Python Basics for DevOps with Practical Examples

🚀 Python for DevOps – Log Monitoring with Timestamp & Alerts (Mini Project) Built a hands-on Python script to analyze logs, generate alerts, and track system health — a small step toward real-world DevOps automation. 📂 Problem: Manually scanning logs is inefficient and error-prone. Needed a way to automatically filter and track critical issues. 💻 Solution (Python Script): from datetime import datetime ERROR_COUNT = 0 WARNING_COUNT = 0 INFO_COUNT = 0 with open("app.log") as f, open("alerts.log", "a") as alert_file: for line in f: timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") if "ERROR" in line: ERROR_COUNT += 1 alert_file.write(f"{timestamp} - {line.strip()}\n") elif "WARNING" in line: WARNING_COUNT += 1 alert_file.write(f"{timestamp} - {line.strip()}\n") elif "INFO" in line: INFO_COUNT += 1 print("============ LOG SUMMARY ============") print("ERROR:", ERROR_COUNT) print("WARNING:", WARNING_COUNT) print("INFO:", INFO_COUNT) Output: ubuntu@satheesha:~/python$ python3 log-mon_alert-time.py ============LOG SUMMARY================ ERROR: 1 WARNING: 1 INFO: 2 ubuntu@satheesha:~/python$ cat alerts.log 2026-04-21 11:37:1776771454 - INFO - INFO: Service startes 2026-04-21 11:37:1776771454 - WARNING - WARNING: High CPU 2026-04-21 11:37:1776771454 - INFO - INFO: Service startes 2026-04-21 11:37:1776771454 - ERROR - ERROR: Disk full 2026-04-21 11:45:59 - INFO - INFO: Service startes 2026-04-21 11:45:59 - WARNING - WARNING: High CPU 2026-04-21 11:45:59 - INFO - INFO: Service startes 2026-04-21 11:45:59 - ERROR - ERROR: Disk full 🔍 What this script does: Reads application logs (app.log) Filters critical log levels (ERROR / WARNING / INFO) Appends important alerts into alerts.log Adds timestamps for better traceability Generates summary metrics for quick insights 📊 Why this matters: Faster troubleshooting in production Clear visibility into system health Reduces manual effort in log analysis 🔥 Key Learning: Python is a powerful tool in DevOps—not just for scripting, but for automation, monitoring, and observability. 📈 Next Steps: Add alerting (Email / Slack integration) Convert logs to structured format (JSON for ELK stack) Build real-time log monitoring (tail -f style) #Python #DevOps #Automation #Logging #Monitoring #Cloud #Scripting #Learning #100DaysOfCode

🚀 Python Basics for DevOps Engineers (With Practical Examples) Python is a must-have skill for DevOps. Here are some basic concepts with real-time examples 👇 🔹 1. Variables name = "server1" cpu_usage = 75 is_running = True print(name) print(cpu_usage) 💡 DevOps Example: server = "web-server" status = "running" print(server, status) 🔹 2. Data Types String → "hello" Integer → 10 Boolean → True / False List → ["app1", "app2"] services = ["nginx", "docker", "jenkins"] print(services[0]) # nginx 🔹 3. Conditions (if-else) Used for decision-making in automation cpu = 85 if cpu > 80: print("High CPU usage") else: print("Normal CPU") 💡 DevOps Example: disk = 90 if disk > 80: print("Alert: Disk almost full") else: print("Disk is normal") 🔹 4. Loops Used to repeat tasks (like checking multiple servers) 👉 for loop: servers = ["web1", "web2", "web3"] for s in servers: print(s) 👉 while loop: i = 1 while i <= 5: print(i) i += 1 🔹 5. Functions Reusable code (very important for scripting) def check_cpu(cpu): if cpu > 80: print("Alert: High CPU") else: print("Normal CPU") check_cpu(85) 🔹 6. Real DevOps Example servers = ["web1", "web2", "web3"] def check_status(server): print(f"Checking {server}...") for s in servers: check_status(s) 🔹 7. Mini Practice cpu = 70 if cpu > 80: print("Alert: scale up server") else: print("Server is stable") 💡 Key Takeaway: Python helps automate repetitive tasks like monitoring, alerts, and server management in DevOps. #DevOps #Python #Automation #Scripting #Learning #AWS #Kubernetes

🚀 Python for DevOps – Log Level Automation Project Today I built a practical DevOps script using Python to analyze logs and separate them based on log levels. 📂 Problem: Manually checking logs is time-consuming. Needed a way to automatically filter and organize logs. 💻 Solution (Python Script): with open("app.log") as f, \ open("error.log", "w") as err, \ open("warning.log", "w") as warn, \ open("info.log", "w") as info: for line in f: if "ERROR" in line: err.write(line) elif "WARNING" in line: warn.write(line) elif "INFO" in line: info.write(line) ####################################### Output: ubuntu@satheesha:~/python$ python3 multiple-log_level.py ubuntu@satheesha:~/python$ ls -ltr error.log warning.log info.log -rw-r--r-- 1 ubuntu ubuntu 18 Apr 21 07:45 warning.log -rw-r--r-- 1 ubuntu ubuntu 44 Apr 21 07:45 info.log -rw-r--r-- 1 ubuntu ubuntu 17 Apr 21 07:45 error.log ubuntu@satheesha:~/python$ cat app.log INFO: Service startes WARNING: High CPU INFO: Service startes ERROR: Disk full ubuntu@satheesha:~/python$ cat error.log ERROR: Disk full ubuntu@satheesha:~/python$ cat warning.log WARNING: High CPU ubuntu@satheesha:~/python$ cat info.log INFO: Service startes INFO: Service startes ######################################### does: Reads app.log Filters logs into: error.log warning.log info.log 📊 Outcome: Faster troubleshooting Organized logs for better monitoring Reduced manual effort 🔥 Real DevOps Use Cases: Production log monitoring CI/CD pipeline validation Incident detection and alerting 💡 Key Learning: Python is a powerful tool for automation in DevOps, especially for handling logs and system data. 📈 Next Step: Enhancing this script to: Count log levels Trigger alerts (email/Slack) Monitor logs in real-time (tail -f style) #Python #DevOps #Automation #Scripting #Cloud #Learning #100DaysOfCode

🚀 Python for DevOps – API Monitoring with requests Practiced using Python’s requests library to check API health, a common real-world DevOps task. 📂 Use Case: In production, services depend on APIs. We need to continuously verify if APIs are reachable and healthy. 💻 Python Script: import requests url = "https://api.github.com" try: res = requests.get(url, timeout=5) if res.status_code == 200: print("✅ GitHub API is UP") else: print("⚠️ GitHub API issue:", res.status_code) except requests.exceptions.RequestException as e: print("🚨 API call failed:", e) Output: Status_code: 200 Response: {'current_user_url': 'https://lnkd.in/guvkNT7k', 'current_user_authorizations_html_url': 'https://lnkd.in/gx-65ERd', 'authorizations_url': 'https://lnkd.in/gzcehbTu', 'code_search_url': 'https://lnkd.in/gQU8cghE', 'commit_search_url': 'https://lnkd.in/g62A-__n', 'emails_url': 'https://lnkd.in/gXaZyEkK', 'emojis_url': 'https://lnkd.in/gp3Scn2Y', 'events_url': 'https://lnkd.in/grbt4NNg', 'feeds_url': 'https://lnkd.in/gCBk-eSN', 'followers_url': 'https://lnkd.in/gQvSEXqB', 'following_url': 'https://lnkd.in/grh4YDpJ', 🔍 What this does: Sends HTTP request to API Uses timeout to avoid hanging Checks response status Handles failures gracefully 🔥 Why this matters in DevOps: Monitor service availability Validate endpoints in CI/CD pipelines Detect outages early Automate health checks 💡 Key Learning: APIs are everywhere in DevOps, and Python makes it easy to integrate, monitor, and automate systems. 📈 Next Steps: Send alerts (Slack/Email) if API fails Combine with log monitoring scripts Build a full monitoring + alerting system #Python #DevOps #API #Automation #Monitoring #Scripting #Cloud #Learning #100DaysOfCode

🚀 Python for DevOps: Real-Time System Monitoring Script (CPU + Memory + Disk) One of the most practical skills in DevOps is automating system monitoring. Instead of manually checking servers, I built a simple Python script that: ✅ Monitors CPU usage ✅ Tracks Memory consumption ✅ Checks Disk utilization ✅ Triggers alerts when thresholds are exceeded 💻 Full Script import psutil import shutil # Thresholds CPU_THRESHOLD = 80 MEM_THRESHOLD = 80 DISK_THRESHOLD = 80 def check_cpu(): cpu = psutil.cpu_percent(interval=1) if cpu > CPU_THRESHOLD: print(f"ALERT: High CPU usage: {cpu}%") else: print(f"OK: CPU usage: {cpu}%") def check_memory(): mem = psutil.virtual_memory() usage = mem.percent if usage > MEM_THRESHOLD: print(f"ALERT: High Memory usage: {usage}%") else: print(f"OK: Memory usage: {usage}%") def check_disk(): disk = shutil.disk_usage("/") usage = (disk.used / disk.total) * 100 if usage > DISK_THRESHOLD: print(f"ALERT: High Disk usage: {usage:.2f}%") else: print(f"OK: Disk usage: {usage:.2f}%") def main(): print("===== System Monitoring =====") check_cpu() check_memory() check_disk() if __name__ == "__main__": main() Output: ubuntu@satheesha:~/python$ python3 full-monitor-script.py ===== System Monitoring ===== OK: CPU usage: 1.0% OK: Memory usage: 30.5% OK: Disk usage: 2.74% ⚙️ How I Used It Installed dependency using: sudo apt install python3-psutil Ran the script to get real-time system health Can be scheduled using cron for continuous monitoring 🔥 Why This Matters in DevOps 👉 Helps detect issues before outages 👉 Reduces manual effort 👉 Can be extended to send alerts (Email / Slack / SNS) 👉 Foundation for tools like monitoring agents 🎯 Key Learning "Don’t just run commands like top or df -h — automate them using Python and build intelligent monitoring." 🚀 Next Steps I’m planning to: Integrate this with Jenkins pipeline Send alerts to Slack Push metrics to monitoring tools 💬 How do you monitor your servers in real-time? #DevOps #Python #Automation #Monitoring #SRE #Cloud #Linux #Jenkins #Learning #100DaysOfCode

🚀 Python for DevOps – Triggering Jenkins Jobs via API Explored how to automate CI/CD by triggering Jenkins jobs using Python and APIs — a key real-world DevOps capability. 📂 Use Case: Instead of manually clicking “Build Now” in Jenkins, we can trigger jobs programmatically using APIs. 💻 Python Script: import requests jenkins_url = "http://your-jenkins-url/job/your-job-name/build" username = "your-username" api_token = "your-api-token" response = requests.post(jenkins_url, auth=(username, api_token)) if response.status_code == 201: print("✅ Jenkins job triggered successfully") else: print("❌ Failed to trigger job:", response.status_code) 🔐 Handling CSRF (Crumb Token): crumb_url = f"{jenkins_url}/crumbIssuer/api/json" crumb_data = requests.get(crumb_url, auth=(username, api_token)).json() headers = { crumb_data['crumbRequestField']: crumb_data['crumb'] } ⚙️ Trigger Job with Parameters: params = { "ENV": "prod", "VERSION": "1.0" } response = requests.post( "http://your-jenkins-url/job/your-job-name/buildWithParameters", params=params, auth=("user", "token") ) 🔍 What this enables: Automate CI/CD pipelines Trigger builds from scripts or monitoring tools Integrate Jenkins with other systems Reduce manual intervention 🔥 Why this matters in DevOps: Automation is the backbone of DevOps. Using APIs, we can connect tools and build end-to-end automated workflows. 💡 Key Learning: Jenkins + APIs + Python = powerful combination for pipeline automation and integration. 📈 Next Steps: Trigger Jenkins from log monitoring script Send build status to Slack Integrate with cloud deployments (AWS) #DevOps #Jenkins #Python #Automation #CICD #API #Cloud #Scripting #Learning #100DaysOfCode

💻 Coding Questions Asked in DevOps/SRE Interviews (Linux, Shell, Python, Terraform, Ansible) Many DevOps interviews now include hands-on coding or scripting rounds to test real problem-solving skills 👇 Here are some commonly asked coding questions across different areas 👇 --- 🐧 Linux / Shell Scripting 🔹 Write a script to find top 5 CPU-consuming processes 🔹 Script to monitor disk usage and send alert if > 80% 🔹 Find and delete files older than 7 days 🔹 Count number of lines, words, characters in a file 🔹 Script to check if a service is running, if not restart it 💡 Example: ps -eo pid,ppid,cmd,%mem,%cpu --sort=-%cpu | head -5 --- 🧾 Shell Scripting (Logic-Based) 🔹 Reverse a string using shell 🔹 Check if a number is prime 🔹 Print fibonacci series 🔹 Find duplicate lines in a file --- 🐍 Python (Very Common Now) 🔹 Script to read a log file and find ERROR lines 🔹 Parse a JSON file and extract specific fields 🔹 Call an API and print response 🔹 Write a script to monitor a website (uptime check) 💡 Example: with open("app.log") as f: for line in f: if "ERROR" in line: print(line) --- 🌍 Terraform (Logic + Understanding) 🔹 Write a Terraform config to: - Create EC2 instance - Attach security group 🔹 Use count or for_each to create multiple resources 🔹 Write a module and reuse it 🔹 Handle dependency using "depends_on" --- ⚙️ Ansible (Automation Tasks) 🔹 Write a playbook to: - Install nginx - Start service 🔹 Use variables for different environments 🔹 Create a role for reusable configuration 🔹 Use handlers to restart service only when config changes --- 🧠 General Coding / Logic Questions 🔹 Reverse a string (any language) 🔹 Find second largest number in array 🔹 Remove duplicates from list 🔹 Count frequency of characters 🔹 Check if string is palindrome --- 🚨 Real-World DevOps Tasks 🔹 Write script to: - Automate backup - Check server health - Deploy application 🔹 Parse logs and generate summary 🔹 Automate cleanup of unused files/resources --- 💡 Pro Tip Interviewers are not expecting perfect coding ❌ They look for: 👉 Logic 👉 Approach 👉 Clean explanation --- 💬 Which type of coding questions have you faced in interviews? #DevOps #SRE #Python #ShellScripting #Linux #Terraform #Ansible #CodingInterview #TechInterviews

🚀 Python for DevOps – Stop Learning, Start Automating Most people learn Python… But very few use it the right way in DevOps. Here’s the truth 👇 👉 You don’t need deep theory. 👉 You need practical automation skills. 🔹 What to Focus On (DevOps Style) ✔ Variables, loops, conditions ✔ Functions ✔ File handling (logs, configs) ✔ Error handling (try/except) ✔ Key modules: os → system operations subprocess → run shell commands json / yaml → config management 🔧 Real Example: Run Linux Command Using Python import subprocess result = subprocess.run(["df", "-h", "/"], capture_output=True, text=True) lines = result.stdout.strip().split("\n") if len(lines) > 1:     parts = lines[1].split()     usage = int(parts[4].replace("%", ""))     if usage > 80:         print(f"🚨 ALERT: Disk usage is {usage}%")     else:         print(f"✅ OK: Disk usage is {usage}%") else:     print("❌ Unexpected output:", result.stdout) Output: ubuntu@satheesha:~/python$ python3 Disk-Usage.py ✅ OK: Disk usage is 3% 💡 What This Shows ✔ You can interact with the OS ✔ You can parse real-time system data ✔ You can build automation scripts ✔ You think like a DevOps Engineer 🎯 How I Explain This in Interviews “I use Python’s subprocess module to execute system commands, parse outputs, and automate monitoring tasks like disk usage alerts.” 🔥 Pro Tip Take it one step further: Send alerts to Slack/Email 📩 Schedule with cron ⏰ Integrate with Jenkins 🔁 💬 If you're learning DevOps, stop just writing scripts… Start solving real problems. #DevOps #Python #Automation #Linux #SRE #Cloud #Jenkins #Learning

In the era of GenAI, which language should I learn - Python or Go? An interesting question from one of my DevOps engineers. At first glance, it sounds like a straightforward choice: Go powers much of the modern cloud-native ecosystem (Kubernetes, Docker, Terraform…) Python has been the backbone of automation, scripting, and now AI/ML But the real answer is a bit uncomfortable: 👉 The language you choose matters less than how you think about building software. The Shift We’re Living Through With LLMs like Claude Sonnet or Opus, generating code is no longer the bottleneck. You can: - Scaffold a REST API in seconds - Generate Terraform modules - Write Kubernetes operators - Automate workflows So if code generation is becoming commoditized… 👉 What actually differentiates engineers going forward? What Still Matters (More Than Ever) 1. Understanding Trade-offs Knowing why Go is used for infrastructure tools: - Concurrency model (goroutines, channels) - Static binaries (ease of distribution) - Performance and low memory footprint Knowing why Python dominates automation: - Rich ecosystem - Faster prototyping - Simplicity and readability AI can generate both but it won’t deeply understand your system constraints unless you do. 2. System Design Thinking Can you answer: - Should this be a long-running service or a batch job? - When do you use event-driven vs polling? - Where does the state live? - How does this scale under failure? These decisions are language-agnostic and AI won’t get them right without strong guidance. 3. Code Quality & Maintainability Generated code often works… until it doesn’t. The real skill is: - Structuring codebases - Applying design patterns appropriately - Writing testable, observable systems - Managing dependencies and versioning In DevOps especially, “quick scripts” often become “critical systems” overnight. 4. Understanding the Runtime Especially in platform engineering: - How does garbage collection impact latency? - What happens under high concurrency? - How do network calls behave under failure? This is where Go shines but only if you understand it beyond syntax. 5. Operational Thinking As DevOps engineers, we don’t just write code, we run it. - Observability - Failure modes - Cost implications - Deployment patterns AI can write code. It cannot own production (yet). The Real Answer Don’t optimize for language choice. Optimize for engineering depth. In a world where AI writes code: - Syntax is cheap - Judgment is expensive The engineers who will stand out are the ones who can: - Ask the right questions - Design the right systems - Validate and evolve solutions over time #DevOps #PlatformEngineering #SoftwareEngineering #CloudNative #Kubernetes #Golang #Python #GenerativeAI #LLM #AICoding #EngineeringLeadership #TechCareers #CareerGrowth #LearningToLearn #SystemDesign #CleanCode #EngineeringExcellence

🚀 Python for DevOps – Log Analysis with Metrics Today I enhanced my log automation script to not only separate logs by level but also count them for quick insights. 📂 Problem: Manually analyzing logs is slow and inefficient. 💻 Solution (Python Script): error = warning = info_count = 0 with open("app.log") as f, \ open("error.log", "w") as err, \ open("warning.log", "w") as warn, \ open("info.log", "w") as info: for line in f: if "ERROR" in line: err.write(line) error += 1 elif "WARNING" in line: warn.write(line) warning += 1 elif "INFO" in line: info.write(line) info_count += 1 print("ERROR:", error) print("WARNING:", warning) print("INFO:", info_count) 🔍 What this does: Reads app.log Splits logs into separate files Counts each log level 📊 Sample Output: ERROR: 1 WARNING: 1 INFO: 2 Output: ubuntu@satheesha:~/python$ python3 multiple-log_level.py ERROR: 1 WARNING: 1 INFO_COUNT: 2 ubuntu@satheesha:~/python$ ls -ltr error.log warning.log info.log -rw-r--r-- 1 ubuntu ubuntu 18 Apr 21 08:20 warning.log -rw-r--r-- 1 ubuntu ubuntu 44 Apr 21 08:20 info.log -rw-r--r-- 1 ubuntu ubuntu 17 Apr 21 08:20 error.log ubuntu@satheesha:~/python$ cat error.log ERROR: Disk full ubuntu@satheesha:~/python$ cat warning.log WARNING: High CPU ubuntu@satheesha:~/python$ cat info.log INFO: Service startes INFO: Service startes 🔥 Why this matters: Quick visibility into system health Helps prioritize issues (ERROR > WARNING > INFO) Reduces manual troubleshooting time 💡 Key Learning: Python can be used not just for automation, but also for real-time insights and monitoring in DevOps. 📈 Next Step: Add alerting when ERROR count exceeds threshold Integrate with monitoring tools Build real-time log monitoring (tail -f in Python) #Python #DevOps #Automation #Scripting #Monitoring #Cloud #Learning