Getting Started with Azure Metrics

Azure metrics are a powerful tool for monitoring and analyzing the performance of your Azure resources. They provide a wealth of information about your resources, including metrics like CPU usage, memory usage, and network traffic.

To get started with Azure metrics, you'll first need to create a metrics namespace. This is a logical grouping of metrics that allows you to organize and track related metrics together. You can create a metrics namespace in the Azure portal or through the Azure CLI.

Understanding the different types of metrics is also crucial. There are two main types: intrinsic and custom metrics. Intrinsic metrics are built into Azure services, such as CPU usage and memory usage, while custom metrics are user-defined and can be created using Azure Monitor.

With Azure metrics, you can set up alerts and notifications to notify you when certain conditions are met, such as when a resource exceeds a certain threshold. This can help you quickly identify and respond to issues before they become major problems.

Prometheus offers a long list of critical monitoring capabilities for Azure environments.

You can check out our Intro to Prometheus metrics article if these concepts are new to you.

Prometheus provides a powerful tool for monitoring Azure environments, making it easier to get started with metrics.

Monitoring your Azure resources' capacity and utilization is crucial for ensuring high performance and availability. Server availability measures the time percentage of uptime, and a 100% uptime is the ideal measure.

Server capacity, on the other hand, measures the used and remaining capability of the total available size. Lower CPU/memory means your application will start to lag in performance. Server utilization measures the percentage of available compute and processing capacity used at a particular time.

Server responsiveness measures the speed at which compute services and servers respond to requests, while server scalability measures how well your compute system expands to handle increasing demand. Network availability, network throughput, and network utilization are also essential metrics to monitor.

Here are some key metrics to track:

Monitoring these metrics will help you identify potential issues and take proactive measures to improve your application's performance and availability. By setting up end-to-end visibility into your compute and processing services, you'll be able to debug performance errors easily and save time when solving issues causing downtime.

Prometheus offers a long list of critical monitoring capabilities for Azure environments.

Prometheus provides a robust set of features that make it an ideal choice for monitoring Azure environments.

Prometheus offers a long list of critical monitoring capabilities for Azure environments including monitoring metrics from Azure services.

Prometheus can help you gain a deeper understanding of your Azure environment by providing insights into system performance and behavior.

Prometheus offers a long list of critical monitoring capabilities for Azure environments including monitoring metrics from Azure services such as Azure Storage and Azure Compute.

Now that you've learned the basics of Azure metrics, it's time to take your skills to the next level.

You can start by troubleshooting any issues you're experiencing with metrics explorer. This will help you identify and resolve any problems that may be affecting your metrics.

To get the most out of Azure metrics, it's essential to review the available metrics for Azure services. This will give you a better understanding of what's possible and help you create more effective dashboards.

Exploring examples of configured charts can also be a great way to learn and get inspiration for your own dashboards. You can find these examples in the Azure documentation or by searching online.

Finally, creating custom KPI dashboards is a crucial step in using Azure metrics effectively. This will allow you to track key performance indicators and make data-driven decisions for your organization.

The Response class represents the response to a metrics query, which can be either a successful request or an error response.

A successful request to get the list of metric values returns a 200 OK response.

Error responses are represented by the ErrorResponse class, which describes why the operation failed.

The ErrorAdditionalInfo class provides additional information about a resource management error.

The LocalizableString class represents a localizable string, which can be used to display error messages in different languages.

The MetadataValue class represents a metric metadata value, which can be used to query metrics.

The Metric class represents the result data of a query, which can be used to retrieve metric values.

The MetricUnit class represents the unit of a metric, which can be used to understand the scale of the metric values.

The MetricValue class represents a metric value, which can be used to query metrics.

The ResultType class reduces the set of data collected, depending on the operation being performed.

Here is a summary of the definitions:

Azure Monitor stores all metrics at 1-minute granularity intervals. This means that during a given minute, a metric might need to be sampled several times.

You can limit the number of raw values that you have to emit and pay for in Azure Monitor by locally preaggregating and emitting the aggregated values. This can be done using metrics like Min, Max, Sum, and Count.

For example, if you have four sign-in transactions to your app during a minute, the resulting measured latencies for each might be: 7 ms, 4 ms, 13 ms, and 16 ms.

Here are the preaggregated values you would emit to Azure Monitor for this scenario:

If your application can't preaggregate locally, you can emit the raw measure values. This means emitting each discrete sample or event immediately upon collection.

To configure Azure Metrics, you need to have Golang installed on your server, as the Azure Metrics Exporter requires Go binaries to operate. This is a crucial step before you can start collecting and sending Azure metrics to your Prometheus server.

You'll also need to create an Azure Active Directory service principal name (SPN) and specify the details in the credentials parameters of the Azure Metrics Exporter configuration YAML. This will allow you to authenticate and authorize access to your Azure resources.

The Azure Metrics Exporter configuration YAML also allows you to list multiple Azure resource targets, such as Azure Virtual Machines, Azure SQL DBs, and other Azure services. By specifying these targets, you can collect metrics from a variety of Azure resources and view them in your Prometheus server.

Here are the five aggregation types available in Azure Metrics Explorer:

To install and configure the Azure Metrics Exporter, you'll need to ensure that Golang is available on your server, as the exporter relies on Go binaries to operate.

First, you'll need to create an Azure Active Directory service principal name (SPN) to use in the exporter's configuration. This involves following the instructions in the document referenced in the Azure Metrics Exporter configuration YAML.

The YAML file itself requires you to specify the SPN details under the "credentials" parameters, allowing the exporter to authenticate with Azure.

In the "Targets" section, you can list multiple Azure resource targets, such as Azure Virtual Machines, Azure SQL DBs, and other Azure services.

To add the Azure Metrics Exporter as a target in Prometheus, you'll need to update your prometheus.yaml file with the necessary configuration.

Here's a summary of the required configuration parameters:

By following these steps and configuring the exporter correctly, you'll be able to collect and send Azure metrics to your Prometheus server.

To set the resource scope in Azure Metrics, you need to have Monitoring Reader permission at the subscription level. This permission allows you to visualize metrics across multiple resources, resource groups, or a subscription.

The resource scope picker lets you scope your chart to view metrics for a single resource or for multiple resources. To view metrics across multiple resources, the resources must be within the same subscription and region location.

To select multiple resources, start by selecting multiple resources within the resource scope picker. The resources you select must be within the same resource type, location, and subscription. Resources that don't meet these criteria aren't selectable.

Here are the steps to follow:

If you want to view the metrics for multiple resources at the same time, or to view metrics across a subscription or resource group, select Upvote.

The time range in Azure Metrics is a crucial aspect to consider when analyzing your data. By default, the chart shows the most recent 24 hours of metrics data.

Most metrics in Azure are stored for 93 days, but you can only query up to 30 days of data on any single chart. The 30-day limitation doesn't apply to log-based metrics.

You can use the time picker to change the time range for your data, such as the last 12 hours or the last 30 days. This gives you more control over the data you're viewing.

The time picker is a useful tool for configuring the time range, and you can also pan and zoom by using the controls in the chart area.

You can use different aggregations to gain more insights about your metrics. Metrics explorer applies a default aggregation, but you can change it to suit your needs.

Metrics explorer has five aggregation types: Sum, Count, Average, Min, and Max. The Sum aggregation is also known as the total aggregation.

The Count aggregation is equal to the Sum aggregation when the metric is always captured with the value of 1, which is common when the metric tracks the count of distinct events.

To change the aggregation, you need to understand how metrics explorer handles them. Metrics explorer determines the time grain based on the time picker in metrics explorer or the currently selected time range by default.

If you set the time granularity to 30 minutes, metrics explorer draws the chart from 48 aggregated data points, which is two data points per hour for 24 hours. If you switch the time granularity to 15 minutes, you get 96 aggregated data points, which is four data points per hour for 24 hours.

Here are the five aggregation types and their descriptions:

To send custom data via REST, you'll need to include a specific set of information with each data point. This includes an authentication token.

The authentication token is a crucial part of the process, as it verifies your identity and allows Azure Monitor to accept your custom metrics. I've found that using a well-structured authentication token helps prevent any issues with data ingestion.

To report custom metrics, you'll need to include a subject, which is a unique identifier for the data point. This helps Azure Monitor understand the context of the metric. For example, if you're reporting a custom metric for a specific application, the subject would be the application name.

You'll also need to specify the region, which is the geographical location where the data point was collected. This is important for Azure Monitor to accurately route the data.

Each data point must include a timestamp, which is the time when the data was collected. This helps Azure Monitor accurately time-stamp the data.

In addition to the above information, you'll also need to include a namespace, which is a unique identifier for the metric. This helps Azure Monitor categorize the metric correctly.

You'll also need to include a name, which is a descriptive name for the metric. This helps Azure Monitor understand the purpose of the metric.

Dimension keys and values are also required, which provide additional context about the data point. These can include things like customer ID, location, or other relevant information.

Finally, you'll need to include the metric values, which are the actual values being reported. These can be numerical values, such as CPU usage or memory usage.

Here is a summary of the required information:

To obtain an authorization token, you'll need to create a managed identity or service principal and assign Monitoring Metrics Publisher permissions. This allows the entity to emit custom metrics about Azure resources.

You can get an authorization token by using a client ID and client secret to authenticate the request. This is done by registering your application with Microsoft Entra ID.

To request a token, specify the resource as https://monitoring.azure.com/. You can also use other resources like https://management.azure.com or https://api.loganalytics.io.

Here are the resources you can use to request a token:

Once you have the token, you can use it to authenticate requests to Azure Monitor. This is a crucial step in sending custom metrics to Azure Monitor.

In Azure Metrics Configuration, you have several options for unit types. You can use a unit type such as BitsPerSecond to measure the rate of binary digits per second.

BitsPerSecond is a string type unit that measures the rate of binary digits per second. This can be useful for measuring data transfer rates.

You can also use ByteSeconds, which is a string type unit that measures the size of data in bytes multiplied by the time it takes to transfer or store the data in seconds.

Some other unit types you might find useful include Bytes, which is a string type unit that measures the size of memory in bytes, and Cores, which is a string type unit that measures the processing power of a CPU.

Here's a list of some common unit types you might use in Azure Metrics Configuration:

To ensure accurate measurements, it's essential to choose the right unit type for your metric. For example, if you're measuring the rate of data transfer, you might choose BitsPerSecond or ByteSeconds. If you're measuring the size of memory, you might choose Bytes.

Monitoring is a crucial aspect of managing your Azure metrics. Azure Monitor, a service for collecting metric data, logs, and traces, provides a holistic view of your infrastructure and application performance.

You can utilize Azure Monitor's Application Insights, Container insights, and VM insights to diagnose issues with your application and connect infrastructure issues. Alternatively, you can use a third-party monitoring solution like Site24x7’s Azure monitoring tool.

Prometheus, a popular cloud monitoring solution, offers native integrations with popular cloud technologies like Kubernetes. It allows you to collect metrics from additional sources with exporters and analyze metrics in real-time with PromQL.

PromQL facilitates real-time analysis and visualization of metrics in Prometheus, and lets you develop custom queries and visualizations to understand application and system performance. You can also configure Prometheus AlertManager to generate notifications whenever certain conditions are met.

Some key Azure metrics to keep an eye on include availability, which assures your cloud workload has a constant 24*7 uptime year-round. You should also monitor storage services, Azure networks, and subnets to ensure they're available and can receive and dispense data.

Some best practices for monitoring availability include drilling down in granularity to identify resources that are now unavailable and the cause of downtime. You can also set up alerts to inform you when system parts become unavailable and the application no longer works well for your end-users.

Here are some key metrics to focus on:

By monitoring these metrics and following best practices, you can maintain your Azure application's performance, availability, and efficiency.