package main

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

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

	// Display SIMD capabilities
	vectorized.PrintSIMDInfo()
	fmt.Println()

	// Demo 1: SIMD vs Scalar Performance Comparison
	fmt.Println("🚀 Demo 1: SIMD vs Scalar Performance Comparison")
	fmt.Println("─────────────────────────────────────────────────")
	runSIMDPerformanceDemo()
	fmt.Println()

	// Demo 2: SIMD-Optimized Filtering
	fmt.Println("⚡ Demo 2: SIMD-Optimized Vectorized Filtering")
	fmt.Println("──────────────────────────────────────────────")
	runSIMDFilterDemo()
	fmt.Println()

	// Demo 3: SIMD Integration in Query Execution
	fmt.Println("🔧 Demo 3: SIMD Integration in Query Execution")
	fmt.Println("───────────────────────────────────────────────")
	runSIMDQueryDemo()
	fmt.Println()

	fmt.Println("📊 SIMD Implementation Summary:")
	fmt.Println("✅ Go Assembly: Highest performance, platform-specific")
	fmt.Println("✅ CGO C++: Good performance, requires C compiler") 
	fmt.Println("✅ Manual Go: Moderate performance, Go compiler auto-vectorization")
	fmt.Println("✅ Automatic fallbacks: Graceful degradation for unsupported operations")
}

func runSIMDPerformanceDemo() {
	tester := vectorized.NewSIMDPerformanceTester()
	
	// Test different data sizes
	dataSizes := []int{1000, 10000, 100000, 1000000}
	
	fmt.Println("Testing SIMD filtering performance across different data sizes:")
	fmt.Println()
	
	for _, size := range dataSizes {
		fmt.Printf("📈 Testing with %d elements:\n", size)
		result := tester.BenchmarkFilterPerformance(size)
		fmt.Print(result.String())
		
		if result.Speedup > 1.0 {
			fmt.Printf("🎯 SIMD is %.2fx faster!\n", result.Speedup)
		} else {
			fmt.Printf("⚠️  Scalar is %.2fx faster (overhead dominates)\n", 1.0/result.Speedup)
		}
		fmt.Println()
	}
}

func runSIMDFilterDemo() {
	// Create test data
	dataSize := 500000
	fmt.Printf("Creating test dataset with %d rows...\n", dataSize)
	
	schema := &vectorized.Schema{
		Fields: []*vectorized.Field{
			{Name: "id", DataType: vectorized.INT64},
			{Name: "value", DataType: vectorized.FLOAT64},
			{Name: "category", DataType: vectorized.STRING},
		},
	}
	
	// Create vectorized data
	batch := vectorized.NewVectorBatch(schema, dataSize)
	categories := []string{"A", "B", "C", "D", "E"}
	
	for i := 0; i < dataSize; i++ {
		batch.AddRow([]interface{}{
			int64(i + 1),
			float64(i%10000) + 100.0, // Values 100-10099
			categories[i%len(categories)],
		})
	}
	
	// Create SIMD-optimized filter operator
	config := vectorized.DefaultSIMDConfig()
	config.MinVectorSize = 1000 // Use SIMD for vectors >= 1000 elements
	
	// Create mock input operator
	mockInput := &MockSIMDOperator{batch: batch}
	
	// Create filters: value > 5000 AND value < 8000
	filters := []*vectorized.VectorizedFilter{
		{ColumnIndex: 1, Operator: vectorized.GT, Value: 5000.0},
		{ColumnIndex: 1, Operator: vectorized.LT, Value: 8000.0},
	}
	
	simdFilter := vectorized.NewSIMDVectorizedFilterOperator(mockInput, filters, config)
	
	// Execute SIMD-optimized filtering
	fmt.Println("Executing SIMD-optimized filtering...")
	start := time.Now()
	result, err := simdFilter.Execute(nil)
	simdTime := time.Since(start)
	
	if err != nil {
		log.Printf("SIMD filter failed: %v", err)
		return
	}
	
	fmt.Printf("✅ SIMD Filter Results:\n")
	fmt.Printf("   • Execution time: %v\n", simdTime)
	fmt.Printf("   • Input rows: %d\n", dataSize)
	fmt.Printf("   • Output rows: %d\n", result.RowCount)
	fmt.Printf("   • Selectivity: %.1f%%\n", float64(result.RowCount)/float64(dataSize)*100)
	fmt.Printf("   • Throughput: %.2f million rows/sec\n", float64(dataSize)/simdTime.Seconds()/1000000)
	
	// Compare with scalar filtering
	fmt.Println("\nComparing with scalar filtering...")
	scalarConfig := &vectorized.SIMDConfig{
		EnableSIMD:  false,
		ForceScalar: true,
	}
	scalarFilter := vectorized.NewSIMDVectorizedFilterOperator(mockInput, filters, scalarConfig)
	
	start = time.Now()
	scalarResult, err := scalarFilter.Execute(nil)
	scalarTime := time.Since(start)
	
	if err != nil {
		log.Printf("Scalar filter failed: %v", err)
		return
	}
	
	fmt.Printf("✅ Scalar Filter Results:\n")
	fmt.Printf("   • Execution time: %v\n", scalarTime)
	fmt.Printf("   • Output rows: %d\n", scalarResult.RowCount)
	fmt.Printf("   • Throughput: %.2f million rows/sec\n", float64(dataSize)/scalarTime.Seconds()/1000000)
	
	// Calculate speedup
	if scalarTime > 0 && simdTime > 0 {
		speedup := float64(scalarTime) / float64(simdTime)
		fmt.Printf("\n🚀 SIMD vs Scalar: %.2fx speedup\n", speedup)
	}
}

func runSIMDQueryDemo() {
	// Demonstrate SIMD integration in full query execution
	fmt.Println("Demonstrating SIMD integration in query execution pipeline...")
	
	// Create larger dataset for realistic performance testing
	dataSize := 1000000 // 1 million rows
	fmt.Printf("Creating large dataset with %d rows...\n", dataSize)
	
	schema := &vectorized.Schema{
		Fields: []*vectorized.Field{
			{Name: "id", DataType: vectorized.INT64},
			{Name: "sales", DataType: vectorized.FLOAT64},
			{Name: "region", DataType: vectorized.STRING},
			{Name: "quarter", DataType: vectorized.INT32},
		},
	}
	
	// Create test data in batches for better memory efficiency
	batchSize := 100000
	var batches []*vectorized.VectorBatch
	regions := []string{"North", "South", "East", "West", "Central"}
	
	for offset := 0; offset < dataSize; offset += batchSize {
		currentBatchSize := batchSize
		if offset+batchSize > dataSize {
			currentBatchSize = dataSize - offset
		}
		
		batch := vectorized.NewVectorBatch(schema, currentBatchSize)
		
		for i := 0; i < currentBatchSize; i++ {
			globalIndex := offset + i
			batch.AddRow([]interface{}{
				int64(globalIndex + 1),
				float64(globalIndex%50000) + 1000.0, // Sales 1000-51000
				regions[globalIndex%len(regions)],
				int32((globalIndex%4) + 1), // Quarters 1-4
			})
		}
		
		batches = append(batches, batch)
	}
	
	// Create data source
	dataSource := &MockSIMDDataSource{
		batches: batches,
		schema:  schema,
		index:   0,
	}
	
	// Test Query: SELECT region, AVG(sales) FROM data WHERE sales > 25000 GROUP BY region
	fmt.Println("\nExecuting query: SELECT region, AVG(sales) FROM data WHERE sales > 25000 GROUP BY region")
	
	// SIMD-optimized execution
	fmt.Println("\n🚀 SIMD-Optimized Execution:")
	start := time.Now()
	simdResults := executeSIMDQuery(dataSource, schema)
	simdTime := time.Since(start)
	
	fmt.Printf("✅ SIMD Execution Results:\n")
	fmt.Printf("   • Execution time: %v\n", simdTime)
	fmt.Printf("   • Throughput: %.2f million rows/sec\n", float64(dataSize)/simdTime.Seconds()/1000000)
	fmt.Printf("   • Result groups: %d\n", len(simdResults))
	
	for region, avgSales := range simdResults {
		fmt.Printf("   • %s: $%.2f average sales\n", region, avgSales)
	}
	
	// Reset data source for comparison
	dataSource.Reset()
	
	// Scalar execution for comparison
	fmt.Println("\n📊 Scalar Execution (for comparison):")
	start = time.Now()
	scalarResults := executeScalarQuery(dataSource, dataSize)
	scalarTime := time.Since(start)
	
	fmt.Printf("✅ Scalar Execution Results:\n")
	fmt.Printf("   • Execution time: %v\n", scalarTime)
	fmt.Printf("   • Throughput: %.2f million rows/sec\n", float64(dataSize)/scalarTime.Seconds()/1000000)
	fmt.Printf("   • Result groups: %d\n", len(scalarResults))
	
	// Calculate overall speedup
	if scalarTime > 0 && simdTime > 0 {
		speedup := float64(scalarTime) / float64(simdTime)
		fmt.Printf("\n🎯 Overall Query Speedup: %.2fx\n", speedup)
		
		if speedup > 1.0 {
			fmt.Printf("✅ SIMD optimization improved performance!\n")
		} else {
			fmt.Printf("⚠️  SIMD overhead exceeded benefits for this workload\n")
		}
	}
	
	fmt.Printf("\n📝 Analysis:\n")
	fmt.Printf("• SIMD excels at: Large datasets, simple filters, mathematical operations\n")
	fmt.Printf("• SIMD overhead: Query planning, data copying, function call costs\n")
	fmt.Printf("• Real-world gains: 2-10x typical, 10-100x with optimization\n")
}

// Mock operators for testing
type MockSIMDOperator struct {
	batch *vectorized.VectorBatch
}

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

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

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

type MockSIMDDataSource struct {
	batches []*vectorized.VectorBatch
	schema  *vectorized.Schema
	index   int
}

func (ds *MockSIMDDataSource) GetNextBatch() (*vectorized.VectorBatch, error) {
	if ds.index >= len(ds.batches) {
		return nil, nil
	}
	batch := ds.batches[ds.index]
	ds.index++
	return batch, nil
}

func (ds *MockSIMDDataSource) GetSchema() *vectorized.Schema {
	return ds.schema
}

func (ds *MockSIMDDataSource) Close() error {
	return nil
}

func (ds *MockSIMDDataSource) HasNext() bool {
	return ds.index < len(ds.batches)
}

func (ds *MockSIMDDataSource) GetEstimatedRowCount() int {
	total := 0
	for _, batch := range ds.batches {
		total += batch.RowCount
	}
	return total
}

func (ds *MockSIMDDataSource) Reset() {
	ds.index = 0
}

// Simplified query execution for demonstration
func executeSIMDQuery(dataSource *MockSIMDDataSource, schema *vectorized.Schema) map[string]float64 {
	results := make(map[string]float64)
	counts := make(map[string]int)
	sums := make(map[string]float64)
	
	// Process batches with SIMD filtering
	for {
		batch, err := dataSource.GetNextBatch()
		if err != nil || batch == nil {
			break
		}
		
		// SIMD filter: sales > 25000
		salesColumn := batch.Columns[1] // sales column
		regionColumn := batch.Columns[2] // region column
		
		salesData := salesColumn.Data.([]float64)
		regionData := regionColumn.Data.([]string)
		
		// Use SIMD filtering
		selection := make([]int, batch.RowCount)
		selectedCount := vectorized.SIMDFilterGreaterThan(salesData, 25000.0, selection)
		
		// Aggregate filtered results
		for i := 0; i < selectedCount; i++ {
			idx := selection[i]
			region := regionData[idx]
			sales := salesData[idx]
			
			sums[region] += sales
			counts[region]++
		}
	}
	
	// Compute averages
	for region, sum := range sums {
		results[region] = sum / float64(counts[region])
	}
	
	return results
}

func executeScalarQuery(dataSource *MockSIMDDataSource, dataSize int) map[string]float64 {
	results := make(map[string]float64)
	counts := make(map[string]int)
	sums := make(map[string]float64)
	
	// Process batches with scalar filtering
	for {
		batch, err := dataSource.GetNextBatch()
		if err != nil || batch == nil {
			break
		}
		
		salesColumn := batch.Columns[1]
		regionColumn := batch.Columns[2]
		
		salesData := salesColumn.Data.([]float64)
		regionData := regionColumn.Data.([]string)
		
		// Scalar filtering and aggregation
		for i := 0; i < batch.RowCount; i++ {
			if salesData[i] > 25000.0 {
				region := regionData[i]
				sums[region] += salesData[i]
				counts[region]++
			}
		}
	}
	
	// Compute averages
	for region, sum := range sums {
		results[region] = sum / float64(counts[region])
	}
	
	return results
}