JavaScript Array & Data Processing Pipeline Optimizer

### 1. Intermediate Allocation Audit - **Current Pipeline Waste:** 1. First `.filter()` creates an intermediate array of ~40,000 elements. 2. `.map()` allocates a second intermediate array of 40,000 **new object literals**, generating 40,000 heap references for the Garbage Collector to sweep. 3. Second `.filter()` creates a third intermediate array of ~25,000 elements. 4. `.sort()` sorts all 25,000 items in $O(N \log N)$ even though the consumer only needs the **top 100**! ### 2. High-Performance Single-Pass + Min-Heap Refactoring Instead of sorting 25,000 items, we evaluate items in a single pass and maintain a bounded 100-element Min-Heap or insertion sort: ```javascript function getTop100Transactions(records, exchangeRates) { const top100 = []; // Bounded array (max 100 items) let minInTop100 = 0; for (let i = 0; i < records.length; i++) { const r = records[i]; if (r.status !== 'COMPLETED' || r.amount <= 0) continue; const rate = exchangeRates[r.currency] || 1; const usdTotal = r.amount * rate; if (usdTotal < 25.0) continue; // If top 100 is not full yet, insert and keep sorted if (top100.length < 100) { top100.push({ id: r.id, merchantId: r.merchantId, usdTotal, fee: r.amount * 0.029 + 0.3, }); if (top100.length === 100) { top100.sort((a, b) => b.usdTotal - a.usdTotal); minInTop100 = top100[99].usdTotal; } } else if (usdTotal > minInTop100) { // Only re-sort when a candidate beats the 100th threshold top100[99] = { id: r.id, merchantId: r.merchantId, usdTotal, fee: r.amount * 0.029 + 0.3, }; top100.sort((a, b) => b.usdTotal - a.usdTotal); minInTop100 = top100[99].usdTotal; } } return top100; } ```