🛠️ Guide
30 min
Infrastructure as Code Best Practices: Terraform, Ansible, Kubernetes
Introduction Infrastructure as Code (IaC) is how modern teams build reliable systems. Instead of manually clicking through cloud consoles or SSHing into servers, you define infrastructure in code—testable, version-controlled, repeatable. This guide shows you practical patterns for Terraform, Ansible, and Kubernetes with real examples, not just theory.
Why Infrastructure as Code? Consider a production outage scenario:
Without IaC:
Database server dies You manually recreate it through AWS console (30 minutes) Forgot to enable backups? Another 15 minutes Need to reconfigure custom security groups? More time Total recovery: 2-4 hours Risk of missing steps = still broken With IaC:
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October 16, 2025 · 30 min · DevOps Engineer
🛠️ Guide
10 min
Layer 4 Load Balancing Guide: TCP/UDP Load Balancing for DevOps/SRE
Executive Summary Layer 4 (Transport Layer) Load Balancing distributes traffic at the TCP/UDP level, before any application-level processing. Unlike Layer 7 (HTTP), L4 LBs don’t inspect request content—they simply route packets based on IP protocol data.
When to use L4:
Raw throughput requirements (millions of requests/sec) Non-HTTP protocols (gRPC, databases, MQTT, game servers) TLS passthrough (encrypted SNI unavailable) Extreme latency sensitivity When NOT to use L4:
HTTP/HTTPS (use Layer 7 instead) Request-based routing (path-based, host-based) Simple workloads with <1M req/sec Fundamentals L4 vs L7: Quick Comparison Aspect Layer 4 (TCP/UDP) Layer 7 (HTTP/HTTPS) What it sees IP/port/protocol HTTP headers, body, cookies Routing based on Destination IP, port, protocol Host, path, query string, cookies Throughput Very high (millions pps) Lower (thousands rps) Latency <1ms typical 5-50ms typical Protocols TCP, UDP, QUIC, SCTP HTTP/1.1, HTTP/2, HTTPS, WebSocket Encryption Can passthrough TLS Can terminate/re-encrypt Best for Databases, non-HTTP, TLS passthrough Web apps, microservices, APIs Core Concepts Listeners: Defined by (protocol, port). Example: TCP:443, UDP:5353
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October 16, 2025 · 10 min · DevOps Engineer
🛠️ Guide
22 min
Layer 7 Load Balancing Guide: Application-Level Routing for DevOps/SRE
Executive Summary Layer 7 (Application Layer) Load Balancing routes traffic based on HTTP/HTTPS semantics: hostnames, paths, headers, cookies, and body content. Unlike Layer 4, L7 LBs inspect and understand application protocols.
When to use L7:
HTTP/HTTPS workloads (99% of web apps) Host-based or path-based routing (SaaS multi-tenant) Advanced features: canary deployments, content-based routing API gateways with authentication/authorization WebSockets, gRPC, Server-Sent Events (SSE) When NOT to use L7:
Non-HTTP protocols (use L4) Ultra-low latency (<5ms) with extreme throughput (use L4) Binary protocols (databases, Kafka) Fundamentals L7 vs L4: What L7 Adds Feature L4 L7 Visibility IP/port/protocol Full HTTP request/response Routing based on Destination IP, port Host, path, headers, cookies, body Request modification None Rewrite, redirect, compress TLS Passthrough only Terminate + re-encrypt Session affinity IP hash (crude) Sticky cookies, affinity headers Compression No Gzip/Brotli inline WebSockets Requires passthrough Native support gRPC Via TLS passthrough Native with trailers, keep-alives Rate limiting App-level only LB-level per path/host Auth App-level only OIDC, JWT, basic @ edge Throughput Millions RPS Thousands-millions RPS Latency <1ms 1-10ms Core L7 Concepts Listeners: HTTP port 80, HTTPS port 443 (often combined as single listener with TLS upgrade)
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October 16, 2025 · 22 min · DevOps Engineer
🛠️ Guide
21 min
Neo4j End-to-End Guide: Deployment, Operations & Best Practices
Executive Summary Neo4j is a native graph database that stores data as nodes (entities) connected by relationships (edges). Unlike relational databases that normalize data into tables, Neo4j excels at traversing relationships.
Quick decision:
Use Neo4j for: Knowledge graphs, authorization/identity, recommendations, fraud detection, network topology, impact analysis Don’t use for: Heavy OLAP analytics, simple key-value workloads, document storage Production deployment: Kubernetes + Helm (managed) or Docker Compose + Causal Cluster (self-managed)
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October 16, 2025 · 21 min · DevOps Engineer
🛠️ Guide
19 min
CI/CD Pipeline Optimization: Build Caching, Parallel Jobs, and Deployment Strategies
Introduction Slow CI/CD pipelines waste developer time and delay releases. This guide covers proven techniques to optimize pipeline performance including build caching, parallel job execution, and efficient deployment strategies across popular CI/CD platforms.
Build Caching Why Caching Matters Without caching:
Build 1: npm install (5 min) → tests (2 min) = 7 min Build 2: npm install (5 min) → tests (2 min) = 7 min Build 3: npm install (5 min) → tests (2 min) = 7 min Total: 21 minutes With caching:
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October 15, 2025 · 19 min · DevOps Engineer
🛠️ Guide
10 min
ELK Stack Tuning: Elasticsearch Index Lifecycle and Logstash Pipelines
Introduction The ELK stack (Elasticsearch, Logstash, Kibana) is powerful for log aggregation and analysis, but requires proper tuning for production workloads. This guide covers Elasticsearch index lifecycle management, Logstash pipeline optimization, and performance best practices.
Elasticsearch Index Lifecycle Management (ILM) Understanding ILM ILM automates index management through lifecycle phases:
Phases:
Hot - Actively writing and querying Warm - No longer writing, still querying Cold - Rarely queried, compressed Frozen - Very rarely queried, minimal resources Delete - Removed from cluster Basic ILM Policy Create policy:
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October 15, 2025 · 10 min · DevOps Engineer
🛠️ Guide
10 min
GitOps with ArgoCD and Flux: Deployment Patterns and Rollback Strategies
Introduction GitOps is a paradigm that uses Git as the single source of truth for declarative infrastructure and applications. ArgoCD and Flux are the leading tools for implementing GitOps on Kubernetes. This guide covers deployment patterns, rollback strategies, and choosing between the two.
GitOps Principles Core Concepts 1. Declarative - Everything defined in Git 2. Versioned - Git history = deployment history 3. Automated - Tools sync Git to cluster 4. Auditable - All changes tracked in Git
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October 15, 2025 · 10 min · DevOps Engineer
🛠️ Guide
15 min
Prometheus Query Optimization: PromQL Tips, Recording Rules, and Performance
Introduction Prometheus queries can become slow and resource-intensive as your metrics scale. This guide covers PromQL optimization techniques, recording rules, and performance best practices to keep your monitoring fast and efficient.
PromQL Optimization Understanding Query Performance Factors affecting query performance:
Number of time series matched Time range queried Query complexity Cardinality of labels Rate of data ingestion Check query stats:
# Grafana: Enable query inspector # Shows: Query time, series count, samples processed 1. Limit Time Series Selection Bad (matches too many series):
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October 15, 2025 · 15 min · DevOps Engineer
🛠️ Guide
11 min
Terraform State Management: Remote Backends, Locking, and Workspaces
Introduction Terraform state is the source of truth for your infrastructure. Proper state management is critical for team collaboration, preventing conflicts, and maintaining infrastructure integrity. This guide covers remote backends, locking mechanisms, and workspace strategies.
Understanding Terraform State What is State? State is Terraform’s way of tracking which real-world resources correspond to your configuration. It’s stored in terraform.tfstate file.
State file contains:
Resource mappings Metadata Resource dependencies Attribute values Why State Matters Without proper state management:
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October 15, 2025 · 11 min · DevOps Engineer
🛠️ Guide
10 min
Docker Best Practices: Multi-stage Builds, Optimization, and Security
Introduction Building efficient and secure Docker images requires following best practices that reduce image size, improve build times, and minimize security vulnerabilities. This guide covers essential techniques for production-ready containers.
Multi-Stage Builds The Problem: Bloated Images Before (single-stage build):
FROM node:18 WORKDIR /app # Install dependencies COPY package*.json ./ RUN npm install # Includes devDependencies # Copy source COPY . . # Build RUN npm run build # Runtime includes build tools and dependencies CMD ["node", "dist/index.js"] Result: 1.2GB image with unnecessary build tools and dependencies.
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October 15, 2025 · 10 min · DevOps Engineer
🛠️ Guide
12 min
Kubernetes Troubleshooting: Pod Crashes, Networking, and Resources
Introduction Kubernetes troubleshooting can be challenging due to its distributed nature and multiple abstraction layers. This guide covers the most common issues and systematic approaches to diagnosing and fixing them.
Pod Crash Loops Understanding CrashLoopBackOff What it means: The pod starts, crashes, restarts, and repeats in an exponential backoff pattern.
Diagnostic Process Step 1: Check pod status
kubectl get pods -n production # Output: # NAME READY STATUS RESTARTS AGE # myapp-7d8f9c6b5-xyz12 0/1 CrashLoopBackOff 5 10m Step 2: Describe the pod
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October 15, 2025 · 12 min · DevOps Engineer
🛠️ Guide
1 min
Kafka Producer Tuning: Practical Recommendations
Introduction Kafka Producer is a key component for sending messages to a Kafka cluster. Proper producer configuration is critical for achieving high performance and system reliability.
Key tuning parameters 1. Batching and Compression # Increase batch size for better throughput batch.size=32768 linger.ms=5 # Enable compression to save bandwidth compression.type=lz4 2. Memory and Buffer # Buffer configuration buffer.memory=67108864 max.block.ms=60000 3. Acknowledgments and Durability # For high reliability acks=all retries=2147483647 enable.idempotence=true Real-world examples High Throughput Scenario For high volume data scenarios:
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August 17, 2025 · 1 min · DevOps Engineer