Consul and Databricks Integration

Input and output integration overview

The Consul Input Plugin collects health check metrics from a Consul server, allowing users to monitor service statuses 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

Consul

The Consul Input Plugin is designed to gather health check statuses from all services registered with Consul, a tool for service discovery and infrastructure management. By querying the Consul API, this plugin helps users monitor the health of their services and ensure that they are operational and meeting service level agreements. It does not provide telemetry data, but users can utilize StatsD if they want to collect those metrics. The plugin offers configuration options to connect to the Consul server, manage authentication, and specify how to handle tags derived from health checks.

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

Consul

[[inputs.consul]]
  ## Consul server address
  # address = "localhost:8500"

  ## URI scheme for the Consul server, one of "http", "https"
  # scheme = "http"

  ## Metric version controls the mapping from Consul metrics into
  ## Telegraf metrics. Version 2 moved all fields with string values
  ## to tags.
  ##
  ##   example: metric_version = 1; deprecated in 1.16
  ##            metric_version = 2; recommended version
  # metric_version = 1

  ## ACL token used in every request
  # token = ""

  ## HTTP Basic Authentication username and password.
  # username = ""
  # password = ""

  ## Data center to query the health checks from
  # datacenter = ""

  ## 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

  ## Consul checks' tag splitting
  # When tags are formatted like "key:value" with ":" as a delimiter then
  # they will be split and reported as proper key:value in Telegraf
  # tag_delimiter = ":"

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

Consul

1. Service Health Monitoring Dashboard: Utilize the Consul Input Plugin to create a comprehensive health monitoring dashboard for all services registered with Consul. This allows operations teams to visualize the health status in real time, enabling quick identification of service issues and facilitating rapid responses to service outages or performance degradation.

2. Automated Alerting System: Implement an automated alerting system that uses the health check data gathered by the Consul Input Plugin to trigger notifications whenever a service status changes to critical. This setup can integrate with notification systems like Slack or email, ensuring that team members are alerted immediately to address potential issues.

3. Integration with Incident Management: Leverage the health check data from the Consul Input Plugin to feed into incident management systems. By analyzing the health status trends, teams can prioritize incidents based on the criticality of the affected services and streamline their resolution processes, improving overall service reliability and customer satisfaction.

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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