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Workflow Performance Optimization

Comprehensive guide to optimizing workflow performance, monitoring resource usage, and scaling Axon OS workflows for production environments.

Overview​

Performance optimization is crucial for ensuring your Axon OS workflows run efficiently and can handle production workloads. This guide covers performance monitoring, optimization techniques, and scaling strategies.

Key Performance Areas​

  • Execution Speed: Minimize workflow execution time
  • Resource Utilization: Optimize CPU, memory, and I/O usage
  • Throughput: Maximize data processing capacity
  • Scalability: Handle increasing workloads efficiently
  • Latency: Reduce response times for real-time workflows
  • Reliability: Maintain performance under various conditions

Performance Monitoring​

Key Performance Metrics​

interface WorkflowPerformanceMetrics {
  execution: ExecutionMetrics;
  resources: ResourceMetrics;
  throughput: ThroughputMetrics;
  latency: LatencyMetrics;
  errors: ErrorMetrics;
}

interface ExecutionMetrics {
  totalExecutionTime: number;
  nodeExecutionTimes: Map<string, number>;
  queueTime: number;
  parallelEfficiency: number;
  bottleneckNodes: string[];
}

interface ResourceMetrics {
  cpuUsage: CPUMetrics;
  memoryUsage: MemoryMetrics;
  diskIO: DiskIOMetrics;
  networkIO: NetworkIOMetrics;
}

interface CPUMetrics {
  averageUsage: number;
  peakUsage: number;
  coreUtilization: number[];
  computeIntensiveNodes: string[];
}

interface MemoryMetrics {
  heapUsed: number;
  heapTotal: number;
  external: number;
  peakMemoryUsage: number;
  memoryLeaks: MemoryLeak[];
}

interface ThroughputMetrics {
  recordsPerSecond: number;
  bytesPerSecond: number;
  workflowsPerHour: number;
  batchProcessingRate: number;
}

interface LatencyMetrics {
  p50: number;  // 50th percentile
  p95: number;  // 95th percentile
  p99: number;  // 99th percentile
  averageLatency: number;
  nodeLatencies: Map<string, LatencyStats>;
}

Performance Monitoring Implementation​

class PerformanceMonitor {
  private metrics: Map<string, WorkflowPerformanceMetrics> = new Map();
  private collectors: PerformanceCollector[] = [];
  
  startMonitoring(executionId: string): void {
    // Initialize metrics collection
    this.metrics.set(executionId, this.createEmptyMetrics());
    
    // Start collectors
    this.collectors.forEach(collector => collector.start(executionId));
  }
  
  recordNodeExecution(
    executionId: string,
    nodeId: string,
    startTime: number,
    endTime: number,
    resourceUsage: ResourceSnapshot
  ): void {
    const metrics = this.metrics.get(executionId);
    if (!metrics) return;
    
    const executionTime = endTime - startTime;
    
    // Update execution metrics
    metrics.execution.nodeExecutionTimes.set(nodeId, executionTime);
    metrics.execution.totalExecutionTime += executionTime;
    
    // Update resource metrics
    this.updateResourceMetrics(metrics.resources, resourceUsage);
    
    // Check for bottlenecks
    if (executionTime > 5000) { // 5 seconds threshold
      metrics.execution.bottleneckNodes.push(nodeId);
    }
  }
  
  generatePerformanceReport(executionId: string): PerformanceReport {
    const metrics = this.metrics.get(executionId);
    if (!metrics) throw new Error(`No metrics found for execution ${executionId}`);
    
    return {
      executionId,
      summary: this.generateSummary(metrics),
      bottlenecks: this.identifyBottlenecks(metrics),
      recommendations: this.generateRecommendations(metrics),
      trends: this.analyzeTrends(metrics),
      comparisons: this.compareWithBaseline(metrics)
    };
  }
  
  private identifyBottlenecks(metrics: WorkflowPerformanceMetrics): Bottleneck[] {
    const bottlenecks: Bottleneck[] = [];
    
    // CPU bottlenecks
    if (metrics.resources.cpuUsage.averageUsage > 80) {
      bottlenecks.push({
        type: 'cpu',
        severity: 'high',
        description: 'High CPU usage detected',
        affectedNodes: metrics.resources.cpuUsage.computeIntensiveNodes,
        recommendation: 'Consider parallelization or algorithm optimization'
      });
    }
    
    // Memory bottlenecks
    if (metrics.resources.memoryUsage.heapUsed > metrics.resources.memoryUsage.heapTotal * 0.9) {
      bottlenecks.push({
        type: 'memory',
        severity: 'critical',
        description: 'High memory usage approaching limits',
        recommendation: 'Optimize data structures or increase memory allocation'
      });
    }
    
    // Execution time bottlenecks
    const slowestNodes = Array.from(metrics.execution.nodeExecutionTimes.entries())
      .sort(([,a], [,b]) => b - a)
      .slice(0, 3);
    
    slowestNodes.forEach(([nodeId, time]) => {
      if (time > 10000) { // 10 seconds
        bottlenecks.push({
          type: 'execution',
          severity: 'medium',
          description: `Node ${nodeId} is slow (${time}ms)`,
          affectedNodes: [nodeId],
          recommendation: 'Review node implementation and consider optimization'
        });
      }
    });
    
    return bottlenecks;
  }
}

Optimization Strategies​

1. Parallelization​

interface ParallelizationConfig {
  enabled: boolean;
  maxConcurrency: number;
  nodeGroups: ParallelNodeGroup[];
  dependencies: NodeDependency[];
  resourceLimits: ResourceLimits;
}

interface ParallelNodeGroup {
  id: string;
  nodeIds: string[];
  executionMode: 'parallel' | 'batch' | 'pipeline';
  batchSize?: number;
  priority: number;
}

class ParallelExecutionOptimizer {
  analyzeParallelizationOpportunities(workflow: Workflow): ParallelizationRecommendations {
    const analysis = this.analyzeWorkflowStructure(workflow);
    
    return {
      independentNodeGroups: this.findIndependentNodes(analysis),
      dataParallelCandidates: this.findDataParallelNodes(analysis),
      pipelineOpportunities: this.findPipelineOpportunities(analysis),
      estimatedSpeedup: this.calculateSpeedupPotential(analysis)
    };
  }
  
  private findIndependentNodes(analysis: WorkflowAnalysis): NodeGroup[] {
    const groups: NodeGroup[] = [];
    const visited = new Set<string>();
    
    for (const node of analysis.nodes) {
      if (visited.has(node.id)) continue;
      
      const independentGroup = this.findConnectedComponent(node, analysis, visited);
      
      if (independentGroup.length > 1) {
        groups.push({
          id: `group_${groups.length}`,
          nodes: independentGroup,
          parallelizable: true,
          estimatedSpeedup: independentGroup.length * 0.8 // Account for overhead
        });
      }
    }
    
    return groups;
  }
  
  optimizeParallelExecution(
    workflow: Workflow,
    config: ParallelizationConfig
  ): OptimizedWorkflow {
    
    // Create execution plan with parallel groups
    const executionPlan = this.createParallelExecutionPlan(workflow, config);
    
    // Optimize resource allocation
    const resourcePlan = this.optimizeResourceAllocation(executionPlan, config.resourceLimits);
    
    // Generate optimized workflow
    return this.generateOptimizedWorkflow(workflow, executionPlan, resourcePlan);
  }
}

// Example: Parallel execution configuration
const parallelConfig: ParallelizationConfig = {
  enabled: true,
  maxConcurrency: 4,
  nodeGroups: [
    {
      id: 'data_processing_group',
      nodeIds: ['transform_1', 'transform_2', 'validate_1', 'validate_2'],
      executionMode: 'parallel',
      priority: 1
    },
    {
      id: 'api_calls_group',
      nodeIds: ['api_call_1', 'api_call_2', 'api_call_3'],
      executionMode: 'batch',
      batchSize: 2,
      priority: 2
    }
  ],
  dependencies: [
    { from: 'input', to: 'data_processing_group' },
    { from: 'data_processing_group', to: 'api_calls_group' },
    { from: 'api_calls_group', to: 'output' }
  ],
  resourceLimits: {
    maxCPU: 80,
    maxMemory: '2GB',
    maxConcurrentNodes: 8
  }
};

2. Caching and Memoization​

interface CacheConfig {
  enabled: boolean;
  strategy: CacheStrategy;
  ttl: number;
  maxSize: number;
  keyGenerator: CacheKeyGenerator;
  storage: CacheStorage;
}

enum CacheStrategy {
  LRU = 'lru',
  LFU = 'lfu',
  TTL = 'ttl',
  SMART = 'smart'
}

class WorkflowCacheManager {
  private caches: Map<string, Cache> = new Map();
  
  createNodeCache(nodeId: string, config: CacheConfig): void {
    const cache = new SmartCache(config);
    this.caches.set(nodeId, cache);
  }
  
  async getCachedResult(
    nodeId: string, 
    input: any, 
    context: ExecutionContext
  ): Promise<CachedResult | null> {
    
    const cache = this.caches.get(nodeId);
    if (!cache) return null;
    
    const cacheKey = this.generateCacheKey(nodeId, input, context);
    const cachedResult = await cache.get(cacheKey);
    
    if (cachedResult) {
      return {
        data: cachedResult.data,
        metadata: {
          cached: true,
          cacheHit: true,
          originalExecutionTime: cachedResult.metadata.executionTime,
          cacheTimestamp: cachedResult.timestamp
        }
      };
    }
    
    return null;
  }
  
  async setCachedResult(
    nodeId: string,
    input: any,
    output: any,
    context: ExecutionContext,
    executionTime: number
  ): Promise<void> {
    
    const cache = this.caches.get(nodeId);
    if (!cache) return;
    
    const cacheKey = this.generateCacheKey(nodeId, input, context);
    
    await cache.set(cacheKey, {
      data: output,
      timestamp: Date.now(),
      metadata: {
        executionTime,
        nodeId,
        inputHash: this.hashInput(input)
      }
    });
  }
  
  private generateCacheKey(
    nodeId: string, 
    input: any, 
    context: ExecutionContext
  ): string {
    const inputHash = this.hashInput(input);
    const contextHash = this.hashContext(context);
    return `${nodeId}:${inputHash}:${contextHash}`;
  }
  
  private hashInput(input: any): string {
    return crypto.createHash('sha256')
      .update(JSON.stringify(input, this.replacer))
      .digest('hex');
  }
  
  getCacheStatistics(): CacheStatistics {
    const stats: CacheStatistics = {
      totalNodes: this.caches.size,
      hitRate: 0,
      missRate: 0,
      memoryUsage: 0,
      entriesCount: 0
    };
    
    let totalHits = 0;
    let totalRequests = 0;
    
    for (const [nodeId, cache] of this.caches) {
      const nodeStats = cache.getStatistics();
      stats.memoryUsage += nodeStats.memoryUsage;
      stats.entriesCount += nodeStats.entriesCount;
      totalHits += nodeStats.hits;
      totalRequests += nodeStats.requests;
    }
    
    stats.hitRate = totalRequests > 0 ? totalHits / totalRequests : 0;
    stats.missRate = 1 - stats.hitRate;
    
    return stats;
  }
}

// Smart caching implementation
class SmartCache {
  constructor(private config: CacheConfig) {}
  
  async get(key: string): Promise<any> {
    // Implement smart caching logic
    const result = await this.storage.get(key);
    
    if (result && this.isValid(result)) {
      this.updateAccessPattern(key);
      return result;
    }
    
    return null;
  }
  
  async set(key: string, value: any): Promise<void> {
    // Apply cache strategy
    await this.applyEvictionPolicy();
    await this.storage.set(key, value);
    this.updateAccessPattern(key);
  }
  
  private isValid(result: CachedItem): boolean {
    const now = Date.now();
    return (now - result.timestamp) < this.config.ttl;
  }
  
  private async applyEvictionPolicy(): Promise<void> {
    const currentSize = await this.storage.size();
    
    if (currentSize >= this.config.maxSize) {
      switch (this.config.strategy) {
        case CacheStrategy.LRU:
          await this.evictLRU();
          break;
        case CacheStrategy.LFU:
          await this.evictLFU();
          break;
        case CacheStrategy.SMART:
          await this.smartEviction();
          break;
      }
    }
  }
}

3. Resource Optimization​

interface ResourceOptimization {
  memory: MemoryOptimization;
  cpu: CPUOptimization;
  io: IOOptimization;
  network: NetworkOptimization;
}

interface MemoryOptimization {
  streaming: boolean;
  chunkSize: number;
  bufferSize: number;
  garbageCollection: GCConfig;
  pooling: ObjectPooling;
}

class ResourceOptimizer {
  optimizeMemoryUsage(workflow: Workflow): MemoryOptimizedWorkflow {
    const analysis = this.analyzeMemoryUsage(workflow);
    
    return {
      ...workflow,
      nodes: workflow.nodes.map(node => this.optimizeNodeMemory(node, analysis)),
      config: {
        ...workflow.config,
        memory: this.generateMemoryConfig(analysis)
      }
    };
  }
  
  private optimizeNodeMemory(node: WorkflowNode, analysis: MemoryAnalysis): WorkflowNode {
    const nodeAnalysis = analysis.nodeAnalysis.get(node.id);
    if (!nodeAnalysis) return node;
    
    const optimizations: any = {};
    
    // Enable streaming for large data processing
    if (nodeAnalysis.dataSize > 100 * 1024 * 1024) { // 100MB
      optimizations.streaming = {
        enabled: true,
        chunkSize: 10 * 1024 * 1024, // 10MB chunks
        parallel: true
      };
    }
    
    // Optimize buffer sizes
    if (nodeAnalysis.ioIntensive) {
      optimizations.buffers = {
        inputBuffer: Math.min(nodeAnalysis.dataSize / 10, 50 * 1024 * 1024),
        outputBuffer: Math.min(nodeAnalysis.outputSize / 10, 50 * 1024 * 1024)
      };
    }
    
    // Configure object pooling for frequently created objects
    if (nodeAnalysis.objectCreationRate > 1000) { // objects per second
      optimizations.pooling = {
        enabled: true,
        maxPoolSize: 100,
        preAllocate: 10
      };
    }
    
    return {
      ...node,
      optimizations
    };
  }
  
  optimizeCPUUsage(workflow: Workflow): CPUOptimizedWorkflow {
    const cpuIntensiveNodes = this.identifyCPUIntensiveNodes(workflow);
    
    return {
      ...workflow,
      nodes: workflow.nodes.map(node => {
        if (cpuIntensiveNodes.includes(node.id)) {
          return this.applyCPUOptimizations(node);
        }
        return node;
      })
    };
  }
  
  private applyCPUOptimizations(node: WorkflowNode): WorkflowNode {
    return {
      ...node,
      optimizations: {
        ...node.optimizations,
        cpu: {
          workerThreads: true,
          maxWorkers: Math.min(require('os').cpus().length, 4),
          loadBalancing: 'round_robin',
          affinity: 'auto'
        }
      }
    };
  }
}

Scaling Strategies​

Horizontal Scaling​

interface ScalingConfig {
  autoScaling: AutoScalingConfig;
  loadBalancing: LoadBalancingConfig;
  clustering: ClusteringConfig;
  distribution: DistributionStrategy;
}

interface AutoScalingConfig {
  enabled: boolean;
  minInstances: number;
  maxInstances: number;
  scaleUpThreshold: number;
  scaleDownThreshold: number;
  cooldownPeriod: number;
  metrics: ScalingMetric[];
}

class WorkflowScaler {
  async scaleWorkflow(
    workflow: Workflow,
    currentLoad: LoadMetrics,
    config: ScalingConfig
  ): Promise<ScalingDecision> {
    
    const decision = await this.analyzeScalingNeed(currentLoad, config);
    
    if (decision.action !== 'none') {
      return await this.executeScaling(workflow, decision, config);
    }
    
    return decision;
  }
  
  private async analyzeScalingNeed(
    load: LoadMetrics,
    config: ScalingConfig
  ): Promise<ScalingDecision> {
    
    const autoScaling = config.autoScaling;
    if (!autoScaling.enabled) {
      return { action: 'none', reason: 'Auto-scaling disabled' };
    }
    
    // Check scale-up conditions
    const shouldScaleUp = autoScaling.metrics.some(metric => {
      const currentValue = this.getMetricValue(load, metric);
      return currentValue > autoScaling.scaleUpThreshold;
    });
    
    if (shouldScaleUp && load.instanceCount < autoScaling.maxInstances) {
      return {
        action: 'scale_up',
        targetInstances: Math.min(
          load.instanceCount + 1,
          autoScaling.maxInstances
        ),
        reason: 'Load threshold exceeded'
      };
    }
    
    // Check scale-down conditions
    const shouldScaleDown = autoScaling.metrics.every(metric => {
      const currentValue = this.getMetricValue(load, metric);
      return currentValue < autoScaling.scaleDownThreshold;
    });
    
    if (shouldScaleDown && load.instanceCount > autoScaling.minInstances) {
      return {
        action: 'scale_down',
        targetInstances: Math.max(
          load.instanceCount - 1,
          autoScaling.minInstances
        ),
        reason: 'Load below threshold'
      };
    }
    
    return { action: 'none', reason: 'Load within acceptable range' };
  }
}

Performance Best Practices​

1. Node-Level Optimizations​

// Efficient data processing
class PerformantDataProcessor {
  async processLargeDataset(data: any[], batchSize: number = 1000): Promise<any[]> {
    const results: any[] = [];
    
    // Process in batches to manage memory
    for (let i = 0; i < data.length; i += batchSize) {
      const batch = data.slice(i, i + batchSize);
      const batchResults = await this.processBatch(batch);
      results.push(...batchResults);
      
      // Allow garbage collection between batches
      if (i % (batchSize * 10) === 0) {
        await this.yield();
      }
    }
    
    return results;
  }
  
  private async processBatch(batch: any[]): Promise<any[]> {
    // Use Promise.all for parallel processing within batch
    return Promise.all(batch.map(item => this.processItem(item)));
  }
  
  private async yield(): Promise<void> {
    return new Promise(resolve => setImmediate(resolve));
  }
}

// Memory-efficient streaming
class StreamingProcessor {
  async processStream(
    inputStream: ReadableStream,
    outputStream: WritableStream,
    transformer: (chunk: any) => any
  ): Promise<void> {
    
    const readable = new ReadableStream({
      start(controller) {
        inputStream.on('data', (chunk) => {
          try {
            const transformed = transformer(chunk);
            controller.enqueue(transformed);
          } catch (error) {
            controller.error(error);
          }
        });
        
        inputStream.on('end', () => {
          controller.close();
        });
        
        inputStream.on('error', (error) => {
          controller.error(error);
        });
      }
    });
    
    return readable.pipeTo(outputStream);
  }
}

2. Workflow-Level Optimizations​

// Workflow optimization patterns
const performancePatterns = {
  // Pattern 1: Early termination
  earlyTermination: {
    description: 'Stop processing when conditions are met',
    implementation: (workflow: Workflow) => {
      return workflow.addNode({
        type: 'condition',
        id: 'early_termination_check',
        config: {
          condition: 'data.length === 0',
          trueAction: 'terminate',
          falseAction: 'continue'
        }
      });
    }
  },
  
  // Pattern 2: Data prefetching
  dataPrefetching: {
    description: 'Fetch data before it\'s needed',
    implementation: (workflow: Workflow) => {
      return workflow.optimizeConnections({
        prefetch: true,
        lookahead: 2 // Prefetch data for next 2 nodes
      });
    }
  },
  
  // Pattern 3: Lazy evaluation
  lazyEvaluation: {
    description: 'Evaluate expressions only when needed',
    implementation: (workflow: Workflow) => {
      return workflow.nodes.map(node => ({
        ...node,
        evaluation: 'lazy',
        caching: true
      }));
    }
  }
};

Performance Testing​

Load Testing​

interface LoadTestConfig {
  concurrent: number;
  duration: number;
  rampUp: number;
  scenarios: LoadScenario[];
  metrics: string[];
}

class WorkflowLoadTester {
  async runLoadTest(workflow: Workflow, config: LoadTestConfig): Promise<LoadTestResult> {
    const testRunner = new LoadTestRunner(config);
    
    // Prepare test scenarios
    const scenarios = config.scenarios.map(scenario => 
      this.prepareScenario(workflow, scenario)
    );
    
    // Execute load test
    const results = await testRunner.execute(scenarios);
    
    // Analyze results
    return this.analyzeResults(results);
  }
  
  private analyzeResults(results: RawLoadTestResult[]): LoadTestResult {
    return {
      summary: this.generateSummary(results),
      performance: this.analyzePerformance(results),
      bottlenecks: this.identifyBottlenecks(results),
      recommendations: this.generateRecommendations(results),
      scalabilityAnalysis: this.analyzeScalability(results)
    };
  }
}

Monitoring and Alerting​

Performance Alerts​

interface PerformanceAlert {
  id: string;
  name: string;
  condition: AlertCondition;
  severity: AlertSeverity;
  actions: AlertAction[];
}

const performanceAlerts: PerformanceAlert[] = [
  {
    id: 'slow_execution',
    name: 'Slow Workflow Execution',
    condition: {
      metric: 'execution_time',
      operator: '>',
      threshold: 300000, // 5 minutes
      duration: '2m'
    },
    severity: 'warning',
    actions: ['notify_team', 'auto_scale']
  },
  
  {
    id: 'high_memory_usage',
    name: 'High Memory Usage',
    condition: {
      metric: 'memory_usage_percent',
      operator: '>',
      threshold: 85,
      duration: '5m'
    },
    severity: 'critical',
    actions: ['immediate_notify', 'scale_up', 'enable_gc']
  },
  
  {
    id: 'low_throughput',
    name: 'Low Throughput',
    condition: {
      metric: 'throughput_rps',
      operator: '<',
      threshold: 10,
      duration: '10m'
    },
    severity: 'warning',
    actions: ['investigate', 'check_bottlenecks']
  }
];

Troubleshooting Performance Issues​

Common Performance Problems​

  1. Memory Leaks

    • Monitor heap usage trends
    • Use memory profiling tools
    • Implement proper cleanup

  2. CPU Bottlenecks

    • Profile CPU usage per node
    • Optimize algorithms
    • Consider parallelization

  3. I/O Bottlenecks

    • Monitor disk and network I/O
    • Implement connection pooling
    • Use asynchronous operations

  4. Inefficient Data Structures

    • Choose appropriate data structures
    • Minimize data copying
    • Use streaming for large datasets

Performance Debugging Tools​

class PerformanceDebugger {
  async profileWorkflow(executionId: string): Promise<PerformanceProfile> {
    const profile = {
      cpuProfile: await this.profileCPU(executionId),
      memoryProfile: await this.profileMemory(executionId),
      ioProfile: await this.profileIO(executionId),
      recommendations: []
    };
    
    profile.recommendations = this.generateRecommendations(profile);
    
    return profile;
  }
  
  async generateRecommendations(profile: PerformanceProfile): Promise<Recommendation[]> {
    const recommendations: Recommendation[] = [];
    
    // CPU recommendations
    if (profile.cpuProfile.averageUsage > 80) {
      recommendations.push({
        type: 'cpu',
        severity: 'high',
        description: 'High CPU usage detected',
        suggestion: 'Consider optimizing algorithms or adding parallelization',
        priority: 1
      });
    }
    
    // Memory recommendations
    if (profile.memoryProfile.trend === 'increasing') {
      recommendations.push({
        type: 'memory',
        severity: 'medium',
        description: 'Memory usage is trending upward',
        suggestion: 'Check for memory leaks or optimize data structures',
        priority: 2
      });
    }
    
    return recommendations;
  }
}