Disk Usage Check Script in Bash

Day 5 of Shell Scripting — Log Analysis Like a Real DevOps Engineer [Writing this at 4:03 PM IST] Most people learn commands. Today I learned to think in pipelines. Day 5 was all about grep, awk, and sort — and how combining them turns raw log files into actual intelligence. What I practiced on a real app.log file: grep — filtered errors, debugs, warnings with -i (case insensitive), -n (line numbers), -c (count), -v (exclude). Found 7 ERROR entries and 4 DEBUG entries instantly. awk — extracted specific fields from those filtered lines. Timestamp + service component in one clean output. sort — sorted that output by service name using -k2 to group all database, api, auth errors together. The final command that clicked for me: grep "ERROR" app.log | awk '{print $2, $4}' | sort -k2 Three tools. One pipeline. Instant clarity on which service is failing most. This is exactly what you need when you're on-call at 2 AM and a production system is throwing errors. No GUI. Just you, the terminal, and your grep flags. Day 5 done. Pipeline thinking unlocked. 🔥 #DevOps #Linux #ShellScripting #BashScripting #100DaysOfCode #DevOpsJourney #Korelium

I'm Sharing Insights from My Interview Journey cicd ----------- 1.cicd work flow 2.if cicd fails what will be the step to check 3.what you will do in test environment  GIT ----------- 4.how will you get back deleted branch 5.what is the diff b/w git pull and git fetch 6.git clone and git fork 7.git rebase 8.In which command we can see latest changes git fetch or git pull Docker ----------- 9.different b/w container and VM 10.how will you enter jenkins container 11.how will you check container in docker Terraform ----------- 12.if terraform state file corrupt what will happens adn how will you debug 13.Terraform work flow AWS ----------- 14.what is the use of Vpc Kubernetes ----------- 15.how will you scedule pod 16.in pod version , metada, Type commands explain these 17.differnce between pod and container 18.how will you create services 19.how will you check services 20.how will you use Node Port IP service 21.if production deployment fails how will you debug Linux --------- 22.How will you check mechine uptime in linux  23.how can you check memory  24. awk command

If you use Qdrant seriously, you've written the same embedding + insert + search wrapper at least three times. qql-go is a CLI for people who want the same retrieval workflow to work the same way in the terminal, in CI, and inside agent tool calls. 🔁 Repeatable by design The same commands work across: → terminal → shell scripts → CI pipelines → agent tool calls 📦 Built for these users: → AI and RAG pipeline builders who script Qdrant but hate rewriting the same glue code → Agent developers who need --json output and stable exit codes they can rely on → DevOps and platform teams who want SHOW COLLECTIONS, DROP, and CREATE INDEX without touching Python ⚡ What the new release 0.1.2 adds: Local/external inference support — qql-go now works with local LM Studio/external embedding and a local Docker Qdrant setup, not just remote cloud inference. That lowers the starting cost significantly. Test locally first, keep the workflow inspectable, then move the same commands into scripts or agent runs when ready. 🛠️ Setup is small: install the binary → curl -fsSL https://lnkd.in/gma5Q4nJ | sh install the agent skill → npx skills add srimon12/qql-go --skill qql-skill After that, the same retrieval surface works for both humans and agents. → Repo: https://lnkd.in/gXjQdjaw #Qdrant #GoLang #OpenSource #Agents #cli #skills

🚨 DevOps Mistake #3: “Works on My Machine” in CI/CD Everything passes locally. Your code works perfectly. You push to repo → CI/CD runs → 💥 FAILURE. ❌ Tests fail ❌ Builds break ❌ Deployment stops And you’re stuck thinking: “But it was working on my machine…” So what’s going wrong? 👉 Environment mismatch What this looks like: 1.Different Python/Java versions 2.Missing environment variables 3.Different OS behavior (Windows vs Linux) 4.Dependency versions not locked 5.Local cache hiding issues Real-world example: -> In pipelines using tools like Docker or Kubernetes: - Locally → app uses cached dependencies - In CI → fresh install breaks due to version mismatch - Result: “Build failed” with no obvious reason How to avoid this: 1.Use Docker for local development (mirror CI environment) 2.Lock dependency versions (requirements.txt / pom.xml) 3. Run tests inside containers, not locally 4.Keep CI as the source of truth (not your laptop) 5.Use .env templates and validate required configs Lesson: In DevOps, if it only works on your machine… it doesn’t work. Consistency > convenience. 💬 What’s the weirdest “works on my machine” bug you’ve faced?

🚀 𝐇𝐎𝐖 𝐓𝐎 𝐃𝐄𝐓𝐄𝐂𝐓 𝐄𝐑𝐑𝐎𝐑𝐒 𝐈𝐍 𝐋𝐎𝐆𝐒 𝐖𝐈𝐓𝐇 --- 𝐀𝐖𝐊🔥 Today I explored how AWK can filter, process, and extract data from logs files. 1️⃣ 𝐁𝐀𝐒𝐈𝐂 𝐏𝐑𝐈𝐍𝐓 awk '{print}' sample.log • Prints entire file line by line 👉 Same as cat, but inside AWK 2️⃣ 𝐏𝐑𝐈𝐍𝐓 𝐒𝐏𝐄𝐂𝐈𝐅𝐈𝐂 𝐅𝐈𝐄𝐋𝐃𝐒(COLOUMNS) awk '{print $1, $2}' sample.log • Prints only 1st and 2nd columns 👉 $1, $2 = fields (space-separated by default) 3️⃣ 𝐅𝐈𝐋𝐓𝐄𝐑 𝐁𝐘 𝐏𝐀𝐓𝐓𝐄𝐑𝐍 awk '/error/ {print $1, $2}' sample.log • Prints lines containing word “error” • Shows only selected columns 4️⃣ 𝐂𝐎𝐔𝐍𝐓 𝐏𝐀𝐓𝐓𝐄𝐑𝐍 awk '/error/ {count++} END {print count}' sample.log • Counts how many lines contain “error” 👉 Very useful for log analysis 5️⃣ 𝐅𝐈𝐋𝐓𝐄𝐑 𝐁𝐘 𝐓𝐈𝐌𝐄 𝐑𝐀𝐍𝐆𝐄 awk '$2 >= "07:51:00" && $2 <= "07:52:00" {print $2}' sample.log • Prints logs between specific time range 👉 Useful for debugging incidents 6️⃣ 𝐏𝐑𝐈𝐍𝐓 𝐒𝐏𝐄𝐂𝐈𝐅𝐈𝐂 𝐋𝐈𝐍𝐄𝐒 awk 'NR >= 3 && NR <= 8 {print NR, $0}' sample.log • Prints line numbers from 3 to 8 👉 NR = current line number 🎯 𝐊𝐄𝐘 𝐓𝐀𝐊𝐄𝐀𝐖𝐀𝐘 AWK is not just a command… it’s a mini programming language for text processing 🧠 If you work with logs → AWK = 🔥 #Linux #AWK #DevOps #Scripting #LearningInPublic #Automation

I thought Docker was just “run containers.” Turns out… that’s the least interesting part 🐳 While prepping for CKA course on YouTube by Varun Joshi, I went deeper—and a few concepts completely changed how I think about containerization. Here’s what actually clicked 👇 The problem Docker solves Before Docker, every environment was slightly different. Different Java versions. Different ports. Different configs. That’s how “works on my machine” became a real production issue 😅 Docker fixes this by packaging your app + dependencies into one consistent unit. How images actually flow Dockerfile → build → image → push → registry → run One pipeline. Repeatable everywhere. Also: • RUN = creates a new image layer • CMD = just metadata (no new layer) Small detail… big impact when debugging. Running containers (the right way) Three flags I now use daily: • -d → run in background • -p → port mapping (left = your machine) • --name → stop memorizing random IDs And base image matters more than you think: python:3.9 ≈ 1 GB vs python:3.9-slim ≈ 162 MB ⚡ Same app. Huge difference. CMD vs ENTRYPOINT (finally makes sense) • CMD = default, easily replaceable • ENTRYPOINT = fixed executable Best practice? Use both together. Multi-stage builds = game changer Keep build tools out of your final image. One small change: 495 MB → 162 MB Same output. ~67% smaller. Less size = faster deploys + fewer vulnerabilities. Big takeaway: Docker isn’t just about containers. It’s about consistency, repeatability, and control. Now moving into Kubernetes — Pods, Nodes, Clusters next 🚀 If you’re learning this stack too, what’s been your biggest “aha” moment so far? #Kubernetes #Docker #CKA #DevOps #CloudNative #K8s #ContinuousLearning #DevOpsEngineer #CNCF

Day 21 of learning and practicing DevOps 🔁 Today was a #revision + consolidation day — built my own Shell Scripting Cheat Sheet I focused on organizing everything from the last few days for shell scripting Covered: • Script basics (shebang, variables, arguments) • Conditions, loops, and functions • Text processing tools like grep, awk, sed • Error handling (set -e, $?, debugging) • Useful one-liners I can use in real scenarios many more...... Important part: Writing a cheat sheet made me realize what I actually understand vs what I just “used once”. This cheat sheet will be something I can refer to anytime during scripting or debugging. Here are my notes: https://lnkd.in/gzXn3Jrv 📍 #DevOps #Linux #ShellScripting #Automation #LearningInPublic #90DaysOfDevOps #TrainWithShubham

I built my own Ansible for 8 nodes and it has more personality than the real thing. I have 8 machines spread across two locations. I call them a fleet. They all have names. A few weeks ago I wanted to check disk usage across all of them at once. I had two options: 1. Learn Ansible, write an inventory file, write a playbook, run it 2. Write fleet_command I wrote fleet_command. It's a Python script that pings every machine, SSHs into the online ones concurrently, runs whatever command I give it, and prints the results in clean colored output with a summary at the bottom. Ansible would have given me the same disk data. It also would have given me YAML files to maintain, a learning curve to climb, and a tool that's honestly designed for teams managing hundreds of servers — not one guy with 8 machines and a naming convention. I'm not knocking Ansible. It's the right tool at the right scale. But I've seen people reach for enterprise tooling when a well-written script would serve them better, be easier to debug, and actually get used. Sometimes the best tool is the one you built yourself. Do you write your own tooling or reach for established solutions? I'd love to hear where you draw the line — drop it in the comments. #Linux #DevOps #Python #Homelab #Infrastructure #SysAdmin #Ansible

🤖 Day 8 of #100DaysOfDevOps — Ansible Today's milestone: installed Ansible 4.8.0 on the jump host and made it available globally to all users. The automation controller is live. But first — what IS Ansible? Ansible is an agentless automation tool. You write simple YAML instructions once, run them from one controller machine, and Ansible SSHes into every server and executes them simultaneously. No agent installed on target servers. No complex setup. Just SSH + Python — both of which I already had from Days 1–7. Why pip3 and not yum? → yum gives you whatever outdated version the OS repo has → pip3 lets you pin an EXACT version: ansible==4.8.0 → sudo pip3 installs it globally so ALL users can run it The commands: 1. python3 --version # confirm Python 3 exists 2. sudo pip3 install ansible==4.8.0 # install exact version globally 3. ansible --version # verify ✅ 4. which ansible # confirms: /usr/local/bin/ansible The architecture now looks like this: [Jump Host — Ansible Controller] ↓ SSH (passwordless from Day 7) [stapp01] [stapp02] [stapp03] The servers need NOTHING installed. Ansible reaches out to them over the SSH keys I set up yesterday. This is where the previous 7 days start paying off: → Day 1: service users → Day 4: file permissions → Day 7: passwordless SSH ← Ansible uses this directly → Day 8: Ansible controller ready "Ansible needs nothing on the servers. Just SSH, Python, and one controller. That's the beauty of agentless automation." #DevOps #Ansible #Automation #Linux #Python #ConfigurationManagement #KodeKloud

KubeOpt now has a CLI scan mode. Run a cost scan against any KubeOpt instance directly from your terminal, no dashboard needed. python cli.py scan python cli.py scan --cluster prod-eks-us-east python cli.py scan --top 10 --json Picks up credentials from your .env file automatically. Outputs a savings report in table or JSON format, so it plugs straight into scripts and pipelines. Repo: https://lnkd.in/g4MuS92S #kubeopt #kubernetes