Production Monitoring

To monitor the pingcap/autoflow project in a production environment, various techniques and tools can be utilized. This section outlines the methods applicable to the components of the project, including logging configurations, environment settings, and key component monitoring.

Docker Configuration

The production setup utilizes Docker containers for each service, which can be monitored using the logs generated from each container. The docker-compose.yml file defines services including backend, frontend, background, redis, and local-embedding-reranker. Each service is configured with logging options to facilitate monitoring.

  backend:
    image: tidbai/backend:0.2.8
    logging:
      driver: json-file
      options:
        max-size: "50m"
        max-file: "6"

  frontend:
    image: tidbai/frontend:0.2.8
    logging:
      driver: json-file
      options:
        max-size: "50m"
        max-file: "6"

The above configuration ensures that logs are retained based on size (50 MB) and file count (6), preventing logs from consuming too much disk space.

Accessing Logs

To access the logs of a specific service in a running Docker environment, use the following commands. Replace {service} with the actual service name (e.g., backend, frontend).

docker-compose logs {service}

For live log streaming, the following command can be utilized:

docker-compose logs -f {service}

Monitoring log files can provide insights into service performance and errors in real-time.

Port Monitoring

The application services expose specific ports, which can be monitored for availability, latency, and error rates.

For example, the frontend service exposes port 3000 as defined in the docker-compose.yml:

  frontend:
    ports:
      - 3000:3000

To check if the service is up and responsive, tools like curl or monitoring solutions can be used:

curl -I http://localhost:3000

A 200 HTTP status code indicates that the service is running correctly.

Health Checks

Although health checks are not explicitly defined in the provided configuration, incorporating health checks is crucial in a production environment. This can involve adding health check endpoints within the application or utilizing Docker’s built-in health check functionality.

An example health check definition within a Dockerfile:

HEALTHCHECK --interval=30s --timeout=3s CMD curl -f http://localhost:3000/health || exit 1

This command can be added to the Dockerfile in the service definition to help monitor the service health.

Resource Monitoring

To ensure each service operates under optimal performance, resource monitoring is necessary. Techniques can include tracking memory and CPU usage.

Using docker stats can provide real-time usage statistics:

docker stats

This command offers insights into how much memory and CPU each container is consuming, which helps identify potential bottlenecks.

Monitoring Infrastructure

In addition to container monitoring, it’s essential to monitor the underlying infrastructure. Tools such as Prometheus or Grafana could be employed to visualize metrics collected from your services and the Docker containers running them. A well-defined metrics endpoint in your application can facilitate this monitoring.

Conclusion

By employing logging features, port monitoring, health checks, resource monitoring, and utilizing monitoring tools, you can effectively observe the behavior and performance of the pingcap/autoflow project in a production environment.

Source: File configurations and practices derived from the docker-compose.yml and Dockerfile.