Add forecasting service

services/forecasting/tests/performance/test_forecasting_performance.py

@@ -0,0 +1,106 @@
+# ================================================================
+# Performance Tests: tests/performance/test_forecasting_performance.py
+# ================================================================
+"""
+Performance tests for forecasting service
+"""
+
+import pytest
+import httpx
+import asyncio
+import time
+from concurrent.futures import ThreadPoolExecutor
+import statistics
+
+class TestForecastingPerformance:
+    """Performance tests for forecasting operations"""
+    
+    @pytest.mark.asyncio
+    async def test_single_forecast_performance(self):
+        """Test single forecast generation performance"""
+        
+        base_url = "http://localhost:8000"
+        
+        forecast_request = {
+            "tenant_id": "perf-test-tenant",
+            "product_name": "Pan Integral",
+            "location": "madrid_centro",
+            "forecast_date": "2024-01-17",
+            "business_type": "individual",
+            "confidence_level": 0.8
+        }
+        
+        times = []
+        
+        async with httpx.AsyncClient() as client:
+            for _ in range(10):
+                start_time = time.time()
+                
+                response = await client.post(
+                    f"{base_url}/api/v1/forecasting/single",
+                    json=forecast_request
+                )
+                
+                end_time = time.time()
+                times.append(end_time - start_time)
+                
+                assert response.status_code == 200
+        
+        # Performance assertions
+        avg_time = statistics.mean(times)
+        p95_time = statistics.quantiles(times, n=20)[18]  # 95th percentile
+        
+        assert avg_time < 2.0, f"Average response time {avg_time}s exceeds 2s"
+        assert p95_time < 5.0, f"95th percentile {p95_time}s exceeds 5s"
+        
+        print(f"Average response time: {avg_time:.2f}s")
+        print(f"95th percentile: {p95_time:.2f}s")
+    
+    @pytest.mark.asyncio
+    async def test_concurrent_forecasts(self):
+        """Test concurrent forecast generation"""
+        
+        base_url = "http://localhost:8000"
+        
+        async def make_forecast_request(product_id):
+            forecast_request = {
+                "tenant_id": "perf-test-tenant",
+                "product_name": f"Product_{product_id}",
+                "location": "madrid_centro",
+                "forecast_date": "2024-01-17",
+                "business_type": "individual"
+            }
+            
+            async with httpx.AsyncClient() as client:
+                start_time = time.time()
+                response = await client.post(
+                    f"{base_url}/api/v1/forecasting/single",
+                    json=forecast_request
+                )
+                end_time = time.time()
+                
+                return {
+                    "status_code": response.status_code,
+                    "response_time": end_time - start_time,
+                    "product_id": product_id
+                }
+        
+        # Run 20 concurrent requests
+        tasks = [make_forecast_request(i) for i in range(20)]
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+        
+        # Analyze results
+        successful = [r for r in results if isinstance(r, dict) and r["status_code"] == 200]
+        failed = [r for r in results if not isinstance(r, dict) or r["status_code"] != 200]
+        
+        success_rate = len(successful) / len(results)
+        
+        assert success_rate >= 0.95, f"Success rate {success_rate} below 95%"
+        
+        if successful:
+            avg_concurrent_time = statistics.mean([r["response_time"] for r in successful])
+            assert avg_concurrent_time < 10.0, f"Average concurrent time {avg_concurrent_time}s exceeds 10s"
+            
+            print(f"Concurrent success rate: {success_rate:.2%}")
+            print(f"Average concurrent response time: {avg_concurrent_time:.2f}s")
+