Change detection monitors allow you to receive alerts when the value of a query changes significantly compared to a previous point in time. This feature is useful for catching unexpected spikes, drops, or shifts in your data trends.

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Creating a Change Detection Monitor

To create a change detection monitor:
  1. Navigate to Monitors and select + New Monitor.
  2. Choose Change Detection as the monitor type.
From here, you will configure the data source, query conditions, and alert thresholds.

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How Change Detection Works

A change detection monitor compares two queries over time:
  1. The monitor first runs a query for the current evaluation window (e.g. the last 15 minutes).
  2. It then runs the same query offset by a chosen timeframe in the past (e.g. 1 hour ago).
  3. The monitor computes the difference between the two query results.
  4. If the difference exceeds the defined threshold, an alert notification is triggered.

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Change Types

When configuring your monitor, you can specify how the change is measured:
  • Change: The raw difference between the current and past values.
a−ba - b
  • % Change: The percentage difference relative to the past value.
(a−b)b×100\frac{(a - b)}{b} \times 100

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Alert Conditions

In the Set Alert Conditions step, you define the threshold for triggering an alert. The following operators are supported:
  • Above
  • Below
  • Above or Equal
  • Below or Equal
An alert will be sent if the computed change meets the specified condition. Note: When monitoring for drops (using a below operator), the threshold value should usually be negative. For example, to alert on a 50% drop in volume, set the threshold to -50 rather than 50. This sends an alert notification when the percentage change falls below -50%, in other words, there has been a 50% drop.

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Configuration Options

When creating a change detection monitor, you’ll need to configure the following:
  1. Source Dataset – Select the dataset(s) on which the queries will be run.
  2. Filters – (Optional) Narrow down results by applying filters.
  3. Aggregation Function – Define how data points are aggregated (e.g. count, average, max, sum).
  4. Change Type – Choose between Change or % Change.
  5. Evaluation Window – The time period over which the aggregation is calculated (N minutes, hours, days, weeks, up to maximum of 1 month).
  6. Timeframe Offset – How far back in time the comparison query should be run (N minutes, hours, days, weeks, up to maximum of 1 month ago).
This example shows the following conditions: The % change for the count of log events with response_status:200 over the past 1 hour compared to 4 hours ago.

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Example Use Cases

  • Detecting unusual traffic spikes compared to the same time yesterday.
  • Monitoring error rates that suddenly increase compared to the previous hour.
  • Tracking a drop in transaction volume relative to the past week.