Containerized Runtime Provisioning

Deploying a telemetry architecture for OpenTenBase requires a stable container orchestration environment. Initialize the Docker engine on a CentOS-based host by removing legacy installations, configuring repository sources, and activating the daemon.

sudo yum remove -y docker-*
sudo yum install -y yum-utils
sudo yum-config-manager --add-repo https://mirrors.aliyun.com/docker-ce/linux/centos/docker-ce.repo
sudo yum makecache fast
sudo yum install -y docker-ce
sudo systemctl enable --now docker

Optimize container pull speeds by defining a registry mirror in /etc/docker/daemon.json. Reload the systemd manager and restart the Docker service to apply the network configuration.

Metrics Aggregation with Prometheus

Deploy the time-series database as a persistent container. Map a host directory to the internal configuration path to enable dynamic service discovery without daemon restarts.

docker run -d \
  --name otb-metrics-server \
  -p 9090:9090 \
  --restart unless-stopped \
  --mount type=bind,source=/opt/monitoring/prometheus,target=/etc/prometheus \
  prom/prometheus:latest

Modify the primary configuration file prometheus.yml to implement file-based target discovery. This approach allows dynamic node additions through JSON manifests.

global:
  scrape_interval: 20s
  evaluation_interval: 20s
scrape_configs:
  - job_name: otb_cluster_nodes
    file_sd_configs:
      - files:
          - /etc/prometheus/targets/*.json
        refresh_interval: 15s

Create a corresponding discovery manifest in the specified directory. Replace placeholder IP addresses with actual OpenTenBase coordinator or data node endpoints.

[
  {
    "targets": ["192.168.10.5:9090"],
    "labels": {
      "instance_role": "primary_coordinator",
      "datacenter": "zone-a"
    }
  }
]

Visualization Engine Deployment

Initialize the dashboard framework using a named Docker volume to persist user configurations, plugins, and panel definitions across container lifecycle events.

docker run -d \
  --name grafana-visualization \
  -p 3000:3000 \
  --restart always \
  --mount type=volume,source=grafana_state,target=/var/lib/grafana \
  grafana/grafana:latest

Access the web console via port 3000, authenticate with default credentials, and navigate to the configuration panel. Register a new data source pointing to http://<prometheus-ip>:9090 to establish the ingestion bridge.

Database Telemetry Extraction

Run the community PostgreSQL exporter containerized with host networking to capture low-latency database metrics. Pass the connection string via environment variables and disable SSL verification for internal cluster communication.

docker run --net=host -d \
  --name db-telemetry-agent \
  -e DATA_SOURCE_NAME="postgresql://telemetry_user:strong_secret@192.168.10.5:5432/postgres?sslmode=disable" \
  quay.io/prometheuscommunity/postgres-exporter

Provision a dedicated, minimally privileged database account to grant the exporter read access to system catalogs. Execute the following SQL block within the OpenTenBase instance:

DO $$
BEGIN
  IF NOT EXISTS (SELECT 1 FROM pg_roles WHERE rolname = 'telemetry_user') THEN
    CREATE ROLE telemetry_user LOGIN PASSWORD 'strong_secret';
  END IF;
END
$$;

ALTER ROLE telemetry_user SET search_path = pg_catalog, pg_monitor;
GRANT CONNECT ON DATABASE postgres TO telemetry_user;
GRANT pg_monitor TO telemetry_user;

Resolving Configuration Parsing Exceptions

Legacy exporter binaries may encounter a fatal panic when processing configuration parameters containing non-numeric suffixes (e.g., session_memory_size = 3M). The metric serialization layer fails to parse these strings into float64 representations during the scrape cycle. Mitigate this issue by upgrading to a patched release or applying a query filter to exclude problematic system settings from the collection routine.

Dashboard Template Provisioning

Navigate to the community marketplace and locate a PostgreSQL-optimized visualization template. Extract the numeric identifier (e.g., 9628), paste it into the import interface, and bind the panel to the configured Prometheus data source. The system will automatically render query throughput, connection pool utilization, replication lag, and storage I/O metrics for real-time cluster assessment.