Rollup

Every metric query is inherently aggregated. However, appending the .rollup() function at the end of a query allows you to perform custom time aggregation that overrides the defaults. This function enables you to define:

• The rollup <interval>: the interval of time your data is aggregated over (if larger than the query-enforced rollup interval).
• The rollup <aggregator>: How your data points are aggregated within a given rollup time interval.

To apply a rollup, navigate to the Add function (Σ) button of the graphing editor:

Note: The Distribution Metric type does not have a rollup aggregator parameter. This metric type is aggregated both in time and space. See the documentation on rollup for distributions with percentiles to learn more.

The function takes two parameters, <AGGREGATOR> and optionally <INTERVAL>: .rollup(<AGGREGATOR>,<INTERVAL>) or .rollup(<AGGREGATOR>).

You can use them individually or together, for instance .rollup(sum,120). The following bar graph displays a week’s worth of CPU usage for a host without using the .rollup() function:

The following bar graph displays the same metric, graphed using a day-long rollup with .rollup(avg,86400):

Moving rollup

Applying the moving_rollup() function to a query allows you to combine points from the most recent specified time range—that is, the last X seconds. Like with .rollup(), <AGGREGATOR> can be sum/min/max/count/avg and defines how data points are aggregated within the given time interval.

Rollup interval: enforced vs custom

When graphing, Datadog imposes a limit on the number of points per graph. To respect this limit, Datadog rolls up data points automatically with the avg method, effectively displaying the average of all data points within a time interval for a given metric. This default time interval varies depending on how the data is being visualized. See the following chart to reference these default time intervals:

A custom .rollup() function can be used to enforce the type of time aggregation applied (avg, min, max, count, or sum) and the time interval to rollup. However, if a custom .rollup() function is applied and uses a smaller time interval than the Datadog limit, the Datadog limit is used instead while still using the specified rollup method. For example, if you’re requesting .rollup(20) for a month-long window, data is returned at a rollup greater than 20 seconds in order to prevent returning more than allotted number of points.

Note: Queries for COUNT and RATE type metrics have the .as_count() modifier appended automatically in the UI, which sets the rollup method used to sum and disables interpolation. This .as_count() is explicitly visible at the end of the query:

For more details about how to use .as_count() and .as_rate() see the Visualize StatsD metrics blog post, or learn more about the effects of those functions with the documentation on in-application modifiers.

Rollup with calendar aligned queries

You can customize how your metrics data is bucketed over time when using the .rollup() function with calendar aligned queries. This feature allows you the flexibility to define:

• Calendar aligned monthly queries with adjustable start date / timezones
• Weekly rollups with adjustable start date / timezones
• Daily rollups with adjustable start time / timezones

Rollups in monitors

Rollups should usually be avoided in monitor queries, because of the possibility of misalignment between the rollup interval and the evaluation window of the monitor. The start and end of rollup intervals are aligned to UNIX time, not to the start and end of monitor queries. Therefore, a monitor may evaluate (and trigger on) an incomplete rollup interval containing only a small sample of data. To avoid this issue, delay the evaluation of your monitor by (at least) the length of the setup rollup interval.

Other functions