sf-agent-framework/sf-core/checklists/performance-optimization-checklist.md

AI Agent Orchestration Framework for Salesforce Development - Two-phase architecture with 70% context reduction

# Performance Optimization Checklist

This comprehensive checklist guides systematic performance optimization for
Salesforce implementations, covering all layers from database to user interface.

## Database Optimization

### Level 1: SOQL Optimization

- [ ] Selective WHERE clauses using indexed fields
- [ ] LIMIT clause used appropriately
- [ ] Fields in SELECT limited to necessary only
- [ ] Relationship queries minimized
- [ ] NOT operators avoided where possible

### Level 2: Query Patterns

- [ ] N+1 query patterns eliminated
- [ ] Bulk query patterns implemented
- [ ] Subqueries used efficiently
- [ ] Aggregate queries optimized
- [ ] SOSL used for text searches

### Level 3: Indexing Strategy

- [ ] Custom indexes requested for large data volume queries
- [ ] Skinny tables implemented for wide objects
- [ ] External IDs indexed
- [ ] Deterministic formula fields for filtering
- [ ] Index usage verified with Query Plan

### Level 4: Data Architecture

- [ ] Archival strategy implemented
- [ ] Data skew addressed
- [ ] Ownership skew resolved
- [ ] Lookup skew mitigated
- [ ] Big objects utilized for historical data

## Apex Code Optimization

### Level 1: Bulkification

- [ ] All triggers bulkified
- [ ] Batch Apex used for large operations
- [ ] Collections used efficiently
- [ ] Loops minimized
- [ ] DML operations consolidated

### Level 2: Governor Limits

- [ ] SOQL queries < 50% of limit
- [ ] DML statements minimized
- [ ] CPU time optimized
- [ ] Heap size managed
- [ ] Callouts batched effectively

### Level 3: Efficient Patterns

- [ ] Lazy loading implemented
- [ ] Caching strategies applied
- [ ] Map data structures used for lookups
- [ ] String concatenation optimized
- [ ] Describe calls cached

### Level 4: Asynchronous Processing

- [ ] Future methods used appropriately
- [ ] Queueable Apex for chaining
- [ ] Batch Apex for large volumes
- [ ] Platform Events for decoupling
- [ ] Scheduled Apex optimized

### Level 5: Code Efficiency

- [ ] Recursive triggers prevented
- [ ] Static variables for caching
- [ ] Efficient algorithms used
- [ ] Early exits implemented
- [ ] Complex calculations minimized

## Lightning Performance

### Level 1: Component Design

- [ ] Component granularity optimized
- [ ] Unnecessary re-renders prevented
- [ ] Reactive properties minimized
- [ ] Component initialization optimized
- [ ] Event handling efficient

### Level 2: Data Management

- [ ] Lightning Data Service utilized
- [ ] Caching implemented
- [ ] Lazy loading for large lists
- [ ] Pagination implemented
- [ ] Progressive rendering used

### Level 3: Network Optimization

- [ ] Server calls minimized
- [ ] Batch operations used
- [ ] Payload sizes reduced
- [ ] Compression enabled
- [ ] CDN utilized for static resources

### Level 4: Rendering Performance

- [ ] DOM manipulation minimized
- [ ] CSS animations used over JavaScript
- [ ] Virtualization for long lists
- [ ] Image optimization applied
- [ ] Web fonts optimized

### Level 5: Mobile Optimization

- [ ] Mobile-specific layouts used
- [ ] Touch interactions optimized
- [ ] Offline capabilities implemented
- [ ] Resource loading prioritized
- [ ] Battery usage considered

## Visualforce Optimization

### Level 1: Page Size

- [ ] View state size < 135KB
- [ ] Unnecessary components removed
- [ ] Transient variables used
- [ ] Read-only pages where applicable
- [ ] Pagination implemented

### Level 2: Rendering

- [ ] Partial page updates used
- [ ] JavaScript remoting for data
- [ ] Lazy loading implemented
- [ ] Static resources combined
- [ ] Browser caching leveraged

### Level 3: Controller Optimization

- [ ] Controller logic minimized
- [ ] Action methods efficient
- [ ] Properties calculated once
- [ ] Database calls batched
- [ ] View state variables minimized

## Flow and Process Builder

### Level 1: Design Efficiency

- [ ] Flows consolidated where possible
- [ ] Decision elements minimized
- [ ] Loops used sparingly
- [ ] Subflows for reusability
- [ ] Fast field updates used

### Level 2: Query Optimization

- [ ] Get Records elements limited
- [ ] Filters selective
- [ ] Related records queried efficiently
- [ ] Collection variables used properly
- [ ] Bulk patterns implemented

### Level 3: Performance Patterns

- [ ] Before-save flows for field updates
- [ ] After-save flows for related records
- [ ] Scheduled flows for batch operations
- [ ] Platform events for async
- [ ] Invocable methods for complex logic

## Integration Performance

### Level 1: API Optimization

- [ ] Composite API used for multiple operations
- [ ] Bulk API for large data volumes
- [ ] Streaming API for real-time
- [ ] Batch sizes optimized
- [ ] Compression enabled

### Level 2: Callout Optimization

- [ ] Connection pooling implemented
- [ ] Timeout values appropriate
- [ ] Retry logic efficient
- [ ] Caching for external data
- [ ] Parallel callouts where possible

### Level 3: Middleware Optimization

- [ ] Message queuing implemented
- [ ] Transformation efficiency verified
- [ ] Routing logic optimized
- [ ] Error handling efficient
- [ ] Monitoring overhead minimized

## Reports and Dashboards

### Level 1: Report Optimization

- [ ] Report filters selective
- [ ] Custom report types efficient
- [ ] Summary levels appropriate
- [ ] Row limits applied
- [ ] Indexed fields used

### Level 2: Dashboard Performance

- [ ] Component count limited
- [ ] Refresh schedules staggered
- [ ] Filtered dashboards used
- [ ] Source reports optimized
- [ ] Dynamic dashboards used sparingly

### Level 3: Analytics Optimization

- [ ] SAQL queries optimized
- [ ] Dataflows scheduled appropriately
- [ ] Recipe performance tuned
- [ ] Data sync optimized
- [ ] Lens performance checked

## Platform Features

### Level 1: Caching

- [ ] Platform Cache utilized
- [ ] Session cache for user data
- [ ] Org cache for shared data
- [ ] Cache partitions sized correctly
- [ ] TTL values appropriate

### Level 2: Big Objects

- [ ] Historical data moved to Big Objects
- [ ] Async SOQL used effectively
- [ ] Indexes defined properly
- [ ] Query patterns optimized
- [ ] Standard objects archived

### Level 3: Event-Driven Architecture

- [ ] Platform Events for decoupling
- [ ] Change Data Capture utilized
- [ ] Streaming API implemented
- [ ] Event replay configured
- [ ] Error handling robust

## Mobile Performance

### Level 1: Offline Optimization

- [ ] Priming queries optimized
- [ ] Cached data minimized
- [ ] Sync frequency appropriate
- [ ] Conflict resolution efficient
- [ ] Storage limits considered

### Level 2: Mobile Layouts

- [ ] Compact layouts used
- [ ] Field count minimized
- [ ] Related lists limited
- [ ] Actions optimized
- [ ] Quick actions utilized

## Testing and Monitoring

### Level 1: Performance Testing

- [ ] Load testing completed
- [ ] Stress testing performed
- [ ] Volume testing done
- [ ] Endurance testing completed
- [ ] Spike testing performed

### Level 2: Monitoring Setup

- [ ] Event monitoring enabled
- [ ] Performance dashboards created
- [ ] Alert thresholds configured
- [ ] Custom metrics tracked
- [ ] Trend analysis automated

### Level 3: Optimization Tracking

- [ ] Baseline metrics captured
- [ ] Improvement targets set
- [ ] Progress tracked regularly
- [ ] ROI calculated
- [ ] Success criteria defined

## User Experience

### Level 1: Page Load Times

- [ ] Initial load < 3 seconds
- [ ] Subsequent loads < 1 second
- [ ] Search responses < 2 seconds
- [ ] Save operations < 2 seconds
- [ ] Navigation instant

### Level 2: Perceived Performance

- [ ] Loading indicators used
- [ ] Progressive disclosure implemented
- [ ] Optimistic UI updates
- [ ] Skeleton screens utilized
- [ ] Async operations for long tasks

## Documentation

### Level 1: Performance Documentation

- [ ] Bottlenecks documented
- [ ] Optimization decisions recorded
- [ ] Trade-offs explained
- [ ] Monitoring guide created
- [ ] Troubleshooting steps defined

### Level 2: Best Practices

- [ ] Coding standards updated
- [ ] Design patterns documented
- [ ] Anti-patterns identified
- [ ] Team training completed
- [ ] Knowledge sharing active

## Continuous Optimization

### Level 1: Regular Reviews

- [ ] Monthly performance reviews
- [ ] Quarterly deep dives
- [ ] Annual architecture review
- [ ] Continuous monitoring active
- [ ] Proactive optimization planned

### Level 2: Capacity Planning

- [ ] Growth projections analyzed
- [ ] Scaling strategies defined
- [ ] Resource planning completed
- [ ] Budget allocated
- [ ] Timeline established

## Sign-off

### Performance Acceptance

- [ ] Performance targets met
- [ ] User satisfaction confirmed
- [ ] Technical debt acceptable
- [ ] Monitoring in place
- [ ] Optimization plan approved