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Azure Monitor and Google Cloud Monitoring 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/azure_monitor-influxdb/">Azure Monitor and InfluxDB</a>.

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Infrastructure Monitoring with Telegraf

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Input and output integration overview

<p>Gather metrics from Azure resources using the Azure Monitor API.</p>

<p>The Stackdriver plugin allows users to send metrics directly to a specified project in Google Cloud Monitoring, facilitating robust monitoring capabilities across their cloud resources.</p>

Integration details

Azure Monitor

<p>The Azure Monitor Telegraf plugin is specifically designed for gathering metrics from various Azure resources using the Azure Monitor API. Users must provide specific credentials such as <code>client_id</code>, <code>client_secret</code>, <code>tenant_id</code>, and <code>subscription_id</code> to authenticate and gain access to their Azure resources. Additionally, the plugin supports functionality to collect metrics from both individual resources and resource groups or subscriptions, allowing for flexible and scalable metric collection tailored to user needs. This plugin is ideal for organizations leveraging Azure cloud infrastructure, providing crucial insights into resource performance and utilization over time, facilitating proactive management and optimization of cloud resources.</p>

Google Cloud Monitoring

<p>This plugin writes metrics to a project in Google Cloud Monitoring, which used to be known as Stackdriver. Authentication is a prerequisite and can be achieved via service accounts or user credentials. The plugin is designed to group metrics by a <code>namespace</code> variable and metric key, facilitating organized data management. However, users are encouraged to use the <code>official</code> naming format for enhanced query efficiency. The plugin supports additional configurations for managing metric representation and allows tags to be treated as resource labels. Notably, it imposes certain restrictions on the data it can accept, such as not allowing string values or points that are out of chronological order.</p>

Configuration

Azure Monitor

<pre><code class="language-toml"># Gather Azure resources metrics from Azure Monitor API [[inputs.azure_monitor]] # can be found under Overview-&gt;Essentials in the Azure portal for your application/service subscription_id = "&lt;<SUBSCRIPTION_ID>&gt;" # can be obtained by registering an application under Azure Active Directory client_id = "&lt;<CLIENT_ID>&gt;" # can be obtained by registering an application under Azure Active Directory. # If not specified Default Azure Credentials chain will be attempted: # - Environment credentials (AZURE_*) # - Workload Identity in Kubernetes cluster # - Managed Identity # - Azure CLI auth # - Developer Azure CLI auth client_secret = "&lt;<CLIENT_SECRET>&gt;" # can be found under Azure Active Directory-&gt;Properties tenant_id = "&lt;<TENANT_ID>&gt;" # Define the optional Azure cloud option e.g. AzureChina, AzureGovernment or AzurePublic. The default is AzurePublic. # cloud_option = "AzurePublic" # resource target #1 to collect metrics from [[inputs.azure_monitor.resource_target]] # can be found under Overview-&gt;Essentials-&gt;JSON View in the Azure portal for your application/service # must start with 'resourceGroups/...' ('/subscriptions/xxxxxxxx-xxxx-xxxx-xxx-xxxxxxxxxxxx' # must be removed from the beginning of Resource ID property value) resource_id = "&lt;<RESOURCE_ID>&gt;" # the metric names to collect # leave the array empty to use all metrics available to this resource metrics = [ "&lt;<METRIC>&gt;", "&lt;<METRIC>&gt;" ] # metrics aggregation type value to collect # can be 'Total', 'Count', 'Average', 'Minimum', 'Maximum' # leave the array empty to collect all aggregation types values for each metric aggregations = [ "&lt;<AGGREGATION>&gt;", "&lt;<AGGREGATION>&gt;" ] # resource target #2 to collect metrics from [[inputs.azure_monitor.resource_target]] resource_id = "&lt;<RESOURCE_ID>&gt;" metrics = [ "&lt;<METRIC>&gt;", "&lt;<METRIC>&gt;" ] aggregations = [ "&lt;<AGGREGATION>&gt;", "&lt;<AGGREGATION>&gt;" ] # resource group target #1 to collect metrics from resources under it with resource type [[inputs.azure_monitor.resource_group_target]] # the resource group name resource_group = "&lt;<RESOURCE_GROUP_NAME>&gt;" # defines the resources to collect metrics from [[inputs.azure_monitor.resource_group_target.resource]] # the resource type resource_type = "&lt;<RESOURCE_TYPE>&gt;" metrics = [ "&lt;<METRIC>&gt;", "&lt;<METRIC>&gt;" ] aggregations = [ "&lt;<AGGREGATION>&gt;", "&lt;<AGGREGATION>&gt;" ] # defines the resources to collect metrics from [[inputs.azure_monitor.resource_group_target.resource]] resource_type = "&lt;<RESOURCE_TYPE>&gt;" metrics = [ "&lt;<METRIC>&gt;", "&lt;<METRIC>&gt;" ] aggregations = [ "&lt;<AGGREGATION>&gt;", "&lt;<AGGREGATION>&gt;" ] # resource group target #2 to collect metrics from resources under it with resource type [[inputs.azure_monitor.resource_group_target]] resource_group = "&lt;<RESOURCE_GROUP_NAME>&gt;" [[inputs.azure_monitor.resource_group_target.resource]] resource_type = "&lt;<RESOURCE_TYPE>&gt;" metrics = [ "&lt;<METRIC>&gt;", "&lt;<METRIC>&gt;" ] aggregations = [ "&lt;<AGGREGATION>&gt;", "&lt;<AGGREGATION>&gt;" ] # subscription target #1 to collect metrics from resources under it with resource type [[inputs.azure_monitor.subscription_target]] resource_type = "&lt;<RESOURCE_TYPE>&gt;" metrics = [ "&lt;<METRIC>&gt;", "&lt;<METRIC>&gt;" ] aggregations = [ "&lt;<AGGREGATION>&gt;", "&lt;<AGGREGATION>&gt;" ] # subscription target #2 to collect metrics from resources under it with resource type [[inputs.azure_monitor.subscription_target]] resource_type = "&lt;<RESOURCE_TYPE>&gt;" metrics = [ "&lt;<METRIC>&gt;", "&lt;<METRIC>&gt;" ] aggregations = [ "&lt;<AGGREGATION>&gt;", "&lt;<AGGREGATION>&gt;" ] &lt;/code&gt;&lt;/pre&gt; </AGGREGATION></AGGREGATION></METRIC></METRIC></RESOURCE_TYPE></AGGREGATION></AGGREGATION></METRIC></METRIC></RESOURCE_TYPE></AGGREGATION></AGGREGATION></METRIC></METRIC></RESOURCE_TYPE></RESOURCE_GROUP_NAME></AGGREGATION></AGGREGATION></METRIC></METRIC></RESOURCE_TYPE></AGGREGATION></AGGREGATION></METRIC></METRIC></RESOURCE_TYPE></RESOURCE_GROUP_NAME></AGGREGATION></AGGREGATION></METRIC></METRIC></RESOURCE_ID></AGGREGATION></AGGREGATION></METRIC></METRIC></RESOURCE_ID></TENANT_ID></CLIENT_SECRET></CLIENT_ID></SUBSCRIPTION_ID></code></pre>

Google Cloud Monitoring

<pre><code class="language-toml">[[outputs.stackdriver]] ## GCP Project project = "project-id" ## Quota Project ## Specifies the Google Cloud project that should be billed for metric ingestion. ## If omitted, the quota is charged to the service account’s default project. ## This is useful when sending metrics to multiple projects using a single service account. ## The caller must have the `serviceusage.services.use` permission on the specified project. # quota_project = "" ## The namespace for the metric descriptor ## This is optional and users are encouraged to set the namespace as a ## resource label instead. If omitted it is not included in the metric name. namespace = "telegraf" ## Metric Type Prefix ## The DNS name used with the metric type as a prefix. # metric_type_prefix = "custom.googleapis.com" ## Metric Name Format ## Specifies the layout of the metric name, choose from: ## * path: 'metric_type_prefix_namespace_name_key' ## * official: 'metric_type_prefix/namespace_name_key/kind' # metric_name_format = "path" ## Metric Data Type ## By default, telegraf will use whatever type the metric comes in as. ## However, for some use cases, forcing int64, may be preferred for values: ## * source: use whatever was passed in ## * double: preferred datatype to allow queries by PromQL. # metric_data_type = "source" ## Tags as resource labels ## Tags defined in this option, when they exist, are added as a resource ## label and not included as a metric label. The values from tags override ## the values defined under the resource_labels config options. # tags_as_resource_label = [] ## Custom resource type # resource_type = "generic_node" ## Override metric type by metric name ## Metric names matching the values here, globbing supported, will have the ## metric type set to the corresponding type. # metric_counter = [] # metric_gauge = [] # metric_histogram = [] ## 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 ## Additional resource labels # [outputs.stackdriver.resource_labels] # node_id = "$HOSTNAME" # namespace = "myapp" # location = "eu-north0" </code></pre>

Input and output integration examples

Azure Monitor

<ol> <li> <p><strong>Dynamic Resource Monitoring</strong>: Use the Azure Monitor plugin to dynamically gather metrics from Azure resources based on specific criteria like tags or resource types. Organizations can automate the process of loading and unloading resource metrics, enabling better performance tracking and optimization based on resource utilization patterns.</p> </li> <li> <p><strong>Multi-Cloud Monitoring Integration</strong>: Integrate metrics collected from Azure Monitor with other cloud providers using a centralized monitoring solution. This allows organizations to view and analyze performance data across multiple cloud deployments, providing a holistic overview of resource performance and costs, and streamlining operations.</p> </li> <li> <p><strong>Anomaly Detection and Alerting</strong>: Leverage the metrics gathered via the Azure Monitor plugin in conjunction with machine learning algorithms to detect anomalies in resource utilization. By establishing baseline performance metrics and automatically alerting on deviations, organizations can mitigate risks and address performance issues before they escalate.</p> </li> <li> <p><strong>Historical Performance Analysis</strong>: Use the collected Azure metrics to conduct historical analysis by feeding the data into a data warehousing solution. This enables organizations to track trends over time, allowing for detailed reporting and decision-making based on historical performance data.</p> </li> </ol>

Google Cloud Monitoring

<ol> <li> <p><strong>Multi-Project Metric Aggregation</strong>: Use this plugin to send aggregated metrics from various applications across different projects into a single Google Cloud Monitoring project. This use case helps centralize metrics for teams managing multiple applications, providing a unified view for performance monitoring and enhancing decision-making. By configuring different quota projects for billing, organizations can ensure proper cost management while benefiting from a consolidated monitoring strategy.</p> </li> <li> <p><strong>Anomaly Detection Setup</strong>: Integrate the plugin with a machine learning-based analytics tool that identifies anomalies in the collected metrics. Using the historical data provided by the plugin, the tool can learn normal baseline behavior and promptly alert the operations team when unusual patterns arise, enabling proactive troubleshooting and minimizing service disruptions.</p> </li> <li> <p><strong>Dynamic Resource Labeling</strong>: Implement dynamic tagging by utilizing the tags_as_resource_label option to adaptively attach resource labels based on runtime conditions. This setup allows metrics to provide context-sensitive information, such as varying environmental parameters or operational states, enhancing the granularity of monitoring and reporting without changing the fundamental metric structure.</p> </li> <li> <p><strong>Custom Metric Visualization Dashboards</strong>: Leverage the data collected by the Google Cloud Monitoring output plugin to feed a custom metrics visualization dashboard using a third-party framework. By visualizing metrics in real-time, teams can achieve better situational awareness, notably by correlating different metrics, improving operational decision-making, and streamlining performance management workflows.</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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