Automating Azure Instrumentation and Monitoring – Part 4: Metric Alerts

One of the most important features of Azure Monitor is its ability to send alerts when something interesting happens – in other words, when our telemetry meets some criteria we have told Azure Monitor that we’re interested in. We might have alerts that indicate when our application is down, or when it’s getting an unusually high amount of traffic, or when the response time or other performance metrics aren’t within the normal range. We can also have alerts based on the contents of log messages, and on the health status of Azure resources as reported by Azure itself. In this post, we’ll look at how alerts work within Azure Monitor and will see how these can be automated using ARM templates. This post will focus on the general workings of the alerts system, including action groups, and on metric alerts; part 5 (coming soon) will look at log alerts and resource health alerts.

This post is part of a series:

    • Part 1 provides an introduction to the series by describing why we should instrument our systems, outlines some of the major tools that Azure provides such as Azure Monitor, and argues why we should be adopting an ‘infrastructure as code’ mindset for our instrumentation and monitoring components.

    • Part 2 describes Azure Application Insights, including its proactive detection and alert features. It also outlines a pattern for deploying instrumentation components based on the requirements we might typically have for different environments, from short-lived development and test environments through to production.

    • Part 3 discusses how to publish custom metrics, both through Application Insights and to Azure Monitor. Custom metrics let us enrich the data that is available to our instrumentation components.

    • Part 4 (this post) covers the basics of alerts and metric alerts. Azure Monitor’s powerful alerting system is a big topic, and in this part we’ll discuss how it works overall, as well as how to get alerts for built-in and custom metrics.

    • Part 5 (coming soon) covers log alerts and resource health alerts, two other major types of alerts that Azure Monitor provides. Log alerts let us alert on information coming into Application Insights logs and Log Analytics workspaces, while resource health alerts us when Azure itself is having an issue that may result in downtime or degraded performance.

    • Part 6 (coming soon) describes dashboards. The Azure Portal has a great dashboard UI, and our instrumentation data can be made available as charts. Dashboards are also possible to automate, and I’ll show a few tips and tricks I’ve learned when doing this.

    • Part 7 (coming soon) covers availability tests, which let us proactively monitor our web applications for potential outages. We’ll discuss deploying and automating both single-step (ping) and multi-step availability tests.

    • Part 8 (coming soon) describes autoscale. While this isn’t exactly instrumentation in and of itself, autoscale is built on much of the same data used to drive alerts and dashboards, and autoscale rules can be automated as well.

    • Finally, part 9 (coming soon) covers exporting data to other systems. Azure Monitor metrics and log data can be automatically exported, as can Application Insights data, and the export rules can be exported and used from automation scripts.

What Are Alerts?

Alerts are described in detail on the Azure Monitor documentation, and I won’t re-hash the entire page here. Here is a quick summary, though.

An alert rule defines the situations under which an alert should fire. For example, an alert rule might be something like when the average CPU utilisation goes above 80% over the last hour, or when the number of requests that get responses with an HTTP 5xx error code goes above 3 in the last 15 minutes. An alert is a single instance in which the alert rule fired. We tell Azure Monitor what alert rules we want to create, and Azure Monitor creates alerts and sends them out.

Alert rules have three logical components:

    • Target resource: the Azure resource that should be monitored for this alert. For example, this might be an app service, a Cosmos DB account, or an Application Insights instance.
    • Rule: the rule that should be applied when determining whether to fire an alert for the resource. For example, this might be a rule like when average CPU usage is greater than 50% within the last 5 minutes, or when a log message is written with a level of Warning. Rules include a number of sub-properties, and often include a time window or schedule that should be used to evaluate the alert rule.
    • Action: the actions that should be performed when the alert has fired. For example, this might be email admin@example.com or invoke a webhook at https://example.com/alert. Azure Monitor provides a number of action types that can be invoked, which we’ll discuss below.

There are also other pieces of metadata that we can set when we create alert rules, including the alert rule name, description, and severity. Severity is a useful piece of metadata that will be propagated to any alerts that fire from this alert rule, and allows for whoever is responding to understand how important the alert is likely to be, and to prioritise their list of alerts so that they deal with the most important alerts first.

Classic Alerts

Azure Monitor currently has two types of alerts. Classic alerts are the original alert type supported by Azure Monitor since its inception, and can be contrasted with the newer alerts – which, confusingly, don’t seem to have a name, but which I’ll refer to as newer alerts for the sake of this post.

There are many differences between classic and newer alerts. One such difference is that in classic alerts, actions and rules are mixed into a single ‘alert’ resource, while in newer alerts, actions and rules are separate resources (as described below in more detail). A second difference is that as Azure migrates from classic to newer alerts, some Azure resource types only support classic alerts, although these are all being migrated across to newer alerts.

Microsoft recently announced that classic alerts will be retired in June 2019, so I won’t spend a lot of time discussing them here, although if you need to create a classic alert with an ARM template before June 2019, you can use this documentation page as a reference.

All of the rest of this discussion will focus on newer alerts.

Alert Action Groups

A key component of Azure Monitor’s alert system is action groups, which define how an alert should be handled. Importantly, action groups are independent of the alert rule that triggered them. An alert rule defines when and why an alert should be fired, while an action group defines how the alert should be sent out to interested parties. For example, an action group can send an email to a specified email address, send an SMS notification, invoke a webhook, trigger a Logic App, or perform a number of other actions. A single action group can perform one or several of these actions.

Action groups are Azure Resource Manager resources in their own right, and alert rules then refer to them. This means we can have shared action groups that work across multiple alerts, potentially spread across multiple applications or multiple teams. We can also create specific action groups for defined purposes. For example, in an enterprise application you might have a set of action groups like this:

Action Group NameResource GroupActionsNotes
CreateEnterpriseIssueShared-OpsTeamInvoke a webhook to create issue in enterprise issue tracking system.This might be used for high priority issues that need immediate, 24×7 attention. It will notify your organisation’s central operations team.
SendSmsToTeamLeadMyApplicationSend an SMS to the development team lead.This might be used for high priority issues that also need 24×7 attention. It will notify the dev team lead.
EmailDevelopmentTeamMyApplicationSend an email to the development team’s shared email alias.This might be used to ensure the development team is aware of all production issues, including lower-priority issues that only need attention during business hours.

Of course, these are just examples; you can set up any action groups that make sense for your application, team, or company.

Automating Action Group Creation

Action groups can be created and updated using ARM templates, using the Microsoft.Insights/actionGroups resource type. The schema is fairly straightforward, but one point to consider is the groupShortName property. The short name is used in several places throughout Azure Monitor, but importantly it is used to identify the action group on email and SMS message alerts that Azure Monitor sends. If you have multiple teams, multiple applications, or even just multiple alert groups, it’s important to choose a meaningful short name that will make sense to someone reading the alert. I find it helpful to put myself in the mind of the person (likely me!) who will be woken at 3am to a terse SMS informing them that something has happened; they will be half asleep while trying to make sense of the alert that they have received. Choosing an appropriate action group short name may help save them several minutes of troubleshooting time, reducing the time to diagnosis (and the time before they can return to bed). Unfortunately these short names must be 12 characters or fewer, so it’s not always easy to find a good name to use.

With this in mind, here is an example ARM template that creates the three action groups listed above:

Note that this will create all three action groups in the same resource group, rather than using separate resource groups for the shared and application-specific action groups.

Once the action groups have been created, any SMS and email recipients will receive a confirmation message to let them know they are now in the action group. They can also unsubscribe from the action group if they choose. If you use a group email alias, it’s important to remember that if one recipient unsubscribes then the whole email address action will be disabled for that alert, and nobody on the email distribution list will get those alerts anymore.

Metric Alerts

Now that we know how to create action groups that are ready to receive alerts and route them to the relevant people and places, let’s look at how we create an alert based on the metrics that Azure Monitor has recorded for our system.

Important: Metric alerts are not free of charge, although there is a small free quota you get. Make sure you remove any test alert rules once you’re done, and take a look at the pricing information for more detail.

A metric alert rule has a number of important properties:

    • Scope is the resource that has the metrics that we want to monitor and alert on.
    • Evaluation frequency is how often Azure Monitor should check the resource to see if it meets the criteria. This is specified as an ISO 8601 period – for example, PT5M means check this alert every 5 minutes.
    • Window size is how far back in time Azure Monitor should look when it checks the criteria. This is also specified as an ISO 8601 period – for example, PT1H means when running this alert, look at the metric history for the last 1 hour. This can be between 5 minutes and 24 hours.
    • Criteria are the specific rules that should be evaluated. There is a sophisticated set of functionality available when specifying criteria, but commonly this will be something like (for an App Service) look at the number of requests that resulted in a 5xx status code response, and alert me if the count is greater than 3 or (for a Cosmos DB database) look at the number of requests where the StatusCode dimension was set to the value 429 (representing a throttled request), and alert me if the count is greater than 1.
    • Actions are references to the action group (or groups) that should be invoked when an alert is fired.

Each of these properties can be set within an ARM template using the resource type Microsoft.Insights/metricAlerts. Let’s discuss a few of these in more detail.

Scope

As we know from earlier in this series, there are three main ways that metrics get into Azure Monitor:

    • Built-in metrics, which are published by Azure itself.
    • Custom resource metrics, which are published by our applications and are attached to Azure resources.
    • Application Insights allows for custom metrics that are also published by our applications, but are maintained within Application Insights rather than tied to a specific Azure resource.

All three of these metric types can have alerts triggered from them. In the case of built-in and custom resource metrics, we will use the Azure resource itself as the scope of the metric alert. For Application Insights, we use the Application Insights resource (i.e. the resource of type Microsoft.Insights/components) as the scope.

Note that Microsoft has recently announced a preview capability of monitoring multiple resources in a single metric alert rule. This currently only works with virtual machines, and as it’s such a narrow use case, I won’t discuss it here. However, keep in mind that the scopes property is specified as an array because of this feature.

Criteria

A criterion is a specification of the conditions under which the alert should fire. Criteria have the following sub-properties:

    • Name: a criterion can have a friendly name specified to help understand what caused an alert to fire.
    • Metric name and namespace: the name of the metric that was published, and if it’s a custom metric, the namespace. For more information on metric namespaces see part 3 of this seriesA list of built-in metrics published by Azure services is available here.
    • Dimensions: if the metric has dimensions associated with it, we can filter the metrics to only consider certain dimension values. Dimension values can be included or excluded.
    • Time aggregation: the way in which the metric should be aggregated – e.g. counted, summed, or have the maximum/minimum values considered.
    • Operator: the comparison operator (e.g. greater than, less than) that should be used when comparing the aggregated metric value to the threshold.
    • Threshold: the critical value at which the aggregated metric should trigger the alert to fire.

These properties can be quite abstract, so let’s consider a couple of examples.

First, let’s consider an example for Cosmos DB. We might have a business rule that says whenever we see more than one throttled request, fire an alert. In this example:

    • Metric name would be TotalRequests, since that is the name of the metric published by Cosmos DB. There is no namespace since this is a built-in alert. Note that, by default, TotalRequests is the count of all requests and not just throttled requests, so…
    • Dimension would be set to filter the StatusCode dimension to only include the value 429, since 429 represents a throttled request.
    • Operator would be GreaterThan, since we are interested in knowing when we see more than a single throttled request.
    • Threshold would be 1, since we want to know whether we received more than one throttled request.
    • Time aggregation would be Maximum. The TotalRequests metric is a count-based metric (i.e. each metric raw value represents the total number of requests for a given period of time), and so we want to look at the maximum value of the metric within the time window that we are considering.

Second, let’s consider an example for App Services. We might have a business rule that says whenever our application returns more than three responses with a 5xx response code, fire an alert. In this example:

    • Metric name would be Http5xx, since that is the name of the metric published by App Services. Once again, there is no namespace.
    • Dimension would be omitted. App Services publishes the Http5xx metric as a separate metric rather than having a TotalRequests metric with dimensions for status codes like Cosmos DB. (Yes, this is inconsistent!)
    • Operator would again be GreaterThan.
    • Threshold would be 3.
    • Time aggregation would again be Maximum.

Note that a single metric alert can have one or more criteria. The odata.type property of the criteria property can be set to different values depending on whether we have a single criterion (in which case use Microsoft.Azure.Monitor.SingleResourceMultipleMetricCriteria) or multiple (Microsoft.Azure.Monitor.MultipleResourceMultipleMetricCriteria). At the time of writing, if we use multiple criteria then all of the criteria must be met for the alert rule to fire.

Static and Dynamic Thresholds

Azure Monitor recently added a new preview feature called dynamic thresholds. When we use dynamic thresholds then rather than specifying the metric thresholds ourselves, we instead let Azure Monitor watch the metric and learn its normal values, and then alert us if it notices a change. The feature is currently in preview, so I won’t discuss it in a lot of detail here, but there are example ARM templates available if you want to explore this.

Example ARM Templates

Let’s look at a couple of ARM templates to create the metric alert rules we discussed above. Each template also creates an action group with an email action, but of course you can have whatever action groups you want; you can also refer to shared action groups in other resource groups.

First, here is the ARM template for the Cosmos DB alert rule (lines 54-99), which uses a dimension to filter the metrics (lines 74-81) like we discussed above:

Second, here is the ARM template for the App Services alert rule (lines 77 to 112):

Note: when I tried to execute the second ARM template, I sometimes found it would fail the first time around, but re-executing it worked. This seems to just be one of those weird things with ARM templates, unfortunately.

Summary

Azure’s built-in metrics provide a huge amount of visibility into the operation of our system components, and of course we can enrich these with our own custom metrics (see part 3 of this series). Once the data is available to Azure Monitor, Azure Monitor can alert us based on whatever criteria we want to establish. The definitions of these metric alert rules is highly automatable using ARM templates, as is the definition of action groups to specify what should happen when an alert is fired.

In the next part of this series we will look at alerts based on log data.

Automating Azure Instrumentation and Monitoring – Part 3: Custom Metrics

One of the core data types that Azure Monitor works with is metrics – numerical pieces of data that represent the state of an Azure resource or of an application component at a specific point in time. Azure publishes built-in metrics for almost all Azure services, and these metrics are available for querying interactively as well as for use within alerts and other systems. In addition to the Azure-published metrics, we can also publish our own custom metrics. In this post we’ll discuss how to do this using both Azure Monitor’s recently announced support for custom metrics, and with Application Insights’ custom metrics features. We’ll start by looking at what metrics are and how they work.

This post is part of a series:

  • Part 1 provides an introduction to the series by describing why we should instrument our systems, outlines some of the major tools that Azure provides such as Azure Monitor, and argues why we should be adopting an ‘infrastructure as code’ mindset for our instrumentation and monitoring components.

  • Part 2 describes Azure Application Insights, including its proactive detection and alert features. It also outlines a pattern for deploying instrumentation components based on the requirements we might typically have for different environments, from short-lived development and test environments through to production.

  • Part 3 (this post) discusses how to publish custom metrics, both through Application Insights and to Azure Monitor. Custom metrics let us enrich the data that is available to our instrumentation components.

  • Part 4 covers the basics of alerts and metric alerts. Azure Monitor’s powerful alerting system is a big topic, and in this part we’ll discuss how it works overall, as well as how to get alerts for built-in and custom metrics.

  • Part 5 (coming soon) covers log alerts and resource health alerts, two other major types of alerts that Azure Monitor provides. Log alerts let us alert on information coming into Application Insights logs and Log Analytics workspaces, while resource health alerts us when Azure itself is having an issue that may result in downtime or degraded performance.

  • Part 6 (coming soon) describes dashboards. The Azure Portal has a great dashboard UI, and our instrumentation data can be made available as charts. Dashboards are also possible to automate, and I’ll show a few tips and tricks I’ve learned when doing this.

  • Part 7 (coming soon) covers availability tests, which let us proactively monitor our web applications for potential outages. We’ll discuss deploying and automating both single-step (ping) and multi-step availability tests.

  • Part 8 (coming soon) describes autoscale. While this isn’t exactly instrumentation in and of itself, autoscale is built on much of the same data used to drive alerts and dashboards, and autoscale rules can be automated as well.

  • Finally, part 9 (coming soon) covers exporting data to other systems. Azure Monitor metrics and log data can be automatically exported, as can Application Insights data, and the export rules can be exported and used from automation scripts.

What Are Metrics?

Metrics are pieces of numerical data. Each metric has both a value and a unit. Here are some example metrics:

ExampleValueUnit
12 requests per second12requests per second
54 gigabytes54gigabytes
7 queue messages7queue messages

Metrics can be captured either in their raw or aggregated form. An aggregated metric is a way of simplifying the metric across a given period of time. For example, consider a system that processes messages from a queue. We could count the number of messages processed by the system in two ways: we could adjust our count every time a message is processed, or we could check the number of messages on the queue every minute, and batch these into five-minute blocks. The latter is one example of an aggregated metric.

Because metrics are numerical in nature, they can be visualised in different ways. For example, a line chart might show the value of a metric over time.

Azure Monitor also supports adding dimensions to metrics. Dimensions are extra pieces of data that help to add context to a metric. For example, the Azure Monitor metric for the number of messages in a Service Bus namespace has the entity (queue or topic) name as a dimension. Queries and visualisations against this metric can then filter down to specific topics, can visualise each topic separately, or can roll up all topics/queues and show the total number of messages across the whole Service Bus namespace.

Azure Monitor Metrics

Azure Monitor currently has two metric systems.

  • Classic metrics were traditionally published by most Azure services. When we use the Azure Portal, the Metrics (Classic) page displays these metrics.
  • Near real-time metrics are the newer type of metrics, and Azure is moving to use this across all services. As their name suggests, these metrics get updated more frequently than classic metrics – where classic metrics might not appear for several minutes, near real-time metrics typically are available for querying within 1-2 minutes of being published, and sometimes much quicker than that. Additionally, near real-time metrics are the only metric type that supports dimensions; classic metrics do not. Custom metrics need to be published as near real-time metrics.

Over time, all Azure services will move to the near real-time metrics system. In the meantime, you can check whether a given Azure service is publishing to the classic or newer metric system by checking this page. In this post we’ll only be dealing with the newer (near real-time) metric system.

Custom Metrics

Almost all Azure services publish their own metrics in some form, although the usefulness and quality varies depending on the specific service. Core Azure services tend to have excellent support for metrics. Publishing of built-in metrics happens automatically and without any interaction on our part. The metrics are available for interactive querying through the portal and API, and for alerts and all of the other purposes we discussed in part 1 of this series.

There are some situations where the built-in metrics aren’t enough for our purposes. This commonly happens within our own applications. For example, if our application has components that process messages from a queue then it can be helpful to know how many messages are being processed per minute, how long each message takes to process, and how many messages are currently on the queues. These metrics can help us to understand the health of our system, to provision new workers to help to process messages more quickly, or to understand bottlenecks that our developers might need to investigate.

There are two ways that we can publish custom metrics into Azure Monitor.

  • Azure Monitor custom metrics, currently a preview service, provides an API for us to send metrics into Azure Monitor. We submit our metrics to an Azure resource, and the metrics are saved alongside the built-in metrics for that resource.
  • Application Insights also provides custom metrics. Our applications can create and publish metrics into Application Insights, and they are accessible by using Application Insights’ UI, and through the other places that we work with metrics. Although the core offering of publishing custom metrics into Application Insights is generally available, some specific features are in preview.

How do we choose which approach to use? Broadly speaking I’d generally suggest using Azure Monitor’s custom metrics API for publishing resource or infrastructure-level metrics – i.e. enriching the data that Azure itself publishes about a resource – and I’d suggest using Application Insights for publishing application-level metrics – i.e. metrics about our own application code.

Here’s a concrete example, again related to queue processing. If we have an application that processes queue messages, we’ll typically want instrumentation to understand how these queues and processors are behaving. If we’re using Service Bus queues or topics then we get a lot of instrumentation about our queues, including the number of messages that are currently on the queue. But if we’re using Azure Storage queues, we’re out of luck. Azure Storage queues don’t have the same metrics, and we don’t get the queue lengths from within Azure Monitor. This is an ideal use case for Azure Monitor’s custom metrics.

We may also want to understand how long it’s taking us to process each message – from the time it was submitted to the queue to the time it completed processing. This is often an important metric to ensure that our data is up-to-date and that users are having the best experience possible. Ultimately this comes down to how long our application is taking to perform its logic, and so this is an application-level concern and not an infrastructure-level concern. We’ll use Application Insights for this custom metric.

Let’s look at how we can write code to publish each of these metrics.

Publishing Custom Resource Metrics

In order to publish a custom resource metric we need to do the following:

  • Decide whether we will add dimensions.
  • Decide whether we will aggregate our metric’s value.
  • Authenticate and obtain an access token.
  • Send the metric data to the Azure Monitor metrics API.

Let’s look at each of these in turn, in the context of an example Azure Function app that we’ll use to send our custom metrics.

Adding Dimensions

As described above, dimensions let us add extra data to our metrics so that we can group and compare them. We can submit metrics to Azure Monitor with or without dimensions. If we want to include dimensions, we need to include two extra properties – dimNames specifies the names of the dimensions we want to add to the metric, and dimValues specifies the values of those dimensions. The order of the dimension names and values must match so that Azure Monitor can relate the value to its dimension name.

Aggregating Metrics

Metrics are typically queried in an aggregated form – for example, counting or averaging the values of metrics to get a picture of how things are going overall. When submitting custom metrics we can also choose to send our metric values in an aggregated form if we want. The main reasons we’d do this are:

  • To save cost. Azure Monitor custom metrics aren’t cheap when you use them at scale, and so pre-aggregating within our application means we don’t need to incur quite as high a cost since we aren’t sending as much raw data to Azure Monitor to ingest.
  • To reduce a very high volume of metrics. If we have a large number of metrics to report on, it will likely be much faster for us to send the aggregated metric to Azure Monitor rather than sending each individual metric.

However, it’s up to us – we can choose to send individual values if we want.

If we send aggregated metrics then we need to construct a JSON object to represent the metric as follows:

For example, let’s imagine we have recorded the following queue lengths (all times in UTC):

TimeLength
11:00am1087
11:01am1124
11:02am826
11:03am888
11:04am1201
11:05am1091

We might send the following pre-aggregated metrics in a single payload:

Azure Monitor would then be able to display the aggregated metrics for us when we query.

If we chose not to send the metrics in an aggregated form, we’d send the metrics across individual messages; here’s an example of the fourth message:

Security for Communicating with Azure Monitor

We need to obtain an access token when we want to communicate with Azure Monitor. When we use Azure Functions, we can make use of managed identities to simplify this process a lot. I won’t cover all the details of managed identities here, but the example ARM template for this post includes the creation and use of a managed identity. Once the function is created, it can use the following code to obtain a token that is valid for communication with Azure Monitor:

The second part of this process is authorising the function’s identity to write metrics to resources. This is done by using the standard Azure role-based access control system. The function’s identity needs to be granted the Monitoring Metrics Publisher role, which has been defined with the well-known role definition ID 3913510d-42f4-4e42-8a64-420c390055eb.

Sending Custom Metrics to Azure Monitor

Now we have our metric object and our access token ready, we can submit the metric object to Azure Monitor. The actual submission is fairly easy – we just perform a POST to a URL. However, the URL we submit to will be different depending on the resource’s location and resource ID, so we dynamically construct the URL as follows:

We might deploy into the West US 2 region, so an example URL might look like this: https://westus2.monitoring.azure.com/subscriptions/377f4fe1-340d-4c5d-86ae-ad795b5ac17d/resourceGroups/MyCustomMetrics/providers/Microsoft.Storage/storageAccounts/mystorageaccount/metrics

Currently Azure Monitor only supports a subset of Azure regions for custom metrics, but this list is likely to grow as the feature moves out of preview.

Here is the full C# Azure Function we use to send our custom metrics:

Testing our Function

I’ve provided an ARM template that you can deploy to test this:

Make sure to deploy this into a region that supports custom metrics, like West US 2.

Once you’ve deployed it, you can create some queues in the storage account (use the storage account that begins with q and not the one that begins with fn). Add some messages to the queues, and then run the function or wait for it to run automatically every five minutes.

Then you can check the metrics for the storage queue, making sure to change the metric namespace to queueprocessing:

You should see something like the following:

As of the time of writing (January 2019), there is a bug where Azure Storage custom metrics don’t display dimensions. This will hopefully be fixed soon.

Publishing Custom Application Metrics

Application Insights also allows for the publishing of custom metrics using its own SDK and APIs. These metrics can be queried through Azure Monitor in the same way as resource-level metrics. The process by which metrics are published into Application Insights is quite different to how Azure Monitor custom metrics are published, though.

The Microsoft documentation on Application Insights custom metrics is quite comprehensive, so rather than restate it here I will simply link to the important parts. I will focus on the C# SDK in this post.

To publish a custom metric to Application Insights you need an instance of the TelemetryClient class. In an Azure Functions app you can set the APPINSIGHTS_INSTRUMENTATIONKEY application setting – for example, within an ARM template – and then create an instance of TelemetryClient. The TelemetryClient will find the setting and will automatically configure itself to send telemetry to the correct place.

Once you have an instance of TelemetryClientyou can use the GetMetric().TrackValue() method to log a new metric value, which is then pre-aggregated and sent to Application Insights after a short delay. Dimensions can also be set using the same method. There are a number of overloads of this method that can be used to submit custom dimensions, too.

Note that as some features are in preview, they don’t work consistently yet – for example, at time of writing custom namespaces aren’t honoured correctly, but this should hopefully be resolved soon.

If you want to send raw metrics rather than pre-aggregated metrics, the legacy TrackMetric() method can be used, but Microsoft discourage its use and are deprecating it.

Here is some example Azure Function code that writes a random value to the My Test Metric metric:

And a full ARM template that deploys this is:

Summary

Custom metrics allow us to enrich our telemetry data with numerical values that can be aggregated and analysed, both manually through portal dashboards and APIs, and automatically using a variety of Azure Monitor features. We can publish custom metrics against any Azure resource by using the new custom metrics APIs, and we can also write application-level metrics to Application Insights.

In the next part of this series we will start to look at alerts, and will specifically look at metric alerts – one way to have Azure Monitor process the data for both built-in and custom metrics and alert us when things go awry.

Automating Azure Instrumentation and Monitoring – Part 2: Application Insights

Application Insights is a component of Azure Monitor for application-level instrumentation. It collects telemetry from your application infrastructure like web servers, App Services, and Azure Functions apps, and from your application code. In this post we’ll discuss how Application Insights can be automated in several key ways: first, by setting up an Application Insights instance in an ARM template; second, by connecting it to various types of Azure application components through automation scripts including Azure Functions, App Services, and API Management; and third, by configuring its smart detection features to emit automatic alerts in a configurable way. As this is the first time in this series that we’ll deploy instrumentation code, we’ll also discuss an approach that can be used to manage the deployment of different types and levels of monitoring into different environments.

This post is part of a series:

  • Part 1 provides an introduction to the series by describing why we should instrument our systems, outlines some of the major tools that Azure provides such as Azure Monitor, and argues why we should be adopting an ‘infrastructure as code’ mindset for our instrumentation and monitoring components.

  • Part 2 (this post) describes Azure Application Insights, including its proactive detection and alert features. It also outlines a pattern for deploying instrumentation components based on the requirements we might typically have for different environments, from short-lived development and test environments through to production.

  • Part 3 discusses how to publish custom metrics, both through Application Insights and to Azure Monitor. Custom metrics let us enrich the data that is available to our instrumentation components.

  • Part 4 covers the basics of alerts and metric alerts. Azure Monitor’s powerful alerting system is a big topic, and in this part we’ll discuss how it works overall, as well as how to get alerts for built-in and custom metrics.

  • Part 5 (coming soon) covers log alerts and resource health alerts, two other major types of alerts that Azure Monitor provides. Log alerts let us alert on information coming into Application Insights logs and Log Analytics workspaces, while resource health alerts us when Azure itself is having an issue that may result in downtime or degraded performance.

  • Part 6 (coming soon) describes dashboards. The Azure Portal has a great dashboard UI, and our instrumentation data can be made available as charts. Dashboards are also possible to automate, and I’ll show a few tips and tricks I’ve learned when doing this.

  • Part 7 (coming soon) covers availability tests, which let us proactively monitor our web applications for potential outages. We’ll discuss deploying and automating both single-step (ping) and multi-step availability tests.

  • Part 8 (coming soon) describes autoscale. While this isn’t exactly instrumentation in and of itself, autoscale is built on much of the same data used to drive alerts and dashboards, and autoscale rules can be automated as well.

  • Finally, part 9 (coming soon) covers exporting data to other systems. Azure Monitor metrics and log data can be automatically exported, as can Application Insights data, and the export rules can be exported and used from automation scripts.

Setting up Application Insights

When using the Azure Portal or Visual Studio to work with various types of resources, Application Insights will often be deployed automatically. This is useful when we’re exploring services or testing things out, but when it comes time to building a production-grade application, it’s better to have some control over the way that each of our components is deployed. Application Insights can be deployed using ARM templates, which is what we’ll do in this post.

Application Insights is a simple resource to create from an ARM template. An instance with the core functionality can be created with a small ARM template:

There are a few important things to note about this template:

  • Application Insights is only available in a subset of Azure regions. This means you may need to deploy it in a region other than the region your application infrastructure is located in. The template above includes a parameter to specify this explicitly.
  • The name of your Application Insights instance doesn’t have to be globally unique. Unlike resources like App Services and Cosmos DB accounts, there are no DNS names attached to an Application Insights instance, so you can use the same name across multiple instances if they’re in different resource groups.
  • Application Insights isn’t free. If you have a lot of data to ingest, you may incur large costs. You can use quotas to manage this if you’re worried about it.
  • There are more options available that we won’t cover here. This documentation page provides further detail.

After an Application Insights instance is deployed, it has an instrumentation key that can be used to send data to the correct Application Insights instance from your application. The instrumentation key can be accessed both through the portal and programmatically, including within ARM templates. We’ll use this when publishing telemetry.

Publishing Telemetry

There are a number of ways to publish telemetry into Application Insights. While I won’t cover them all, I’ll give a quick overview of some of the most common ways to get data from your application into Application Insights, and how to automate each.

Azure Functions

Azure Functions has built-in integration with Application Insights. If you create an Azure Functions app through the portal it asks whether you want to set up this integration. Of course, since we’re using automation scripts, we have to do a little work ourselves. The magic lies in an app setting, APPINSIGHTS_INSTRUMENTATIONKEY, which we can attach to any function app. Here is an ARM template that deploys an Azure Functions app (and associated app service plan and storage account), an Application Insights instance, and the configuration to link the two:

App Services

If we’re deploying a web app into an app service using ASP.NET, we can use the ASP.NET integration directly. In fact, this works across many different hosting environments, and is described in the next section.

If you’ve got an app service that is not using ASP.NET, though, you can still get telemetry from your web app into Application Insights by using an app service extension. Extensions augment the built-in behaviour of an app service by installing some extra pieces of logic into the web server. Application Insights has one such extension, and we can configure it using an ARM template:

As you can see, similarly to the Azure Functions example, the APPINSIGHTS_INSTRUMENTATIONKEY is used here to link the app service with the Application Insights instance.

One word of warning – I’ve found that the site extension ARM resource isn’t always deployed correctly the first time the template is deployed. If you get an error the first time you deploy, try it again and see if the problem goes away. I’ve tried, but have never fully been able to understand why this happens or how to stop it.

ASP.NET Applications

If you have an ASP.NET application running in an App Service or elsewhere, you can install a NuGet package into your project to collect Application Insights telemetry. This process is documented here. If you do this, you don’t need to install the App Services extension from the previous section. Make sure to set the instrumentation key in your configuration settings and then flow it through to Application Insights from your application code.

API Management

If you have an Azure API Management instance, you might be aware that this can publish telemetry into Application Insights too. This allows for monitoring of requests all the way through the request handling pipeline. When it comes to automation, Azure API Management has very good support for ARM templates, and its Application Insights integration is no exception.

At a high level there are two things we need to do: first, we create a logger resource to establish the API Management-wide connection with Application Insights; and second, we create a diagnosticresource to instruct our APIs to send telemetry to the Application Insights instance we have configured. We can create a diagnostic resource for a specific API or to cover all APIs.

The diagnostic resource includes a sampling rate, which is the percentage of requests that should have their telemetry sent to Application Insights. There is a lot of detail to be aware of with this feature, such as the performance impact and the ways in which sampling can reduce that impact. We won’t get into that here, but I encourage you to read more detail from Microsoft’s documentationbefore using this feature.

Here’s an example ARM template that deploys an API Management instance, an Application Insights instance, and configuration to send telemetry from every request into Application Insights:

Smart Detection

Application Insights provides a useful feature called smart detection. Application Insights watches your telemetry as it comes in, and if it notices unusual changes, it can send an alert to notify you. For example, it can detect the following types of issues:

  • An application suddenly sends back a higher rate of 5xx (error)-class status responses than it was sending previously.
  • The time it takes for an application to communicate with a database has increased significantly above the previous average.

Of course, this feature is not foolproof – for example, in my experience it won’t detect slow changes in error rates over time that may still indicate an issue. Nevertheless, it is a very useful feature to have available to us, and it has helped me identify problems on numerous occasions.

Smart detection is enabled by default. Unless you configure it otherwise, smart detection alerts are sent to all owners of the Azure subscription in which the Application Insights instance is located. In many situations this is not desirable: when your Azure subscription contains many different applications, each with different owners; or when the operations or development team are not granted the subscription owner role (as they should not be!); or when the subscriptions are managed by a central subscription management team who cannot possibly deal with the alerts they receive from all applications. We can configure each smart detection alert using the proactiveDetectionConfigs ARM resource type.

Here is an example ARM template showing how the smart detection alerts can be redirected to an email address you specify:

In development environments, you may not want to have these alerts enabled at all. Development environments can be used sporadically, and can have a much higher error rate than normal, so the signals that Application Insights uses to proactively monitor for problems aren’t as useful. I find that it’s best to configure smart detection myself so that I can switch it on or off for different environments, and for those environments that do need it, I’ll override the alert configuration to send to my own alert email address and not to the subscription owners. This requires us to have different instrumentation configuration for different environments.

Instrumentation Environments

In most real-world applications, we end up deploying the application in at least three environments: development environments, which are used by software developers as they actively work on a feature or change; non-production environments, which are used by testers, QA engineers, product managers, and others who need to access a copy of the application before it goes live; and production environments, which are used by customers and may be monitored by a central operations team. Within these categories, there can be multiple actual environments too – for example, there can be different non-production environments for different types of testing (e.g. functional testing, security testing, and performance testing), and some of these may be long-lived while others are short-lived.

Each of these different environments also has different needs for instrumentation:

  • Production environments typically need the highest level of alerting and monitoring since an issue may affect our customers’ experiences. We’ll typically have many alerts and dashboards set up for production systems. But we also may not want to collect large volumes of telemetry from production systems, especially if doing so may cause a negative impact on our application’s performance.
  • Non-production environments may need some level of alerting, but there are certain types of alerts that may not make sense compared to production environments. For example, we may run our non-production systems on a lower tier of infrastructure compared to our production systems, and so an alert based on the application’s response time may need different thresholds to account for the lower expected performance. But in contrast to non-production environments, we may consider it to be important to collect a lot of telemetry in case our testers do find any issues and we need to diagnose them interactively, so we may allow for higher levels of telemetry sampling than we would in a production environment.
  • Development environments may only need minimal instrumentation. Typically in development environments I’ll deploy all of the telemetry collection that I would deploy for non-production environments, but turn all alerts and dashboards off. In the event of any issues, I’ll be interactively working with the telemetry myself anyway.

Of course, your specific needs may be different, but in general I think it’s good to categorise our instrumentation across types of environments. For example, here is how I might typically deploy Application Insights components across environments:

Instrumentation TypeDevelopmentNonProductionProduction
Application Insights smart detectionOffOn, sending alerts to developersOn, sending alerts to production monitoring group
Application Insights Azure Functions integrationOnOnOn
Application Insights App Services integrationOnOnOn
Application Insights API Management integrationOn, at 100% samplingOn, at 100% samplingOn, at 30% sampling

Once we’ve determined those basic rules, we can then implement them. In the case of ARM templates, I tend to use ARM template parameters to handle this. As we go through this series we’ll see examples of how we can use parameters to achieve this conditional logic. I’ll also present versions of this table with my suggestions for the components that you might consider deploying for each environment.

Configuring Smart Detection through ARM Templates

Now that we have a basic idea of how we’ll configure instrumentation in each environment, we can reconsider how we might configure Application Insights. Typically I suggest deploying a single Application Insights instance for each environment the system will be deployed into. If we’re building up a complex ARM template with all of the system’s components, we can embed the conditional logic required to handle different environments in there.

Here’s a large ARM template that includes everything we’ve created in this post, and has the three environment type modes:

Summary

Application Insights is a very useful tool for monitoring our application components. It collects telemetry from a range of different sources, which can all be automated. It provides automatic analysis of some of our data and has smart detection features, which again we can configure through our automation scripts. Furthermore, we can publish data into it ourselves as well. In fact, in the next post this series, we’ll discuss how we can publish custom metrics into Application Insights.

Automating Azure Instrumentation and Monitoring – Part 1: Introduction

Instrumentation and monitoring is a critical part of managing any application or system. By proactively monitoring the health of the system as a whole, as well as each of its components, we can mitigate potential issues before they affect customers. And if issues do occur, good instrumentation alerts us to that fact so that we can respond quickly.

Azure provides a set of powerful monitoring and instrumentation tools to instrument almost all Azure services as well as our own applications. By taking advantage of these tools we can can improve the quality of our systems. However, there isn’t a lot of documentation on how to script and automate the instrumentation components that we build. Alerts, dashboards, and other instrumentation components are important parts of our systems and deserve as much attention as our application code or other parts of our infrastructure. In this series, we’ll cover many of the common types of instrumentation used in Azure-hosted systems and will outline how many of these can be automated, usually with a combination of ARM templates and scripting. The series consists of nine parts:

  • Part 1 (this post) provides an introduction to the series by describing why we should instrument our systems, outlines some of the major tools that Azure provides such as Azure Monitor, and argues why we should be adopting an ‘infrastructure as code’ mindset for our instrumentation and monitoring components.

  • Part 2 describes Azure Application Insights, including its proactive detection and alert features. It also outlines a pattern for deploying instrumentation components based on the requirements we might typically have for different environments, from short-lived development and test environments through to production.

  • Part 3 discusses how to publish custom metrics, both through Application Insights and to Azure Monitor. Custom metrics let us enrich the data that is available to our instrumentation components.

  • Part 4 covers the basics of alerts and metric alerts. Azure Monitor’s powerful alerting system is a big topic, and in this part we’ll discuss how it works overall, as well as how to get alerts for built-in and custom metrics.

  • Part 5 (coming soon) covers log alerts and resource health alerts, two other major types of alerts that Azure Monitor provides. Log alerts let us alert on information coming into Application Insights logs and Log Analytics workspaces, while resource health alerts us when Azure itself is having an issue that may result in downtime or degraded performance.

  • Part 6 (coming soon) describes dashboards. The Azure Portal has a great dashboard UI, and our instrumentation data can be made available as charts. Dashboards are also possible to automate, and I’ll show a few tips and tricks I’ve learned when doing this.

  • Part 7 (coming soon) covers availability tests, which let us proactively monitor our web applications for potential outages. We’ll discuss deploying and automating both single-step (ping) and multi-step availability tests.

  • Part 8 (coming soon) describes autoscale. While this isn’t exactly instrumentation in and of itself, autoscale is built on much of the same data used to drive alerts and dashboards, and autoscale rules can be automated as well.

  • Finally, part 9 (coming soon) covers exporting data to other systems. Azure Monitor metrics and log data can be automatically exported, as can Application Insights data, and the export rules can be exported and used from automation scripts.

While the posts will cover the basics of each of these topics, the focus will be on deploying and automating each of these components. I’ll provide links to more details on the inner workings where needed to supplement the basic overview I’ll provide. Also, I’ll assume some basic familiarity with ARM templates and PowerShell.

Let’s start by reviewing the landscape of instrumentation on Azure.

Azure’s Instrumentation Platform

As Azure has evolved, it’s built up an increasingly comprehensive suite of tools for monitoring the individual components of a system as well as complete systems as a whole. The key piece of the Azure monitoring puzzle is named, appropriately enough, Azure Monitor. Azure Monitor is a built-in service that works with almost all Azure services. Many of its features are free. It automatically captures telemetry, consolidates it, and makes the data available for interactive querying as well as for a variety of other purposes that we’ll discuss throughout the series.

This isn’t quite the whole story, though. While Azure Monitor works well most of the time, and it appears to be the strategic direction that Azure is heading in, there are a number of exceptions, caveats, and complexities – and these become more evident when you try to automate it. I’ll cover some of these in more detail below.

Metrics

Metrics are numeric values that represent a distinct piece of information about a component at a point in time. The exact list of metrics depends on what makes sense for a given service. For example, a virtual machine publishes metrics for the CPU and memory used; a SQL database has metrics for the number of connections and the database throughput units used; a Cosmos DB account publishes metrics for the number of requests issued to the database engine; and an App Service has metrics for the number of requests flowing through. There can be dozens of different metrics published for any given Azure service, and they are all documented for reference. We’ll discuss metrics in more detail throughout the series, as there are some important things to be aware of when dealing with metrics.

As well as Azure Monitor’s metrics support, some Azure services have their metrics systems. For example, SQL Azure has a large amount of telemetry that can be accessed through dynamic management views. Some of the key metrics are also published into Azure Monitor, but if you want to use metrics that are only available in dynamic management views then you won’t be able to use the analysis and processing features of Azure Monitor. We’ll discuss a potential workaround for this in part 3 of this series.

A similar example is Azure Storage queues. Azure Storage has an API that can be used to retrieve the approximate number of messages sitting in a queue, but this metric isn’t published into Azure Monitor and so isn’t available for alerting or dashboarding. Again, we’ll discuss a potential workaround for this in part 3 of this series.

Nevertheless, in my experience, almost all of the metrics I work with on a regular basis are published through Azure Monitor, and so in this series we’ll predominantly focus on these.

Logs

Logs are structured pieces of data, usually with a category, a level, and a textual message, and often with a lot of additional contextual data as well. Broadly speaking, there are several general types of logs that Azure deals with:

  • Resource activity logs are essentially the logs for management operations performed on Azure resources through the Azure Resource Management (ARM) API, and a few other types of management-related logs. They can be interactively queried using the Azure Portal blades for any resource, as well as resource groups and subscriptions. You can typically view these by looking at the Activity log tab from any Azure resource blade in the portal. Activity logs contain all write operations that pass through the ARM API. If you use the ARM API directly, or indirectly through the Azure Portal, CLI, PowerShell, or anything else, you’ll see logs appear in here. More details on activity logs is available here.
  • Azure AD activity logs track Active Directory sign-ins and management actions. These can be viewed from within the Azure AD portal blade. We won’t be covering Azure AD much in this series, but you can read more detail about Azure AD logs here.
  • Diagnostic logs are published by individual Azure services. They provide information about the actions and work that the service itself is doing. By default these are not usually available for interactive querying. Diagnostic logs often work quite differently between different services. For example, Azure Storage can publish its own internal logs into a $logs blob container; App Services provides web server and application logs and can save these to a number of different places as well as view them in real time; and Azure SQL logs provide a lot of optional diagnostic information and again have to be explicitly enabled.
  • Application logs are written by application developers. These can be sent to a number of different places, but a common destination is Application Insights. If logs are published into Application Insights they can be queried interactively, and used as part of alerts and dashboards. We’ll discuss these in more detail in later parts of this series.

Azure Log Analytics is a central log consolidation, aggregation, and querying service. Some of the above logs are published automatically into Log Analytics, while others have to be configured to do so. Log Analytics isn’t a free service, and needs to be provisioned separately if you want to configure logs to be sent into it. We’ll discuss it more detail throughout this series.

Ingestion of Telemetry

Azure services automatically publish metrics into Azure Monitor, and these built-in metrics are ingested free of charge. Custom metrics can also be ingested by Azure Monitor, which we’ll discuss in more detail in part 3 of this series.

As described in the previous section, different types of logs are ingested in different ways. Azure Monitor automatically ingests resource activity logs, and does so free of charge. The other types of logs are not ingested by Azure Monitor unless you explicitly opt into that, either by configuring Application Insights to receive custom logs, or by provisioning a Log Analytics workspace and then configuring your various components to send their logs to that.

Processing and Working With Telemetry

Once data has been ingested into Azure Monitor, it becomes available for a variety of different purposes. Many of these will be discussed in later parts of this series. For example, metrics can be used for dashboards (see part 6, coming soon) and for autoscale rules (see part 8, coming soon); logs that have been routed to Azure Monitor can be used as part of alerts (see part 5, coming soon); and all of the data can be exported (see part 9, coming soon).

Application Insights

Application Insights has been part of the Azure platform for around two years. Microsoft recently announced that it is considered to be part of the umbrella Azure Monitor service. However, Application Insights is deployed as a separate service, and is billable based on the amount of data it ingests. We’ll cover Application Insights in more detail in part 2 of this series.

Summary of Instrumentation Components

There’s a lot to take in here! The instrumentation story across Azure isn’t always easy to understand, and although the complexity is reducing as Microsoft consolidates more and more of these services into Azure Monitor, there is still a lot to unpack. Here’s a very brief summary:

Azure Monitor is the primary instrumentation service we generally interact with. Azure Monitor captures metrics from every Azure service, and it also captures some types of logs as well. More detailed diagnostic and activity logging can be enabled on a per-service or per-application basis, and depending on how you configure it, it may be routed to Azure Monitor or somewhere else like an Azure Storage account.

Custom data can be published into Azure Monitor through custom metrics (which we’ll cover in part 3 of the series), through publishing custom logs into Log Analytics, and through Application Insights. Application Insights is a component that is deployed separately, and provides even more metrics and logging capabilities. It’s built off the same infrastructure as the rest of Azure Monitor and is mostly queryable from the same places.

Once telemetry is published into Azure Monitor it’s available for a range of different purposes including interactive querying, alerting, dashboarding, and exporting. We’ll cover all of these in more detail throughout the series.

Instrumentation as Infrastructure

The idea of automating all of our infrastructure – scripting the setup of virtual machines or App Services, creating databases, applying schema updates, deploying our applications, and so forth – has become fairly uncontroversial. The benefits are so compelling, and the tools are getting so good, that generally most teams don’t take much convincing that expressing their infrastructure as code is worthwhile. But in my experience working with a variety of customers, I’ve found that this often isn’t the case with instrumentation.

Instrumentation components like dashboards, alerts, and availability tests are still frequently seen as being of a different category to the rest of an application. While it may seem perfectly reasonable to script out the creation of some compute resources, and for these scripts to be put into a version control system and built alongside the app itself, instrumentation is frequently handled manually and without the same level of automation rigour as the application code and scripts. As I’ll describe below, I’m not opposed to using the Azure Portal and other similar tools to explore the metrics and logs associated with an application. But I believe that the instrumentation artifacts that come out of this exploration – saved queries, dashboard widgets, alert rules, etc – are just as important as the rest of our application components, and should be treated with the same level of diligence.

As with any other type of infrastructure, there are some clear benefits to expressing instrumentation components as code compared to using the Azure Portal including:

  • Reducing risk of accidental mistakes: I find that expressing my instrumentation logic explicitly in code, scripts, or ARM templates makes me far less likely to make a typo, or to do something silly like confuse different units of measurement when I’m setting an alert threshold.
  • Peer review: For teams that use a peer review process in their version control system, treating infrastructure as code means that someone else on the team is expected to review the changes I’m making. If I do end up making a dumb mistake then it’s almost always caught by a coworker during a review, and and even if there are no mistakes, having someone else on the team review the change means that someone else understands what’s going on.
  • Version control: Keeping all of our instrumentation logic and alert rules in a version control system is helpful when we want to understand how instrumentation has evolved over time, and for auditability.
  • Keeping related changes together: I’m a big fan of keeping related changes together. For example, if I create a pull request to add a new application component then I can add the application code, the deployment logic, and the instrumentation for that new component all together. This makes it easier to understand the end-to-end scope of the feature being added. If we include instrumentation in our ‘definition of done’ for a feature then we can easily see that this requirement is met during the code review stage.
  • Managing multiple environments: When instrumentation rules and components aren’t automated, it’s easy for them to get out of sync between environments. In most applications there is at least one dev/test environment as well as production. While it might seem unnecessary to have alerts and monitoring in a dev environment, I will argue in part 2 of this series that it’s important to do so, even if you have slightly different rules and thresholds. Deploying instrumentation as code means that these environments can be kept in sync. Similarly, you may deploy your production environment to multiple regions for georedundancy or for performance reasons. If your instrumentation components are kept alongside the rest of your infrastructure, you’ll get the same alerts and monitoring for all of your regions.
  • Avoid partial automation: In my experience, partially automating an application can sometimes result in more complexity than not automating it at all. For example, if you use ARM templates and (as I typically suggest) use the ‘complete’ deployment mode, then any components you may have created manually through the Azure Portal can be removed. Many of the instrumentation components we’ll discuss are ARM resources and so can be subject to this behaviour. Therefore, a lack of consistency across how we deploy all of our infrastructure and instrumentation can result in lost work, missed alerts, hard-to-find bugs, and generally odd instrumentation behaviour.

Using the Azure Portal

Having an instrumentation-first mindset doesn’t mean that we can’t or shouldn’t ever use the Azure Portal. In fact, I tend to use it quite a lot – but for specific purposes.

First, I tend to use it a lot for interactively querying metrics and logs in response to an issue, or just to understand how my systems are behaving. I’ll use Metrics Explorer to create and view charts of potentially interesting metrics, and I’ll write log queries and execute them from Application Insights or Log Analytics.

Second, when I’m responding to alerts, I’ll make use of the portal’s tooling to view details, track the status of the alert, and investigate what might be happening. We’ll discuss alerts more later in this series.

Third, I use the portal for monitoring my dashboards. We’ll talk about dashboards in part 6 (coming soon). Once they’re created, I’ll often check on them to make sure that all of my metrics look to be in a normal range and that everything appears healthy.

Fourth, when I’m developing new alerts, dashboard widgets, or other components, I’ll create test resources using the portal. I’lll use my existing automation scripts to deploy a short-term copy of my environment temporarily, then deploy a new alert or autoscale rule using the portal, and then export them to an ARM template or manually construct a template based on what gets deployed by the portal. This way I can see how things should work, and get to use the portal’s built-in validation and assistance with creating the components, but still get everything into code form eventually. Many of the ARM templates I’ll provide throughout this series were created in this way.

Finally, during an emergency – when a system is down, or something goes wrong in the middle of the night – I’ll sometimes drop the automation-first requirement and create alerts on the fly, even on production, but knowing that I’ll need to make sure I add it into the automation scripts as soon as possible to ensure everything stays in sync.

Summary

This post has outlined the basics of Azure’s instrumentation platform. The two main types of data we tend to work with are metrics and logs. Metrics are numerical values that represent the state of a system at a particular point in time. Logs come in several variants, some of which are published automatically and some of which need to be enabled and then published to a suitable location before they can be queried. Both metrics and logs can be processed by Azure Monitor, and over the course of this series we’ll look at how we can script and automate the ingestion, processing, and handling of a variety of types of instrumentation data.

Automation of Azure Monitor and other instrumentation components is something that I’ve found to be quite poorly documented, so in writing this series I’ve aimed to provide both explanations of how these parts can be built, and set of sample ARM templates and scripts that you can adapt to your own environment.

In the next part we’ll discuss Application Insights, and some of the automation we can achieve with that. We’ll also look at a pattern I typically use for deploying different levels of instrumentation into different environments.

Monitoring Azure Storage Queues with Application Insights and Azure Monitor

Azure Queues provides an easy queuing system for cloud-based applications. Queues allow for loose coupling between application components, and applications that use queues can take advantage of features like peek-locking and multiple retry attempts to enable application resiliency and high availability. Additionally, when Azure Queues are used with Azure Functions or Azure WebJobs, the built-in poison queue support allows for messages that repeatedly fail processing attempts to be moved to a dedicated queue for later inspection.
An important part of operating a queue-based application is monitoring the length of queues. This can tell you whether the back-end parts of the application are responding, whether they are keeping up with the amount of work they are being given, and whether there are messages that are causing problems. Most applications will have messages being added to and removed from queues as part of their regular operation. Over time, an operations team will begin to understand the normal range for each queue’s length. When a queue goes out of this range, it’s important to be alerted so that corrective action can be taken.
Azure Queues don’t have a built-in queue length monitoring system. Azure Application Insights allows for the collection of large volumes of data from an application, but it does not support monitoring queue lengths with its built-in functionality. In this post, we will create a serverless integration between Azure Queues and Application Insights using an Azure Function. This will allow us to use Application Insights to monitor queue lengths and set up Azure Monitor alert emails if the queue length exceeds a given threshold.

Solution Architecture

There are several ways that Application Insights could be integrated with Azure Queues. In this post we will use Azure Functions. Azure Functions is a serverless platform, allowing for blocks of code to be executed on demand or at regular intervals. We will write an Azure Function to poll the length of a set of queues, and publish these values to Application Insights. Then we will use Application Insights’ built-in analytics and alerting tools to monitor the queue lengths.

Base Application Setup

For this sample, we will use the Azure Portal to create the resources we need. You don’t even need Visual Studio to follow along. I will assume some basic familiarity with Azure.
First, we’ll need an Azure Storage account for our queues. In our sample application, we already have a storage account with two queues to monitor:

  • processorders: this is a queue that an API publishes to, and a back-end WebJob reads from the queue and processes its items. The queue contains orders that need to be processed.
  • processorders-poison: this is a queue that WebJobs has created automatically. Any messages that cannot be processed by the WebJob (by default after five attempts) will be moved into this queue for manual handling.

Next, we will create an Azure Functions app. When we create this through the Azure Portal, the portal helpfully asks if we want to create an Azure Storage account to store diagnostic logs and other metadata. We will choose to use our existing storage account, but if you prefer, you can have a different storage account than the one your queues are in. Additionally, the portal offers to create an Application Insights account. We will accept this, but you can create it separately later if you want.
1-FunctionsApp
Once all of these components have been deployed, we are ready to write our function.

Azure Function

Now we can write an Azure Function to connect to the queues and check their length.
Open the Azure Functions account and click the + button next to the Functions menu. Select a Timer trigger. We will use C# for this example. Click the Create this function button.
2-Function
By default, the function will run every five minutes. That might be sufficient for many applications. If you need to run the function on a different frequency, you can edit the schedule element in the function.json file and specify a cron expression.
Next, paste the following code over the top of the existing function:

This code connects to an Azure Storage account and retrieves the length of each queue specified. The key parts here are:

var connectionString = System.Configuration.ConfigurationManager.AppSettings["AzureWebJobsStorage"];

Azure Functions has an application setting called AzureWebJobsStorage. By default this refers to the storage account created when we provisioned the functions app. If you wanted to monitor a queue in another account, you could reference the storage account connection string here.

var queue = queueClient.GetQueueReference(queueName);
queue.FetchAttributes();
var length = queue.ApproximateMessageCount;

When you obtain a reference to a queue, you must explicitly fetch the queue attributes in order to read the ApproximateMessageCount. As the name suggests, this count may not be completely accurate, especially in situations where messages are being added and removed at a high rate. For our purposes, an approximate message count is enough for us to monitor.

log.Info($"{queueName}: {length}");

For now, this line will let us view the length of the queues within the Azure Functions log window. Later, we will switch this out to log to Application Insights instead.
Click Save and run. You should see something like the following appear in the log output window below the code editor:

2017-09-07T00:35:00.028 Function started (Id=57547b15-4c3e-42e7-a1de-1240fdf57b36)
2017-09-07T00:35:00.028 C# Timer trigger function executed at: 9/7/2017 12:35:00 AM
2017-09-07T00:35:00.028 processorders: 1
2017-09-07T00:35:00.028 processorders-poison: 0
2017-09-07T00:35:00.028 Function completed (Success, Id=57547b15-4c3e-42e7-a1de-1240fdf57b36, Duration=9ms)

Now we have our function polling the queue lengths. The next step is to publish these into Application Insights.

Integrating into Azure Functions

Azure Functions has integration with Appliation Insights for logging of each function execution. In this case, we want to save our own custom metrics, which is not currently supported by the built-in integration. Thankfully, integrating the full Application Insights SDK into our function is very easy.
First, we need to add a project.json file to our function app. To do this, click the View files tab on the right pane of the function app. Then click the + Add button, and name your new file project.json. Paste in the following:

This adds a NuGet reference to the Microsoft.ApplicationInsights package, which allows us to use the full SDK from our function.
Next, we need to update our function so that it writes the queue length to Application Insights. Click on the run.csx file on the right-hand pane, and replace the current function code with the following:

The key new parts that we have just added are:

private static string key = TelemetryConfiguration.Active.InstrumentationKey = System.Environment.GetEnvironmentVariable("APPINSIGHTS_INSTRUMENTATIONKEY", EnvironmentVariableTarget.Process);
private static TelemetryClient telemetry = new TelemetryClient() { InstrumentationKey = key };

This sets up an Application Insights TelemetryClient instance that we can use to publish our metrics. Note that in order for Application Insights to route the metrics to the right place, it needs an instrumentation key. Azure Functions’ built-in integration with Application Insights means that we can simply reference the instrumentation key it has set up for us. If you did not set up Application Insights at the same time as the function app, you can configure this separately and set your instrumentation key here.
Now we can publish our custom metrics into Application Insights. Note that Application Insights has several different types of custom diagnostics that can be tracked. In this case, we use metrics since they provide the ability to track numerical values over time, and set up alerts as appropriate. We have added the following line to our foreach loop, which publishes the queue length into Application Insights.

telemetry.TrackMetric($"Queue length - {queueName}", (double)length);

Click Save and run. Once the function executes successfully, wait a few minutes before continuing with the next step – Application Insights takes a little time (usually less than five minutes) to ingest new data.

Exploring Metrics in Application Insights

In the Azure Portal, open your Application Insights account and click Metrics Explorer in the menu. Then click the + Add chart button, and expand the Custom metric collection. You should see the new queue length metrics listed.
4-AppInsights
Select the metrics, and as you do, notice that a new chart is added to the main panel showing the queue length count. In our case, we can see the processorders queue count fluctuate between one and five messages, while the processorders-poison queue stays empty. Set the Aggregation property to Max to better see how the queue fluctuates.
5-AppInsightsChart
You may also want to click the Time range button in the main panel, and set it to Last 30 minutes, to fully see how the queue length changes during your testing.

Setting up Alerts

Now that we can see our metrics appearing in Application Insights, we can set up alerts to notify us whenever a queue exceeds some length we specify. For our processorders queue, this might be 10 messages – we know from our operations history that if we have more than ten messages waiting to be processed, our WebJob isn’t processing them fast enough. For our processorders-poison queue, though, we never want to have any messages appear in this queue, so we can have an alert if more than zero messages are on the queue. Your thresholds may differ for your application, depending on your requirements.
Click the Alert rules button in the main panel. Azure Monitor opens. Click the + Add metric alert button. (You may need to select the Application Insights account in the Resource drop-down list if this is disabled.)
On the Add rule pane, set the values as follows:

  • Name: Use a descriptive name here, such as `QueueProcessOrdersLength`.
  • Metric: Select the appropriate `Queue length – queuename` metric.
  • Condition: Set this to the value and time period you require. In our case, I have set the rule to `Greater than 10 over the last 5 minutes`.
  • Notify via: Specify how you want to be notified of the alert. Azure Monitor can send emails, call a webhook URL, or even start a Logic App. In our case, I have opted to receive an email.

Click OK to save the rule.
6-Alert.PNG
If the queue count exceeds your specified limit, you will receive an email shortly afterwards with details:
7-Alert

Summary

Monitoring the length of your queues is a critical part of operating a modern application, and getting alerted when a queue is becoming excessively long can help to identify application failures early, and thereby avoid downtime and SLA violations. Even though Azure Storage doesn’t provide a built-in monitoring mechanism, one can easily be created using Azure Functions, Application Insights, and Azure Monitor.

Know Your Cloud Resource Costs on Azure

An organisation used to invest their IT infrastructure mostly for computers, network or data centre. Over time, they spent their budget for hosting spaces. Nowadays, in cloud environments, they mostly spend their funds to purchase computing power. Here’s a simple diagram about the cloud computing evolution. From left to right, expenditure shifts from infrastructure to computing power.

In the cloud environment, when we need resources, we just create and use them, and when we don’t need them any longer, we just delete them. But let’s think about this. If your organisation runs dev, test and production environment on cloud, the cost of resources running on dev or test environment is likely to be overlooked unless carefully monitored. In this case, your organisation might be receiving an invoice with massive amount of cost! That has to be avoided. In this post, we are going to have a look at the Azure Billing API that was released in preview and build a simple application to monitor costs in an effective way.

The sample codes used for this post can be found here.

Azure Billing API Structure

There are two distinctive APIs for Azure Billing – one is Usage API and the other is Rate Card API. Therefore, we can calculate how much we spent during a particular period.

Usage API

This API is based on a subscription. Within a subscription, we can send a request to calculate how much resources we used in a specified period. Here are the parameters we can use for these requests.

  • ReportedStartTime: Starting date/time reported in the billing system.
  • ReportedEndTime: Ending date/time reported in the billing system.
  • Granularity: Either Daily or Hourly. Hourly can return more detailed result but takes far longer time to get the result.
  • Details: Either true or false. This determines how usage is split into instance level or not. If false is selected, all same instance types are aggregated.

Here’s an interesting point on the term Reported. When we USE cloud resources, that can be interpreted from two different perspectives. The term, USE, might mean that the resources were actually used at the specified date/time, or the resource used events were reported to the billing system at the specified date/time. This happens because Azure is basically a distributed system scattered all around the world, and based on the data centre the resources are situated, the actual usage date/time can be reported to the billing system in a delayed manner. Therefore, even though we send requests based on the reported date/time, the responses containing usage data show the actual usage date/time.

Rate Card API

When you open a new Azure subscription, you might have noticed a code looking like MS-AZR-****P. Have you seen that code before? This is called Offer Durable ID and, based on this, different rates on resources apply. Please refer to this page to see more details about various types of offers. In order to send requests for this, we can use the following query parameters.

  • OfferDurableId: This is the offer Id. eg) MS-AZR-0017P (EA Subscription)
  • Currency: Currency that you want to look for. eg) AUD
  • Locale: Locale of your search region. eg) en-AU
  • Region: Two-letter ISO country code that you purchased this offer. eg) AU

Therefore, in order to calculate the actual spending, we need to combine these two API responses. Fortunately, there’s a good NuGet library called CodeHollow.AzureBillingApi. So we just use it to figure out Azure resource consumption costs.

Scenario

Kloud, as a cloud consulting firm, offers all consultants access to the company’s subscription without restriction so that they can create resources to develop/test scenarios for their clients. However, once resources are created, there’s high chance that those resources are not destroyed in a timely manner, which brings about unnecessary cost spending. Therefore, management team has made a decision to perform cost control by resource groups 1) assigning resource group owners, 2) setting total spend limit, and 3) setting daily spend limit, using tags. By virtue of these tags, resource group owners are notified via email when cost approaches 90% of the total spend limit, and when it reaches the total spend limit. They also get notified if the cost exceeds the daily spend limit so they can take appropriate actions for their resource groups.
Sounds simple, right? Let’s code it!
When the application is written, it should be run daily to aggregate all costs, store it to database, and send notifications to resource group owners that meets the conditions above.

Writing Common Libraries

The common libraries consist of three parts. Firstly, it calls Azure Billing API, and aggregates data by date and resource group. Secondly, it stores those aggregated data into database. Finally, it sends notification to resource group owners who have resource groups that exceeds either total spend limit or daily spend limit.

Azure Billing API Call & Data Aggregation

CodeHollow.AzureBillingApi can reduce huge amount of API calling work. Its simple implementation might look like:

First of all, like the code above, we need to fetch all resource usage/cost data then, like below, those data needs to be grouped by dates and resource groups.

We now have all cost related data per resource group. We then need to fetch tag values from resource groups using another API call and merge it with the data previously populated.

We can look up all resource groups in a given subscription like above, and merge this result with the cost data that we previously found, like below.

Date Storage

This is the simplest part. Just use Entity Framework and store data into the database.

We’ve so far implemented data aggregation part.

Notification

First of all, we need to fetch resource groups that meet conditions, which is not that hard to write.

The code above is self-explanatory: it only returns resource groups that 1) approach the total spend limit or 2) exceed the total spend limit, or 3) exceed the daily spend limit. It works well, even though it looks smelly.
The following code bits show how to send notifications to the resource group owners.

It only writes alarms onto the screen, but we can implement SendGrid for email notification or Twillio for SMS alert, in here.
Now we’ve got the basic application structure. How can we execute it, by the way? We might have two approaches – Azure WebJobs and Azure Functions. Let’s move on.

Monitoring Application on Azure WebJob

A console application might be the simplest way for this purpose. Once the console app is built, it can be deployed to an Azure WebJob straight away. Here’s the simple console application code.

Aggregator service collects and store data and Reminder service sends alerts to resource group owners. In order to deploy this to Azure WebJob, we need to create two extra files, run.cmd and settings.job.

  • settings.job: It contains CRON expression for scheduled job. For example, if this WebJob runs every night at 00:20, the JSON object might look like:
  • run.cmd: When this WebJob is run, it always looks up run.cmd first, which is a simple batch command file. Therefore, if necessary, we can enter the actual executable command with appropriate arguments into this file.

That’s how we can use Azure WebJob for monitoring.

Monitoring Application on Azure Function

We can use Azure Functions instead. But in this case we HAVE TO make sure:

Azure Functions instance MUST be with App Service Plan, NOT Consumption Plan

Basically, this app runs for 1-2 minutes at the shortest or 30-40 minutes at the longest. This execution time is not affordable for Consumption Plan, which charges costs based on execution time. On the other hand, as we have already paid for App Service Plan, we don’t need to pay extra for the Function instance, if we create it under the App Service Plan.
Timer Trigger Function code suits our purpose. Also using Precompiled Azure Functions approach would be more helpful and the function code might look like:

Here’s the function.json for this Timer Trigger one:

Here we have shown how to quickly write a simple application for cost monitoring, using the Azure Billing API. Cloud resources can certainly be used effectively and efficiently, but the flipside of it is, of course, that we have to be very careful not to be wasteful. Therefore, implementing a monitoring application would help in preventing unwanted cost leak.

Monitoring Azure WebJobs Health with Application Insights

Introduction

Azure WebJobs have been available for quite some time and have become very popular for running background tasks with programs or scripts. WebJobs are deployed as part of Azure App Services (Web Apps), which include their companion site Kudu. Kudu provides a lot of features, including a REST API, which provides operations for source code management (SCM), virtual file system, deployments, accessing logs, and for WebJob management as well. The Kudu WebJobs API provides different operations including listing WebJobs, uploading a WebJob, or triggering it. One of the operations of this API allows to get the status of a specific WebJob by name.

Another quite popular Azure service is Application Insights. This provides functionality to monitor and diagnose application issues and to analyse usage and performance as well. One of these features are web tests, which provide a way to monitor the availability and health of a web site.

In this blog post I will go through the required configuration on Application Insights to monitor the health of WebJobs using Application Insights web tests calling the Kudu WebJobs API.

Calling the Kudu WebJobs API.

For this exercise, it is worth getting familiar with the WebJobs API, particularly with the endpoint to get a WebJob status. Through this post, I will be working with a triggered WebJob scheduled with a CRON expression, but you can apply the same principles for a continuous WebJob. I will be using postman to call this API.

To get a WebJob status, we need to call the corresponding Kudu WebJob API endpoint. In the case of triggered WebJobs, the endpoint looks something like:

https://{webapp-name}.scm.azurewebsites.net/api/triggeredwebjobs/{webjob-name}/

Before calling the endpoint, we need to add the Authorization header to the GET request. To create the header value, we need to use the corresponding Kudu API credentials, as explained here. Considering we want to monitor the status of a WebJob under a particular web site, I prefer to use site-level credentials (or publishing profile credentials) instead of the user-level ones.

Getting the Publishing Profile Credentials from the Azure Portal

You can get the publishing profile credentials, by downloading the publishing profile from the portal, as shown in the figure below. Once downloaded, the XML document will contain the site-level credentials.

Getting the Publishing Profile Credentials via PowerShell

We can also get the site-level credentials via PowerShell. I’ve created a PowerShell function which returns the publishing credentials of an Azure Web App or a Deployment Slot, as shown below.

Bear in mind that you need to be logged in to Azure in your PowerShell session before calling these cmdlets.

Getting the Kudu REST API Authorisation header via PowerShell

Once we have the credentials, we are able to get the Authorization header value. The instructions to construct the header are described here. I’ve created another PowerShell function, which relies on the previous one, to get the header value, as follows.

Once we have the header value, we can call the api. Let’s call it using postman.

You should be getting a response similar to the one shown below:

Note that for this triggered WebJob, there are status and duration fields.

Now that we are familiar with the response, we can start designing an App Insights web test to monitor the health of our WebJob.

Configuring an App Insights Web Test to Monitor the Health of an Azure WebJob

You can find here detailed documentation on how to create web tests to monitor availability and responsiveness of web end points. In the following sections of this post, I will cover how to create an App Insights web test to Monitor the Health of a WebJob.

As we saw above, to call the WebJobs API we need to add an Authorization Header to the GET request. And once we get the API response, to check the status of the WebJob, we would need to interpret the response in JSON format.

To create the web test on App Insights to monitor a WebJob, I will first create a simple web test via the Azure Portal, and enrich it later.

Creating a Web Test on Application Insights.

I will create a basic web test with the following configuration. You should change it to the values which suit your scenario:

  • Test type: URL ping test
  • URL: My WebJob Rest API, e.g. https://{webapp-name}.scm.azurewebsites.net/api/triggeredwebjobs/{webjob-name}/
  • Test frequency: 5 minutes
  • Test locations: SG Singapore and AU Sydney
  • Success criteria:
    • Test timeout: 120 seconds
    • HTTP Response: (checked)
    • Status code must equal: 200
    • Content match: (checked)
    • Content must contain: “status”:”success”
  • Alerts
    • Status: Enabled
    • Alert threshold location: 1
    • Alert failure time window: 5 minutes
    • Send alert emails to these email addresses: <my email address>

You could also keep email alerts disabled or configure them later.

If you enable the web test as is, you will see that it will start failing. The reason being that we are not adding the required Authorization header to the GET request.

To add headers to the test, you could record web tests on Visual Studio Enterprise or Ultimate. This is explained in details in the Azure documentation. Additionally, in these multi-steps web tests you can add more than one validation rule.

Knowing that not everybody has access to a VS Enterprise or Ultimate license, I will explain here how to create a web test using the corresponding XML format. The first step is to extract the web test XML definition from the test manually created on the portal.

Extracting the Web Test XML Definition from a Test Manually Created on the Portal.

Once we have created the web test manually on the portal, to get its XML definition, we have to open the resource explorer on https://resources.azure.com/ and navigate to subscriptions/<subscription-guid>/resourceGroups/<resourcegroup>/providers/microsoft.insights/webtests/<webtest>-<app-insight> until you are on the definition of the web test you have just created.

Once there, you need to find the member: “WebTest”, which should be something similar to:

Now, we need to extract the XML document by removing the escape characters of the double quotes, and get something like:

which is the XML definition of the web test we created manually on the portal.

Adding a Header to the Application Insights Web Test Request by updating the Web Test XML definition.

Now we should be ready to edit our web test XML definition to add the Authorization header.

To do this, we just need to add a Headers child element to the Request record, similar to the one shown below. You would need to get the Base 64 encoded Authorization header value, similarly to how we did it previously when calling the API via Postman.

Extending the Functionality of the Web Test.

When we created the web test on the portal, we said that we wanted the status to be “success”, however, we might want to add “running” as another valid value. Additionally, in my case, I wanted to check that duration is less than 10 minutes. For this I have updated the Validation Rules to use regular expressions and to have a second rule. The final web test XML definition resulted as follows:

You could play around with the web test XML definition and update or extend it according to your needs. In case you are interested on the capabilities of web tests, here the documentation.

Once our web test XML definition is ready, we save it with a “.webtest” extension.

Uploading the (Multi-Step) Web Test to Application Insights

Having the web test XML definition ready, we can update our Application Insights web test with it. For this, on the portal, we open the Edit Test blade and:

  • Change the Test Type to: Multi-step test, and
  • Upload the web test xml definition file we just saved with the “.webtest” extension.

This will update the web test, and now with the proper Authorization header and the added validation rules, we can monitor the health of our triggered WebJob.

With Application Insights web tests, we can monitor the WebJob via the dashboard as shown above, or configuring alerts to be sent via email.

Summary

Through this post I have shown how to monitor the health of an Azure WebJob using Application Insights web tests. But on the journey, I also showed some tricks which I hope can be useful in other scenarios as well, including

  1. How to call the Azure WebJobs API via Postman, including how to get the Kudu API Authorization header via PowerShell.
  2. How to manually configure App Insights web tests,
  3. How to get the XML definition of a manually created web test using the Azure Resource Explorer,
  4. How to update the web test XML definition to add a request
    header and expand the validation rules. This without requiring Visual Studio Enterprise or Ultimate, and
  5. How to update the Application Insights web test by uploading the updated multi-step web test file.

Thanks for reading, and feel free to add your comments or queries below. 🙂 

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