python Fundamentals for DevOps

### 🎯 Goal: Learn the basics of Python for scripting and automation.

✅ Python syntax, variables, loops, functions, and error handling  
✅ Working with files (reading, writing, and parsing logs)  
✅ Interacting with OS processes (`subprocess`, `os` modules)  
✅ Installing and managing packages with `pip` and `venv`  

**🔨 Hands-on Project:**
- Write a Python script to automate log parsing from `/var/log/` and extract useful insights.
- Create a script that monitors CPU & memory usage using the `psutil` library.

## 🛠 Week 2: Automating DevOps Tasks with Python

### 🎯 Goal: Automate daily DevOps operations.

✅ Working with APIs (`requests` library for REST APIs)  
✅ Automating SSH tasks (`paramiko` for remote execution)  
✅ Automating cloud operations with AWS/GCP SDKs (`boto3` and `google-cloud-sdk`)  
✅ Writing CLI tools with `argparse`  

**🔨 Hands-on Project:**
- Build a Python script that automates AWS EC2 instance management (start, stop, terminate instances).
- Create a CLI tool that checks the status of Kubernetes pods using `kubectl` and `subprocess`.

## 🐳 Week 3: Docker & Python for Containerized Applications

### 🎯 Goal: Learn to containerize Python applications for DevOps automation.

✅ Writing Dockerfiles for Python apps  
✅ Running Python scripts inside containers  
✅ Docker Compose for multi-container apps  
✅ Working with Python SDK for Docker (`docker-py`)  

**🔨 Hands-on Project:**
- Build a Python script to manage Docker containers (start, stop, restart, delete containers).
- Create a Flask API that returns system metrics (CPU, RAM) and deploy it in a Docker container.

## 🔧 Week 4: Infrastructure as Code (IaC) with Python

### 🎯 Goal: Automate infrastructure provisioning with Python.

✅ Terraform automation with Python (`python-terraform`)  
✅ Working with Ansible and Python (`ansible-runner`)  
✅ Writing Python scripts to automate Kubernetes YAML generation  
✅ Using `Fabric` for remote automation  

**🔨 Hands-on Project:**
- Write a Python script that provisions AWS infrastructure (VPC, EC2, S3) using Terraform.
- Automate Ansible playbook execution using Python.

## ☸️ Week 5: Kubernetes Automation with Python

### 🎯 Goal: Automate Kubernetes operations using Python.

✅ Working with Kubernetes Python SDK (`kubernetes` library)  
✅ Managing Kubernetes objects dynamically with Python  
✅ Writing Admission Webhooks in Python  
✅ Automating Helm deployments with Python  

**🔨 Hands-on Project:**
- Write a Python script that dynamically creates and deletes Kubernetes namespaces.
- Build a Mutating Admission Webhook to enforce security policies in a cluster.

## 🔍 Week 6: Python for Security & Monitoring in DevOps.

### 🎯 Goal: Secure infrastructure and monitor logs with Python.

✅ Parsing and analyzing logs (`loguru`, `logging`)  
✅ Security automation (checking misconfigurations with `PyInfra`)  
✅ Python for **SIEM (Security Information & Event Management)**  
✅ Automating RBAC checks for Kubernetes  

**🔨 Hands-on Project:**
- Build a tool that checks Kubernetes RBAC permissions and finds over-privileged service accounts.
- Automate security scanning of container images using Trivy and Python.

## 🤖 Week 7: GenAI & LlamaIndex for DevOps

### 🎯 Goal: Use AI for DevOps workflows with Python.

✅ Introduction to **LlamaIndex & GenAI for DevOps**  
✅ Automating incident response with AI-driven bots  
✅ Generating YAML/JSON configurations using AI  
✅ AI-powered log analysis using `LangChain` & `OpenAI API`  

**🔨 Hands-on Project:**
- Build an AI-powered chatbot that suggests Kubernetes troubleshooting steps.
- Develop an AI-based log anomaly detection system that detects security threats.


## 🤖 Week 8: Agentic AI 

### 🎯 Goal: Implement advanced AI-driven DevOps automation

✅ Understanding Agentic AI for DevOps  
✅ AI-driven CI/CD pipeline optimization  
✅ Automating incident remediation using AI agents  
✅ Integrating AI with monitoring tools (Prometheus, Grafana)  

## 💼 Final Capstone Projects

1️⃣ **DevOps Dashboard API:** Build a FastAPI app that displays real-time Kubernetes metrics.  
2️⃣ **Self-Healing Kubernetes System:** AI-powered Kubernetes operator that auto-heals pods based on anomaly detection.  
3️⃣ **Intelligent CI/CD Analyzer:** AI-driven CI/CD log analyzer that suggests fixes based on failure patterns.

## 📢 Next Steps

- Contribute to Open Source Python DevOps projects
- Deploy Python automation scripts in production Kubernetes environments
- Prepare for DevOps interviews with Python scripting questions
- Build AI-powered DevOps tools using LangChain & LlamaIndex

🔥 Ready to Master Python for DevOps? Let’s Get Started! 🚀



================================================
FILE: awsrdsmysqlp.py
================================================
import boto3
import json
import mysql.connector
import tabulate

# Initialize AWS Secrets Manager client
secrets_manager = boto3.client('secretsmanager')
db_host = "DB_HOST"

def get_secretvalue():
    secret_name = secrets_manager.get_secret_value(SecretId='YOUR_SECRET_ARN')
    secret_dict = json.loads(secret_name['SecretString'])
    db_username = secret_dict['username']
    db_password = secret_dict['password']
    return db_username, db_password
    
def execute_query(db_name, query):
    db_username, db_password = get_secretvalue()

    for db in db_name:
        try:
            connection = mysql.connector.connect(
                host=db_host,
                user=db_username,
                password=db_password,
                database=db
            )
            cursor = connection.cursor()
            cursor.execute(query)
            result = cursor.fetchall()
            row_count = cursor.rowcount

            if query.upper().startswith(("SELECT", "SHOW", "DESCRIBE")):
                if result:
                    header = [column_name[0] for column_name in cursor.description]
                    table_output = [header] + list(result)
                    print(f"Running query on: {db}")
                    print(tabulate.tabulate(table_output, tablefmt="pipe"))
                else:
                    print("Empty result")
            else:
                connection.commit()
                print(f"{row_count} rows affected in {db}")          
        
        except Exception as e:
            print("Error:", e)
        finally:
            if 'connection' in locals():
                connection.close()

db_names = input("Enter the DB names followed by space:").split()
query = input("Enter the query:")
execute_query(db_names, query)


================================================
FILE: azure-openapi/basic-api/main.py
================================================
import os
from openai import AzureOpenAI

endpoint = "<your-endpoint>"
model_name = "o4-mini"
deployment = "o4-mini"

subscription_key = os.getenv("AZURE_OPENAI_API_KEY")
api_version = "2024-12-01-preview"

client = AzureOpenAI(
    api_version=api_version,
    azure_endpoint=endpoint,
    api_key=subscription_key,
)

response = client.chat.completions.create(
    messages=[
        {
            "role": "system",
            "content": "You are a senior DevOps engineer helping beginners.",
        },
        {
            "role": "user",
            "content": "How do I set up CI/CD using GitHub Actions for a Node.js app?",
        }
    ],
    max_completion_tokens=100000,
    model=deployment
)

print(response.choices[0].message.content)

================================================
FILE: azure-openapi/basic-api/requirements.txt
================================================
openai~=1.60.2


================================================
FILE: boto3/dynamodb/README.md
================================================
## Working With DynamoDB Using Boto3

This example shows loading sample data to dynamoDB and using boto3 to acess the data from the table.

Code Credits: [fernandomc.com Guide](https://www.fernandomc.com/posts/ten-examples-of-getting-data-from-dynamodb-with-python-and-boto3/)

================================================
FILE: boto3/dynamodb/createTable.py
================================================
import boto3

dynamodb = boto3.client("dynamodb")

response = dynamodb.create_table(
  TableName="basicSongsTable",
  AttributeDefinitions=[
    {
      "AttributeName": "artist",
      "AttributeType": "S"
    },
    {
      "AttributeName": "song",
      "AttributeType": "S"
    }
  ],
  KeySchema=[
    {
      "AttributeName": "artist",
      "KeyType": "HASH"
    },
    {
      "AttributeName": "song",
      "KeyType": "RANGE"
    }
  ],
  ProvisionedThroughput={
    "ReadCapacityUnits": 1,
    "WriteCapacityUnits": 1
  }
)

print(response)

================================================
FILE: boto3/dynamodb/data.json
================================================
[
    {
      "artist": "Romero Allen",
      "song": "Atomic Dim",
      "id": "b4b0da3f-36e3-4569-b196-3ad982f72bbd",
      "priceUsdCents": 392,
      "publisher": "QUAREX"
    },
    {
      "artist": "Hilda Barnes",
      "song": "Almond Dutch",
      "id": "eeb58c73-603f-4d6b-9e3b-cf587488f488",
      "priceUsdCents": 161,
      "publisher": "LETPRO"
    },
    {
      "artist": "Hall Ramos",
      "song": "Carmine Drab",
      "id": "951637bd-59ef-4448-93f5-e179bfbd20a0",
      "priceUsdCents": 370,
      "publisher": "BRAINQUIL"
    },
    {
      "artist": "Chang Vance",
      "song": "Aureolin Dodger",
      "id": "29f5dea2-2bd1-4d13-bf33-7aadc4f14ef2",
      "priceUsdCents": 324,
      "publisher": "DIGIAL"
    },
    {
      "artist": "May Tanner",
      "song": "Cadmium Cultured",
      "id": "483b1787-a9fd-4ec8-bf16-5ec122bb559a",
      "priceUsdCents": 146,
      "publisher": "CHORIZON"
    },
    {
      "artist": "Tillman Watkins",
      "song": "Cameo Dark",
      "id": "18f42c1e-8a91-47f9-829e-836ee1a49a17",
      "priceUsdCents": 173,
      "publisher": "OTHERWAY"
    },
    {
      "artist": "Naomi Petersen",
      "song": "Candy Earth",
      "id": "0ed05345-3dfa-4ea4-a59a-6c015153c9d5",
      "priceUsdCents": 249,
      "publisher": "SONIQUE"
    },
    {
      "artist": "Kristin Olson",
      "song": "Banana Eerie",
      "id": "f4ea8c5a-1a5b-4d4b-94cf-74da68c543e6",
      "priceUsdCents": 246,
      "publisher": "AMTAP"
    },
    {
      "artist": "Marta Frost",
      "song": "Apple Chinese",
      "id": "5cd6c545-655c-491d-bc7c-1aaa8569b26c",
      "priceUsdCents": 181,
      "publisher": "ZAGGLES"
    },
    {
      "artist": "Etta Anderson",
      "song": "Antique English",
      "id": "ba865a03-8666-4074-8e47-15bcc707fa6e",
      "priceUsdCents": 143,
      "publisher": "DATACATOR"
    },
    {
      "artist": "Charlotte Cooley",
      "song": "Amaranth Dim",
      "id": "fb4aad81-24c0-4d79-ac34-264923e99464",
      "priceUsdCents": 121,
      "publisher": "KEENGEN"
    },
    {
      "artist": "Elise Pugh",
      "song": "Bright Cerulean",
      "id": "5ac17e68-e6c3-43f6-a305-5c63ac39307f",
      "priceUsdCents": 389,
      "publisher": "CUJO"
    },
    {
      "artist": "Tamika Austin",
      "song": "Camel Flickr",
      "id": "a89ac751-89ba-41e9-9e86-60605b3139f4",
      "priceUsdCents": 392,
      "publisher": "TECHMANIA"
    },
    {
      "artist": "Claire Sharp",
      "song": "Beaver Electric",
      "id": "f9ab6076-3582-479e-877e-f27d5c0919d9",
      "priceUsdCents": 209,
      "publisher": "PORTICO"
    },
    {
      "artist": "Lilian Horton",
      "song": "Bright Coral",
      "id": "634d096f-edc1-4452-a73c-d417c7e46c04",
      "priceUsdCents": 227,
      "publisher": "GUSHKOOL"
    },
    {
      "artist": "Reba George",
      "song": "Bone Coquelicot",
      "id": "a0d5a2c1-7086-46a5-98ea-36724a296728",
      "priceUsdCents": 124,
      "publisher": "MANGLO"
    },
    {
      "artist": "Ruiz Vaughn",
      "song": "Byzantium Feldgrau",
      "id": "202cb417-0b2b-4809-8993-0916aa62ff67",
      "priceUsdCents": 223,
      "publisher": "RONBERT"
    },
    {
      "artist": "Wilcox Contreras",
      "song": "Brink Cocoa",
      "id": "c7763312-b732-4ad9-8d58-295f36e3f411",
      "priceUsdCents": 214,
      "publisher": "ZENTIA"
    },
    {
      "artist": "Jodi Ruiz",
      "song": "Auburn Chartreuse",
      "id": "ab89362d-3ef7-461e-a093-60aa2e28eb07",
      "priceUsdCents": 215,
      "publisher": "ZOLAVO"
    },
    {
      "artist": "Potter Keith",
      "song": "Arctic CG",
      "id": "e52cb1f0-2459-4c60-9841-4497fcdab5a9",
      "priceUsdCents": 173,
      "publisher": "CIRCUM"
    },
    {
      "artist": "Wilson Randall",
      "song": "Black Coral",
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      "priceUsdCents": 206,
      "publisher": "GEEKUS"
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    {
      "artist": "Morrow Camacho",
      "song": "Bittersweet Poetic",
      "id": "23edaed1-d443-4e53-bbbd-c54dc8d3bf5f",
      "priceUsdCents": 182,
      "publisher": "MEDESIGN"
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      "artist": "Clayton Sears",
      "song": "Bitter Dodger",
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      "publisher": "ZOXY"
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      "priceUsdCents": 193,
      "publisher": "MYOPIUM"
    },
    {
      "artist": "Madeleine Rivers",
      "song": "Bisque Congo",
      "id": "596626a8-c54b-4d88-9925-0415d4ab589d",
      "priceUsdCents": 137,
      "publisher": "LINGOAGE"
    },
    {
      "artist": "Jeannie Benton",
      "song": "Caribbean Denim",
      "id": "802afc05-3e72-4c51-b234-7e16a63150e2",
      "priceUsdCents": 122,
      "publisher": "XURBAN"
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      "publisher": "FUTURIZE"
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      "song": "Bittersweet Sunday",
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      "song": "Candy Flax",
      "id": "d043213f-01fc-4086-83df-e852b6450605",
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      "publisher": "RETRACK"
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    {
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      "song": "Buff Cerulean",
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      "publisher": "NITRACYR"
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      "publisher": "UNI"
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    {
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      "song": "Brown English",
      "id": "9790b013-ae7c-4afb-b166-37a0a012cc5b",
      "priceUsdCents": 180,
      "publisher": "INTERODEO"
    },
    {
      "artist": "Hardy Alexander",
      "song": "Aqua November",
      "id": "464cd8a0-b5f6-43c1-a6d6-b548c13602f3",
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      "publisher": "ISOPOP"
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    {
      "artist": "Jerry Floyd",
      "song": "Brown Dartmouth",
      "id": "6bef9c2e-0610-49fa-b162-3446aa88757e",
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      "publisher": "HOMETOWN"
    },
    {
      "artist": "Nichole Phelps",
      "song": "Blue Cultured",
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      "publisher": "XIXAN"
    },
    {
      "artist": "Marguerite Mcclure",
      "song": "Cedar Columbia",
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      "publisher": "TELEQUIET"
    },
    {
      "artist": "Beach Evans",
      "song": "Bright Coffee",
      "id": "26d74b66-5bb7-46e6-89cb-b8864b8111bf",
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      "publisher": "OHMNET"
    },
    {
      "artist": "Clarke Peterson",
      "song": "Carnelian Falu",
      "id": "d708c366-0580-410b-a0d7-1f7c91ecc7c4",
      "priceUsdCents": 339,
      "publisher": "LUMBREX"
    },
    {
      "artist": "Bertie Michael",
      "song": "Ash Chinese",
      "id": "d25ff9f9-25ae-459d-8591-7650106a3d11",
      "priceUsdCents": 204,
      "publisher": "CRUSTATIA"
    },
    {
      "artist": "Barr Good",
      "song": "Black Earth",
      "id": "882c720a-a0ba-4fe1-9e02-eb2640a3677a",
      "priceUsdCents": 153,
      "publisher": "OCEANICA"
    },
    {
      "artist": "Rios Sheppard",
      "song": "Blanched Davy",
      "id": "38ad21d5-679d-4352-bab6-11a03ea167d3",
      "priceUsdCents": 222,
      "publisher": "BUZZMAKER"
    },
    {
      "artist": "Natalia Wooten",
      "song": "Alloy Charleston",
      "id": "107adc93-dcbb-4ffe-9259-ebe67cdfce32",
      "priceUsdCents": 273,
      "publisher": "GORGANIC"
    },
    {
      "artist": "Hopkins Levine",
      "song": "Blue China",
      "id": "5df65ddf-afe7-4514-93c2-93f53885cf71",
      "priceUsdCents": 248,
      "publisher": "GINKLE"
    },
    {
      "artist": "Monique Curry",
      "song": "Aqua Cyclamen",
      "id": "5148a24f-5c13-4861-96d7-ca957af1b16d",
      "priceUsdCents": 226,
      "publisher": "INTRADISK"
    },
    {
      "artist": "Kirsten Decker",
      "song": "Ash Cinnamon",
      "id": "ca90e78f-d4d3-4af3-9b20-254254a4d922",
      "priceUsdCents": 193,
      "publisher": "COMTOURS"
    },
    {
      "artist": "DJ DW",
      "song": "Battleship Cinnamon",
      "id": "ba66eb9d-f8b5-4924-a9e2-e8817e02b03a",
      "priceUsdCents": 365,
      "publisher": "MUSICMAN INC"
    },
    {
      "artist": "Arturus Ardvarkian",
      "song": "Bright Cerulean",
      "id": "84024726-d9ba-4dd4-830d-1022f79eca97",
      "priceUsdCents": 166,
      "publisher": "GLOBOMANTICS MUSIC"
    },
    {
      "artist": "DJ DW",
      "song": "Black Crimson",
      "id": "c79bcf38-d459-4479-a385-a27a9bdb558a",
      "priceUsdCents": 370,
      "publisher": "GLOBOMANTICS MUSIC"
    },
    {
      "artist": "DJ DW",
      "song": "Caribbean Citron",
      "id": "8643a896-a7a9-4139-95cc-583e1710b2b1",
      "priceUsdCents": 360,
      "publisher": "MUSICMAN INC"
    },
    {
      "artist": "Arturus Ardvarkian",
      "song": "Carnelian Cobalt",
      "id": "5e8d8ff7-25a7-41c6-a1fa-ac823fda0161",
      "priceUsdCents": 311,
      "publisher": "GLOBOMANTICS MUSIC"
    },
    {
      "artist": "Professor RattyB",
      "song": "Big Cyan",
      "id": "209e59fb-68a5-45a1-aebb-f84447229a4e",
      "priceUsdCents": 261,
      "publisher": "MUSICMAN INC"
    },
    {
      "artist": "Arturus Ardvarkian",
      "song": "Bright Electric",
      "id": "ced73077-f8c7-40d0-96ee-8d995baa080b",
      "priceUsdCents": 273,
      "publisher": "GLOBOMANTICS MUSIC"
    },
    {
      "artist": "Professor RattyB",
      "song": "Blanched Cornsilk",
      "id": "88ca6ef9-8cd4-4a52-9c85-f7d7ac81c275",
      "priceUsdCents": 192,
      "publisher": "MUSICMAN INC"
    },
    {
      "artist": "DJ DW",
      "song": "Amaranth Fever",
      "id": "00ea572d-a3ea-41dc-97f6-50af8b26cd13",
      "priceUsdCents": 199,
      "publisher": "GLOBOMANTICS MUSIC"
    },
    {
      "artist": "Arturus Ardvarkian",
      "song": "Cadmium Eggplant",
      "id": "30af38c6-f32d-4e7b-a571-e8641247b975",
      "priceUsdCents": 168,
      "publisher": "GLOBOMANTICS MUSIC"
    },
    {
      "artist": "Arturus Ardvarkian",
      "song": "Bleu Cinnamon",
      "id": "f90cef82-bd0a-44a9-bd3a-bd743c140ced",
      "priceUsdCents": 153,
      "publisher": "GLOBOMANTICS MUSIC"
    },
    {
      "artist": "Arturus Ardvarkian",
      "song": "Carrot Eton",
      "id": "dbea9bd8-fe1f-478a-a98a-5b46d481cf57",
      "priceUsdCents": 161,
      "publisher": "MUSICMAN INC"
    },
    {
      "artist": "Arturus Ardvarkian",
      "song": "Baker Firebrick",
      "id": "1a4e5bc5-4fa3-4b37-9d36-e15dc9ab6b21",
      "priceUsdCents": 312,
      "publisher": "MUSICMAN INC"
    },
    {
      "artist": "Arturus Ardvarkian",
      "song": "Cadet Celadon",
      "id": "fd7667cb-3a41-4777-93bb-ed2d0d8d7458",
      "priceUsdCents": 142,
      "publisher": "MUSICMAN INC"
    },
    {
      "artist": "DJ DW",
      "song": "Byzantine Charcoal",
      "id": "5ab5351f-212a-4fcc-9a76-b5bfbc5d18a0",
      "priceUsdCents": 174,
      "publisher": "MUSICMAN INC"
    },
    {
      "artist": "Arturus Ardvarkian",
      "song": "Brick Cerulean",
      "id": "65ce7700-c32d-415c-9bf4-26f4e4911372",
      "priceUsdCents": 381,
      "publisher": "GLOBOMANTICS MUSIC"
    }
  ]

================================================
FILE: boto3/dynamodb/loadData.py
================================================
import boto3
import json

dynamodb = boto3.client('dynamodb')

def upload():
    with open('data.json', 'r') as datafile:
        records = json.load(datafile)
    for song in records:
        print(song)
        item = {
                'artist':{'S':song['artist']},
                'song':{'S':song['song']},
                'id':{'S': song['id']},
                'priceUsdCents':{'S': str(song['priceUsdCents'])},
                'publisher':{'S': song['publisher']}
        }
        print(item)
        response = dynamodb.put_item(
            TableName='basicSongsTable', 
            Item=item
        )
        print("UPLOADING ITEM")
        print(response)

upload()

================================================
FILE: boto3/dynamodb/query.py
================================================
import boto3
import json
from boto3.dynamodb.conditions import Key

TABLE_NAME = "basicSongsTable"

# Creating the DynamoDB Client
dynamodb_client = boto3.client('dynamodb', region_name="us-west-2")

# Creating the DynamoDB Table Resource
dynamodb = boto3.resource('dynamodb', region_name="us-west-2")
table = dynamodb.Table(TABLE_NAME)


artists = table.scan(AttributesToGet=['artist.'])


specific_artist = table.query(
  KeyConditionExpression=Key('artist').eq('Arturus Ardvarkian')
)

artists_object = json.dumps(artists, indent = 4) 
print(artists_object)

specific_artist = json.dumps(specific_artist, indent = 4) 
print(specific_artist)

================================================
FILE: boto3/ec2/create-instance.py
================================================
#! /bin/python3

import boto3, os
from botocore.exceptions import ClientError

vpc_id = "vpc-0d42bf2f27be967ff"
subnet_id = "subnet-00b5ede5e160caa59"
ami_id = "ami-0ddf424f81ddb0720"
instance_type = "t2.small"
app_name = "flask"

create_key = True
key_name = "dev-key"
key_location = "/Users/bibinwilson/.ssh/devops-class/"

ec2 = boto3.client('ec2')

def createSecurityGroup():
    global security_group_id
    try:
        response = ec2.create_security_group(GroupName=app_name + "-sg",
                                            Description=app_name + " Security Group",
                                            VpcId=vpc_id,
                                            TagSpecifications=[
                                                    {
                                                        'ResourceType': 'security-group',
                                                        'Tags': [
                                                            {
                                                                'Key': 'Name',
                                                                'Value': app_name + "-sg"
                                                            }
                                                        ]
                                                    },
                                                ],
                                            )
        security_group_id = response['GroupId']
        print('Security Group Created %s in vpc %s.' % (security_group_id, vpc_id))

        ingress = ec2.authorize_security_group_ingress(
                            GroupId=security_group_id,
                            IpPermissions=[
                                {
                                    'IpProtocol': 'tcp',
                                    'FromPort': 80,
                                    'ToPort': 80,
                                    'IpRanges': [
                                        {
                                            'CidrIp': '0.0.0.0/0'
                                        }
                                    ]
                                },
                                {
                                    'IpProtocol': 'tcp',
                                    'FromPort': 22,
                                    'ToPort': 22,
                                    'IpRanges': [
                                        {
                                            'CidrIp': '0.0.0.0/0'
                                        }
                                    ]
                                }
                            ])
        print('Ingress Successfully Set %s' % ingress)
    except ClientError as e:
        print(e)

def createKeyPair():

    try:
        key_pair = ec2.create_key_pair(KeyName=key_name)

        ssh_private_key = key_pair["KeyMaterial"]
        
        with os.fdopen(os.open(key_location + key_name + ".pem", os.O_WRONLY | os.O_CREAT, 0o400), "w+") as handle:
            handle.write(ssh_private_key)
    except ClientError as e:
        print(e)
    
def createInstance():
    blockDeviceMappings = [
        {
            'DeviceName': "/dev/sda1",
            'Ebs': {
                'DeleteOnTermination': True,
                'VolumeSize': 20,
                'VolumeType': 'gp2'
            }
        },
    ]

    instances = ec2.run_instances(
        ImageId= ami_id,
        MinCount=1,
        MaxCount=1,
        InstanceType=instance_type,
        SubnetId=subnet_id,
        KeyName=key_name,
        SecurityGroupIds=[security_group_id],
        BlockDeviceMappings=blockDeviceMappings,
        TagSpecifications=[
            {
                'ResourceType': 'instance',
                'Tags': [
                    {
                        'Key': 'Name',
                        'Value': app_name + "-server"
                    }
                ]
            },
            {
                'ResourceType': 'volume',
                'Tags': [
                    {
                        'Key': 'Name',
                        'Value': app_name + "-root-disk"
                    }
                ]
            }
        ]
    )

    print(instances["Instances"][0]["InstanceId"])


if __name__ == "__main__":
    
    createSecurityGroup()

    if create_key == True:
        createKeyPair();

    createInstance()


# References
# [1] https://codeflex.co/boto3-create-ec2-with-tags/
# [2] https://www.learnaws.org/2020/12/16/aws-ec2-boto3-ultimate-guide/
# [3] https://arjunmohnot.medium.com/aws-ec2-management-with-python-and-boto-3-59d849f1f58f



================================================
FILE: database/postgresql.py
================================================
import psycopg2

try:
    connection = psycopg2.connect(database="dvdrental",
                        host="192.168.5.5",
                        user="postgres",
                        password="myPassword",
                        port="5432")
    cursor = connection.cursor()
    sql_query = "select * from actor"

    cursor.execute(sql_query)
    print("Selecting rows from mobile table using cursor.fetchall")
    actor_records = cursor.fetchall()

    print("Print each row and it's columns values")
    for row in actor_records:
        print("Id = ", row[0], )
        print("Model = ", row[1])
        print("Price  = ", row[2], "\n")

except (Exception, psycopg2.Error) as error:
    print("Error while fetching data from PostgreSQL", error)

finally:
    if connection:
        cursor.close()
        connection.close()
        print("PostgreSQL connection is closed")


================================================
FILE: generic/command-line-argument.py
================================================


================================================
FILE: generic/exception-handling.py
================================================


================================================
FILE: json/files/example.json
================================================
{
    "name" : "test",
    "ip" : "198.168.23.45",
    "country" : "United States",
    "project" : "Data Analytics",
    "website" : "scriptscrunch.com"
}


================================================
FILE: json/parse-json.py
================================================
import json
import os

# Script to create absolute path of the JSON file.

script_dir = os.path.dirname(__file__)
print("The Script is located at:" + script_dir )
script_absolute_path = os.path.join(script_dir, 'files/example.json')
print("The Script Path is:" + script_absolute_path)

# Script to parse JSON

json = json.loads(open(script_absolute_path).read())
value = json['name']
print(value)

# Loop through JSON

for key in json:
    value = json[key]
    print("The key and value are ({}) = ({})".format(key, value))

================================================
FILE: operating-system/README.md
================================================
## Python Operating Sytem Based Tasks

1. Create Directory
2. Delete Directory
3. List Directory
4. List total number of folder in a Directory
5. List total numbers of files in a Directory

================================================
FILE: operating-system/command-execution.py
================================================
import os

os.system('echo "hello"')

================================================
FILE: operating-system/files/param.sh
================================================
#!/bin/sh
echo $1
echo $2
echo $3

echo "$@"
exit 0

================================================
FILE: operating-system/files/script.sh
================================================
#! /bin/bash

echo "This is a shell script"

================================================
FILE: operating-system/shell-execution.py
================================================
import os
import subprocess

# Script execution without parameters

script_dir = os.path.dirname(__file__)

script_abosulte_path = os.path.join( script_dir + "/files/script.sh")

subprocess.call(['sh', script_abosulte_path])

# Script execution with parameters

param_script_abosulte_path = os.path.join( script_dir + "/files/param.sh")

subprocess.call(['sh', param_script_abosulte_path, 'param1 param2'])