Module 7: Performance & Cost Optimization
Overview
This module is CRITICAL for Principal-level engineers. Every architectural decision has cost implications. This module covers storage optimization (small files problem, compaction, compression, Z-Ordering), compute optimization (spot instances, rightsizing, serverless), query performance (caching, materialized views, join strategies), and FinOps (tagging, chargeback, lifecycle management).
Module Contents
Storage Optimization
| Document | Description | Status |
|---|---|---|
| Storage Optimization Overview | Storage strategies | ✅ Complete |
| Small Files Problem | Impact and solutions | ✅ Complete |
| Compaction Strategies | File merging strategies | ✅ Complete |
| Compression Codecs | Codec comparison | ✅ Complete |
| Data Skipping | Predicate pushdown | ✅ Complete |
| Partition Pruning | Partition optimization | ✅ Complete |
| Z-Ordering Clustering | Multi-dimensional clustering | ✅ Complete |
Compute Optimization
| Document | Description | Status |
|---|---|---|
| Compute Optimization Overview | Compute strategies | ✅ Complete |
| Spark Dynamic Allocation | Auto-scaling Spark | ✅ Complete |
| Spot Preemptible Instances | Spot instances | ✅ Complete |
| Cluster Rightsizing | Data-driven sizing | ✅ Complete |
| Serverless vs. Provisioned | Compute model choice | ✅ Complete |
Query Performance
| Document | Description | Status |
|---|---|---|
| Query Performance Overview | Query strategies | ✅ Complete |
| Caching Strategies | Result caching | ✅ Complete |
| Join Strategies | Join optimization | ✅ Complete |
| Materialized Views | Pre-computed results | ✅ Complete |
| Vectorization | CPU optimization | ✅ Complete |
FinOps
| Document | Description | Status |
|---|---|---|
| FinOps Overview | Financial operations | ✅ Complete |
| Tagging Strategies | Cost attribution | ✅ Complete |
| Chargeback Models | Cost allocation | ✅ Complete |
| Lifecycle Management | Data lifecycle | ✅ Complete |
| Cost Monitoring | Cost observability | ✅ Complete |
The Cost Optimization Framework
Cost Optimization Impact
| Optimization | Typical Savings | Complexity | Priority |
|---|---|---|---|
| Spot instances | 60-80% | Low | HIGH |
| Compression (ZSTD) | 15-30% storage | Low | HIGH |
| File size optimization | 10-50% query | Medium | HIGH |
| Z-Ordering | 50-80% query | Medium | HIGH |
| Materialized views | 70-90% query | Medium | MEDIUM |
| Right-sizing | 20-40% compute | Medium | HIGH |
| Lifecycle management | 30-70% storage | Medium | MEDIUM |
The Small Files Problem
Problem: Thousands of small files → slow queries, metadata explosion.
Impact:
- Query planning overhead
- Inefficient I/O (many small reads)
- NameNode/metastore pressure
Solutions:
- Proper file sizing (256MB-1GB)
- Continuous compaction
- Partition optimization
- Streaming configuration
Cost Impact: Unchecked small files can increase query costs by 10-100x.
FinOps Maturity Model
Learning Objectives
After completing this module, you will:
- Solve the small files problem: Compaction strategies and automation
- Optimize compression: Select codecs for cost/performance balance
- Implement Z-Ordering: Multi-dimensional data skipping
- Use spot instances: 60-80% compute savings with fault tolerance
- Right-size clusters: Data-driven cluster sizing
- Choose serverless vs. provisioned: Cost model analysis
- Implement materialized views: Query cost reduction
- Design FinOps practices: Tagging, chargeback, monitoring
Module Dependencies
This module is critical for Principal-level interviews. Cost optimization is a primary architectural concern.
Next Steps
- Start with Small Files Problem
- Study Compaction Strategies
- Learn Spot Instances
- Implement FinOps
Estimated Time to Complete Module 7: 12-15 hours (CRITICAL MODULE)
Total Files: 22 markdown files with 80+ diagrams