feat(db): implement PostgreSQL monitoring and maintenance features

Added PostgreSQL monitoring and maintenance capabilities to the API, including new endpoints for instance-level metrics, maintenance operations, and job scheduling. Updated the HTTP API to support PostgreSQL monitoring routes and integrated a background scheduler for metrics collection. Enhanced the CLI with database commands for maintenance tasks. Updated documentation to reflect these changes.

internal/pgmonitor/recommendations.go

@@ -0,0 +1,104 @@
+package pgmonitor
+
+import (
+	"context"
+	"time"
+)
+
+// Recommendations builds heuristic items from live stats and snapshots.
+func (s *Service) Recommendations(ctx context.Context) (RecommendationsResponse, error) {
+	now := time.Now().UTC()
+	var items []RecommendationItem
+
+	ov, err := s.Overview(ctx)
+	if err == nil {
+		if ov.Database.CacheHitPct > 0 && ov.Database.CacheHitPct < 90 {
+			items = append(items, RecommendationItem{
+				Severity: "warn",
+				Code:     "low_cache_hit",
+				Title:    "Низкий cache hit ratio",
+				Detail:   "Buffer cache hit ниже 90%; проверьте shared_buffers и горячие seq scan.",
+			})
+		}
+		if ov.Database.Deadlocks > 0 {
+			items = append(items, RecommendationItem{
+				Severity: "warn",
+				Code:     "deadlocks",
+				Title:    "Зафиксированы deadlocks",
+				Detail:   "Проверьте конкурирующие транзакции и порядок блокировок.",
+			})
+		}
+		if ov.Connections.MaxConnections > 0 &&
+			float64(ov.Connections.Total)/float64(ov.Connections.MaxConnections) > 0.8 {
+			items = append(items, RecommendationItem{
+				Severity: "critical",
+				Code:     "connections_high",
+				Title:    "Много подключений к PostgreSQL",
+				Detail:   "Использование max_connections выше 80%; увеличьте pool tuning или лимит.",
+			})
+		}
+	}
+
+	tables, err := s.Tables(ctx, 30)
+	if err == nil {
+		for _, t := range tables {
+			if t.SeqScan > 1000 && t.IdxScan < t.SeqScan/10 {
+				items = append(items, RecommendationItem{
+					Severity: "warn",
+					Code:     "missing_index",
+					Title:    "Высокий seq_scan",
+					Detail:   "Таблица часто сканируется последовательно; рассмотрите индекс.",
+					Refs:     []string{t.Relname},
+				})
+			}
+			if t.BloatRatio > 0.2 && t.DeadTuples > 5000 {
+				items = append(items, RecommendationItem{
+					Severity: "info",
+					Code:     "autovacuum_lag",
+					Title:    "Возможный bloat / мёртвые строки",
+					Detail:   "Высокая доля n_dead_tup; запланируйте VACUUM.",
+					Refs:     []string{t.Relname},
+				})
+			}
+		}
+	}
+
+	if snap, ok, _ := s.loadSnapshot(ctx, "unused_indexes", 30*time.Minute); ok {
+		type unused struct {
+			Index     string `json:"index"`
+			SizeBytes int64  `json:"size_bytes"`
+		}
+		var list []unused
+		if decodePayload(snap.Payload, &list) == nil {
+			for _, u := range list {
+				if u.SizeBytes < 1024*1024 {
+					continue
+				}
+				items = append(items, RecommendationItem{
+					Severity: "info",
+					Code:     "unused_index",
+					Title:    "Неиспользуемый индекс",
+					Detail:   "idx_scan=0; проверьте перед удалением.",
+					Refs:     []string{u.Index},
+				})
+			}
+		}
+	}
+
+	q, err := s.TopQueries(ctx, 5)
+	if err == nil {
+		for _, qs := range q.Items {
+			if qs.MeanExecMs > 500 {
+				items = append(items, RecommendationItem{
+					Severity: "warn",
+					Code:     "slow_query",
+					Title:    "Медленный запрос",
+					Detail:   "Среднее время выполнения выше 500ms.",
+					Refs:     []string{qs.Query},
+				})
+			}
+		}
+	}
+
+	return RecommendationsResponse{CollectedAt: now, Items: items}, nil
+}