Compute Optimization
Cluster Sizing, Spot Instances, and Auto-Scaling
Overview
Compute optimization reduces compute costs through rightsizing, spot instances, dynamic allocation, and choosing between serverless and provisioned models.
Optimization Strategies
Strategy Overview
Optimization Impact
Cost Savings
| Optimization | Savings | Complexity | Use Case |
|---|---|---|---|
| Spot instances | 60-80% | Medium | Fault-tolerant workloads |
| Rightsizing | 20-40% | Medium | All clusters |
| Dynamic allocation | 30-50% | Low | Variable workloads |
| Serverless | Variable | Low | Infrequent usage |
Combined Impact: 70-90% compute savings possible.
Compute Optimization Guides
| Document | Description | Status |
|---|---|---|
| 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 |
Quick Wins
Immediate Actions
- Enable spot instances: 60-80% cost savings
- Enable dynamic allocation: Auto-scale executors
- Right-size clusters: Based on actual usage
- Choose serverless: For infrequent workloads
- Monitor utilization: Track CPU, memory, storage
Long-Term Strategy
- Implement auto-scaling: Dynamic allocation
- Use spot instances: For fault-tolerant workloads
- Regular rightsizing: Quarterly cluster review
- Hybrid approach: Mix serverless and provisioned
- Automation: Automated scaling and rightsizing
Key Takeaways
- Spot instances: 60-80% savings for fault-tolerant workloads
- Rightsizing: Data-driven cluster sizing
- Dynamic allocation: Auto-scaling for variable workloads
- Serverless: Zero idle cost for infrequent usage
- Provisioned: Predictable cost for constant usage
- Monitoring: Track utilization, costs, performance
- Automation: Automate scaling and rightsizing
- Use When: All compute, cost optimization focus
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