Monitoring & Observability in Search Engine Databases

1. Monitoring

Monitoring refers to the continuous observation of system performance and operational metrics.

2. Observability

Observability extends monitoring by providing insights into system operation through logs, metrics, and traces.

3. Metrics

Metrics are quantitative measures that help assess performance, such as query response times and resource utilization.

4. Logs

Logs are records of events and transactions, providing contextual information about system behavior.

5. Traces

Traces represent the journey of a request through the system, helping identify latency and bottlenecks.

1. Performance Metrics Collection

Use tools like Prometheus or Grafana to collect and visualize performance metrics.

# Example of Prometheus config for a search engine database
scrape_configs:
  - job_name: 'search_engine'
    static_configs:
      - targets: ['localhost:9100']
            

2. Health Checks

Implement health checks to verify the availability of the search engine service.

# Example of a simple health check in Python
import requests

def health_check():
    response = requests.get("http://localhost:9200/_cluster/health")
    return response.json()
            

3. Error Rate Monitoring

Monitor error rates and alert on unexpected spikes in errors.

1. Centralized Logging

Aggregate logs from multiple sources using tools like ELK Stack or Fluentd.

2. Distributed Tracing

Implement distributed tracing with tools such as Jaeger or Zipkin to track requests across services.

3. Correlation IDs

Use correlation IDs to trace requests through the system, improving the observability of user journeys.