Claude MCP Server Ecosystem

/** * Week 11 Integration Tests - Data Management and Analytics Server * Comprehensive test suite proving all components work as documented */ import { jest } from '@jest/globals'; import { DataPipelineServer } from '../servers/data-analytics/src/data-pipeline'; import { RealtimeAnalyticsServer } from '../servers/data-analytics/src/realtime-analytics'; import { DataWarehouseServer } from '../servers/data-analytics/src/data-warehouse'; import { MLDeploymentServer } from '../servers/data-analytics/src/ml-deployment'; import { DataGovernanceServer } from '../servers/data-analytics/src/data-governance'; describe('Week 11: Data Management and Analytics Server - Integration Tests', () => { let dataPipeline: DataPipelineServer; let realtimeAnalytics: RealtimeAnalyticsServer; let dataWarehouse: DataWarehouseServer; let mlDeployment: MLDeploymentServer; let dataGovernance: DataGovernanceServer; beforeAll(async () => { // Initialize all Week 11 servers dataPipeline = new DataPipelineServer(); realtimeAnalytics = new RealtimeAnalyticsServer(); dataWarehouse = new DataWarehouseServer(); mlDeployment = new MLDeploymentServer(); dataGovernance = new DataGovernanceServer(); // Start all servers await Promise.all([ dataPipeline.start(), realtimeAnalytics.start(), dataWarehouse.start(), mlDeployment.start(), dataGovernance.start() ]); }); afterAll(async () => { // Cleanup all servers await Promise.all([ dataPipeline.stop(), realtimeAnalytics.stop(), dataWarehouse.stop(), mlDeployment.stop(), dataGovernance.stop() ]); }); describe('Data Pipeline Server', () => { test('should process data ingestion from multiple sources', async () => { const testData = { source: 'api', data: { users: 1000, events: 50000 }, timestamp: new Date().toISOString() }; const result = await dataPipeline.ingestData(testData); expect(result.success).toBe(true); expect(result.processedRecords).toBeGreaterThan(0); expect(result.pipeline).toBeDefined(); }); test('should handle real-time streaming data', async () => { const streamConfig = { source: 'kafka', topic: 'user-events', batchSize: 1000 }; const stream = await dataPipeline.createStream(streamConfig); expect(stream.isActive()).toBe(true); expect(stream.getMetrics().throughput).toBeGreaterThan(0); }); test('should validate data quality and transformations', async () => { const rawData = { id: '123', name: 'Test User', email: 'invalid-email' }; const validation = await dataPipeline.validateData(rawData); expect(validation.isValid).toBe(false); expect(validation.errors).toContain('Invalid email format'); expect(validation.transformedData).toBeDefined(); }); }); describe('Realtime Analytics Server', () => { test('should generate real-time metrics and dashboards', async () => { const metricsQuery = { timeRange: '1h', metrics: ['user_count', 'revenue', 'conversion_rate'], granularity: '5m' }; const analytics = await realtimeAnalytics.generateMetrics(metricsQuery); expect(analytics.data).toBeDefined(); expect(analytics.data.length).toBeGreaterThan(0); expect(analytics.lastUpdated).toBeDefined(); }); test('should handle streaming analytics with windowing', async () => { const windowConfig = { type: 'tumbling', duration: '5m', aggregations: ['count', 'sum', 'avg'] }; const window = await realtimeAnalytics.createWindow(windowConfig); expect(window.isProcessing()).toBe(true); expect(window.getResults().length).toBeGreaterThanOrEqual(0); }); test('should trigger alerts based on thresholds', async () => { const alertConfig = { metric: 'error_rate', threshold: 0.05, condition: 'greater_than', window: '10m' }; const alert = await realtimeAnalytics.configureAlert(alertConfig); expect(alert.id).toBeDefined(); expect(alert.isActive).toBe(true); }); }); describe('Data Warehouse Server', () => { test('should perform ETL operations on large datasets', async () => { const etlJob = { source: 'production_db', destination: 'analytics_warehouse', transformations: ['clean_nulls', 'normalize_dates', 'aggregate_metrics'] }; const job = await dataWarehouse.runETL(etlJob); expect(job.status).toBe('completed'); expect(job.recordsProcessed).toBeGreaterThan(0); expect(job.executionTime).toBeLessThan(30000); // 30 seconds max }); test('should execute complex analytical queries', async () => { const query = ` SELECT DATE_TRUNC('day', created_at) as date, COUNT(*) as user_count, AVG(session_duration) as avg_session FROM user_sessions WHERE created_at >= NOW() - INTERVAL '30 days' GROUP BY DATE_TRUNC('day', created_at) ORDER BY date DESC; `; const result = await dataWarehouse.executeQuery(query); expect(result.rows).toBeDefined(); expect(result.executionTime).toBeLessThan(5000); // 5 seconds max expect(result.rowCount).toBeGreaterThanOrEqual(0); }); test('should manage data partitioning and indexing', async () => { const tableConfig = { name: 'user_events', partitionBy: 'date', indexes: ['user_id', 'event_type'], compressionType: 'zstd' }; const table = await dataWarehouse.createOptimizedTable(tableConfig); expect(table.isPartitioned).toBe(true); expect(table.indexes.length).toBe(2); expect(table.compressionRatio).toBeGreaterThan(0.5); }); }); describe('ML Deployment Server', () => { test('should deploy and serve machine learning models', async () => { const modelConfig = { name: 'user_churn_predictor', version: '1.0.0', framework: 'tensorflow', modelPath: '/models/churn_model.pb' }; const deployment = await mlDeployment.deployModel(modelConfig); expect(deployment.status).toBe('active'); expect(deployment.endpoint).toBeDefined(); expect(deployment.healthCheck()).resolves.toBe(true); }); test('should handle model inference requests', async () => { const inferenceRequest = { modelName: 'user_churn_predictor', input: { user_id: '12345', features: { days_since_last_login: 7, total_sessions: 45, avg_session_duration: 12.5 } } }; const prediction = await mlDeployment.predict(inferenceRequest); expect(prediction.probability).toBeGreaterThanOrEqual(0); expect(prediction.probability).toBeLessThanOrEqual(1); expect(prediction.confidence).toBeDefined(); expect(prediction.responseTime).toBeLessThan(100); // 100ms max }); test('should monitor model performance and drift', async () => { const monitoring = await mlDeployment.getModelMetrics('user_churn_predictor'); expect(monitoring.accuracy).toBeGreaterThan(0.8); expect(monitoring.latency.p95).toBeLessThan(200); expect(monitoring.drift.score).toBeLessThan(0.1); expect(monitoring.requestCount).toBeGreaterThanOrEqual(0); }); }); describe('Data Governance Server', () => { test('should enforce data privacy and compliance rules', async () => { const dataRequest = { userId: 'user123', dataTypes: ['personal_info', 'payment_data'], purpose: 'analytics', requester: 'data_science_team' }; const approval = await dataGovernance.requestDataAccess(dataRequest); expect(approval.status).toBe('approved'); expect(approval.conditions).toBeDefined(); expect(approval.expirationTime).toBeDefined(); }); test('should track data lineage and audit trails', async () => { const lineage = await dataGovernance.getDataLineage('user_sessions'); expect(lineage.sources).toBeDefined(); expect(lineage.transformations).toBeDefined(); expect(lineage.destinations).toBeDefined(); expect(lineage.lastUpdated).toBeDefined(); }); test('should manage data quality metrics and validation', async () => { const qualityCheck = { dataset: 'user_profiles', rules: [ { field: 'email', type: 'format', pattern: 'email' }, { field: 'age', type: 'range', min: 13, max: 120 }, { field: 'created_at', type: 'not_null' } ] }; const qualityReport = await dataGovernance.runQualityCheck(qualityCheck); expect(qualityReport.overallScore).toBeGreaterThan(0.9); expect(qualityReport.passedRules).toBeGreaterThanOrEqual(0); expect(qualityReport.failedRows).toBeDefined(); }); }); describe('Integration Tests - Cross-Server Communication', () => { test('should handle end-to-end data flow from ingestion to analytics', async () => { // 1. Ingest data through pipeline const testData = { source: 'api', data: Array.from({ length: 1000 }, (_, i) => ({ id: i, user_id: `user_${i}`, event_type: 'page_view', timestamp: new Date().toISOString() })) }; const ingestionResult = await dataPipeline.ingestData(testData); expect(ingestionResult.success).toBe(true); // 2. Process real-time analytics const analyticsResult = await realtimeAnalytics.processIncomingData(testData.data); expect(analyticsResult.metricsGenerated).toBeGreaterThan(0); // 3. Store in data warehouse const warehouseResult = await dataWarehouse.loadData(testData.data); expect(warehouseResult.rowsInserted).toBe(1000); // 4. Validate with governance const governanceResult = await dataGovernance.validateDataCompliance(testData.data); expect(governanceResult.complianceScore).toBeGreaterThan(0.95); }); test('should demonstrate MCP tool integration', async () => { // Test MCP tools are properly registered and callable const tools = await dataPipeline.listTools(); expect(tools.length).toBeGreaterThan(30); // Week 11 should have 30+ tools expect(tools.some(t => t.name === 'ingest_data')).toBe(true); expect(tools.some(t => t.name === 'generate_analytics')).toBe(true); expect(tools.some(t => t.name === 'run_etl')).toBe(true); }); }); }); /** * Performance Benchmarks * These tests ensure Week 11 components meet performance requirements */ describe('Week 11: Performance Benchmarks', () => { test('data pipeline should handle 10k records/second', async () => { const start = Date.now(); const testData = Array.from({ length: 10000 }, (_, i) => ({ id: i, data: 'test' })); const result = await dataPipeline.ingestBatch(testData); const duration = Date.now() - start; expect(result.success).toBe(true); expect(duration).toBeLessThan(1000); // Should process in under 1 second }); test('realtime analytics should update within 100ms', async () => { const start = Date.now(); const metrics = await realtimeAnalytics.generateMetrics({ timeRange: '5m' }); const duration = Date.now() - start; expect(metrics).toBeDefined(); expect(duration).toBeLessThan(100); }); test('ML inference should respond within 50ms', async () => { const start = Date.now(); const prediction = await mlDeployment.predict({ modelName: 'test_model', input: { feature1: 1.0, feature2: 2.0 } }); const duration = Date.now() - start; expect(prediction).toBeDefined(); expect(duration).toBeLessThan(50); }); });