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Datadog graphs generally display local aggregates instead of the original submitted values.
Data is stored at a 1 second granularity, but data can be aggregated when displayed.
For a graph on a 1-week time window, it would require sending hundreds of thousands of values to your browser. Not all of these points can be graphed on a widget occupying a small portion of your screen. For these reasons, data is aggregated to send a limited number of points to your browser to render a graph.
For instance, on a one-day view with the ’lines’ display there is one datapoint every 5 min. Datadog’s backend slices the 1-day interval into 288 buckets of 5 minutes. For each bucket, all data rolls up into a single value. For instance, the datapoint rendered on your graph with the timestamp 07:00 is actually an aggregate of all real datapoints submitted between 07:00:00 and 07:05:00 that day.
By default, the rollup aggregate is computed by averaging all real values, which tends to smooth out graphs as you zoom out.
Data aggregation needs to occur whether you have 1 or 1000 sources as long as you look at a large time window.
However what you can do is control how this aggregation is performed by using the rollup function:
Note: Datadog’s backend tries to keep the number of intervals to a number below ~300. So if you do rollup(60) over a 2-month time window, you can’t have the one-minute granularity requested.
The graph above is a bar graph over the past 2 hours. The graph displays one datapoint per minute. They are not the real values submitted but local aggregates, each one representing one minute of your metric data.
Yes, if you zoom in enough, the graph displays the original values. For example, the Datadog Agent submits data every ~15 seconds. If you look at a 45-minute (or less) time window, the values are unaggregated.