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Azure Monitor and DuckDB 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>This plugin enables Telegraf to write structured metrics into DuckDB using SQLite-compatible SQL connections, supporting lightweight local analytics and offline metric analysis.</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>

DuckDB

<p>Use the Telegraf SQL plugin to write metrics into a local DuckDB database. DuckDB is an in-process OLAP database designed for efficient analytical queries on columnar data. Although it does not provide a traditional client-server interface, DuckDB can be accessed via SQLite-compatible drivers in embedded mode. This allows Telegraf to store time series metrics in DuckDB using SQL, enabling powerful analytics workflows using familiar SQL syntax, Jupyter notebooks, or integration with data science tools like Python and R. DuckDB’s columnar storage and vectorized execution make it ideal for compact and high-performance metric archives.</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>

DuckDB

<pre><code class="language-toml">[[outputs.sql]] ## Use the SQLite driver to connect to DuckDB via Go's database/sql driver = "sqlite3" ## DSN should point to the DuckDB database file dsn = "file:/var/lib/telegraf/metrics.duckdb" ## SQL INSERT statement with placeholders for metrics table_template = "INSERT INTO metrics (timestamp, name, value, tags) VALUES (?, ?, ?, ?)" ## Optional: manage connection pooling # max_open_connections = 1 # max_idle_connections = 1 # conn_max_lifetime = "0s" ## DuckDB does not require TLS or authentication by default</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>

DuckDB

<ol> <li> <p><strong>Embedded Metric Warehousing for Notebooks</strong>: Write metrics to a local DuckDB file from Telegraf and analyze them in Jupyter notebooks using Python or R. This workflow supports reproducible analytics, ideal for data science experiments or offline troubleshooting.</p> </li> <li> <p><strong>Batch Time-Series Processing on the Edge</strong>: Use Telegraf with DuckDB on edge devices to log metrics locally in SQL format. The compact storage and fast analytical capabilities of DuckDB make it ideal for batch processing and low-bandwidth environments.</p> </li> <li> <p><strong>Exploratory Querying of Historical Metrics</strong>: Accumulate system metrics over time in DuckDB and perform exploratory data analysis (EDA) using SQL joins, window functions, and aggregates. This enables insights that go beyond what typical time-series dashboards provide.</p> </li> <li> <p><strong>Self-Contained Metric Snapshots</strong>: Use DuckDB as a portable metrics archive by shipping <code>.duckdb</code> files between systems. Telegraf can collect and store data in this format, and analysts can later load and query it using the DuckDB CLI or integrations with tools like Tableau and Apache Arrow.</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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