TheTree_mock/scripts/core/performance_budget.gd

class_name SimulationPerformanceBudget
extends RefCounted

## Lightweight runtime profiler with explicit integer-microsecond samples.
## It is diagnostic state only and deliberately stays outside Save snapshots.

signal budget_exceeded(metric_id: StringName, report: Dictionary)

enum Aggregation {
	AVERAGE,
	P95,
	MAXIMUM,
}

const DEFAULT_SAMPLE_CAPACITY := 512

const DEFAULT_BUDGETS := {
	&"simulation_x1": {&"aggregation": Aggregation.AVERAGE, &"limit_us": 4_000},
	&"simulation_x3": {&"aggregation": Aggregation.P95, &"limit_us": 12_000},
	&"path_query": {&"aggregation": Aggregation.P95, &"limit_us": 2_000},
	&"main_thread_stall": {&"aggregation": Aggregation.MAXIMUM, &"limit_us": 50_000},
	&"save": {&"aggregation": Aggregation.MAXIMUM, &"limit_us": 5_000_000},
	&"load": {&"aggregation": Aggregation.MAXIMUM, &"limit_us": 8_000_000},
}

var sample_capacity := DEFAULT_SAMPLE_CAPACITY
var _budgets: Dictionary = {}
var _samples: Dictionary = {}
var _totals: Dictionary = {}
var _sample_counts: Dictionary = {}


func _init(capacity := DEFAULT_SAMPLE_CAPACITY) -> void:
	sample_capacity = maxi(int(capacity), 1)
	for raw_metric_id: Variant in DEFAULT_BUDGETS:
		_budgets[StringName(raw_metric_id)] = (
			DEFAULT_BUDGETS[raw_metric_id] as Dictionary
		).duplicate(true)


func set_budget(
	metric_id: StringName,
	limit_us: int,
	aggregation: Aggregation = Aggregation.P95
) -> bool:
	if metric_id.is_empty() or limit_us <= 0 \
		or aggregation < Aggregation.AVERAGE or aggregation > Aggregation.MAXIMUM:
		return false
	_budgets[metric_id] = {
		&"aggregation": aggregation,
		&"limit_us": limit_us,
	}
	return true


func record_sample(metric_id: StringName, duration_us: int) -> Dictionary:
	if metric_id.is_empty() or duration_us < 0:
		return {&"ok": false, &"error": "Metric ID and non-negative duration are required."}
	if not _samples.has(metric_id):
		_samples[metric_id] = []
	var values: Array = _samples[metric_id]
	values.append(duration_us)
	if values.size() > sample_capacity:
		values.pop_front()
	_totals[metric_id] = int(_totals.get(metric_id, 0)) + duration_us
	_sample_counts[metric_id] = int(_sample_counts.get(metric_id, 0)) + 1
	var report := get_report(metric_id)
	if bool(report.get(&"budget_exceeded", false)):
		budget_exceeded.emit(metric_id, report.duplicate(true))
	return {&"ok": true, &"report": report}


func record_simulation_tick(duration_us: int, speed: int) -> Dictionary:
	var metric_id := &"simulation_x3" if speed == 3 else &"simulation_x1"
	return record_sample(metric_id, duration_us)


## Records the full main-thread cost of one rendered/process frame, including
## all game-minute ticks consumed at accelerated speed. This keeps x3 from
## accidentally reporting four individually-fast ticks while the frame stalls.
func record_simulation_frame(duration_us: int, speed: int, ticks_run: int) -> Dictionary:
	if ticks_run <= 0:
		return {&"ok": false, &"error": "At least one completed tick is required."}
	var simulation_result := record_simulation_tick(duration_us, speed)
	var stall_result := record_sample(&"main_thread_stall", duration_us)
	return {
		&"ok": bool(simulation_result[&"ok"]) and bool(stall_result[&"ok"]),
		&"ticks_run": ticks_run,
		&"simulation": simulation_result,
		&"main_thread": stall_result,
	}


func get_report(metric_id: StringName) -> Dictionary:
	var values: Array = _samples.get(metric_id, [])
	var count := values.size()
	var lifetime_count := int(_sample_counts.get(metric_id, 0))
	var lifetime_total := int(_totals.get(metric_id, 0))
	var average_us := lifetime_total / lifetime_count if lifetime_count > 0 else 0
	var maximum_us := 0
	for raw_value: Variant in values:
		maximum_us = maxi(maximum_us, int(raw_value))
	var p95_us := _percentile(values, 95)
	var budget: Dictionary = _budgets.get(metric_id, {})
	var aggregation := int(budget.get(&"aggregation", Aggregation.P95))
	var measured_us := _aggregated_value(aggregation, average_us, p95_us, maximum_us)
	var limit_us := int(budget.get(&"limit_us", 0))
	return {
		&"metric_id": metric_id,
		&"window_sample_count": count,
		&"lifetime_sample_count": lifetime_count,
		&"average_us": average_us,
		&"p95_us": p95_us,
		&"maximum_us": maximum_us,
		&"aggregation": aggregation,
		&"measured_us": measured_us,
		&"limit_us": limit_us,
		&"budget_exceeded": limit_us > 0 and measured_us > limit_us,
	}


func get_all_reports() -> Array[Dictionary]:
	var metric_ids: Array[StringName] = []
	for raw_metric_id: Variant in _budgets:
		metric_ids.append(StringName(raw_metric_id))
	for raw_metric_id: Variant in _samples:
		var metric_id := StringName(raw_metric_id)
		if not metric_ids.has(metric_id):
			metric_ids.append(metric_id)
	metric_ids.sort_custom(func(a: StringName, b: StringName) -> bool:
		return String(a) < String(b)
	)
	var reports: Array[Dictionary] = []
	for metric_id: StringName in metric_ids:
		reports.append(get_report(metric_id))
	return reports


func clear(metric_id: StringName = &"") -> void:
	if metric_id.is_empty():
		_samples.clear()
		_totals.clear()
		_sample_counts.clear()
		return
	_samples.erase(metric_id)
	_totals.erase(metric_id)
	_sample_counts.erase(metric_id)


func _aggregated_value(
	aggregation: int,
	average_us: int,
	p95_us: int,
	maximum_us: int
) -> int:
	match aggregation:
		Aggregation.AVERAGE:
			return average_us
		Aggregation.MAXIMUM:
			return maximum_us
		_:
			return p95_us


func _percentile(raw_values: Array, percentile: int) -> int:
	if raw_values.is_empty():
		return 0
	var values: Array[int] = []
	for raw_value: Variant in raw_values:
		values.append(int(raw_value))
	values.sort()
	var rank_numerator := clampi(percentile, 0, 100) * values.size()
	var index := maxi((rank_numerator + 99) / 100 - 1, 0)
	return values[mini(index, values.size() - 1)]