The task involved building a backend Express.js service for a real-time analytics dashboard that tracks user interactions on an e-commerce platform. The service needed to fetch data from three internal sources: User Activity Service, Transaction Service, and Inventory Service. Each source differed in response characteristics, data formats, and reliability. The User Activity Service tracked events like page views, clicks, and session data, returning a high volume of detailed events with occasional partial results or errors. The Transaction Service returned slower but more reliable financial transaction data, differing in format from user activity data. The Inventory Service provided fast but XML-like alerts for stock levels, requiring parsing. Sample detailed JavaScript code was provided demonstrating the asynchronous fetching of data from all three services, simulating delays and potential errors. Functions implemented: ```js async function fetchUserActivity(timeWindow) { return new Promise((resolve, reject) => { const delay = 100 + Math.random() * 100; // 100-200ms setTimeout(() => { if (Math.random() < 0.15) { resolve({ events: generateRandomEvents(Math.floor(Math.random() * 10) + 5), complete: false, error: "Service degraded, partial data returned" }); } else if (Math.random() < 0.05) { reject(new Error("User activity service temporarily unavailable")); } else { resolve({ events: generateRandomEvents(Math.floor(Math.random() * 20) + 10), complete: true }); } }, delay); }); function generateRandomEvents(count) { const events = []; const eventTypes = ["page_view", "click", "scroll", "search"]; for (let i = 0; i < count; i++) { events.push({ eventId: `evt-${Math.random().toString(36).substring(2, 10)}`, userId: `user-${Math.floor(Math.random() * 1000)}`, type: eventTypes[Math.floor(Math.random() * eventTypes.length)], timestamp: Date.now() - Math.floor(Math.random() * 60000), data: { page: Math.random() < 0.5 ? "/products" : "/checkout", duration: Math.floor(Math.random() * 300) } }); } return events; } } async function fetchTransactions(timeWindow) { return new Promise((resolve, reject) => { const delay = 200 + Math.random() * 150; // 200-350ms setTimeout(() => { if (Math.random() < 0.08) { reject(new Error("Transaction service unavailable")); } else { const transactions = []; const count = Math.floor(Math.random() * 8) + 2; for (let i = 0; i < count; i++) { transactions.push({ transactionId: `tx-${Date.now()}-${Math.random().toString(36).substring(2, 7)}`, customerData: { id: `user-${Math.floor(Math.random() * 1000)}`, }, amount: +(Math.random() * 200 + 10).toFixed(2), status: Math.random() < 0.9 ? "completed" : "pending", items: Math.floor(Math.random() * 5) + 1, createdAt: new Date(Date.now() - Math.floor(Math.random() * 3600000)).toISOString() }); } resolve({ data: transactions, meta: { total: transactions.length, timeframe: timeWindow } }); } }, delay); }); } async function fetchInventoryAlerts(urgencyLevel = "all") { return new Promise((resolve, reject) => { const delay = 50 + Math.random() * 100; // 50-150ms setTimeout(() => { if (Math.random() < 0.1) { reject(new Error("Inventory service connection refused")); } else { const alertCount = Math.floor(Math.random() * 5); let xmlResponse = ``; for (let i = 0; i < alertCount; i++) { const productId = Math.floor(Math.random() * 1000); const currentStock = Math.floor(Math.random() * 10); const threshold = 5; const urgency = currentStock < 2 ? "high" : "medium"; if (urgencyLevel === "all" || urgencyLevel === urgency) { xmlResponse += `

${currentStock}

${threshold} `; } } xmlResponse += ''; resolve(xmlResponse); } }, delay); }); } The core requirement was to create an Express.js GET /api/dashboard endpoint accepting a timeWindow parameter. This endpoint concurrently fetched data from all three services, handled differences in data format including XML parsing for inventory alerts, managed errors, partial data scenarios, and enforced a strict 300ms total response timeout. An example successful response from the API: ```json { "dashboard": { "timeWindow": "15m", "summary": { "activeUsers": 42, "revenueTotal": 1234.56, "conversionRate": 2.7, "inventoryAlerts": 3 }, "details": { "userActivity": { "complete": true, // Additional processed details... }, "transactions": { // Processed transaction details }, "inventory": { // Parsed inventory alert details } }, "status": { "servicesResponded": ["userActivity", "transactions", "inventory"], "servicesFailed": [], "responseTime": 298 } } } ``` This implementation emphasizes robust asynchronous data fetching with concurrency, resilience to partial/failure responses, unification of heterogeneous data, and real-time performance requirements for dashboard API service.