⚡ Bolt: Optimize priority distribution calculations

.jules/bolt.md

@@ -11,3 +11,7 @@
 ## 2024-05-18 - [Extract Repeated array filter into O(N) reduce]
 **Learning:** Multiple consecutive `.filter()` assignments on identical arrays (like `P.tickets`) in heavily invoked functions (`buildContext`, `generateLocalResponse`) degrade overall component efficiency by repeatedly traversing arrays O(N) over N times when a single loop suffices.
 **Action:** Consolidate multiple independent `.filter()` passes and sum calculations (`.reduce`) into a single `.reduce()` step. By pushing matches to category-specific accumulator arrays and counting aggregate fields (like sums/counts) simultaneously, you accomplish what took ~5 traversals into one O(N) pass, reducing time complexity significantly (benchmarked from ~350ms to ~45ms for large arrays).
+
+## 2024-05-18 - Optimize nested filtering mapping in Dashboard and Reports
+**Learning:** In React components like `DashboardModule.jsx` and `ReportsModule.jsx`, mapping over static category lists (like priorities) and then calling `.filter(t => t.priority === p).length` inside the loop causes an O(N*M) time complexity traversal where N is the number of tickets and M is the number of priorities.
+**Action:** Replace the nested `.filter().length` with an O(N) `.reduce()` that builds a hash map of frequencies (`const counts = tickets.reduce(...)`), and then use O(1) hash map lookups (`counts[p] || 0`) inside the `.map()` loop to significantly improve rendering performance.

src/components/DashboardModule.jsx

@@ -233,13 +233,18 @@ export default function Dash({ P }) {
     weekday: "long", day: "numeric", month: "long",
   });
 
-  const distribution = useMemo(() => (
-    ["critical", "high", "medium", "low"].map(p => {
-      const cnt = P.tickets.filter(t => t.priority === p).length;
+  // ⚡ Bolt Optimization: Replace O(N*M) nested loop with O(N) hash map lookup
+  const distribution = useMemo(() => {
+    const counts = P.tickets.reduce((acc, t) => {
+      acc[t.priority] = (acc[t.priority] || 0) + 1;
+      return acc;
+    }, {});
+    return ["critical", "high", "medium", "low"].map(p => {
+      const cnt = counts[p] || 0;
       const pct = P.tickets.length ? Math.round((cnt / P.tickets.length) * 100) : 0;
       return { p, cnt, pct };
-    })
-  ), [P.tickets]);
+    });
+  }, [P.tickets]);
 
   return (
     <motion.div

src/components/ReportsModule.jsx

@@ -14,11 +14,16 @@ export default function Rpt({ P }) {
   }, [P.tickets, P.tasks]);
 
   // Memoize priority distributions
+  // ⚡ Bolt Optimization: Replace O(N*M) nested loop with O(N) hash map lookup
   const priorityDist = useMemo(() => {
+    const counts = P.tickets.reduce((acc, t) => {
+      acc[t.priority] = (acc[t.priority] || 0) + 1;
+      return acc;
+    }, {});
     const priorities = ["critical", "high", "medium", "low"];
     const colors = { critical: "#dc2626", high: "#ea580c", medium: "#d97706", low: "#2563eb" };
     return priorities.map(p => {
-      const cnt = P.tickets.filter(t => t.priority === p).length;
+      const cnt = counts[p] || 0;
       const pct = stats.totalTickets > 0 ? Math.round((cnt / stats.totalTickets) * 100) : 0;
       return { p, cnt, pct, color: colors[p] };
     });