Azure Monitor and Zabbix Integration

Input and output integration overview

Gather metrics from Azure resources using the Azure Monitor API.

This plugin sends metrics to Zabbix via traps, allowing for efficient monitoring of systems and applications. It supports automated configuration and data sending based on dynamic metrics collected by Telegraf.

Integration details

Azure Monitor

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 client_id, client_secret, tenant_id, and subscription_id 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.

Zabbix

The Telegraf Zabbix plugin is designed to send metrics to Zabbix, an open-source monitoring solution, using the trap protocol. It supports various versions from 3.0 to 6.0, ensuring compatibility with recent updates. The plugin facilitates easy integration with the Zabbix ecosystem, allowing users to send collected metrics and monitor system performance seamlessly. Key functionalities include the ability to define the address and port of the Zabbix server, options for prefixing keys, determining the type of data sent (active vs. trapper), and features for low-level discovery (LLD) enabling dynamic item creation based on the metrics observed. Configuration options also allow for autoregistration and resending intervals for LLD data, ensuring that the metrics are up-to-date and relevant. Additionally, the trap format used for sending metrics is structured to facilitate efficient data transfer and processing in Zabbix.

Configuration

Azure Monitor

# Gather Azure resources metrics from Azure Monitor API
[[inputs.azure_monitor]]
  # can be found under Overview->Essentials in the Azure portal for your application/service
  subscription_id = "<>"
  # can be obtained by registering an application under Azure Active Directory
  client_id = "<>"
  # 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 = "<>"
  # can be found under Azure Active Directory->Properties
  tenant_id = "<>"
  # 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->Essentials->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 = "<>"
    # the metric names to collect
    # leave the array empty to use all metrics available to this resource
    metrics = [ "<>", "<>" ]
    # 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 = [ "<>", "<>" ]

  # resource target #2 to collect metrics from
  [[inputs.azure_monitor.resource_target]]
    resource_id = "<>"
    metrics = [ "<>", "<>" ]
    aggregations = [ "<>", "<>" ]

  # 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 = "<>"

    # defines the resources to collect metrics from
    [[inputs.azure_monitor.resource_group_target.resource]]
      # the resource type
      resource_type = "<>"
      metrics = [ "<>", "<>" ]
      aggregations = [ "<>", "<>" ]

    # defines the resources to collect metrics from
    [[inputs.azure_monitor.resource_group_target.resource]]
      resource_type = "<>"
      metrics = [ "<>", "<>" ]
      aggregations = [ "<>", "<>" ]

  # resource group target #2 to collect metrics from resources under it with resource type
  [[inputs.azure_monitor.resource_group_target]]
    resource_group = "<>"

    [[inputs.azure_monitor.resource_group_target.resource]]
      resource_type = "<>"
      metrics = [ "<>", "<>" ]
      aggregations = [ "<>", "<>" ]

  # subscription target #1 to collect metrics from resources under it with resource type
  [[inputs.azure_monitor.subscription_target]]
    resource_type = "<>"
    metrics = [ "<>", "<>" ]
    aggregations = [ "<>", "<>" ]

  # subscription target #2 to collect metrics from resources under it with resource type
  [[inputs.azure_monitor.subscription_target]]
    resource_type = "<>"
    metrics = [ "<>", "<>" ]
    aggregations = [ "<>", "<>" ]
</code></pre>

Zabbix

[[outputs.zabbix]]
  ## Address and (optional) port of the Zabbix server
  address = "zabbix.example.com:10051"

  ## Send metrics as type "Zabbix agent (active)"
  # agent_active = false

  ## Add prefix to all keys sent to Zabbix.
  # key_prefix = "telegraf."

  ## Name of the tag that contains the host name. Used to set the host in Zabbix.
  ## If the tag is not found, use the hostname of the system running Telegraf.
  # host_tag = "host"

  ## Skip measurement prefix to all keys sent to Zabbix.
  # skip_measurement_prefix = false

  ## This field will be sent as HostMetadata to Zabbix Server to autoregister the host.
  ## To enable this feature, this option must be set to a value other than "".
  # autoregister = ""

  ## Interval to resend auto-registration data to Zabbix.
  ## Only applies if autoregister feature is enabled.
  ## This value is a lower limit, the actual resend should be triggered by the next flush interval.
  # autoregister_resend_interval = "30m"

  ## Interval to send LLD data to Zabbix.
  ## This value is a lower limit, the actual resend should be triggered by the next flush interval.
  # lld_send_interval = "10m"

  ## Interval to delete stored LLD known data and start capturing it again.
  ## This value is a lower limit, the actual resend should be triggered by the next flush interval.
  # lld_clear_interval = "1h"

Input and output integration examples

Azure Monitor

1. Dynamic Resource Monitoring: 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.

2. Multi-Cloud Monitoring Integration: 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.

3. Anomaly Detection and Alerting: 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.

4. Historical Performance Analysis: 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.

Zabbix

1. Dynamic Monitoring of Containerized Applications: Integration of the Zabbix plugin can be leveraged to monitor Docker containers dynamically. As containers are created and removed, the plugin can automatically update Zabbix with the appropriate metrics, ensuring that monitoring stays current without manual configuration. This enhances visibility into resource usage and performance metrics for microservices orchestrated with Kubernetes or Docker Swarm.

2. Real-Time Performance Monitoring with Auto-registration: By enabling the autoregister feature, the plugin can automatically register hosts in Zabbix based on the metrics received. This scenario provides a streamlined approach to add new hosts to monitoring without manual setup, which is particularly useful in environments where hosts may frequently spin up and down, such as serverless architectures or cloud-based deployments.

3. Leveraging Low-level Discovery for Flexible Metric Capture: Using low-level discovery, this plugin allows Zabbix to adaptively create items for metrics that are not predefined. In a scenario involving multiple network devices reporting different performance metrics, the plugin can dynamically inform Zabbix about new metrics as they appear, thus ensuring comprehensive monitoring capabilities that evolve with the monitored systems.

4. Centralized Monitoring of Distributed Systems: The Zabbix plugin can be utilized in a centralized monitoring setup for distributed systems where multiple Telegraf instances are running across different geographical locations. By sending all metrics to a central Zabbix server, organizations can achieve a holistic view of their infrastructure’s performance and make informed operational decisions.

Feedback

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