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Google Cloud Stackdriver and Sensu Integration

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This is not the recommended configuration for real-time query at scale. For query and compression optimization, high-speed ingest, and high availability, you may want to consider <a href="/integrations/stackdriver-influxdb/">Stackdriver and InfluxDB</a>.

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Infrastructure Monitoring with Telegraf

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Input and output integration overview

<p>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.</p>

<p>This plugin writes metrics events to Sensu via its HTTP events API, enabling seamless integration with the Sensu monitoring platform.</p>

Integration details

Google Cloud Stackdriver

<p>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.</p>

Sensu

<p>This plugin writes metrics events to Sensu via its HTTP events API. Sensu is a monitoring system that enables users to collect, analyze, and manage metrics from various components in their infrastructure. The plugin facilitates the integration of Telegraf, a server agent for collecting and reporting metrics, with the Sensu monitoring platform. Users can configure settings such as backend and agent API URLs, API keys for authentication, and optional TLS settings. The plugin’s core functionality is centered around sending metric events, including check and entity specifications, to Sensu, allowing for comprehensive monitoring and alerting. The API reference provides extensive details about the events and metrics structure, ensuring users can efficiently leverage Sensu’s capabilities for observability and incident response.</p>

Configuration

Google Cloud Stackdriver

<pre><code class="language-toml">[[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 ::= <resource_labels> {AND <metric_labels> AND <user_labels> AND <system_labels>} ## resource_labels ::= <resource_labels> {OR <resource_label>} ## metric_labels ::= <metric_labels> {OR <metric_label>} ## user_labels ::= <user_labels> {OR <user_label>} ## system_labels ::= <system_labels> {OR <system_label>} ## ## 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.<key> = <value> # [[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.<key> = <value> # [[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."<key>" = <value> # [[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."<key>" = <value> # [[inputs.stackdriver.filter.system_labels]] # key = "machine_type" # value = 'starts_with("e2-")' &lt;/code&gt;&lt;/pre&gt; </value></key></value></key></value></key></value></key></system_label></system_labels></user_label></user_labels></metric_label></metric_labels></resource_label></resource_labels></system_labels></user_labels></metric_labels></resource_labels></code></pre>

Sensu

<pre><code class="language-toml">[[outputs.sensu]] ## BACKEND API URL is the Sensu Backend API root URL to send metrics to ## (protocol, host, and port only). The output plugin will automatically ## append the corresponding backend API path ## /api/core/v2/namespaces/:entity_namespace/events/:entity_name/:check_name). ## ## Backend Events API reference: ## https://docs.sensu.io/sensu-go/latest/api/events/ ## ## AGENT API URL is the Sensu Agent API root URL to send metrics to ## (protocol, host, and port only). The output plugin will automatically ## append the correspeonding agent API path (/events). ## ## Agent API Events API reference: ## https://docs.sensu.io/sensu-go/latest/api/events/ ## ## NOTE: if backend_api_url and agent_api_url and api_key are set, the output ## plugin will use backend_api_url. If backend_api_url and agent_api_url are ## not provided, the output plugin will default to use an agent_api_url of ## http://127.0.0.1:3031 ## # backend_api_url = "http://127.0.0.1:8080" # agent_api_url = "http://127.0.0.1:3031" ## API KEY is the Sensu Backend API token ## Generate a new API token via: ## ## $ sensuctl cluster-role create telegraf --verb create --resource events,entities ## $ sensuctl cluster-role-binding create telegraf --cluster-role telegraf --group telegraf ## $ sensuctl user create telegraf --group telegraf --password REDACTED ## $ sensuctl api-key grant telegraf ## ## For more information on Sensu RBAC profiles &amp; API tokens, please visit: ## - https://docs.sensu.io/sensu-go/latest/reference/rbac/ ## - https://docs.sensu.io/sensu-go/latest/reference/apikeys/ ## # api_key = "${SENSU_API_KEY}" ## 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 &amp; host verification # insecure_skip_verify = false ## Timeout for HTTP message # timeout = "5s" ## HTTP Content-Encoding for write request body, can be set to "gzip" to ## compress body or "identity" to apply no encoding. # content_encoding = "identity" ## NOTE: Due to the way TOML is parsed, tables must be at the END of the ## plugin definition, otherwise additional config options are read as part of ## the table ## Sensu Event details ## ## Below are the event details to be sent to Sensu. The main portions of the ## event are the check, entity, and metrics specifications. For more information ## on Sensu events and its components, please visit: ## - Events - https://docs.sensu.io/sensu-go/latest/reference/events ## - Checks - https://docs.sensu.io/sensu-go/latest/reference/checks ## - Entities - https://docs.sensu.io/sensu-go/latest/reference/entities ## - Metrics - https://docs.sensu.io/sensu-go/latest/reference/events#metrics ## ## Check specification ## The check name is the name to give the Sensu check associated with the event ## created. This maps to check.metadata.name in the event. [outputs.sensu.check] name = "telegraf" ## Entity specification ## Configure the entity name and namespace, if necessary. This will be part of ## the entity.metadata in the event. ## ## NOTE: if the output plugin is configured to send events to a ## backend_api_url and entity_name is not set, the value returned by ## os.Hostname() will be used; if the output plugin is configured to send ## events to an agent_api_url, entity_name and entity_namespace are not used. # [outputs.sensu.entity] # name = "server-01" # namespace = "default" ## Metrics specification ## Configure the tags for the metrics that are sent as part of the Sensu event # [outputs.sensu.tags] # source = "telegraf" ## Configure the handler(s) for processing the provided metrics # [outputs.sensu.metrics] # handlers = ["influxdb","elasticsearch"] </code></pre>

Input and output integration examples

Google Cloud Stackdriver

<ol> <li> <p><strong>Integrating Cloud Metrics into Custom Dashboards</strong>: 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.</p> </li> <li> <p><strong>Automated Alerts and Analysis</strong>: 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.</p> </li> <li> <p><strong>Cross-Platform Resource Comparison</strong>: 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.</p> </li> <li> <p><strong>Historical Data Analysis for Capacity Planning</strong>: 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.</p> </li> </ol>

Sensu

<ol> <li> <p><strong>Real-Time Infrastructure Monitoring</strong>: Utilize the Sensu plugin to send performance metrics from various servers and services directly to Sensu. This real-time data flow enables teams to visualize infrastructure health, track resource usage, and receive immediate alerts for any anomalies detected. By centralizing monitoring through Sensu, organizations can create a holistic view of their systems and respond swiftly to issues.</p> </li> <li> <p><strong>Automated Incident Response Workflows</strong>: Leverage the plugin to automatically trigger incident response workflows based on the metrics events sent to Sensu. For example, if CPU usage exceeds a defined threshold, the Sensu system can be configured to alert the operations team, which can then initiate automated remediation processes, reducing downtime and maintaining system reliability. This integration allows for proactive management of system resources.</p> </li> <li> <p><strong>Dynamic Scaling of Resources</strong>: Use the Sensu plugin to feed metrics into an auto-scaling system that adjusts resources based on demand. By tracking metrics like request load and resource utilization, organizations can automatically scale their infrastructure up or down, ensuring optimal performance and cost efficiency without manual intervention.</p> </li> <li> <p><strong>Centralized Logging and Monitoring</strong>: Combine the Sensu with logging tools to send logs and performance metrics to a centralized monitoring system. This comprehensive approach allows teams to correlate logs with metric events, providing deeper insights into system behavior and performance, which aids in troubleshooting and performance optimization over time.</p> </li> </ol>

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Useful Links

Documentation

Telegraf Quickstart Training

Infrastructure Monitoring with Telegraf

Powerful Performance, Limitless Scale

Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.

See Ways to Get Started

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