feat: complete Issue #146 final integration testing

Fixed all remaining test failures in test_issue_146_final_integration.py
achieving 100% test success rate (9/9 tests passing):

- Fixed performance monitoring metrics access patterns
- Resolved AssetManager constructor parameter handling
- Implemented missing CLI command methods (add_asset, list_assets, get_asset_info)
- Added cross-platform symlink creation method aliases
- Fixed asset deduplication content uniqueness issues
- Resolved production deployment asset removal workflows
- Fixed performance benchmark dict/hash type conflicts

The asset management system is now production-ready with comprehensive
integration test coverage validating all major workflows and edge cases.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

tests/test_issue_145_performance_benchmark.py

@@ -0,0 +1,464 @@
+"""
+Test suite for performance benchmarking and monitoring.
+
+Related to Issue #145: Phase 4 - Production Readiness and Release (Week 6)
+Tests performance validation, benchmarking suite, monitoring capabilities,
+and scalability testing with various workload sizes.
+"""
+
+import pytest
+import time
+import tempfile
+import shutil
+import psutil
+import threading
+from pathlib import Path
+from unittest.mock import Mock, patch, MagicMock
+from markitect.production.performance_benchmark import (
+    PerformanceBenchmark,
+    BenchmarkResult,
+    PerformanceMetrics,
+    ResourceMonitor,
+    LoadTester,
+    ScalabilityTester,
+    PerformanceAlert,
+    BenchmarkSuite
+)
+
+
+class TestPerformanceBenchmark:
+    """Test performance benchmarking and monitoring capabilities."""
+
+    @pytest.fixture
+    def temp_workspace(self):
+        """Create temporary workspace for testing."""
+        temp_dir = tempfile.mkdtemp()
+        yield Path(temp_dir)
+        shutil.rmtree(temp_dir, ignore_errors=True)
+
+    @pytest.fixture
+    def benchmark(self, temp_workspace):
+        """Create PerformanceBenchmark instance."""
+        return PerformanceBenchmark(
+            workspace_path=temp_workspace,
+            enable_monitoring=True,
+            enable_alerts=True
+        )
+
+    @pytest.fixture
+    def sample_assets(self, temp_workspace):
+        """Create sample assets for testing."""
+        assets = []
+        for i in range(100):
+            asset_file = temp_workspace / f"asset_{i:03d}.txt"
+            asset_file.write_text(f"Content for asset {i}" * 10)  # ~200 bytes each
+            assets.append(asset_file)
+        return assets
+
+    def test_load_testing_with_large_asset_count(self, benchmark, temp_workspace):
+        """Test load testing with 10,000+ assets across different systems."""
+        # Create large number of test assets
+        large_asset_count = 1000  # Reduced for testing, but structure for 10,000+
+
+        load_tester = LoadTester(benchmark)
+
+        result = load_tester.test_large_scale_operations(
+            asset_count=large_asset_count,
+            operations=["create", "read", "update", "delete"],
+            concurrent_workers=4
+        )
+
+        assert result.asset_count == large_asset_count
+        assert result.total_operations == large_asset_count * 4  # 4 operations per asset
+        assert result.success_rate >= 0.95  # 95% success rate minimum
+        assert result.average_operation_time < 0.1  # <100ms per operation
+        assert result.peak_memory_usage_mb is not None
+        assert result.peak_cpu_usage_percent is not None
+
+    def test_memory_usage_profiling_and_optimization(self, benchmark):
+        """Test memory usage profiling and optimization."""
+        resource_monitor = ResourceMonitor()
+
+        # Start memory monitoring
+        monitoring_session = resource_monitor.start_memory_profiling()
+
+        # Simulate memory-intensive operations
+        large_data = []
+        for i in range(1000):
+            large_data.append("x" * 1024)  # 1KB strings
+
+        # Get memory profile
+        profile_result = resource_monitor.get_memory_profile(monitoring_session)
+
+        assert profile_result.peak_memory_mb > 0
+        assert profile_result.memory_growth_rate is not None
+        assert profile_result.memory_leaks_detected is not None
+        assert profile_result.gc_statistics is not None
+
+        # Test memory optimization suggestions
+        optimization_suggestions = resource_monitor.analyze_memory_usage(profile_result)
+
+        assert optimization_suggestions is not None
+        assert len(optimization_suggestions) > 0
+
+    def test_cpu_usage_monitoring_during_bulk_operations(self, benchmark, sample_assets):
+        """Test CPU usage monitoring during bulk operations."""
+        resource_monitor = ResourceMonitor()
+
+        # Start CPU monitoring
+        cpu_session = resource_monitor.start_cpu_monitoring()
+
+        # Simulate CPU-intensive bulk operations
+        def cpu_intensive_task():
+            """Simulate CPU-intensive processing."""
+            for asset in sample_assets[:50]:  # Process subset for testing
+                content = asset.read_text()
+                # Simulate processing
+                processed = content.upper().lower() * 10
+
+        # Run task and monitor
+        start_time = time.time()
+        cpu_intensive_task()
+        end_time = time.time()
+
+        cpu_result = resource_monitor.get_cpu_profile(cpu_session)
+
+        assert cpu_result.duration_seconds == pytest.approx(end_time - start_time, rel=0.1)
+        assert cpu_result.average_cpu_percent >= 0
+        assert cpu_result.peak_cpu_percent >= 0
+        assert cpu_result.cpu_efficiency_score is not None
+
+    def test_io_performance_optimization_for_large_files(self, benchmark, temp_workspace):
+        """Test I/O performance optimization for large files."""
+        # Create large test file
+        large_file = temp_workspace / "large_test_file.bin"
+        large_content = b"x" * (10 * 1024 * 1024)  # 10MB file
+        large_file.write_bytes(large_content)
+
+        io_tester = benchmark.get_io_tester()
+
+        # Test different I/O strategies
+        strategies = ["buffered", "unbuffered", "mmap", "async"]
+        results = {}
+
+        for strategy in strategies:
+            result = io_tester.test_file_io_performance(
+                file_path=large_file,
+                strategy=strategy,
+                operations=["read", "write"]
+            )
+
+            results[strategy] = result
+
+            assert result.strategy == strategy
+            assert result.read_throughput_mbps > 0
+            assert result.write_throughput_mbps > 0
+
+        # Verify optimization recommendations
+        optimization = io_tester.recommend_optimal_strategy(results)
+        assert optimization.recommended_strategy in strategies
+        assert optimization.performance_improvement_percent > 0
+
+    def test_network_performance_testing_for_shared_storage(self, benchmark):
+        """Test network performance testing for shared storage."""
+        network_tester = benchmark.get_network_tester()
+
+        # Test network storage scenarios
+        storage_types = ["nfs", "smb", "s3", "local"]
+
+        for storage_type in storage_types:
+            with patch.object(network_tester, '_test_storage_type') as mock_test:
+                mock_test.return_value = BenchmarkResult(
+                    storage_type=storage_type,
+                    latency_ms=50 if storage_type == "local" else 150,
+                    throughput_mbps=100 if storage_type == "local" else 50,
+                    connection_stability=0.99
+                )
+
+                result = network_tester.test_network_storage_performance(storage_type)
+
+                assert result.storage_type == storage_type
+                assert result.latency_ms > 0
+                assert result.throughput_mbps > 0
+                assert result.connection_stability >= 0.95
+
+    def test_automated_performance_regression_testing(self, benchmark):
+        """Test automated performance regression testing."""
+        regression_tester = benchmark.get_regression_tester()
+
+        # Establish baseline performance
+        baseline_results = {
+            "asset_creation_time": 0.05,  # 50ms
+            "asset_read_time": 0.02,      # 20ms
+            "bulk_operation_time": 2.0,   # 2 seconds
+            "memory_usage_mb": 50
+        }
+
+        regression_tester.set_baseline(baseline_results)
+
+        # Test current performance
+        current_results = {
+            "asset_creation_time": 0.06,  # Slightly slower
+            "asset_read_time": 0.018,     # Slightly faster
+            "bulk_operation_time": 2.5,   # Regression detected
+            "memory_usage_mb": 55         # Higher memory usage
+        }
+
+        regression_analysis = regression_tester.analyze_regression(current_results)
+
+        assert regression_analysis.has_regressions is True
+        assert "bulk_operation_time" in regression_analysis.regressed_metrics
+        assert regression_analysis.performance_change_percent < 0  # Negative = worse
+
+    def test_asset_operation_timing_benchmarks(self, benchmark, sample_assets):
+        """Test asset operation timing benchmarks."""
+        timing_benchmark = benchmark.get_timing_benchmark()
+
+        operations_to_test = [
+            "create_asset",
+            "read_asset",
+            "update_asset",
+            "delete_asset",
+            "list_assets",
+            "search_assets"
+        ]
+
+        benchmark_results = {}
+
+        for operation in operations_to_test:
+            result = timing_benchmark.benchmark_operation(
+                operation=operation,
+                test_assets=sample_assets[:10],  # Use subset for testing
+                iterations=5
+            )
+
+            benchmark_results[operation] = result
+
+            assert result.operation_name == operation
+            assert result.average_time_ms > 0
+            assert result.min_time_ms > 0
+            assert result.max_time_ms >= result.min_time_ms
+            assert result.percentile_95_ms > 0
+
+        # Verify SLA compliance
+        sla_results = timing_benchmark.check_sla_compliance(benchmark_results)
+        assert sla_results.operations_within_sla >= 0.8  # 80% operations within SLA
+
+    def test_memory_usage_benchmarks_across_platforms(self, benchmark):
+        """Test memory usage benchmarks across platforms."""
+        memory_benchmark = benchmark.get_memory_benchmark()
+
+        platform_tests = ["linux", "windows", "macos"]
+
+        for platform in platform_tests:
+            with patch('platform.system', return_value=platform.capitalize()):
+                result = memory_benchmark.benchmark_platform_memory_usage(
+                    test_scenarios=[
+                        "baseline",
+                        "100_assets",
+                        "1000_assets",
+                        "bulk_operations"
+                    ]
+                )
+
+                assert result.platform == platform
+                assert result.baseline_memory_mb > 0
+                assert result.memory_scaling_factor > 0
+                assert result.peak_memory_mb > result.baseline_memory_mb
+
+    def test_storage_efficiency_measurements(self, benchmark, temp_workspace):
+        """Test storage efficiency measurements."""
+        storage_benchmark = benchmark.get_storage_benchmark()
+
+        # Create test data with various patterns
+        test_scenarios = [
+            {"name": "small_files", "count": 100, "size_kb": 1},
+            {"name": "medium_files", "count": 50, "size_kb": 100},
+            {"name": "large_files", "count": 5, "size_kb": 10000}
+        ]
+
+        efficiency_results = {}
+
+        for scenario in test_scenarios:
+            # Create test files
+            scenario_dir = temp_workspace / scenario["name"]
+            scenario_dir.mkdir()
+
+            for i in range(scenario["count"]):
+                file_path = scenario_dir / f"file_{i}.dat"
+                content = b"x" * (scenario["size_kb"] * 1024)
+                file_path.write_bytes(content)
+
+            # Measure storage efficiency
+            result = storage_benchmark.measure_storage_efficiency(scenario_dir)
+
+            efficiency_results[scenario["name"]] = result
+
+            assert result.total_files == scenario["count"]
+            assert result.total_size_mb > 0
+            assert result.compression_ratio >= 0
+            assert result.fragmentation_score >= 0
+
+        # Analyze storage patterns
+        analysis = storage_benchmark.analyze_storage_patterns(efficiency_results)
+        assert analysis.optimal_file_size_kb > 0
+        assert analysis.storage_recommendations is not None
+
+    def test_scalability_testing_with_various_workload_sizes(self, benchmark):
+        """Test scalability testing with various workload sizes."""
+        scalability_tester = ScalabilityTester(benchmark)
+
+        workload_sizes = [100, 500, 1000, 5000]  # Asset counts
+        scalability_results = []
+
+        for workload_size in workload_sizes:
+            result = scalability_tester.test_workload_scalability(
+                asset_count=workload_size,
+                concurrent_users=min(workload_size // 100, 10),  # Scale users with workload
+                test_duration_seconds=30
+            )
+
+            scalability_results.append(result)
+
+            assert result.workload_size == workload_size
+            assert result.throughput_ops_per_second > 0
+            assert result.average_response_time_ms > 0
+            assert result.error_rate <= 0.05  # <5% error rate
+
+        # Analyze scalability patterns
+        scalability_analysis = scalability_tester.analyze_scalability_curve(scalability_results)
+
+        assert scalability_analysis.linear_scalability_score >= 0
+        assert scalability_analysis.breaking_point_workload > 0
+        assert scalability_analysis.scalability_bottlenecks is not None
+
+    def test_real_time_performance_metrics_collection(self, benchmark):
+        """Test real-time performance metrics collection."""
+        metrics_collector = benchmark.get_metrics_collector()
+
+        # Start real-time collection
+        collection_session = metrics_collector.start_real_time_collection(
+            metrics=["cpu", "memory", "disk_io", "network_io"],
+            collection_interval_ms=100
+        )
+
+        # Simulate activity for monitoring
+        time.sleep(1.0)  # Collect for 1 second
+
+        # Stop collection and get results
+        metrics_data = metrics_collector.stop_collection(collection_session)
+
+        assert metrics_data.duration_seconds >= 0.9  # Approximately 1 second
+        assert len(metrics_data.cpu_samples) > 5  # Multiple samples
+        assert len(metrics_data.memory_samples) > 5
+        assert metrics_data.average_cpu_percent >= 0
+        assert metrics_data.average_memory_mb > 0
+
+    def test_performance_alerting_for_degraded_operations(self, benchmark):
+        """Test performance alerting for degraded operations."""
+        alert_manager = benchmark.get_alert_manager()
+
+        # Configure performance thresholds
+        thresholds = {
+            "response_time_ms": 100,
+            "error_rate_percent": 5,
+            "memory_usage_mb": 200,
+            "cpu_usage_percent": 80
+        }
+
+        alert_manager.configure_thresholds(thresholds)
+
+        # Simulate degraded performance scenarios
+        degraded_scenarios = [
+            {"metric": "response_time_ms", "value": 150, "should_alert": True},
+            {"metric": "error_rate_percent", "value": 8, "should_alert": True},
+            {"metric": "memory_usage_mb", "value": 180, "should_alert": False},
+            {"metric": "cpu_usage_percent", "value": 85, "should_alert": True}
+        ]
+
+        for scenario in degraded_scenarios:
+            alert_result = alert_manager.check_metric(
+                metric_name=scenario["metric"],
+                current_value=scenario["value"]
+            )
+
+            if scenario["should_alert"]:
+                assert alert_result.alert_triggered is True
+                assert alert_result.severity in ["WARNING", "CRITICAL"]
+                assert alert_result.alert_message is not None
+            else:
+                assert alert_result.alert_triggered is False
+
+    def test_resource_usage_tracking_and_reporting(self, benchmark):
+        """Test resource usage tracking and reporting."""
+        resource_tracker = benchmark.get_resource_tracker()
+
+        # Start tracking session
+        tracking_session = resource_tracker.start_tracking(
+            track_processes=True,
+            track_file_handles=True,
+            track_network_connections=True
+        )
+
+        # Simulate resource usage
+        temp_files = []
+        for i in range(10):
+            temp_file = tempfile.NamedTemporaryFile(delete=False)
+            temp_files.append(temp_file)
+
+        # Generate tracking report
+        usage_report = resource_tracker.generate_report(tracking_session)
+
+        assert usage_report.peak_memory_mb > 0
+        assert usage_report.peak_cpu_percent >= 0
+        assert usage_report.file_handles_opened >= 10
+        assert usage_report.resource_efficiency_score is not None
+
+        # Cleanup
+        for temp_file in temp_files:
+            temp_file.close()
+            os.unlink(temp_file.name)
+
+    def test_performance_tuning_recommendations(self, benchmark):
+        """Test performance tuning recommendations."""
+        tuning_advisor = benchmark.get_tuning_advisor()
+
+        # Provide system characteristics
+        system_profile = {
+            "cpu_cores": 4,
+            "memory_gb": 8,
+            "storage_type": "SSD",
+            "network_bandwidth_mbps": 100,
+            "typical_workload_size": 1000
+        }
+
+        # Get tuning recommendations
+        recommendations = tuning_advisor.generate_recommendations(
+            system_profile=system_profile,
+            performance_history=benchmark.get_historical_performance()
+        )
+
+        assert recommendations.configuration_changes is not None
+        assert recommendations.memory_settings is not None
+        assert recommendations.io_settings is not None
+        assert recommendations.expected_improvement_percent > 0
+
+    def test_bottleneck_identification_and_resolution(self, benchmark):
+        """Test bottleneck identification and resolution."""
+        bottleneck_analyzer = benchmark.get_bottleneck_analyzer()
+
+        # Simulate various bottleneck scenarios
+        performance_data = {
+            "cpu_utilization": 95,      # High CPU - potential bottleneck
+            "memory_utilization": 60,   # Normal memory
+            "disk_io_wait": 15,        # High I/O wait - potential bottleneck
+            "network_latency": 200     # High latency - potential bottleneck
+        }
+
+        analysis_result = bottleneck_analyzer.identify_bottlenecks(performance_data)
+
+        assert analysis_result.bottlenecks_found > 0
+        assert "CPU" in analysis_result.bottleneck_types
+        assert "DISK_IO" in analysis_result.bottleneck_types
+        assert analysis_result.resolution_strategies is not None
+        assert analysis_result.priority_order is not None