It Infrastructure Sysadmin Engine

IT Infrastructure & Sysadmin Engine

Purpose

Provide expert-level IT infrastructure knowledge covering networking, system administration, troubleshooting methodologies, and cloud/on-premises infrastructure management.

Networking Fundamentals

OSI Model (Troubleshooting Layer by Layer)

DNS Deep Dive

• Record Types: A (IPv4), AAAA (IPv6), CNAME (alias), MX (mail), TXT (verification/SPF/DKIM), NS (nameserver), SOA (authority), SRV (service), PTR (reverse)
• Resolution Flow: Client cache → Local DNS → Root → TLD → Authoritative
• Troubleshooting: nslookup, dig, host, check TTL, check propagation
• Common Issues: Stale cache, misconfigured records, TTL too high/low, DNSSEC validation failures

DHCP

• DORA Process: Discover → Offer → Request → Acknowledge
• Scope Management: Address pools, reservations, exclusions, lease duration
• Troubleshooting: Check scope exhaustion, relay agents, lease conflicts, ipconfig /release && ipconfig /renew

Subnetting Quick Reference

System Administration

Windows Server

• Active Directory: Domain controllers, OUs, GPOs, FSMO roles, replication, trusts
• Group Policy: Computer vs User config, precedence (Local → Site → Domain → OU), gpupdate /force, gpresult /r
• File Services: NTFS permissions, share permissions, DFS, quotas
• Certificate Services: PKI, CA hierarchy, certificate templates, auto-enrollment
• Hyper-V: Virtual switches, checkpoints, live migration, replication

Linux Server

• Package Management: apt (Debian/Ubuntu), yum/dnf (RHEL/CentOS), pacman (Arch)
• Service Management: systemctl start/stop/enable/status service
• User Management: useradd, usermod, passwd, /etc/passwd, /etc/shadow, sudo
• Cron Jobs: crontab -e, format: min hour dom month dow command
• Log Management: /var/log/, journalctl, logrotate, syslog/rsyslog
• Performance: top/htop, vmstat, iostat, sar, free -h, df -h

Troubleshooting Methodology

The 7-Step IT Troubleshooting Framework

1. Identify the Problem: What exactly is broken? Who is affected? When did it start? What changed?
2. Establish a Theory: Based on symptoms, what are the most likely causes? (Start with the simplest)
3. Test the Theory: Can you reproduce? Does the evidence support your theory?
4. Establish a Plan: What's the fix? What's the rollback plan? Who needs to be notified?
5. Implement the Fix: Apply the change, monitor for side effects
6. Verify Full Functionality: Test the fix AND test that nothing else broke
7. Document: Root cause, fix applied, prevention measures

The "5 Whys" for Root Cause Analysis

• Problem: Website is down
• Why? The web server crashed
• Why? It ran out of memory
• Why? A memory leak in the application
• Why? An unclosed database connection in a loop
• Why? No connection pooling was implemented
• Root Cause: Missing connection pooling → Fix: Implement connection pooling

Cloud Infrastructure

AWS Core Services

Azure Equivalent Services

Infrastructure as Code

• Terraform: Multi-cloud, declarative, state management, modules
• CloudFormation: AWS-native, JSON/YAML templates
• Ansible: Agentless, SSH-based, playbooks for configuration management
• Pulumi: Infrastructure as real code (TypeScript, Python, Go)

Virtualization & Containers

Virtualization

• Type 1 Hypervisors: VMware ESXi, Hyper-V, KVM (bare metal)
• Type 2 Hypervisors: VirtualBox, VMware Workstation (hosted)
• Key Concepts: vCPU allocation, memory ballooning, thin provisioning, snapshots, live migration

Containers (Docker/Kubernetes)

• Docker: Images, containers, Dockerfile, docker-compose, volumes, networks
• Kubernetes: Pods, Deployments, Services, Ingress, ConfigMaps, Secrets, PVCs
• Best Practices: Multi-stage builds, non-root users, health checks, resource limits, image scanning

Monitoring & Alerting

The 4 Golden Signals (Google SRE)

1. Latency: How long requests take (track p50, p95, p99)
2. Traffic: How much demand (requests/sec, concurrent users)
3. Errors: Rate of failed requests (5xx, timeouts, exceptions)
4. Saturation: How full is the system (CPU, memory, disk, connections)

Monitoring Stack Options

• Metrics: Prometheus + Grafana, Datadog, CloudWatch
• Logs: ELK Stack (Elasticsearch, Logstash, Kibana), Loki, Splunk
• Traces: Jaeger, Zipkin, AWS X-Ray, Datadog APM
• Alerting: PagerDuty, OpsGenie, Prometheus Alertmanager

Backup & Disaster Recovery

3-2-1 Backup Rule

• 3 copies of data
• 2 different storage media
• 1 offsite/cloud copy

Recovery Objectives

• RPO (Recovery Point Objective): How much data loss is acceptable?
• RTO (Recovery Time Objective): How quickly must service be restored?
• Design backup frequency and DR strategy around these objectives