Create baseline alert conditions

In New Relic Alerts, you can use baseline alert conditions to define thresholds that adjust to the behavior of your data.

Baseline alerting is useful for creating alert conditions that:

  • only notify you when data is behaving abnormally.
  • dynamically adjust to changing data and trends, including daily or weekly trends.
  • works well out-of-the-box for new applications with as-yet-unknown behaviors.

Access to baseline alert conditions is available with a Pro subscription to any New Relic product. If all of your subscriptions are at the Essentials level, upgrade your subscription level to Pro on at least one New Relic product to gain baseline alerting access.

How it works

Access to baseline alert conditions is available with a Pro subscription to any New Relic product.

When you choose a data source (for example, an APM metric) for a baseline alert condition, we use the past values of that data to dynamically predict the data's near-future behavior.

The line of that predicted future behavior for that value is called a baseline; it appears as a dotted black line on the preview chart in the baseline condition UI.

You use the baseline alert condition UI to:

  • Adjust how sensitive the alert condition is to fluctuations in the data source.
  • Set the behavior that will trigger a violation (for example: "deviating for more than five minutes")
  • Set whether you want the condition to check for upper violations or lower violations, or both.

When your data escapes the predicted "normal" behavior and meets the criteria you've chosen, you'll receive an alert notification.

Set baseline thresholds

new-relic-alerts-baseline-thresholds.png
alerts.newrelic.com > Alert policies > (create or select policy) > Create alert condition: Baseline alert conditions give you the ability to set intelligent, self-adjusting thresholds that only generate violations when abnormal behavior is detected.

To create a baseline alert condition: When you start to create an alert condition, choose one of the following data sources:

  • APM: Application metric baseline
  • Browser: Metric baseline
  • NRQL (and then choose a baseline type threshold)

Here are some tips for setting baseline thresholds:

  • Set the baseline direction to monitor violations that happen either above or below the baseline.
  • Set the preview chart to either 2 days or 7 days of displayed data. (Not applicable for NRQL alert conditions.)
  • Use the slider bar to adjust the icon-alert-critical.png Critical threshold sensitivity, represented in the preview chart by the light gray area around the baseline. The tighter the band around the baseline, the more sensitive it is and the more violations it will generate.
  • Optional: You can create a icon-alert-warning.png Warning threshold (the darker gray area around the baseline).
  • For NRQL alerts, see the allowed types of NRQL queries.
  • If the alert condition applies to multiple apps, you can select a choice from the dropdown above the chart to use different metrics. (Not applicable for NRQL alert conditions.)

Baseline rules and settings

Here are some details about how the UI works:

Rules governing creation of baseline

The algorithm for baseline conditions is mathematically complex. Here are some of the major rules governing its predictive abilities:

Data trait Baseline rules
Age of data

On initial creation, the baseline is calculated using between 1 to 4 weeks of data, depending on data availability and baseline type. After its creation, the algorithm will take into account ongoing data fluctuations over a long time period, although greater weight is given to more recent data.

For data that has only existed for a short time, the baseline will likely fluctuate a good deal and not be very accurate. This is because there is not yet enough data to determine its usual values and behavior. The more history the data has, the more accurate the baseline and thresholds will become.

Consistency of data

For metric values that remain in a consistent range or that trend slowly and steadily, their more predictable behavior means that their thresholds will become tighter around the baseline. Data that is more varied and unpredictable will have looser (wider) thresholds.

Regular fluctuations

For shorter-than-one-week cyclical fluctuations (such as weekly Wednesday 1 pm deployments or nightly reports), the baseline algorithm looks for these cyclical fluctuations and attempts to adjust to them.

Baseline direction: select upper or lower ranges

You can choose whether you want the condition to violate for behavior that goes above the baseline ("upper") or that goes below the baseline ("lower"), or that goes either above or below. You choose these with the Baseline direction selector.

Example use cases for this:

  • You might use the Upper setting for a data source like error rate, because you generally are only concerned if it goes up, and aren't concerned if it goes down.
  • You might use the Lower setting for a data source like throughput, because sudden upward fluctuations are quite common, but a large sudden downswing would be a sign of a problem.

Here are examples of how large fluctuations in your data would be treated under the different baseline direction settings. The red areas represent violations.

New Relic baseline alerts direction image examples
Preview chart: select 2 or 7 days

When setting thresholds, the preview chart has an option for displaying Since 2 days ago or Since 7 days ago. These selections are not the time period used to compute the baseline; they are only the time range used for a preview display. For more about the time range used to calculate the baseline, see the algorithm rules.

For more help

Recommendations for learning more: