⚡ Bolt: Optimize profile data aggregation with single O(n) pass

Co-authored-by: KxlSys <116387953+KxlSys@users.noreply.github.com>

Author: google-labs-jules[bot]

.jules/bolt.md

@@ -17,3 +17,7 @@
 ## 2024-06-25 - DebouncedInput Component Memoization
 **Learning:** The `DebouncedInput` component was used in several places to prevent excessive parent re-renders while typing. However, because `DebouncedInput` itself was not wrapped in `React.memo()`, it was still re-rendering unnecessarily whenever its parent components re-rendered for reasons unrelated to the input (e.g. other form fields changing, complex page state updates). This unnecessary re-rendering could destroy and recreate its internal debouncing logic and cause extra reconciliation work.
 **Action:** When creating utility wrapper components designed to optimize performance (like `DebouncedInput`), ensure the component itself is memoized with `React.memo()` so it fully shields itself from irrelevant parent state updates.
+
+## 2024-07-10 - O(n) Single Pass Data Aggregation
+**Learning:** Using chained array methods (e.g. `.map().filter()`, `.flatMap()`, and repeated `.filter()` over the same large list like `profiles`) leads to redundant O(n) passes over data and allocates unnecessary intermediate arrays. This blocks the main thread longer than necessary when calculating basic statistics like `uniqueCities`, `uniqueTechs`, or total counts.
+**Action:** Consolidate statistical aggregations over large object arrays into a single O(n) `for` loop that updates multiple accumulators (like `Set` objects and primitive counters) in one pass, thereby improving performance and reducing memory overhead.

src/components/hero/hero-section.tsx

@@ -11,9 +11,25 @@ interface HeroSectionProps {
 export function HeroSection({ totalContributors, profiles }: HeroSectionProps) {
   const { user } = useAuthStore();
 
-  const uniqueCities = new Set(profiles.map((p) => p.city).filter(Boolean)).size;
-  const uniqueTechs = new Set(profiles.flatMap((p) => p.tech_stack)).size;
-  const openToCollab = profiles.filter((p) => p.open_to_collaboration).length;
+  // ⚡ Bolt: Consolidate data aggregation into a single O(n) pass
+  // Avoids multiple redundant iterations and intermediate array allocations
+  const citySet = new Set<string>();
+  const techSet = new Set<string>();
+  let openToCollab = 0;
+
+  for (let i = 0; i < profiles.length; i++) {
+    const p = profiles[i];
+    if (p.city) citySet.add(p.city);
+    if (p.open_to_collaboration) openToCollab++;
+    if (p.tech_stack) {
+      for (let j = 0; j < p.tech_stack.length; j++) {
+        techSet.add(p.tech_stack[j]);
+      }
+    }
+  }
+
+  const uniqueCities = citySet.size;
+  const uniqueTechs = techSet.size;
 
   return (
     <section className="ambient-bg relative overflow-hidden border-b border-white/10">

src/hooks/use-platform-stats.ts

@@ -14,14 +14,29 @@ function computeStats(
 ): PlatformStats {
   const total = profiles.length;
   if (total === 0) return { totalUsers: 0, totalCities: 0, collaborationRate: 0, techCount: 0 };
-  const cities = new Set(profiles.map((p) => p.city).filter(Boolean)).size;
-  const collab = profiles.filter((p) => p.open_to_collaboration).length;
-  const allTechs = new Set(profiles.flatMap((p) => p.tech_stack ?? [])).size;
+
+  // ⚡ Bolt: Consolidate data aggregation into a single O(n) pass
+  // Avoids multiple redundant iterations and intermediate array allocations
+  const citySet = new Set<string>();
+  const techSet = new Set<string>();
+  let collab = 0;
+
+  for (let i = 0; i < total; i++) {
+    const p = profiles[i];
+    if (p.city) citySet.add(p.city);
+    if (p.open_to_collaboration) collab++;
+    if (p.tech_stack) {
+      for (let j = 0; j < p.tech_stack.length; j++) {
+        techSet.add(p.tech_stack[j]);
+      }
+    }
+  }
+
   return {
     totalUsers: total,
-    totalCities: cities,
+    totalCities: citySet.size,
     collaborationRate: Math.round((collab / total) * 100),
-    techCount: allTechs,
+    techCount: techSet.size,
   };
 }
 

src/pages/about-page.tsx

@@ -210,19 +210,32 @@ function computeStats(profiles: RawProfile[]): ComputedStats {
     value: Math.round((m.count / maxCount) * 100),
   }));
 
-  const collab = profiles.filter((p) => p.open_to_collaboration).length;
-  const totalCities = new Set(profiles.map((p) => p.city).filter(Boolean)).size;
-  const allTechs = new Set(profiles.flatMap((p) => p.tech_stack ?? [])).size;
+  // ⚡ Bolt: Consolidate data aggregation into a single O(n) pass
+  // Avoids multiple redundant iterations and intermediate array allocations
+  let collab = 0;
+  let activeLastWeek = 0;
+  const citySet = new Set<string>();
+  const techSet = new Set<string>();
   const weekAgo = new Date(Date.now() - 7 * 24 * 60 * 60 * 1000);
-  const activeLastWeek = profiles.filter(
-    (p) => p.last_seen_at && new Date(p.last_seen_at) >= weekAgo
-  ).length;
+
+  for (let i = 0; i < profiles.length; i++) {
+    const p = profiles[i];
+    if (p.open_to_collaboration) collab++;
+    if (p.city) citySet.add(p.city);
+    if (p.last_seen_at && new Date(p.last_seen_at) >= weekAgo) activeLastWeek++;
+
+    if (p.tech_stack) {
+      for (let j = 0; j < p.tech_stack.length; j++) {
+        techSet.add(p.tech_stack[j]);
+      }
+    }
+  }
 
   return {
     total,
     collabRate: Math.round((collab / total) * 100),
-    totalCities,
-    allTechs,
+    totalCities: citySet.size,
+    allTechs: techSet.size,
     activeLastWeek,
     roles,
     techs,

src/pages/admin-page.tsx

@@ -177,10 +177,20 @@ export function AdminPage() {
     return p.full_name.toLowerCase().includes(q) || p.username.toLowerCase().includes(q) || p.city?.toLowerCase().includes(q);
   });
 
+  // ⚡ Bolt: Consolidate data aggregation into a single O(n) pass
+  // Avoids multiple redundant iterations and intermediate array allocations
+  const citySet = new Set<string>();
+  let collaboratingCount = 0;
+  for (let i = 0; i < profiles.length; i++) {
+    const p = profiles[i];
+    if (p.city) citySet.add(p.city);
+    if (p.open_to_collaboration) collaboratingCount++;
+  }
+
   const stats = {
     total: profiles.length,
-    cities: new Set(profiles.map((p) => p.city).filter(Boolean)).size,
-    collaborating: profiles.filter((p) => p.open_to_collaboration).length,
+    cities: citySet.size,
+    collaborating: collaboratingCount,
   };
   const collabRate = stats.total > 0 ? (stats.collaborating / stats.total) * 100 : 0;
   const pendingInvitations = invitations.filter((i) => i.status === "pending").length;