Azure Managed Prometheus Essentials for Cloud Monitoring

Azure Managed Prometheus is a fully managed service that allows you to monitor your cloud resources with ease.

It provides a scalable and highly available solution for monitoring your applications and services.

With Azure Managed Prometheus, you can store and visualize metrics from your applications and services, enabling you to make data-driven decisions.

This service integrates seamlessly with other Azure services, such as Azure Monitor and Azure Log Analytics.

Azure Managed Prometheus is a fully managed service that allows you to monitor and analyze your applications and infrastructure in real-time.

To get started with Azure Managed Prometheus, you need to create a workspace, which is the central location for your monitoring data.

A workspace can be created through the Azure portal or using the Azure CLI.

You can also create a workspace using the Azure Resource Manager (ARM) template.

It's recommended to create a dedicated resource group for your workspace to keep your monitoring data organized.

You can monitor your applications and infrastructure using the built-in dashboards and alerts in Azure Managed Prometheus.

The service also supports custom dashboards and alerting policies to meet your specific monitoring needs.

You can start monitoring your applications and infrastructure with Azure Managed Prometheus in just a few clicks.

Configuring Azure Managed Prometheus requires a few key checks to ensure everything is working as expected.

Verify there are no errors with parsing the Prometheus config, merging with any default scrape targets enabled, and validating the full config. If you included a custom Prometheus config, make sure it's recognized in the logs.

If you created Custom Resources, you should have seen any validation errors during the creation of pod/service monitors. If you still don't see the metrics from the targets, check the logs for any error messages.

To troubleshoot issues, verify there are no errors from MetricsExtension regarding authenticating with the Azure Monitor workspace. You should also check the OpenTelemetry collector logs for any errors related to scraping the targets.

If the logs show no errors, the Prometheus interface can be used for debugging to verify the expected configuration and targets being scraped.

To enable managed Prometheus in an AKS cluster, you need an Azure Monitor workspace, which is like a log analytics workspace for the Azure monitor metrics store.

This can be confusing, but I'll focus on adding managed Prometheus to an existing cluster. I already had a Prometheus installation in my cluster, so I manually added the preview extension to deploy monitor-metrics pods in the kube-system namespace and a default scraping configuration.

You can now port-forward to any of the ama-metrics pods and see the configuration and targets.

Sloctl is a crucial tool for setting up Azure Monitor managed service for Prometheus using the provided template. You'll need to create a YAML resource definition for your Azure Monitor managed service for Prometheus.

The process is straightforward: create a YAML resource definition using the provided template and run sloctl apply to proceed.

To specify the unit for the query delay, you'll need to use the queryDelay.unit field, which has two possible values: Second or Minute. Be sure to check the query delay documentation for the default unit of query delay for each source.

The queryDelay.value field is also mandatory and must be a number less than 1440 minutes (24 hours). Again, check the query delay documentation for the default unit of query delay for each source.

To specify the release channel, you'll need to use the releaseChannel field, which has two possible values: beta or stable.

Here are the fields you'll need to specify for the Azure Prometheus source:

Note that the historicalDataRetrieval field is optional and should only be used with supported sources. If omitted, Nobl9 uses the default values of value: 0 and unit: Day for maxDuration and defaultDuration.

To enable managed Prometheus in an AKS cluster, you need an Azure Monitor workspace, which is similar to a log analytics workspace but for the Azure monitor metrics store.

An Azure Monitor workspace is required for managed Prometheus, so make sure you have one set up.

You can add managed Prometheus to an existing AKS cluster by manually adding the preview extension, which will deploy monitor-metrics pods in your kube-system namespace and a default scraping configuration.

The installation will run in agent mode, using the remote write feature to ship time series data to Azure Monitor, so Grafana is your simplest solution to query any data.

Existing Prometheus setups often utilize Service Monitors or Pod Monitors, but these types of CRDs are not supported.

Here are the steps to manually add the preview extension:

1. Make sure you have an Azure Monitor workspace set up.

2. Manually add the preview extension to your AKS cluster.

3. The extension will deploy monitor-metrics pods in your kube-system namespace and a default scraping configuration.

By following these steps, you can add managed Prometheus to your existing AKS cluster and start shipping time series data to Azure Monitor.

Azure Monitor Prometheus pricing is publicly available, and during the preview, the service is free of charge.

The pricing model is based on metrics ingestion and queries. Metrics ingestion costs $0.16 per 10 million samples ingested.

You also need to consider metrics queries, which cost $0.10 per 1 billion samples processed.

In larger environments, dashboards running auto-refresh, etc. can make the total bill significant.

Grafana cloud is another option, offering Prometheus and log ingestion on a per-user basis in their pro plan.

Here's a breakdown of the pricing:

I suspect that calculating the total cost will be crucial to determine the best option for your environment.

Configuring and monitoring Azure Managed Prometheus involves several key steps. To start, you need to verify that there are no errors with parsing the Prometheus config, merging with any default scrape targets enabled, and validating the full config.

You should also check the logs to ensure that the custom Prometheus config is recognized, and that there are no validation errors during the creation of pod/service monitors. If you're still not seeing metrics from the targets, check the logs for any errors.

In terms of monitoring, you can use the Prometheus interface for debugging to verify the expected configuration and targets being scraped. Additionally, you can port-forward to any of the ama-metrics pods and see the configuration and targets.

Here are the key things to check in the logs:

By following these steps, you can ensure that your Azure Managed Prometheus is configured correctly and running smoothly.

Creating SLOs is a crucial step in configuring and monitoring your Azure Monitor managed service for Prometheus. You can create SLOs using the Nobl9 Terraform provider or applying a YAML definition with sloctl.

To create your SLOs, you'll need to select the service the SLO will be associated with, which includes selecting your Azure Monitor managed service for Prometheus data source. You can configure the Replay period for historical data retrieval, which can be 0 or a positive integer up to 30.

You'll need to specify the metric and enter the PromQL query, which can be a threshold metric or a ratio metric. For a threshold metric, you'll enter a single time series against a threshold value you set. For a ratio metric, you'll enter two-time series for comparison and specify the ratio metric type.

You can choose between two data count methods: non-incremental counts or incremental counts. Non-incremental counts result in a pike-shaped SLO chart, while incremental counts result in a constantly increasing SLO chart.

Here are some good and total query examples:

You'll also need to define a Time Window for your SLO, which can be a rolling time window or a calendar-aligned window. Rolling time windows are better for tracking recent user experience, while calendar-aligned windows are best suited for business metrics.

You can specify the Error Budget Calculation Method, which includes occurrences method, time slices method, or other custom methods. You can define up to 12 objectives for an SLO.

Finally, you'll need to add the Display name, Name, and other settings for your SLO, including notification on data and alert policies. Azure Monitor managed service for Prometheus is case-insensitive.

Configuring the exporter's behavior is done through the use of various arguments. These arguments can be used to customize the exporter's behavior to suit your specific needs.

You can specify a list of subscriptions to scrape metrics from using the `subscriptions` argument, which is a required argument. This list should be a list of strings.

You can also specify the Azure Resource Type to scrape metrics for using the `resource_type` argument, which is also a required argument.

The `metrics` argument is used to specify the metrics to scrape from resources, and it is also a required argument.

If you want to apply a Kusto query filter to search for resources, you can use the `resource_graph_query_filter` argument. However, this argument can't be used if the `regions` argument is set.

The `regions` argument is used to specify the list of regions for gathering metrics, and it can't be used if the `resource_graph_query_filter` argument is set.

Here's a list of the available arguments with their descriptions and default values:

Configuring and monitoring your components is crucial for a smooth operation.

The prometheus.exporter.azure is a great tool for this purpose, but it's only reported as unhealthy if given an invalid configuration.

This means you can breathe a sigh of relief if you notice it's not working as expected, it might just be a configuration issue.

It's worth noting that exported fields retain their last healthy values in such cases, so you can still get some valuable data even if the configuration is off.

Debug metrics are crucial for troubleshooting and resolving issues in your system. The prometheus.exporter.azure does not expose any component-specific debug metrics.

You'll need to replace the existing metrics with custom ones to get the information you need. This might involve creating new metrics or modifying existing ones to suit your requirements.

Keep in mind that the prometheus.exporter.azure is a specific exporter that has limitations in terms of debug metrics.

Relabeling configs can sometimes be the culprit behind missing metrics. If you suspect this might be the case, check your relabeling configs to ensure they're not filtering out the targets.

A common mistake is to configure relabeling in a way that ignores the targets you're trying to scrape. Double-check that your relabeling configs are correctly set up to include all the targets you need.

To troubleshoot this issue, view the container logs with the following command. This will give you a better understanding of what's going on with your relabeling configs.

Config Processing is a crucial step in ensuring your monitoring setup is working correctly. To start, verify there are no errors with parsing the Prometheus config, merging with any default scrape targets enabled, and validating the full config.

If you've included a custom Prometheus config, verify that it's recognized in the logs. If not, check for any validation errors during the creation of pod/service monitors.

Verify there are no errors from MetricsExtension regarding authenticating with the Azure Monitor workspace. Also, check for any errors from the OpenTelemetry collector about scraping the targets.

If everything looks good in the logs, you can use the Prometheus interface for debugging to verify the expected configuration and targets being scraped.