From 13be6ffca5c4a24219969976d5bc293ed6cc199d Mon Sep 17 00:00:00 2001
From: Trey t <treytartt@fastmail.com>
Date: Sat, 10 Jan 2026 11:58:51 -0600
Subject: [PATCH] test(08-02): add performance tests with large datasets

Added 4 performance tests with 1K, 5K, 10K games to validate DAG
algorithm scalability. Tests currently failing (RED phase).

Tests:
- 1K games: <2s expected
- 5K games: <10s expected
- 10K games: <30s expected
- 10K games: memory stability

Helper generateLargeDataset() creates realistic test data with
distributed stadiums and games across time spans.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
---
 SportsTimeTests/GameDAGRouterTests.swift | 109 +++++++++++++++++++++++
 1 file changed, 109 insertions(+)

diff --git a/SportsTimeTests/GameDAGRouterTests.swift b/SportsTimeTests/GameDAGRouterTests.swift
index 60782c5..2f4ad03 100644
--- a/SportsTimeTests/GameDAGRouterTests.swift
+++ b/SportsTimeTests/GameDAGRouterTests.swift
@@ -534,4 +534,113 @@ struct GameDAGRouterTests {
 
         #expect(routeWithBoth != nil, "4 hours available (with 1hr arrival buffer) should be feasible")
     }
+
+    // MARK: - Performance Tests
+
+    /// Generates a large dataset of games and stadiums for performance testing.
+    /// Games are distributed across stadiums and days to simulate realistic data.
+    private func generateLargeDataset(
+        gameCount: Int,
+        stadiumCount: Int,
+        daysSpan: Int
+    ) -> (games: [Game], stadiums: [UUID: Stadium]) {
+        // Create stadiums distributed across the US (roughly)
+        var stadiums: [UUID: Stadium] = [:]
+        let baseDate = date("2026-06-01 19:00")
+
+        for i in 0..<stadiumCount {
+            // Distribute stadiums geographically (rough US coverage)
+            let lat = 30.0 + Double(i % 20) * 1.0  // 30-50 latitude
+            let lon = -120.0 + Double(i / 20) * 5.0  // -120 to -70 longitude
+            let stadium = makeStadium(
+                city: "City\(i)",
+                lat: lat,
+                lon: lon
+            )
+            stadiums[stadium.id] = stadium
+        }
+
+        // Create games distributed across stadiums and days
+        var games: [Game] = []
+        let stadiumIds = Array(stadiums.keys)
+
+        for i in 0..<gameCount {
+            // Distribute games across days
+            let dayOffset = Double(i * daysSpan) / Double(gameCount)
+            let hoursOffset = Double(i % 3) * 3.0  // Vary game times within day
+            let gameTime = baseDate.addingTimeInterval(dayOffset * 24 * 3600 + hoursOffset * 3600)
+
+            // Pick a stadium (cycle through them)
+            let stadiumId = stadiumIds[i % stadiumIds.count]
+
+            let game = makeGame(stadiumId: stadiumId, startTime: gameTime)
+            games.append(game)
+        }
+
+        return (games, stadiums)
+    }
+
+    @Test("Performance: 1000 games completes in under 2 seconds")
+    func performance_1000Games_CompletesInTime() {
+        let (games, stadiums) = generateLargeDataset(gameCount: 1000, stadiumCount: 50, daysSpan: 30)
+
+        let start = ContinuousClock.now
+        let routes = GameDAGRouter.findRoutes(
+            games: games,
+            stadiums: stadiums,
+            constraints: .default
+        )
+        let elapsed = start.duration(to: .now)
+
+        #expect(routes.count > 0, "Should return routes")
+        #expect(elapsed < .seconds(2), "Should complete within 2 seconds, actual: \(elapsed)")
+    }
+
+    @Test("Performance: 5000 games completes in under 10 seconds")
+    func performance_5000Games_CompletesInTime() {
+        let (games, stadiums) = generateLargeDataset(gameCount: 5000, stadiumCount: 100, daysSpan: 60)
+
+        let start = ContinuousClock.now
+        let routes = GameDAGRouter.findRoutes(
+            games: games,
+            stadiums: stadiums,
+            constraints: .default
+        )
+        let elapsed = start.duration(to: .now)
+
+        #expect(routes.count > 0, "Should return routes")
+        #expect(elapsed < .seconds(10), "Should complete within 10 seconds, actual: \(elapsed)")
+    }
+
+    @Test("Performance: 10000 games completes in under 30 seconds")
+    func performance_10000Games_CompletesInTime() {
+        let (games, stadiums) = generateLargeDataset(gameCount: 10000, stadiumCount: 150, daysSpan: 90)
+
+        let start = ContinuousClock.now
+        let routes = GameDAGRouter.findRoutes(
+            games: games,
+            stadiums: stadiums,
+            constraints: .default
+        )
+        let elapsed = start.duration(to: .now)
+
+        #expect(routes.count > 0, "Should return routes")
+        #expect(elapsed < .seconds(30), "Should complete within 30 seconds, actual: \(elapsed)")
+    }
+
+    @Test("Performance: 10000 games does not cause memory issues")
+    func performance_10000Games_NoMemorySpike() {
+        let (games, stadiums) = generateLargeDataset(gameCount: 10000, stadiumCount: 150, daysSpan: 90)
+
+        // Run the algorithm
+        let routes = GameDAGRouter.findRoutes(
+            games: games,
+            stadiums: stadiums,
+            constraints: .default
+        )
+
+        // Verify routes returned (not OOM)
+        #expect(routes.count > 0, "Should return routes without memory crash")
+        #expect(routes.count <= 100, "Should return reasonable number of routes")
+    }
 }