A metric is a particular class of measurement, state, or event in the monitored environment. Many defaults relate to the overall performance of the application or business transaction, such as request load, average response time, or error rate. Others describe the state of the server infrastructure, such as percentage CPU busy or percentage of memory used.

Agents register the metrics they detect with the Controller Tenant. They then report measurements or occurrences of the metrics at regular intervals. You can view metrics using the Metric Browser in the Controller Tenant UI.

Information points are a type of metric that enables you to report on how your business (as opposed to your application) is performing. For example, you could set up an information point to total the revenue from the purchase on your website of a specific product or set of products. You can also use information points to report on how your code is performing, for example, how many times a specific method is called and how long it is taking to execute.

You can create extensions that use the machine agent to report custom metrics that you define. Just like the built-in Splunk AppDynamics metrics, these metrics incur a baseline that reports to the Controller Tenant.

As an alternative to using the Controller Tenant UI, you can access metrics programmatically with the Splunk AppDynamics APIs.

Splunk AppDynamics uses both self-learned baselines and configurable thresholds to identify application issues. A complex distributed application has a large number of performance metrics, and each metric is important in one or more contexts. It can be difficult to:

• Determine the values or ranges that are normal for a particular metric.
• Set meaningful thresholds on which to base and receive relevant alerts.
• Determine what is a 'normal' metric when the application or infrastructure undergoes change.

For these reasons, we provide anomaly detection based on dynamic baselines or thresholds.

Splunk AppDynamics automatically calculates dynamic baselines for your metrics, defining what is 'normal' for each metric according to actual usage. The platform uses these baselines to identify subsequent metric values that fall out of range. You no longer need to set up static thresholds in rapidly changing, error-prone application environments.

You can create health rules with conditions that use baselines to trigger alerts or other actions when performance problems are occurring or about to happen. See Alert and Respond and Health Rules and Dynamic Baselines.

Splunk AppDynamics thresholds help you to maintain service level agreements (SLAs) and ensure optimum performance levels by detecting slow, very slow, and stalled transactions. Thresholds provide a way to associate the right business context with a slow request to isolate the root cause. See Transaction Thresholds.

Splunk AppDynamics uses dynamic baselining to establish typical behavior for your application automatically. You can set up health rules according to those standard baselines (or use other health indicators) to track non-optimal conditions.

Policies that allow you to connect such problematic events (such as the health rule critical event) with actions that can trigger alerts/remedial behavior addresses the system's issues long before your users will be affected.

Splunk AppDynamics supplies default health rules. You can customize the default health rules and create new rules specific to your environment. See Alert and Respond.

The out-of-the-box health rules test business transaction performance as follows:

Monitoring infrastructure performance can add insight into underlying factors about performance. Splunk AppDynamics can alert you of the problem at the Business Transaction and infrastructure level.

Splunk AppDynamics provides preconfigured application infrastructure metrics and default health rules to enable you to discover and correct infrastructure problems. You can configure additional persistent metrics to implement a monitoring strategy specific to your business needs and application architecture.

Infrastructure Visibility contributes to your view of the data center by adding valuable information on the performance of the machines and networks in your environment.

You can view infrastructure metrics in the Metric Browser. In this context, the Correlation Analysis and Scalability Analysis graphs are useful to understand how infrastructure metrics can correlate or relate to Business Transaction performance.

The Splunk AppDynamics Exchange contains many extensions that you can download and if you cannot find what you need, you can develop your own.

Splunk AppDynamics extensions are available in the following categories:

• Monitoring Extensions add metrics to the existing set of metrics that agents collect and report to the Tenant. These can include metrics that you obtain from other monitoring systems. They can also include metrics that your system extracts from services that are not instrumented by Splunk AppDynamics, such as databases, LDAP servers, web servers, or C programs. See Extensions and Custom Metrics.
• Alerting Extensions enable you to integrate Splunk AppDynamics with external alerting or ticketing systems and create custom notification actions. See Email Templates and HTTP Request Actions and Templates.
• Performance testing extensions consist of performance-testing extensions.
• Built-in integration extensions that you must enable or configure. These include:
  • Integrate Splunk AppDynamics with Splunk
  • Integrate Splunk AppDynamics with DB CAM

Review Splunk AppDynamics to create custom extensions and integration components.