Google Cloud Stackdriver and Databricks Integration

Input and output integration overview

This plugin enables the collection of monitoring data from Google Cloud services through the Stackdriver Monitoring API. It is designed to help users monitor their cloud infrastructure’s performance and health by gathering relevant metrics.

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

Google Cloud Stackdriver

The Stackdriver Telegraf plugin allows users to query timeseries data from Google Cloud Monitoring using the Cloud Monitoring API v3. With this plugin, users can easily integrate Google Cloud monitoring metrics into their monitoring stacks. This API provides a wealth of insights about resources and applications running in Google Cloud, including performance, uptime, and operational metrics. The plugin supports various configuration options to filter and refine the data retrieved, enabling users to customize their monitoring setup according to their specific needs. This integration facilitates a smoother experience in maintaining the health and performance of cloud resources and assists teams in making data-driven decisions based on historical and current performance statistics.

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

Google Cloud Stackdriver

[[inputs.stackdriver]]
  ## GCP Project
  project = "erudite-bloom-151019"

  ## Include timeseries that start with the given metric type.
  metric_type_prefix_include = [
    "compute.googleapis.com/",
  ]

  ## Exclude timeseries that start with the given metric type.
  # metric_type_prefix_exclude = []

  ## Most metrics are updated no more than once per minute; it is recommended
  ## to override the agent level interval with a value of 1m or greater.
  interval = "1m"

  ## Maximum number of API calls to make per second.  The quota for accounts
  ## varies, it can be viewed on the API dashboard:
  ##   https://cloud.google.com/monitoring/quotas#quotas_and_limits
  # rate_limit = 14

  ## The delay and window options control the number of points selected on
  ## each gather.  When set, metrics are gathered between:
  ##   start: now() - delay - window
  ##   end:   now() - delay
  #
  ## Collection delay; if set too low metrics may not yet be available.
  # delay = "5m"
  #
  ## If unset, the window will start at 1m and be updated dynamically to span
  ## the time between calls (approximately the length of the plugin interval).
  # window = "1m"

  ## TTL for cached list of metric types.  This is the maximum amount of time
  ## it may take to discover new metrics.
  # cache_ttl = "1h"

  ## If true, raw bucket counts are collected for distribution value types.
  ## For a more lightweight collection, you may wish to disable and use
  ## distribution_aggregation_aligners instead.
  # gather_raw_distribution_buckets = true

  ## Aggregate functions to be used for metrics whose value type is
  ## distribution.  These aggregate values are recorded in in addition to raw
  ## bucket counts; if they are enabled.
  ##
  ## For a list of aligner strings see:
  ##   https://cloud.google.com/monitoring/api/ref_v3/rpc/google.monitoring.v3#aligner
  # distribution_aggregation_aligners = [
  #  "ALIGN_PERCENTILE_99",
  #  "ALIGN_PERCENTILE_95",
  #  "ALIGN_PERCENTILE_50",
  # ]

  ## Filters can be added to reduce the number of time series matched.  All
  ## functions are supported: starts_with, ends_with, has_substring, and
  ## one_of.  Only the '=' operator is supported.
  ##
  ## The logical operators when combining filters are defined statically using
  ## the following values:
  ##   filter ::=  {AND  AND  AND }
  ##   resource_labels ::=  {OR }
  ##   metric_labels ::=  {OR }
  ##   user_labels ::=  {OR }
  ##   system_labels ::=  {OR }
  ##
  ## For more details, see https://cloud.google.com/monitoring/api/v3/filters
  #
  ## Resource labels refine the time series selection with the following expression:
  ##   resource.labels. = 
  # [[inputs.stackdriver.filter.resource_labels]]
  #   key = "instance_name"
  #   value = 'starts_with("localhost")'
  #
  ## Metric labels refine the time series selection with the following expression:
  ##   metric.labels. = 
  #  [[inputs.stackdriver.filter.metric_labels]]
  #    key = "device_name"
  #    value = 'one_of("sda", "sdb")'
  #
  ## User labels refine the time series selection with the following expression:
  ##   metadata.user_labels."" = 
  #  [[inputs.stackdriver.filter.user_labels]]
  #    key = "environment"
  #    value = 'one_of("prod", "staging")'
  #
  ## System labels refine the time series selection with the following expression:
  ##   metadata.system_labels."" = 
  #  [[inputs.stackdriver.filter.system_labels]]
  #    key = "machine_type"
  #    value = 'starts_with("e2-")'
</code></pre>

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

Google Cloud Stackdriver

1. Integrating Cloud Metrics into Custom Dashboards: With this plugin, teams can funnel metrics from Google Cloud into personalized dashboards, allowing for real-time monitoring of application performance and resource utilization. By customizing the visual representation of cloud metrics, operations teams can easily identify trends and anomalies, enabling proactive management before issues escalate.

2. Automated Alerts and Analysis: Users can set up automated alerting mechanisms leveraging the plugin’s metrics to track resource thresholds. This capability allows teams to act swiftly in response to performance degradation or outages by providing immediate notifications, thus reducing the mean time to recovery and ensuring continued operational efficiency.

3. Cross-Platform Resource Comparison: The plugin can be used to draw metrics from various Google Cloud services and compare them with on-premise resources. This cross-platform visibility helps organizations make informed decisions about resource allocation and scaling strategies, as well as optimize cloud spending versus on-premise infrastructure.

4. Historical Data Analysis for Capacity Planning: By collecting historical metrics over time, the plugin empowers teams to conduct thorough capacity planning. Understanding past performance trends facilitates accurate forecasting for resource needs, leading to better budgeting and investment strategies.

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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