Python DevOps Automation with subprocess for Disk Monitoring

🚀 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

“How much Python should a DevOps Engineer know?” 🤔 After working through different DevOps tasks, one thing became clear: It’s not about how much Python you know… It’s about how effectively you can use it in real scenarios. Here’s how I see it 👇 💡 Core Skills (Non-negotiable) ✔ Writing clean scripts ✔ Working with files & logs ✔ Handling errors properly 👉 This is the foundation for automation 💡 Practical DevOps Usage ✔ Calling APIs (cloud / tools) ✔ Parsing JSON & YAML ✔ Automating workflows 👉 This is where Python becomes powerful 💡 Advanced Usage (Context-driven) ✔ Building CLI tools ✔ Writing reusable modules ✔ Optimizing scripts for scale 👉 Needed when you're solving larger problems ⚡ Key Insight: In DevOps, Python is not a goal… 👉 It’s a tool to automate, integrate, and scale systems 🚀 For hands-on practice, I found this repo really useful: Check out Abhishek Veeramalla's work 🫡 : 👉 https://lnkd.in/dTqaK8fQ 🧠 Final Thought: Strong DevOps engineers don’t just “know Python”… They use it to eliminate manual work and improve systems How are you using Python in your DevOps workflow? #DevOps #Python #Automation #Cloud #Learning #Engineering

🚀 Python for DevOps – Log Monitoring with File Output Today I built a simple automation script to read logs and write alerts to a separate file. 📂 Scenario: Instead of manually checking logs, automate detection of ERROR messages and store them in another file. 💻 Python Code: with open("app.log") as f, open("alerts.log", "w") as out: for line in f: if "ERROR" in line: out.write(line) output: root@satheesha:~# python3 Python 3.12.3 (main, Mar 3 2026, 12:15:18) [GCC 13.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> with open("app.log") as f, open("Alert.log", "w") as out: ...   for line in f: ...     if "ERROR" in line: ...       out.write(line) ...  17 >>> exit() root@satheesha:~# cat Alert.log ERROR: Disk full 🔍 What this does: Reads app.log line by line Filters only ERROR logs Writes them into alerts.log 📌 Why this is useful: Helps in faster troubleshooting Reduces manual log scanning Can be integrated with monitoring systems 🔥 Real DevOps Use Cases: Production log monitoring CI/CD pipeline validation Incident detection and alerting 📈 Next Step: Enhance this script to: Handle multiple log levels (ERROR / WARNING / INFO) Send alerts to email or Slack Monitor logs in real-time (like tail -f) #Python #DevOps #Automation #Scripting #Cloud #Learning #100DaysOfCode

🚀 Learning Python for DevOps – Hands-on Practice with Code Today I worked on a practical DevOps task: Log File Analysis using Python Started with a sample app.log: ERROR: Disk full WARNING: High CPU usage INFO: Service started INFO: Health check passed 🔍 Step 1: Read log file with open("app.log", "r") as f: data = f.read() print(data) ⚠️ Step 2: Filter only ERROR logs with open("app.log", "r") as f: for line in f: if "ERROR" in line: print(line) 📊 Step 3: Count total errors error_count = 0 with open("app.log") as f: for line in f: if "ERROR" in line: error_count += 1 print("Total Errors:", error_count) 🧠 Step 4: Handle multiple log levels (Real-world scenario) error = 0 warning = 0 info = 0 with open("app.log") as f: for line in f: if "ERROR" in line: error += 1 elif "WARNING" in line: warning += 1 elif "INFO" in line: info += 1 print("ERROR:", error) print("WARNING:", warning) print("INFO:", info) 🚨 Step 5: DevOps-style alert output with open("app.log") as f: for line in f: if "ERROR" in line: print("ALERT:", line.strip()) 💡 Key Learning: Python is a powerful tool in DevOps for log monitoring, automation, and faster troubleshooting. 🔥 Even simple scripts like this can help in: Production monitoring CI/CD pipelines Incident detection 📈 Next Goal: Build a real-time log monitoring script (like tail -f) using Python #Python #DevOps #Automation #Scripting #Learning #Cloud #100DaysOfCode

🚀 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

🚀 Monitor Disk Usage with Python (DevOps Practical Tip) As a DevOps Engineer, keeping an eye on disk usage is critical to avoid unexpected outages in production. Here’s a simple Python script I use to check disk usage of the root filesystem: import subprocess result = subprocess.run(["df", "-h", "/"], capture_output=True, text=True) print(result.stdout) 🔍 What this does: Executes df -h / to fetch disk usage Displays output in a human-readable format 💡 Real-world use case: You can extend this script to trigger alerts when disk usage crosses a threshold (say 80%): for line in result.stdout.split("\n"): if "/" in line: usage_percent = int(line.split()[4].replace("%", "")) if usage_percent > 80: print("⚠️ Alert: Disk usage is above 80%") Output: ubuntu@satheesha:~/python$ python3 check-disk_usage.py Filesystem Size Used Avail Use% Mounted on /dev/sdd 1007G 28G 928G 3% / 🔥 Where this helps: Production server monitoring Preventing downtime due to full disk Automating health checks via cron jobs Integrating with alerting tools (Slack, Email, etc.) Simple scripts like this can save hours of troubleshooting and keep systems stable. #DevOps #Python #Automation #Monitoring #Linux #SRE

🚀 Simple Python Script for DevOps Practice In DevOps, even small scripts can make a big difference in automation and monitoring. Here’s a simple Python script I practiced to simulate basic server details and loop operations 👇 name = "server1" cpu_usage = 75 is_running = True print(f"Name: {name}") print(f"CPU Usage: {cpu_usage}%") print(f"Is Running: {is_running}") print("-" * 30) for i in range(6): print(i) print("-" * 30) for i in range(1, 12, 2): print(i) print("-" * 30) for i in range(10, -1, -1): print(i) print("-" * 30) Output: ubuntu@satheesha:~/python$ python3 variable-for_loop.py Name: server1 CPU Usage: 75% Is Running: True ------------------------------  0 ------------------------------ 1 ------------------------------ 2 ------------------------------ 3 ------------------------------ 4 ------------------------------ 5 ------------------------------ 1 ------------------------------ 3 ------------------------------ 5 ------------------------------ 7 ------------------------------ 9 ------------------------------ 11 ------------------------------ 10 ------------------------------ 9 ------------------------------ 8 ------------------------------ 7 ------------------------------ 6 ------------------------------ 5 ------------------------------ 4 ------------------------------ 3 ------------------------------ 2 ------------------------------ 1 ------------------------------ 0 ------------------------------ 🔹 Key concepts used: ✔ Variables & data types ✔ f-strings for clean output ✔ Loops with range() ✔ Reverse iteration 💡 These basics are very useful for: Automation scripts Monitoring tasks Log analysis Learning step by step and practicing regularly helps build strong DevOps scripting skills. #DevOps #Python #Automation #Scripting #Learning #AWS #Kubernetes #Jenkins

🐍📈 DevOps With Python — With this learning path you'll sample a variety of skills and technologies that any DevOps engineer working with Python should know #python #learnpython

🚀 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

🐍 Want to start Python from scratch? This FREE resource is gold! 📘 Complete Python for Beginners – Notes by Rishabh Mishra If you're planning to learn Python (or strengthen your basics), this book gives a clear and structured foundation. Here’s what it covers 👇 ✅ Python basics – variables, data types, operators ✅ Control flow – if-else, loops, conditions ✅ Functions & arguments (real coding examples) ✅ Core concepts like lists, strings, and type casting ✅ Step-by-step setup + beginner-friendly explanations 💡 Why this is useful: Python is one of the most popular languages used in DevOps, Data Science, AI, and Automation (GeeksforGeeks) And this guide makes it simple to start without confusion. 🔥 As a DevOps Engineer, I’m focusing on improving Python skills for: • Automation • Scripting • Cloud & Infrastructure tasks 💬 Let’s discuss: 👉 Are you learning Python for DevOps, Data Science, or Development? 🔗 My LinkedIn Profile: https://lnkd.in/gpakHghj #Python #DevOps #Programming #Automation #Coding #Beginners #Learning #Tech #SoftwareDevelopment