Platform Engineering Workbook

Date: 2025-01-27
Problem: Pods are getting OOMKilled due to insufficient memory limits
Force Approach: Increase memory limits for all pods across all namespaces, then monitor for resource waste
Leverage Alternative: Analyze actual memory usage patterns, implement horizontal pod autoscaling, and set realistic limits based on data
Key Learning: Instead of fighting against resource constraints, work with the system's natural scaling patterns

Date: ___________
Problem: _________________________________
Force Approach: __________________________
Leverage Alternative: ____________________
Key Learning: ____________________________

Before: "I need to implement a service mesh for microservices communication"
Leverage Questions:
- What's the smallest change? → Start with just ingress/egress traffic, not all inter-service communication
- Work with existing systems? → Use existing load balancer configuration as a foundation
- Use constraints as catalysts? → Limited budget forces me to choose the most critical services first

Week of: 2025-01-20

Force Example:
Problem: Need to migrate PostgreSQL database to a new version with zero downtime
Approach: Built complex custom migration scripts with multiple rollback scenarios, extensive testing
Outcome: Took 4 weeks to build and test, worked but was overly complex and fragile

Leverage Example:
Problem: Same database migration requirement
Approach: Used PostgreSQL's logical replication to create a read replica, then promoted it
Outcome: Completed in 3 days using the database's native capabilities

Key Insight: Instead of fighting against database limitations, I worked with its built-in replication features

Problem: Implement comprehensive monitoring for a Kubernetes cluster
Original Approach: Deploy Prometheus, Grafana, Jaeger, and custom dashboards for all services
Artificial Constraints: Can only use existing monitoring tools, no new deployments, must work with current resource limits
Constrained Solution: 
- Extend existing Prometheus configuration to scrape new metrics
- Create Grafana dashboards using existing data sources
- Use Kubernetes native metrics (kube-state-metrics) instead of custom exporters
- Implement alerting rules that work with existing notification channels
Key Insights: The constraints forced me to work with existing systems, resulting in a more maintainable solution

Constraint: "This legacy system doesn't support modern container orchestration"
Reframing:
- What constraint? → Limited to traditional VM-based deployment
- How could this help? → Forces me to focus on application-level monitoring and logging
- What opportunities? → Creates a clean separation between infrastructure and application concerns

Problem: Need to implement CI/CD for a new microservice
Minimum Solution: 
- Use existing GitHub Actions workflow as template
- Deploy to existing Kubernetes cluster using existing Helm charts
- Use existing container registry and monitoring
Value Created: Automated deployment with minimal new infrastructure
Where to Add Complexity: 
- Custom deployment strategies (blue-green, canary)
- Advanced testing (integration, performance)
- Custom monitoring and alerting
Why: Basic CI/CD provides immediate value, advanced features can be added incrementally

Existing System: Basic Kubernetes cluster with simple deployments
What's Working: 
- Stable cluster with good uptime
- Basic monitoring with Prometheus
- Simple deployment process
How We Can Build On It: 
- Add horizontal pod autoscaling (leverage existing metrics)
- Implement network policies (build on existing CNI)
- Add service mesh (extend existing service discovery)
Next Steps: 
- Week 1: Implement HPA using existing metrics
- Week 2: Add network policies for security
- Week 3: Evaluate service mesh options

Project Goal: Migrate from manual server provisioning to Terraform
Existing Energy Sources: 
- DevOps team already using Terraform for some services
- Management wants cost optimization (Terraform can help with resource management)
- Developers frustrated with manual deployment processes
How to Channel Energy: 
- Start with services the DevOps team already manages
- Frame as cost optimization initiative (management buy-in)
- Show developers how it reduces deployment friction
Potential Resistance: 
- "We don't have time to learn new tools"
- "Current process works fine"
Mitigation Strategy: 
- Start with non-critical services to prove value
- Provide training sessions during lunch hours
- Show concrete time savings from automation

Today's Small Improvement: Document one Terraform module with clear input/output descriptions
Expected Compound Effect: 
- Future team members understand infrastructure faster
- Reduces time spent debugging configuration issues
- Creates culture of self-documenting infrastructure
- Makes infrastructure changes safer and more predictable
How to Measure Progress: 
- Track number of documented modules
- Measure time to understand infrastructure during onboarding
- Count questions asked about infrastructure configuration

Project: Design infrastructure for new microservices architecture
Leverage Approach: 
- Use existing Kubernetes cluster instead of building new infrastructure
- Extend current monitoring setup rather than implementing new tools
- Leverage existing CI/CD pipeline patterns
Constraint Strategy: 
- Limited to existing cloud provider and services
- Must work with current security and compliance requirements
- Can't modify existing services (only new ones)
Momentum Building: 
- Build on team's existing Kubernetes knowledge
- Use established deployment patterns
- Follow current monitoring and alerting standards
Precision Focus: 
- Focus on critical infrastructure components (networking, security, monitoring)
- Skip nice-to-have features that don't impact reliability
- Prioritize observability over complex orchestration

Opportunity: Manual infrastructure provisioning is error-prone and time-consuming
Judo Principle: Leverage over Force - Use existing tools and processes more effectively
Action Plan: 
- Create Terraform modules for common infrastructure patterns
- Use existing CI/CD pipeline to deploy infrastructure changes
- Implement infrastructure testing using existing testing frameworks
Expected Impact: 
- Faster infrastructure provisioning (less manual work)
- Better reliability (automated testing catches issues)
- More consistent infrastructure (standardized modules)

Team Member: Alex (Platform Engineer)
Judo Moment: Instead of rebuilding our entire monitoring stack, I extended the existing Prometheus setup to handle the new microservices by creating reusable Helm chart templates
Principle Applied: Momentum over Muscle - Built on existing systems instead of starting from scratch
Key Learning: The existing monitoring was actually well-designed; I just needed to understand how to extend it rather than replace it
Team Discussion: 
- "This approach saved us 3 weeks of work"
- "We should document this pattern for future microservices"
- "Let's create a template library for common monitoring patterns"

Day 1: Force: ___ Leverage: ___
Day 2: Force: ___ Leverage: ___
Day 3: Force: ___ Leverage: ___
Day 4: Force: ___ Leverage: ___
Day 5: Force: ___ Leverage: ___

Total Force: ___ Total Leverage: ___
Ratio: ___% Leverage

Goal: Increase leverage ratio by 10% each week

Week 1: ___/10
Week 2: ___/10
Week 3: ___/10
Week 4: ___/10

Average: ___/10
Trend: ________________

Week 1: ___/10
Week 2: ___/10
Week 3: ___/10
Week 4: ___/10

Average: ___/10
Trend: ________________

High-Impact Work: ___ hours
Low-Impact Work: ___ hours
Ratio: ___% High-Impact

Goal: Increase high-impact ratio by 5% each week