package main

import (
	"bytedb/vectorized"
	"fmt"
	"runtime"
	"time"
)

func main() {
	fmt.Println("=== ByteDB SIMD Implementation Guide ===")
	fmt.Println()

	// Show system information
	fmt.Printf("System Information:\n")
	fmt.Printf("• Go Version: %s\n", runtime.Version())
	fmt.Printf("• Architecture: %s\n", runtime.GOARCH)
	fmt.Printf("• OS: %s\n", runtime.GOOS)
	fmt.Printf("• CPU Cores: %d\n", runtime.NumCPU())
	fmt.Println()

	// Demo 1: SIMD Implementation Approaches
	fmt.Println("🔧 SIMD Implementation Approaches in Go")
	fmt.Println("─────────────────────────────────────────")
	demonstrateSIMDApproaches()
	fmt.Println()

	// Demo 2: Performance Comparison
	fmt.Println("📊 Performance Comparison: Manual vs Vectorized")
	fmt.Println("───────────────────────────────────────────────")
	runPerformanceComparison()
	fmt.Println()

	// Demo 3: Real-world Integration
	fmt.Println("🚀 SIMD Integration in ByteDB")
	fmt.Println("─────────────────────────────")
	demonstrateIntegration()
	fmt.Println()

	// Demo 4: Implementation Roadmap
	fmt.Println("🗺️  SIMD Implementation Roadmap")
	fmt.Println("──────────────────────────────")
	showImplementationRoadmap()
}

func demonstrateSIMDApproaches() {
	fmt.Println("There are several approaches to implement SIMD in Go:")
	fmt.Println()

	fmt.Println("1. 🔥 Go Assembly (Highest Performance)")
	fmt.Println("   • Write .s files with AVX2/AVX-512 instructions")
	fmt.Println("   • Example: simd_amd64.s with VADDPD, VCMPGTPD")
	fmt.Println("   • 5-10x performance improvement")
	fmt.Println("   • Platform-specific, requires assembly knowledge")
	fmt.Println()

	fmt.Println("2. ⚡ CGO with C++ (Good Performance)")
	fmt.Println("   • Use C++ intrinsics like _mm256_add_pd()")
	fmt.Println("   • 3-8x performance improvement") 
	fmt.Println("   • Requires C compiler, more complex build")
	fmt.Println()

	fmt.Println("3. 🔧 Manual Go Vectorization (Moderate)")
	fmt.Println("   • Loop unrolling and compiler hints")
	fmt.Println("   • Go compiler auto-vectorization")
	fmt.Println("   • 1.5-3x performance improvement")
	fmt.Println("   • Pure Go, portable")
	fmt.Println()

	fmt.Println("4. 📚 Third-party Libraries")
	fmt.Println("   • Libraries like GoNum, Apache Arrow Go")
	fmt.Println("   • Pre-optimized SIMD operations")
	fmt.Println("   • Easy integration, maintained by community")
}

func runPerformanceComparison() {
	// Test different vector sizes
	sizes := []int{1000, 10000, 100000, 1000000}
	
	fmt.Println("Testing filtering performance (element > threshold):")
	fmt.Println()

	for _, size := range sizes {
		fmt.Printf("📈 Dataset: %d elements\n", size)
		
		// Create test data
		data := make([]float64, size)
		for i := 0; i < size; i++ {
			data[i] = float64(i%1000) + 100.0
		}
		
		threshold := 500.0
		selection := make([]int, size)
		
		// Manual optimized version (simulates SIMD)
		start := time.Now()
		count1 := manualOptimizedFilter(data, threshold, selection)
		optimizedTime := time.Since(start)
		
		// Simple scalar version
		start = time.Now()
		count2 := simpleScalarFilter(data, threshold)
		scalarTime := time.Since(start)
		
		// Calculate metrics
		speedup := float64(scalarTime) / float64(optimizedTime)
		throughput := float64(size) / optimizedTime.Seconds() / 1000000
		
		fmt.Printf("   • Optimized: %v (%.1f M elem/sec, %d results)\n", 
			optimizedTime, throughput, count1)
		fmt.Printf("   • Scalar: %v (%d results)\n", scalarTime, count2)
		fmt.Printf("   • Speedup: %.2fx\n", speedup)
		fmt.Println()
	}
}

// Manual optimized filter (simulates SIMD with loop unrolling)
func manualOptimizedFilter(data []float64, threshold float64, selection []int) int {
	count := 0
	i := 0
	
	// Process 8 elements at a time (manual unrolling)
	for ; i < len(data)-7; i += 8 {
		// Check 8 elements in parallel (simulates AVX-512)
		if data[i] > threshold {
			selection[count] = i
			count++
		}
		if data[i+1] > threshold {
			selection[count] = i + 1
			count++
		}
		if data[i+2] > threshold {
			selection[count] = i + 2
			count++
		}
		if data[i+3] > threshold {
			selection[count] = i + 3
			count++
		}
		if data[i+4] > threshold {
			selection[count] = i + 4
			count++
		}
		if data[i+5] > threshold {
			selection[count] = i + 5
			count++
		}
		if data[i+6] > threshold {
			selection[count] = i + 6
			count++
		}
		if data[i+7] > threshold {
			selection[count] = i + 7
			count++
		}
	}
	
	// Handle remaining elements
	for ; i < len(data); i++ {
		if data[i] > threshold {
			selection[count] = i
			count++
		}
	}
	
	return count
}

// Simple scalar filter
func simpleScalarFilter(data []float64, threshold float64) int {
	count := 0
	for _, val := range data {
		if val > threshold {
			count++
		}
	}
	return count
}

func demonstrateIntegration() {
	fmt.Println("SIMD integration in ByteDB vectorized execution:")
	fmt.Println()

	// Create test data
	schema := &vectorized.Schema{
		Fields: []*vectorized.Field{
			{Name: "id", DataType: vectorized.INT64},
			{Name: "value", DataType: vectorized.FLOAT64},
			{Name: "category", DataType: vectorized.STRING},
		},
	}

	dataSize := 100000
	batch := vectorized.NewVectorBatch(schema, dataSize)
	categories := []string{"A", "B", "C", "D", "E"}

	fmt.Printf("Creating test batch with %d rows...\n", dataSize)
	for i := 0; i < dataSize; i++ {
		batch.AddRow([]interface{}{
			int64(i + 1),
			float64(i%5000) + 1000.0,
			categories[i%len(categories)],
		})
	}

	// Test current vectorized filter
	fmt.Println("Testing current vectorized filter implementation...")
	
	mockInput := &MockOperator{batch: batch}
	filters := []*vectorized.VectorizedFilter{
		{ColumnIndex: 1, Operator: vectorized.GT, Value: 3000.0},
	}

	filterOp := vectorized.NewVectorizedFilterOperator(mockInput, filters)

	start := time.Now()
	result, err := filterOp.Execute(nil)
	execTime := time.Since(start)

	if err != nil {
		fmt.Printf("❌ Filter failed: %v\n", err)
		return
	}

	fmt.Printf("✅ Filter Results:\n")
	fmt.Printf("   • Input rows: %d\n", dataSize)
	fmt.Printf("   • Output rows: %d\n", result.RowCount)
	fmt.Printf("   • Execution time: %v\n", execTime)
	fmt.Printf("   • Throughput: %.2f million rows/sec\n", 
		float64(dataSize)/execTime.Seconds()/1000000)
	fmt.Printf("   • Selectivity: %.1f%%\n", 
		float64(result.RowCount)/float64(dataSize)*100)

	fmt.Println()
	fmt.Println("💡 With real SIMD implementation, this could be 3-10x faster!")
}

func showImplementationRoadmap() {
	fmt.Println("Step-by-step guide to implement real SIMD in ByteDB:")
	fmt.Println()

	fmt.Println("Phase 1: Foundation (✅ Completed)")
	fmt.Println("• ✅ Vectorized data structures (VectorBatch, Vector)")
	fmt.Println("• ✅ Columnar operators (filter, project, aggregate)")
	fmt.Println("• ✅ Query integration and planning")
	fmt.Println()

	fmt.Println("Phase 2: SIMD Implementation (🚧 Next Steps)")
	fmt.Println("• 🔧 CPU feature detection (AVX2, AVX-512 support)")
	fmt.Println("• 🔧 Go assembly files for core operations:")
	fmt.Println("  - SIMD filtering (GT, LT, EQ comparisons)")
	fmt.Println("  - SIMD arithmetic (ADD, SUB, MUL, DIV)")
	fmt.Println("  - SIMD aggregation (SUM, MIN, MAX)")
	fmt.Println("• 🔧 CGO fallbacks for complex operations")
	fmt.Println("• 🔧 Memory alignment optimization")
	fmt.Println()

	fmt.Println("Phase 3: Advanced Optimizations (🔮 Future)")
	fmt.Println("• 🚀 JIT compilation for expressions")
	fmt.Println("• 🚀 Vectorized string operations")
	fmt.Println("• 🚀 SIMD hash joins and sorts")
	fmt.Println("• 🚀 GPU acceleration (CUDA/OpenCL)")
	fmt.Println()

	fmt.Println("Implementation Files Needed:")
	fmt.Println("```")
	fmt.Println("vectorized/")
	fmt.Println("├── simd_amd64.s          # Go assembly for x86_64")
	fmt.Println("├── simd_arm64.s          # Go assembly for ARM64") 
	fmt.Println("├── simd_native.go        # SIMD interface and CGO")
	fmt.Println("├── simd_detection.go     # CPU feature detection")
	fmt.Println("├── simd_filters.go       # SIMD-optimized filters")
	fmt.Println("├── simd_aggregates.go    # SIMD-optimized aggregations")
	fmt.Println("└── simd_benchmark.go     # Performance testing")
	fmt.Println("```")
	fmt.Println()

	fmt.Println("Expected Performance Gains:")
	fmt.Println("• Simple filters: 4-8x speedup (AVX2)")
	fmt.Println("• Mathematical operations: 8-16x speedup (AVX-512)")
	fmt.Println("• Aggregations: 3-6x speedup")
	fmt.Println("• Overall query performance: 2-5x improvement")
	fmt.Println()

	fmt.Println("Real-world Examples:")
	fmt.Println("• ClickHouse: 10-100x faster analytics with SIMD")
	fmt.Println("• DuckDB: 5-50x speedup on analytical queries")
	fmt.Println("• Apache Arrow: 3-20x improvement in columnar operations")
	fmt.Println()

	fmt.Println("📚 Learning Resources:")
	fmt.Println("• Intel Intrinsics Guide: https://software.intel.com/sites/landingpage/IntrinsicsGuide/")
	fmt.Println("• Go Assembly: https://go.dev/doc/asm")
	fmt.Println("• SIMD Programming: https://chryswoods.com/vector_c++/")
}

// Mock operator for testing
type MockOperator struct {
	batch *vectorized.VectorBatch
}

func (m *MockOperator) Execute(input *vectorized.VectorBatch) (*vectorized.VectorBatch, error) {
	return m.batch, nil
}

func (m *MockOperator) GetOutputSchema() *vectorized.Schema {
	return m.batch.Schema
}

func (m *MockOperator) GetEstimatedRowCount() int {
	return m.batch.RowCount
}