Icinga and Databricks Integration

Input and output integration overview

This plugin gathers services & hosts status using Icinga2 Remote API, providing an interface to monitor your infrastructure effectively.

Use Telegraf’s HTTP output plugin to push metrics straight into a Databricks Lakehouse by calling the SQL Statement Execution API with a JSON-wrapped INSERT or volume PUT command.

Integration details

Icinga

The Icinga2 Plugin enables users to gather status information from Icinga2’s Remote API. Icinga2 is a powerful monitoring system that checks the health of hosts and services and provides detailed monitoring capabilities. The plugin facilitates retrieving metrics such as the state of hosts and services, as well as detailed API status metrics. This integration is vital for users looking to keep an eye on their infrastructure’s health and performance metrics automatically, leveraging the Icinga2’s extensive API. By utilizing this plugin, users can easily integrate Icinga2 monitoring data with other systems, providing a comprehensive view of their infrastructure status.

Databricks

This configuration turns Telegraf into a lightweight ingestion agent for the Databricks Lakehouse. It leverages the Databricks SQL Statement Execution API 2.0, which accepts authenticated POST requests containing a JSON payload with a statement field. Each Telegraf flush dynamically renders a SQL INSERT (or, for file-based workflows, a PUT ... INTO /Volumes/... command) that lands the metrics into a Unity Catalog table or volume governed by Lakehouse security. Under the hood Databricks stores successful inserts as Delta Lake transactions, enabling ACID guarantees, time-travel, and scalable analytics. Operators can point the warehouse_id at any serverless or classic SQL warehouse, and all authentication is handled with a PAT or service-principal token—no agents or JDBC drivers required. Because Telegraf’s HTTP output supports custom headers, batching, TLS, and proxy settings, the same pattern scales from edge IoT gateways to container sidecars, consolidating infrastructure telemetry, application logs, or business KPIs directly into the Lakehouse for BI, ML, and Lakehouse Monitoring. Unity Catalog volumes provide a governed staging layer when file uploads and COPY INTO are preferred, and the approach aligns with Databricks’ recommended ingestion practices for partners and ISVs.

Configuration

Icinga

[[inputs.icinga2]]
  ## Required Icinga2 server address
  # server = "https://localhost:5665"

  ## Collected Icinga2 objects ("services", "hosts")
  ## Specify at least one object to collect from /v1/objects endpoint.
  # objects = ["services"]

  ## Collect metrics from /v1/status endpoint
  ## Choose from:
  ##     "ApiListener", "CIB", "IdoMysqlConnection", "IdoPgsqlConnection"
  # status = []

  ## Credentials for basic HTTP authentication
  # username = "admin"
  # password = "admin"

  ## Maximum time to receive response.
  # response_timeout = "5s"

  ## Optional TLS Config
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = true

Databricks

[[outputs.http]]
  ## Databricks SQL Statement Execution API endpoint
  url = "https://{{ env "DATABRICKS_HOST" }}/api/2.0/sql/statements"

  ## Use POST to submit each Telegraf batch as a SQL request
  method = "POST"

  ## Personal-access token (PAT) for workspace or service principal
  headers = { Authorization = "Bearer {{ env "DATABRICKS_TOKEN" }}" }

  ## Send JSON that wraps the metrics batch in a SQL INSERT (or PUT into a Volume)
  content_type = "application/json"

  ## Serialize metrics as JSON so they can be embedded in the SQL statement
  data_format = "json"
  json_timestamp_units = "1ms"

  ## Build the request body.  Telegraf replaces the template variables at runtime.
  ## Example inserts a row per metric into a Unity-Catalog table.
  body_template = """
  {
    \"statement\": \"INSERT INTO ${TARGET_TABLE} VALUES {{range .Metrics}}(from_unixtime({{.timestamp}}/1000), {{.fields.usage}}, '{{.tags.host}}'){{end}}\",
    \"warehouse_id\": \"${WAREHOUSE_ID}\"
  }
  """

  ## Optional: add batching limits or TLS settings
  # batch_size = 500
  # timeout     = "10s"

Input and output integration examples

Icinga

1. Centralized Monitoring Dashboard: Integrate the Icinga2 plugin with a visualization tool to create a centralized monitoring dashboard that presents real-time statuses of all monitored services and hosts. This setup allows teams to quickly identify issues and to respond proactively, ensuring minimal downtime.

2. Automated Incident Response: Use the metrics collected by the plugin to trigger automated incident response workflows. For instance, if a service is reported as critical, an automated system could notify relevant team members and even attempt to restart the service, reducing manual intervention and speeding resolution times.

3. Service Reliability Reporting: Combine data from the Icinga with business reporting systems to generate insights on service reliability. By analyzing trends in service states over time, organizations can identify weak points in their infrastructure and improve service availability based on factual data.

4. Cross-System Alerting: Leverage the collected metrics to integrate with various alerting systems. This could route notifications based on specific Icinga2 service states to different departments or teams depending on their roles, enabling tailored and timely responses to potential issues in the infrastructure.

Databricks

1. Edge-to-Lakehouse Telemetry Pipe: Deploy Telegraf on factory PLCs to sample vibration metrics and post them every second to a serverless SQL warehouse. Delta tables power PowerBI dashboards that alert engineers when thresholds drift.
2. Blue-Green CI/CD Rollout Metrics: Attach a Telegraf sidecar to each Kubernetes canary pod; it inserts container stats into a Unity Catalog table tagged by deployment_id, letting Databricks SQL compare error-rate percentiles and auto-rollback underperforming versions.
3. SaaS Usage Metering: Insert per-tenant API-call counters via the HTTP plugin; a nightly Lakehouse query aggregates usage into invoices, eliminating custom metering micro-services.
4. Security Forensics Lake: Upload JSON batches of Suricata IDS events to a Unity Catalog volume using PUT commands, then run COPY INTO for near-real-time enrichment with Delta Live Tables, producing a searchable threat-intel lake that joins network logs with user session data.

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