sf-agent-framework

# Build Performance Optimization Task This task guides the systematic optimization of Salesforce build and deployment performance to reduce cycle times and improve developer productivity. ## Purpose Enable build engineers to: - Analyze and optimize build pipeline performance - Reduce deployment and testing cycle times - Improve resource utilization efficiency - Minimize build failures and retries - Enhance developer experience and productivity ## Prerequisites - Access to CI/CD pipeline configuration - Build and deployment metrics history - Understanding of Salesforce deployment mechanisms - Performance monitoring tools access - Build infrastructure administration rights ## Build Performance Framework ### 1. Performance Analysis Baseline **Build Metrics Collection** ```yaml Performance_Metrics: Pipeline_Duration: Total_Build_Time: 'End-to-end pipeline execution' Stage_Breakdown: - Source_Checkout: 'Code retrieval from repository' - Dependency_Resolution: 'Package and library downloads' - Compilation: 'Code compilation and validation' - Testing: 'Unit and integration test execution' - Packaging: 'Deployment package creation' - Deployment: 'Target environment deployment' - Validation: 'Post-deployment verification' Resource_Utilization: CPU_Usage: 'Build agent processor utilization' Memory_Consumption: 'RAM usage during build' Network_Bandwidth: 'Data transfer rates' Storage_I/O: 'Disk read/write operations' Quality_Metrics: Success_Rate: 'Percentage of successful builds' Failure_Rate: 'Build failure frequency' Retry_Rate: 'Number of retries required' Test_Coverage: 'Code coverage percentage' ``` **Performance Benchmarking** ``` Algorithm: Build Performance Baseline Establishment INPUT: metrics_collector, historical_days (default: 30) PROCESS: 1. GET historical_builds from metrics for specified days 2. CALCULATE baseline metrics: - average_build_duration from historical builds - percentile_durations (50th, 75th, 95th percentiles) - success_rate from historical builds - failure_patterns analysis - resource_utilization analysis 3. STORE baseline_metrics for future comparisons 4. RETURN complete baseline OUTPUT: performance_baseline_metrics ``` ``` Algorithm: Performance Bottleneck Identification INPUT: build_data PROCESS: 1. INITIALIZE bottlenecks = empty list 2. ANALYZE stage performance: FOR each stage, metrics in stage_analysis: IF metrics.duration > (metrics.expected_duration * 1.5) THEN CREATE bottleneck_entry: - type: "stage_performance" - stage, current_duration, expected_duration - impact: "high" if duration > 300 else "medium" ADD to bottlenecks 3. ANALYZE resource constraints: FOR each resource, constraint in resource_analysis: IF constraint.utilization > 0.9 THEN CREATE resource_bottleneck: - type: "resource_constraint" - resource, utilization - impact: "high" ADD to bottlenecks 4. RETURN identified bottlenecks OUTPUT: performance_bottleneck_list ``` ### 2. Optimization Strategies **Parallel Execution Optimization** ```yaml Parallelization_Strategy: Test_Execution: Parallel_Test_Classes: 'Run test classes in parallel' Test_Partitioning: 'Divide tests by execution time' Resource_Allocation: 'Optimize test runner resources' Deployment_Components: Metadata_Chunking: 'Deploy metadata in parallel chunks' Component_Grouping: 'Group related components' Dependency_Management: 'Resolve dependencies efficiently' Multi_Environment: Concurrent_Deployments: 'Deploy to multiple envs simultaneously' Environment_Isolation: 'Isolate environment-specific operations' Resource_Sharing: 'Share common resources across environments' ``` **Build Caching Implementation** ``` Algorithm: Dependency Caching Strategy Implementation INPUT: cache_configuration PROCESS: 1. DEFINE cache_layers = ["dependency", "compilation", "test_results"] 2. CREATE cache_strategy with components: a. package_dependencies: - cache_key: "package-lock-hash" - cache_paths: ["node_modules/", ".sfdx/"] - invalidation: "on_package_change" - compression: true b. salesforce_metadata: - cache_key: "metadata-hash" - cache_paths: ["force-app/", "sfdx-project.json"] - invalidation: "on_metadata_change" - compression: true c. test_data: - cache_key: "test-data-hash" - cache_paths: ["test-data/", "test-config/"] - invalidation: "on_test_change" - compression: false 3. RETURN complete cache_strategy OUTPUT: dependency_caching_strategy ``` ``` Algorithm: Cache Utilization Optimization INPUT: build_context PROCESS: 1. INITIALIZE optimization_actions = empty list 2. ANALYZE cache_performance for all layers 3. FOR each layer, stats in cache_stats: a. IF hit_rate < 0.7 (less than 70%) THEN ADD optimization_action: - action: "improve_cache_key_strategy" - layer, current_hit_rate - recommendation from analysis b. IF cache size_mb > 1000 THEN ADD optimization_action: - action: "optimize_cache_size" - layer, current_size - recommendation: "Implement cache pruning strategy" 4. RETURN optimization_actions OUTPUT: cache_optimization_recommendations ``` **Incremental Build Implementation** ``` Algorithm: Incremental Change Detection INPUT: base_commit, target_commit, source_control PROCESS: 1. GET changes from source_control between base_commit and target_commit 2. INITIALIZE categorized_changes with categories: - apex_classes, lightning_components, flows - metadata, test_classes, static_resources 3. FOR each change in changes: a. CATEGORIZE change based on file path and type b. IF category identified THEN ADD change to categorized_changes[category] 4. RETURN categorized_changes OUTPUT: categorized_incremental_changes ``` ``` Algorithm: Incremental Deployment Package Creation INPUT: categorized_changes PROCESS: 1. INITIALIZE package_components = empty list 2. FOR each category, changed_files in changes: a. IF changed_files not empty THEN RESOLVE dependencies for changed_files in category ADD dependencies to package_components 3. OPTIMIZE package structure: - REMOVE duplicates - ORDER components by deployment priority 4. DETERMINE test_scope from changes 5. GET incremental_deployment_options based on changes 6. RETURN deployment_package with: - optimized components - test_classes scope - deployment options OUTPUT: incremental_deployment_package ``` ## Implementation Steps ### Step 1: Build Pipeline Analysis **Performance Profiling Setup** ``` Algorithm: Build Performance Profiler Script INPUT: build_id, performance_tracking_configuration PROCESS: 1. CONFIGURE script execution: - SET error_exit_mode = true - ENABLE detailed timing logs - RECORD build_start_time 2. LOG profiler initialization with build_id and start_time 3. DEFINE stage_timing_logger function: - INPUT: stage_name, start_time - CALCULATE duration = current_time - start_time - LOG stage completion with duration - APPEND metrics to build_metrics.csv 4. EXECUTE build stages with timing: a. SOURCE_CHECKOUT: - RECORD stage_start_time - RUN git fetch with shallow clone - LOG timing for "source_checkout" b. DEPENDENCY_RESOLUTION: - RECORD stage_start_time - RUN npm ci for production dependencies - INSTALL sfdx scanner plugin - LOG timing for "dependency_resolution" c. CODE_QUALITY_ANALYSIS: - RECORD stage_start_time - RUN sfdx scanner on force-app - OUTPUT results to quality-results.json - LOG timing for "code_quality" d. UNIT_TESTS: - RECORD stage_start_time - RUN apex tests with code coverage - OUTPUT results to test-results directory - LOG timing for "unit_tests" e. PACKAGE_CREATION: - RECORD stage_start_time - CONVERT source to mdapi format - LOG timing for "package_creation" f. DEPLOYMENT_VALIDATION: - RECORD stage_start_time - RUN check-only deployment with local tests - LOG timing for "deployment_validation" 5. CALCULATE total_duration = build_end_time - build_start_time 6. LOG build completion with total duration and end time 7. GENERATE performance report from build_metrics.csv OUTPUT: build_performance_profile_with_metrics ``` **Resource Monitoring** ``` Algorithm: Resource Monitoring Initialization INPUT: sampling_interval (default: 5 seconds) PROCESS: 1. SET sampling_interval for metric collection frequency 2. INITIALIZE metrics = empty list for storing snapshots 3. SET monitoring = false (initial state) 4. RETURN configured resource monitor OUTPUT: resource_monitoring_system ``` ``` Algorithm: Background Resource Monitoring INPUT: resource_monitor PROCESS: 1. SET monitoring = true to start collection 2. WHILE monitoring is active: a. CREATE metrics_snapshot with current timestamp: - cpu_percent (1-second interval measurement) - memory_percent and memory_used_gb - disk_io_counters statistics - network_io_counters statistics - active_process_count - system_load_average b. ADD metrics_snapshot to metrics collection c. WAIT for sampling_interval seconds 3. CONTINUE until monitoring stopped OUTPUT: continuous_resource_metrics_collection ``` ``` Algorithm: Resource Monitoring Report Generation INPUT: collected_metrics PROCESS: 1. IF no metrics collected THEN RETURN error: "No metrics collected" 2. EXTRACT cpu_values and memory_values from all metrics 3. CALCULATE monitoring_duration = sample_count * sampling_interval 4. COMPUTE cpu_statistics: - average, maximum, minimum CPU usage 5. COMPUTE memory_statistics: - average, maximum, minimum memory usage - peak_usage_gb from all samples 6. GENERATE optimization_recommendations based on patterns 7. COMPILE comprehensive report with: - monitoring duration and sample count - cpu and memory statistics - optimization recommendations 8. RETURN complete resource_usage_report OUTPUT: comprehensive_resource_analysis ``` ### Step 2: Performance Optimization Implementation **Parallel Test Execution** ```yaml # parallel-test-config.yml parallel_test_configuration: max_parallel_classes: 5 test_partitioning_strategy: 'by_execution_time' test_groups: fast_tests: max_execution_time: 30 # seconds parallel_instances: 3 classes: - AccountTriggerTest - ContactTriggerTest - OpportunityTriggerTest medium_tests: max_execution_time: 120 # seconds parallel_instances: 2 classes: - DataMigrationTest - IntegrationTest slow_tests: max_execution_time: 300 # seconds parallel_instances: 1 classes: - PerformanceTest - EndToEndTest ``` **Smart Deployment Packaging** ``` Algorithm: Optimized Package Creation INPUT: changed_components, metadata_analyzer PROCESS: 1. DEFINE optimization_strategies: - minimize_metadata_size - optimize_component_order - bundle_related_components - exclude_unnecessary_metadata 2. INITIALIZE optimized_package = copy of changed_components 3. FOR each strategy in optimization_strategies: APPLY strategy to optimized_package UPDATE optimized_package with strategy results 4. GENERATE optimization_report comparing original and optimized packages 5. RETURN package_result with: - optimized package - optimization report OUTPUT: optimized_deployment_package ``` ``` Algorithm: Metadata Size Minimization INPUT: deployment_package PROCESS: 1. INITIALIZE size_optimizations = empty list 2. INITIALIZE optimized_package = empty dictionary 3. FOR each component_type, components in package: a. IF component_type = "CustomObject" THEN INITIALIZE optimized_components = empty list FOR each component in components: STRIP non-essential metadata (help text, descriptions for non-prod) ADD optimized_component to optimized_components SET optimized_package[component_type] = optimized_components b. ELSE SET optimized_package[component_type] = components (unchanged) 4. RETURN optimized_package OUTPUT: size_optimized_package ``` ### Step 3: Advanced Performance Techniques **Dynamic Resource Scaling** ``` Algorithm: Dynamic Build Resource Scaling INPUT: build_requirements, container_orchestrator PROCESS: 1. LOAD scaling_policies from configuration 2. CALCULATE required_resources from build_requirements 3. GET current_resource_allocation from orchestrator 4. MAKE scaling_decision: - scale_up: required_cpu > (current_cpu * 0.8) - scale_down: required_cpu < (current_cpu * 0.3) - target_resources: calculated requirements 5. IF scale_up needed THEN RETURN scale_up_resources with target_resources 6. ELSE IF scale_down needed THEN RETURN scale_down_resources with target_resources 7. ELSE RETURN no_scaling_required with current_resources OUTPUT: resource_scaling_action ``` ``` Algorithm: Build Resource Requirements Calculation INPUT: build_requirements PROCESS: 1. INITIALIZE base_requirements: - cpu: 2, memory_gb: 4, storage_gb: 20 2. ADJUST based on build characteristics: a. IF test_count > 100 THEN ADD 2 to cpu, ADD 4 to memory_gb b. IF package_size_mb > 100 THEN ADD 2 to memory_gb, ADD 10 to storage_gb c. IF parallel_jobs > 1 THEN MULTIPLY cpu by parallel_jobs count 3. RETURN adjusted base_requirements OUTPUT: calculated_resource_requirements ``` **Build Artifact Optimization** ``` Algorithm: Build Artifact Storage Optimization INPUT: build_artifacts, artifact_store PROCESS: 1. INITIALIZE optimization_results = empty list 2. FOR each artifact in build_artifacts: a. IF artifact size_mb > 50 THEN COMPRESS artifact using compression algorithm ADD optimization_result: - artifact name, action: "compressed" - original_size, compressed_size - compression_ratio calculation b. IF artifact checksum exists in store THEN ADD optimization_result: - artifact name, action: "deduplicated" - savings_mb: original artifact size c. IF artifact age_days > 30 THEN ARCHIVE artifact to cold storage ADD optimization_result: - artifact name, action: "archived" - storage_tier: "cold_storage" 3. RETURN complete optimization_results OUTPUT: artifact_optimization_report ``` ## Monitoring and Continuous Improvement ### Performance Metrics Dashboard ``` Algorithm: Real-time Performance Dashboard Generation INPUT: metrics_database PROCESS: 1. COMPILE dashboard_data components: - current_builds: active build status - recent_performance: performance trends - optimization_opportunities: identified improvements - resource_utilization: system resource summary - success_rates: build success statistics - performance_alerts: active performance warnings 2. RETURN complete dashboard_data OUTPUT: real_time_performance_dashboard ``` ``` Algorithm: Optimization Opportunities Identification INPUT: metrics_database PROCESS: 1. INITIALIZE opportunities = empty list 2. GET recent_builds from last 7 days 3. ANALYZE build duration trends: IF duration_regression detected (>20% increase) THEN ADD opportunity: - type: "duration_regression", priority: "high" - description: duration increase details - recommended_actions: ["Review recent changes", "Check resource constraints"] 4. ANALYZE test execution trends: GET test_duration_trend from recent builds IF test duration increasing THEN ADD opportunity: - type: "test_performance", priority: "medium" - description: test execution time increase percentage - recommended_actions: ["Implement parallel test execution", "Review slow test classes"] 5. RETURN identified opportunities OUTPUT: build_optimization_opportunities ``` ## Success Criteria ✅ Build performance baseline established ✅ Bottlenecks identified and prioritized ✅ Optimization strategies implemented ✅ Parallel execution configured ✅ Caching mechanisms active ✅ Resource utilization optimized ✅ Performance monitoring dashboard operational ✅ Continuous improvement process established