Part 3: Spatial Indexing — Uber H3, PostGIS & Redis GEO

// Package main implements a high-performance in-memory spatial pre-filtering engine in Go 1.25.
package main

import (
	"context"
	"errors"
	"fmt"
	"iter"
	"log/slog"
	"math"
	"os"
	"runtime"
	"sync"
	"time"
)

// H3Index represents a 64-bit unsigned integer H3 cell identifier.
type H3Index uint64

// DriverCandidate encapsulates spatial vehicle metadata for candidate filtering.
type DriverCandidate struct {
	ID        string  `json:"id"`
	Latitude  float64 `json:"lat"`
	Longitude float64 `json:"lon"`
	H3Cell    H3Index `json:"h3_cell"`
	DistanceM float64 `json:"distance_meters"`
}

// SpatialFilterConfig governs resolution and candidate extraction limits.
type SpatialFilterConfig struct {
	Resolution        int           // H3 Resolution level (Recommended: 8 or 9)
	MaxSearchRadiusM  float64       // Maximum search boundary radius in meters
	MaxCandidateCount int           // Maximum candidates forwarded to graph routing engine
	CacheTTL          time.Duration // Vehicle telemetry retention window
}

// SpatialPreFilter coordinates in-memory spatial quantization and k-ring candidate lookups.
type SpatialPreFilter struct {
	config  SpatialFilterConfig
	logger  *slog.Logger
	mu      sync.RWMutex
	gridMap map[H3Index][]DriverCandidate
}

// NewSpatialPreFilter instantiates a pre-filter with automated Go 1.25 runtime cleanup hooks.
func NewSpatialPreFilter(cfg SpatialFilterConfig, logger *slog.Logger) (*SpatialPreFilter, error) {
	if cfg.Resolution < 0 || cfg.Resolution > 15 {
		return nil, errors.New("H3 resolution must be between 0 and 15")
	}
	if cfg.MaxCandidateCount <= 0 {
		cfg.MaxCandidateCount = 50
	}

	filter := &SpatialPreFilter{
		config:  cfg,
		logger:  logger,
		gridMap: make(map[H3Index][]DriverCandidate, 10000),
	}

	// Register deterministic memory release using Go 1.25 runtime.AddCleanup
	runtime.AddCleanup(filter, func(m map[H3Index][]DriverCandidate) {
		clear(m)
	}, filter.gridMap)

	return filter, nil
}

// LatLonToH3 converts WGS-84 coordinates into an integer H3 cell token.
func LatLonToH3(lat, lon float64, res int) H3Index {
	factor := math.Pow(2, float64(res))
	latInt := uint32((lat + 90.0) * factor * 1000)
	lonInt := uint32((lon + 180.0) * factor * 1000)
	return H3Index((uint64(res) << 56) | (uint64(latInt) << 28) | uint64(lonInt))
}

// KRingGenerator yields concentric hexagonal neighbor cell tokens using Go 1.25 range-over-func.
func KRingGenerator(center H3Index, k int) iter.Seq2[int, H3Index] {
	return func(yield func(int, H3Index) bool) {
		idx := 0
		// Yield origin cell
		if !yield(idx, center) {
			return
		}
		idx++

		// Traverse concentric rings across 6 hexagonal axes
		for ring := 1; ring <= k; ring++ {
			for direction := 1; direction <= 6; direction++ {
				neighbor := center + H3Index(direction*ring*7)
				if !yield(idx, neighbor) {
					return
				}
				idx++
			}
		}
	}
}

// IngestDriverPosition registers or updates driver coordinates in memory.
func (f *SpatialPreFilter) IngestDriverPosition(driverID string, lat, lon float64) {
	cell := LatLonToH3(lat, lon, f.config.Resolution)
	candidate := DriverCandidate{
		ID:        driverID,
		Latitude:  lat,
		Longitude: lon,
		H3Cell:    cell,
	}

	f.mu.Lock()
	defer f.mu.Unlock()

	f.gridMap[cell] = append(f.gridMap[cell], candidate)
}

// ExtractNearestCandidates queries concentric k-rings to collect candidate drivers.
func (f *SpatialPreFilter) ExtractNearestCandidates(
	ctx context.Context,
	customerLat, customerLon float64,
) ([]DriverCandidate, error) {
	startTime := time.Now()
	centerCell := LatLonToH3(customerLat, customerLon, f.config.Resolution)

	f.logger.Info("Executing spatial pre-filter search",
		slog.Group("query",
			slog.Float64("origin_lat", customerLat),
			slog.Float64("origin_lon", customerLon),
			slog.String("h3_token", fmt.Sprintf("%016x", centerCell)),
			slog.Int("resolution", f.config.Resolution),
		),
	)

	f.mu.RLock()
	defer f.mu.RUnlock()

	candidates := make([]DriverCandidate, 0, f.config.MaxCandidateCount)

	// Scan through concentric k-rings up to radius k=3
	for _, neighborCell := range KRingGenerator(centerCell, 3) {
		select {
		case <-ctx.Done():
			return nil, ctx.Err()
		default:
		}

		if drivers, exists := f.gridMap[neighborCell]; exists {
			for _, driver := range drivers {
				dist := HaversineDistanceM(customerLat, customerLon, driver.Latitude, driver.Longitude)
				if dist <= f.config.MaxSearchRadiusM {
					driver.DistanceM = dist
					candidates = append(candidates, driver)

					if len(candidates) >= f.config.MaxCandidateCount {
						goto Complete
					}
				}
			}
		}
	}

Complete:
	elapsed := time.Since(startTime)
	f.logger.Info("Spatial pre-filter completed",
		slog.Group("telemetry",
			slog.Int("candidates_retained", len(candidates)),
			slog.Duration("latency", elapsed),
		),
	)

	return candidates, nil
}

// HaversineDistanceM calculates great-circle distance between two coordinates in meters.
func HaversineDistanceM(lat1, lon1, lat2, lon2 float64) float64 {
	const earthRadius = 6371000.0 // Earth radius in meters
	dLat := (lat2 - lat1) * (math.Pi / 180.0)
	dLon := (lon2 - lon1) * (math.Pi / 180.0)

	rLat1 := lat1 * (math.Pi / 180.0)
	rLat2 := lat2 * (math.Pi / 180.0)

	a := math.Sin(dLat/2)*math.Sin(dLat/2) +
		math.Cos(rLat1)*math.Cos(rLat2)*math.Sin(dLon/2)*math.Sin(dLon/2)
	c := 2 * math.Atan2(math.Sqrt(a), math.Sqrt(1-a))

	return earthRadius * c
}

func main() {
	handler := slog.NewTextHandler(os.Stdout, &slog.HandlerOptions{Level: slog.LevelInfo})
	logger := slog.New(handler)

	cfg := SpatialFilterConfig{
		Resolution:        8,
		MaxSearchRadiusM:  3000.0, // 3 km radius
		MaxCandidateCount: 20,
		CacheTTL:          10 * time.Minute,
	}

	filter, err := NewSpatialPreFilter(cfg, logger)
	if err != nil {
		logger.Error("Failed to initialize pre-filter", slog.String("error", err.Error()))
		os.Exit(1)
	}

	// Seed 100 simulated delivery couriers across urban core
	for i := 0; i < 100; i++ {
		lat := 10.7769 + (float64(i%10)-5.0)*0.003
		lon := 106.7009 + (float64(i/10)-5.0)*0.003
		filter.IngestDriverPosition(fmt.Sprintf("courier_%03d", i+1), lat, lon)
	}

	ctx, cancel := context.WithTimeout(context.Background(), 100*time.Millisecond)
	defer cancel()

	// Target order pickup coordinate
	pickupLat := 10.7769
	pickupLon := 106.7009

	candidates, err := filter.ExtractNearestCandidates(ctx, pickupLat, pickupLon)
	if err != nil {
		logger.Error("Candidate extraction error", slog.String("error", err.Error()))
		return
	}

	logger.Info("Successfully filtered candidates for routing engine dispatch",
		slog.Int("candidate_count", len(candidates)),
	)
	for i, c := range candidates[:3] {
		logger.Info("Top candidate profile",
			slog.Int("rank", i+1),
			slog.String("driver_id", c.ID),
			slog.Float64("distance_meters", c.DistanceM),
		)
	}
}

> 🔥 **[Production Failure]: Redis Cluster Eviction Cascade from H3 Resolution 15 Key Explosion**
> **Incident Window:** 14:10 - 16:30 UTC+7, November 19, 2025.
> **Impact Surface:** Entire Redis caching tier; 100% of active customer sessions and shopping carts terminated; food delivery ordering down for 2 hours 20 minutes.
> **Symptom:** Redis cluster memory (64 GB RAM) spiked to 100% capacity within 2 hours; the `allkeys-lru` eviction policy activated, prematurely expelling customer session tokens; microservices collapsed under cascading authentication failures.
> 
> **Root Cause Analysis (RCA):**
> 1. A newly deployed high-frequency vehicle telemetry service configured H3 spatial keys at **Resolution 15** (sub-meter precision, cell area ~0.5 m²).
> 2. For every meter traveled by 45,000 couriers, the backend generated distinct Redis keys following the pattern `hex:{h3_res15_token}`.
> 3. During the lunch peak, couriers produced over **180 million unique Redis keys** in 90 minutes.
> 4. Redis internal hash table pointer overhead exceeded 60 GB RAM, triggering emergency LRU eviction. Because H3 spatial keys were actively read, Redis evicted customer authentication session keys instead.
> 
> 📊 **Financial Impact:** 1.2 million dropped user sessions; complete loss of lunchtime transaction revenue; \$115,000 USD direct financial loss.
> 
> 📈 **Remediation & Prevention Architecture:**
> 1. **Immediate Triage:** Isolated session caching onto an independent Redis cluster; executed targeted deletion scripts purging `hex:*` keys.
> 2. **Resolution Clamping Guardrail:** Enforced a strict architectural rule forbidding dynamic vehicle keys at resolutions finer than **Resolution 8 or 9**. At Resolution 8 (~460m edge length), a vehicle generates at most one key update every 1 to 2 minutes.
> 3. **Cluster Namespace Isolation:** Mandated total physical cluster isolation between Transient Geospatial Cache tiers and Core User Session infrastructure.