// ABOUTME: Accumulates system metrics samples across a collection run.
// ABOUTME: Computes peak, average, and P95 statistics for CPU and memory on demand.
package summary

import (
	"fmt"
	"sort"
	"time"

	"edp.buildth.ing/DevFW-CICD/forgejo-runner-optimiser/internal/metrics"
)

// containerAccumulator tracks metrics for a single container
type containerAccumulator struct {
	cpuCoresValues    []float64
	memoryBytesValues []float64
}

// Accumulator collects metric samples and computes run-level statistics
type Accumulator struct {
	topN           int
	cpuValues      []float64
	memBytesValues []float64
	memPctValues   []float64
	processPeaks   map[string]*ProcessPeak
	containers     map[string]*containerAccumulator
	startTime      time.Time
	endTime        time.Time
	sampleCount    int
}

// NewAccumulator creates an accumulator that tracks the top N processes
func NewAccumulator(topN int) *Accumulator {
	return &Accumulator{
		topN:         topN,
		processPeaks: make(map[string]*ProcessPeak),
		containers:   make(map[string]*containerAccumulator),
	}
}

// Add records a single metrics sample
func (a *Accumulator) Add(m *metrics.SystemMetrics) {
	a.sampleCount++
	if a.sampleCount == 1 {
		a.startTime = m.Timestamp
	}
	a.endTime = m.Timestamp

	a.cpuValues = append(a.cpuValues, m.CPU.TotalPercent)
	a.memBytesValues = append(a.memBytesValues, float64(m.Memory.UsedBytes))
	a.memPctValues = append(a.memPctValues, m.Memory.UsedPercent)

	for _, p := range m.TopCPU {
		a.updateProcessPeak(p)
	}
	for _, p := range m.TopMemory {
		a.updateProcessPeak(p)
	}

	// Track per-container metrics
	for name, cgroup := range m.Cgroups {
		ca, ok := a.containers[name]
		if !ok {
			ca = &containerAccumulator{}
			a.containers[name] = ca
		}
		// Only record CPU when a valid delta was computed (skip first sample and underflow)
		if cgroup.CPU.HasDelta {
			ca.cpuCoresValues = append(ca.cpuCoresValues, cgroup.CPU.UsedCores)
		}
		ca.memoryBytesValues = append(ca.memoryBytesValues, float64(cgroup.Memory.TotalRSSBytes))
	}
}

// Summarize computes and returns the run summary, or nil if no samples were added
func (a *Accumulator) Summarize() *RunSummary {
	if a.sampleCount == 0 {
		return nil
	}

	return &RunSummary{
		StartTime:       a.startTime,
		EndTime:         a.endTime,
		DurationSeconds: a.endTime.Sub(a.startTime).Seconds(),
		SampleCount:     a.sampleCount,
		CPUTotal:        computeStats(a.cpuValues),
		MemUsedBytes:    computeStats(a.memBytesValues),
		MemUsedPercent:  computeStats(a.memPctValues),
		TopCPUProcesses: a.topProcesses(func(p *ProcessPeak) float64 { return p.PeakCPU }),
		TopMemProcesses: a.topProcesses(func(p *ProcessPeak) float64 { return float64(p.PeakMem) }),
		Containers:      a.containerSummaries(),
	}
}

// containerSummaries computes summaries for all tracked containers
func (a *Accumulator) containerSummaries() []ContainerSummary {
	summaries := make([]ContainerSummary, 0, len(a.containers))
	for name, ca := range a.containers {
		summaries = append(summaries, ContainerSummary{
			Name:        name,
			CPUCores:    computeStats(ca.cpuCoresValues),
			MemoryBytes: computeStats(ca.memoryBytesValues),
		})
	}
	// Sort by name for consistent output
	sort.Slice(summaries, func(i, j int) bool {
		return summaries[i].Name < summaries[j].Name
	})
	return summaries
}

// SampleCount returns the number of samples added
func (a *Accumulator) SampleCount() int {
	return a.sampleCount
}

// computeStats calculates peak, percentiles (p99, p95, p75, p50), and average from a sorted copy of the values
func computeStats(values []float64) StatSummary {
	n := len(values)
	if n == 0 {
		return StatSummary{}
	}

	sorted := make([]float64, n)
	copy(sorted, values)
	sort.Float64s(sorted)

	var sum float64
	for _, v := range sorted {
		sum += v
	}

	return StatSummary{
		Peak: sorted[n-1],
		P99:  sorted[percentileIndex(n, 0.99)],
		P95:  sorted[percentileIndex(n, 0.95)],
		P75:  sorted[percentileIndex(n, 0.75)],
		P50:  sorted[percentileIndex(n, 0.50)],
		Avg:  sum / float64(n),
	}
}

// percentileIndex returns the index for the given percentile (0.0-1.0)
func percentileIndex(n int, percentile float64) int {
	return int(float64(n-1) * percentile)
}

// updateProcessPeak merges a process observation into the peak tracking map
func (a *Accumulator) updateProcessPeak(p metrics.ProcessMetrics) {
	key := fmt.Sprintf("%d:%s", p.PID, p.Name)
	existing, ok := a.processPeaks[key]
	if !ok {
		a.processPeaks[key] = &ProcessPeak{
			PID:     p.PID,
			Name:    p.Name,
			PeakCPU: p.CPUPercent,
			PeakMem: p.MemRSS,
		}
		return
	}
	if p.CPUPercent > existing.PeakCPU {
		existing.PeakCPU = p.CPUPercent
	}
	if p.MemRSS > existing.PeakMem {
		existing.PeakMem = p.MemRSS
	}
}

// topProcesses returns the top N processes sorted by the given key function (descending)
func (a *Accumulator) topProcesses(keyFn func(*ProcessPeak) float64) []ProcessPeak {
	all := make([]ProcessPeak, 0, len(a.processPeaks))
	for _, p := range a.processPeaks {
		all = append(all, *p)
	}

	sort.Slice(all, func(i, j int) bool {
		return keyFn(&all[i]) > keyFn(&all[j])
	})

	if len(all) > a.topN {
		all = all[:a.topN]
	}
	return all
}