Vector Database Observability

Overview

Vector database observability involves monitoring system health, performance, and data quality to ensure reliable production operations.

Key Metrics

Query Performance

• Latency: p50, p95, p99 query times
• Throughput: Queries per second (QPS)
• Recall: % of true neighbors found
• Error Rate: Failed queries

Resource Utilization

• CPU: Index build and query processing
• Memory: Index size and query buffers
• Disk I/O: For disk-based indexes
• Network: Distributed systems

Index Health

• Size: Number of vectors indexed
• Freshness: Lag between insert and searchability
• Quality: Index fragmentation, build status
• Segment Count: For segmented indexes

Monitoring Tools

Native Database Metrics

# Milvus metrics
stats = collection.get_stats()
print(f"Row count: {stats['row_count']}")
print(f"Index status: {collection.indexes}")

External Monitoring

• Datadog: Integration with Zilliz Cloud
• Prometheus: Metrics collection
• Grafana: Dashboards
• OpenTelemetry: Distributed tracing

Alerting

Critical Alerts

• Query latency > threshold
• Error rate spike
• Memory exhaustion
• Index build failures

Warning Alerts

• Slow trend in recall
• Increasing latency
• Resource approaching limits

Logging

Query Logs

{
  "timestamp": "2024-01-15T10:30:00Z",
  "query_id": "q123",
  "collection": "documents",
  "latency_ms": 45,
  "results_count": 10,
  "filters": {"category": "tech"}
}

Error Logs

• Failed queries
• Index build errors
• Resource exhaustion

Best Practices

1. Dashboard: Real-time metrics visualization
2. SLOs: Define service level objectives
3. Runbooks: Document issue resolution
4. Regular Review: Analyze trends
5. Capacity Planning: Predict resource needs

Pricing

Monitoring tool costs (Datadog, Prometheus hosting, etc.).