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//go:build !(js && wasm)
package ratelimit
import (
"runtime"
"sync"
"sync/atomic"
"time"
"github.com/dgraph-io/badger/v4"
"next.orly.dev/pkg/interfaces/loadmonitor"
)
// BadgerMonitor implements loadmonitor.Monitor for the Badger database.
// It collects metrics from Badger's LSM tree, caches, and Go runtime.
type BadgerMonitor struct {
db *badger.DB
// Target memory for pressure calculation
targetMemoryBytes atomic.Uint64
// Latency tracking with exponential moving average
queryLatencyNs atomic.Int64
writeLatencyNs atomic.Int64
latencyAlpha float64 // EMA coefficient (default 0.1)
// Cached metrics (updated by background goroutine)
metricsLock sync.RWMutex
cachedMetrics loadmonitor.Metrics
lastL0Tables int
lastL0Score float64
// Background collection
stopChan chan struct{}
stopped chan struct{}
interval time.Duration
}
// Compile-time check that BadgerMonitor implements loadmonitor.Monitor
var _ loadmonitor.Monitor = (*BadgerMonitor)(nil)
// NewBadgerMonitor creates a new Badger load monitor.
// The updateInterval controls how often metrics are collected (default 100ms).
func NewBadgerMonitor(db *badger.DB, updateInterval time.Duration) *BadgerMonitor {
if updateInterval <= 0 {
updateInterval = 100 * time.Millisecond
}
m := &BadgerMonitor{
db: db,
latencyAlpha: 0.1, // 10% new, 90% old for smooth EMA
stopChan: make(chan struct{}),
stopped: make(chan struct{}),
interval: updateInterval,
}
// Set a default target (1.5GB)
m.targetMemoryBytes.Store(1500 * 1024 * 1024)
return m
}
// GetMetrics returns the current load metrics.
func (m *BadgerMonitor) GetMetrics() loadmonitor.Metrics {
m.metricsLock.RLock()
defer m.metricsLock.RUnlock()
return m.cachedMetrics
}
// RecordQueryLatency records a query latency sample using exponential moving average.
func (m *BadgerMonitor) RecordQueryLatency(latency time.Duration) {
ns := latency.Nanoseconds()
for {
old := m.queryLatencyNs.Load()
if old == 0 {
if m.queryLatencyNs.CompareAndSwap(0, ns) {
return
}
continue
}
// EMA: new = alpha * sample + (1-alpha) * old
newVal := int64(m.latencyAlpha*float64(ns) + (1-m.latencyAlpha)*float64(old))
if m.queryLatencyNs.CompareAndSwap(old, newVal) {
return
}
}
}
// RecordWriteLatency records a write latency sample using exponential moving average.
func (m *BadgerMonitor) RecordWriteLatency(latency time.Duration) {
ns := latency.Nanoseconds()
for {
old := m.writeLatencyNs.Load()
if old == 0 {
if m.writeLatencyNs.CompareAndSwap(0, ns) {
return
}
continue
}
// EMA: new = alpha * sample + (1-alpha) * old
newVal := int64(m.latencyAlpha*float64(ns) + (1-m.latencyAlpha)*float64(old))
if m.writeLatencyNs.CompareAndSwap(old, newVal) {
return
}
}
}
// SetMemoryTarget sets the target memory limit in bytes.
func (m *BadgerMonitor) SetMemoryTarget(bytes uint64) {
m.targetMemoryBytes.Store(bytes)
}
// Start begins background metric collection.
func (m *BadgerMonitor) Start() <-chan struct{} {
go m.collectLoop()
return m.stopped
}
// Stop halts background metric collection.
func (m *BadgerMonitor) Stop() {
close(m.stopChan)
<-m.stopped
}
// collectLoop periodically collects metrics from Badger.
func (m *BadgerMonitor) collectLoop() {
defer close(m.stopped)
ticker := time.NewTicker(m.interval)
defer ticker.Stop()
for {
select {
case <-m.stopChan:
return
case <-ticker.C:
m.updateMetrics()
}
}
}
// updateMetrics collects current metrics from Badger and runtime.
func (m *BadgerMonitor) updateMetrics() {
if m.db == nil || m.db.IsClosed() {
return
}
metrics := loadmonitor.Metrics{
Timestamp: time.Now(),
}
// Calculate memory pressure from Go runtime
var memStats runtime.MemStats
runtime.ReadMemStats(&memStats)
targetBytes := m.targetMemoryBytes.Load()
if targetBytes > 0 {
// Use HeapAlloc as primary memory metric
// This represents the actual live heap objects
metrics.MemoryPressure = float64(memStats.HeapAlloc) / float64(targetBytes)
}
// Get Badger LSM tree information for write load
levels := m.db.Levels()
var l0Tables int
var maxScore float64
for _, level := range levels {
if level.Level == 0 {
l0Tables = level.NumTables
}
if level.Score > maxScore {
maxScore = level.Score
}
}
// Calculate write load based on L0 tables and compaction score
// L0 tables stall at NumLevelZeroTablesStall (default 16)
// We consider write pressure high when approaching that limit
const l0StallThreshold = 16
l0Load := float64(l0Tables) / float64(l0StallThreshold)
if l0Load > 1.0 {
l0Load = 1.0
}
// Compaction score > 1.0 means compaction is needed
// We blend L0 tables and compaction score for write load
compactionLoad := maxScore / 2.0 // Score of 2.0 = fully loaded
if compactionLoad > 1.0 {
compactionLoad = 1.0
}
// Blend: 60% L0 (immediate backpressure), 40% compaction score
metrics.WriteLoad = 0.6*l0Load + 0.4*compactionLoad
// Calculate read load from cache metrics
blockMetrics := m.db.BlockCacheMetrics()
indexMetrics := m.db.IndexCacheMetrics()
var blockHitRatio, indexHitRatio float64
if blockMetrics != nil {
blockHitRatio = blockMetrics.Ratio()
}
if indexMetrics != nil {
indexHitRatio = indexMetrics.Ratio()
}
// Average cache hit ratio (0 = no hits = high load, 1 = all hits = low load)
avgHitRatio := (blockHitRatio + indexHitRatio) / 2.0
// Invert: low hit ratio = high read load
// Use 0.5 as the threshold (below 50% hit ratio is concerning)
if avgHitRatio < 0.5 {
metrics.ReadLoad = 1.0 - avgHitRatio*2 // 0% hits = 1.0 load, 50% hits = 0.0 load
} else {
metrics.ReadLoad = 0 // Above 50% hit ratio = minimal load
}
// Store latencies
metrics.QueryLatency = time.Duration(m.queryLatencyNs.Load())
metrics.WriteLatency = time.Duration(m.writeLatencyNs.Load())
// Update cached metrics
m.metricsLock.Lock()
m.cachedMetrics = metrics
m.lastL0Tables = l0Tables
m.lastL0Score = maxScore
m.metricsLock.Unlock()
}
// GetL0Stats returns L0-specific statistics for debugging.
func (m *BadgerMonitor) GetL0Stats() (tables int, score float64) {
m.metricsLock.RLock()
defer m.metricsLock.RUnlock()
return m.lastL0Tables, m.lastL0Score
}