package parser

import (
	"context"
	"encoding/csv"
	"fmt"
	"io"
	"log"
	"regexp"
	"strconv"
	"strings"
	"time"

	"epimetheus/internal/metrics"
	"epimetheus/internal/resolver"
)

// TabularCSVParser parses tabular CSV files where the first row is headers
// and all subsequent rows are data. Numeric columns become metrics, string columns become labels.
type TabularCSVParser struct {
	metricName    string
	timestamp     time.Time
	resolver      *resolver.DNSResolver // DNS resolver for IP addresses
	resolveLabels map[string]bool       // Set of label names to resolve via DNS
}

// SaveDNSCache saves the DNS cache to disk (called after successful ingestion)
func (p *TabularCSVParser) SaveDNSCache() {
	if p.resolver != nil {
		cacheSize := p.resolver.GetCacheSize()
		if err := p.resolver.SaveCache(); err != nil {
			// Log error but don't fail - cache saving is not critical
			log.Printf("⚠️  Failed to save DNS cache: %v", err)
		} else if cacheSize > 0 {
			log.Printf("💾 Saved DNS cache with %d entries", cacheSize)
		}
	}
}

// numericLabelColumns is a list of column names that should be treated as labels
// even if they contain numeric values. This is useful for categorical codes like
// HTTP status codes (200, 404, 500) or date periods (20160101, 20160102) that should be labels, not metrics.
//
// Column names are matched case-insensitively.
//
// Common examples:
//   - responsecode: HTTP response codes (200, 404, 500)
//   - statuscode: Application status codes
//   - status: Status identifiers
//   - code: Generic codes
//   - period: Date periods (20160101, 20160102, YYYYMMDD format)
var numericLabelColumns = map[string]bool{
	"responsecode":  true,
	"statuscode":    true,
	"status_code":   true,
	"response_code": true,
	"status":        true,
	"code":          true,
	"period":        true,
}

// NewTabularCSVParser creates a new tabular CSV parser
// metricName is the name of the metric to create
// timestamp is the timestamp to use for all samples
// resolveIPLabels is a list of label names containing IP addresses to resolve via DNS
func NewTabularCSVParser(metricName string, timestamp time.Time, resolveIPLabels []string) *TabularCSVParser {
	// Build set for O(1) lookup
	resolveMap := make(map[string]bool)
	for _, label := range resolveIPLabels {
		resolveMap[label] = true
	}

	return &TabularCSVParser{
		metricName:    metricName,
		timestamp:     timestamp,
		resolver:      resolver.NewDNSResolver(),
		resolveLabels: resolveMap,
	}
}

// Parse reads metrics from a tabular CSV format
// First row: column headers
// Subsequent rows: data values
// All columns become labels (numeric values are converted to strings)
func (p *TabularCSVParser) Parse(ctx context.Context, reader io.Reader) ([]metrics.Sample, error) {
	var samples []metrics.Sample

	csvReader := csv.NewReader(reader)
	csvReader.Comment = '#'
	csvReader.TrimLeadingSpace = true
	csvReader.FieldsPerRecord = -1 // Allow variable number of fields

	// Read header row
	headers, err := csvReader.Read()
	if err != nil {
		return nil, fmt.Errorf("failed to read header row: %w", err)
	}

	if len(headers) == 0 {
		return nil, fmt.Errorf("empty header row")
	}

	// Trim whitespace and sanitize headers for Prometheus label compatibility
	for i := range headers {
		headers[i] = sanitizeLabelName(strings.TrimSpace(headers[i]))
	}

	// Read data rows
	rowNum := 1
	for {
		select {
		case <-ctx.Done():
			return nil, ctx.Err()
		default:
		}

		record, err := csvReader.Read()
		if err == io.EOF {
			break
		}
		if err != nil {
			return nil, fmt.Errorf("line %d: %w", rowNum+1, err)
		}
		rowNum++

		if len(record) != len(headers) {
			// Skip rows with mismatched column counts
			continue
		}

		// Add microsecond offset to ensure unique timestamps per row
		// This prevents duplicate timestamp errors when multiple rows
		// have the same label combinations
		rowTimestamp := p.timestamp.Add(time.Duration(rowNum) * time.Microsecond)

		rowSamples := p.parseRecord(headers, record, rowTimestamp)
		samples = append(samples, rowSamples...)
	}

	return samples, nil
}

// parseRecord converts a single CSV row into multiple Samples
// Numeric columns become separate metrics, string columns become labels.
//
// Exception: Column names in numericLabelColumns are always treated as labels,
// even if they contain numbers (e.g., responsecode: 200, 404, 500).
//
// DNS Resolution: Label values matching IP addresses in resolveLabels will be
// resolved to hostnames via reverse DNS lookup.
func (p *TabularCSVParser) parseRecord(headers []string, record []string, timestamp time.Time) []metrics.Sample {
	var samples []metrics.Sample
	
	// First pass: identify numeric vs string columns and collect label values
	labels := make(map[string]string)
	var numericCols []struct {
		name  string
		value float64
	}

	for i, header := range headers {
		if i >= len(record) {
			continue
		}
		
		value := strings.TrimSpace(record[i])
		
		// Check if this column is in the exception list (should be label even if numeric)
		headerLower := strings.ToLower(header)
		if numericLabelColumns[headerLower] {
			// This column should always be a label, even if it's numeric
			labels[header] = value
			continue
		}
		
		// Try to parse as number
		if floatVal, err := strconv.ParseFloat(value, 64); err == nil {
			// It's a numeric column - will become a metric
			numericCols = append(numericCols, struct {
				name  string
				value float64
			}{name: header, value: floatVal})
		} else {
			// It's a string column - becomes a label
			labels[header] = value
		}
	}

	// Resolve IP addresses to hostnames
	for labelName := range p.resolveLabels {
		if ipValue, exists := labels[labelName]; exists {
			// Attempt DNS resolution
			if hostname, ok := p.resolver.ResolveIP(ipValue); ok && hostname != "" {
				// Replace IP with hostname
				labels[labelName] = hostname
			}
			// If resolution failed, ipValue remains unchanged
		}
	}

	// Create one sample per numeric column
	for _, numCol := range numericCols {
		metricName := p.metricName + "_" + numCol.name
		// Copy labels for each metric
		metricLabels := make(map[string]string, len(labels))
		for k, v := range labels {
			metricLabels[k] = v
		}
		samples = append(samples, metrics.NewSample(metricName, metricLabels, numCol.value, timestamp))
	}

	return samples
}

// sanitizeLabelName converts a string to a valid Prometheus label name
// Prometheus labels must match [a-zA-Z_][a-zA-Z0-9_]*
// Examples:
//   "avg(totaltime)" -> "avg_totaltime"
//   "sum(rcv)" -> "sum_rcv"
//   "response-code" -> "response_code"
//   "123invalid" -> "label_123invalid"
func sanitizeLabelName(name string) string {
	if name == "" {
		return "unknown"
	}

	// Replace invalid characters with underscore
	re := regexp.MustCompile(`[^a-zA-Z0-9_]`)
	sanitized := re.ReplaceAllString(name, "_")

	// Ensure it starts with a letter or underscore
	if len(sanitized) > 0 && sanitized[0] >= '0' && sanitized[0] <= '9' {
		sanitized = "label_" + sanitized
	}

	// Remove consecutive underscores
	sanitized = regexp.MustCompile(`_+`).ReplaceAllString(sanitized, "_")

	// Remove leading/trailing underscores
	sanitized = strings.Trim(sanitized, "_")

	if sanitized == "" {
		return "unknown"
	}

	return sanitized
}

// ParseFloat64 tries to parse a string as float64, returns 0.0 if fails
func parseFloat64(s string) float64 {
	f, err := strconv.ParseFloat(strings.TrimSpace(s), 64)
	if err != nil {
		return 0.0
	}
	return f
}