Hashicorp Nomad and Databricks Integration

Input and output integration overview

This plugin allows users to collect metrics from Hashicorp Nomad agents in distributed environments.

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

Hashicorp Nomad

The Hashicorp Nomad input plugin is designed to gather metrics from every Nomad agent within a cluster. By deploying Telegraf on each node, it can connect to the local Nomad agent, typically available at ‘http://127.0.0.1:4646’. With this setup, users can systematically collect and monitor metrics related to the performance and status of their Nomad environment, ensuring they maintain a healthy and efficient cluster operational state. This plugin enables visibility into the operational aspects of Nomad, which is essential for maintaining reliable cloud infrastructure.

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

Hashicorp Nomad

[[inputs.nomad]]
  ## URL for the Nomad agent
  # url = "http://127.0.0.1:4646"

  ## Set response_timeout (default 5 seconds)
  # response_timeout = "5s"

  ## Optional TLS Config
  # tls_ca = /path/to/cafile
  # tls_cert = /path/to/certfile
  # tls_key = /path/to/keyfile

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

Hashicorp Nomad

1. Cluster Health Monitoring: Use the Hashicorp Nomad plugin to aggregate metrics across all nodes in a Nomad deployment. By monitoring health metrics such as allocation status, job performance, and resource utilization, operations teams can gain insights into the overall health of their deployment, quickly identify and resolve issues, and optimize resource allocation based on real-time data.

2. Performance Analytics for Job Execution: Leverage the metrics provided by Nomad to analyze job execution times and resource consumption. This use case enables developers to adjust job parameters effectively, optimize task performance, and illustrate trends over time, ultimately leading to increased efficiency and reduced costs in resource allocation.

3. Alerting on Critical Conditions: Implement alerting mechanisms based on metrics scraped from Nomad agents. By setting thresholds for critical metrics like CPU usage or failed job allocations, teams can proactively respond to potential issues before they escalate, ensuring higher uptime and reliability for applications running on the Nomad platform.

4. Integration with Visualization Tools: Use the data collected by the Hashicorp Nomad plugin to feed into visualization tools for real-time dashboards. This setup allows teams to monitor cluster workloads, job states, and system performance at a glance, facilitating better decision-making and strategic planning based on visual insights into the Nomad environment.

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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