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Archana posted a question in Ask the CommunityHi, I am trying to collect the average/max value of a field or metric only for a particular time range which we choose. I am unaware through which kind of Widget I can show the average/max value of a particular time period as a single value. I tried using Gauge & Big Number Widget to show my single average value for a specific time period. But the problems are as follows: 1. Big Number Widget - will show only the single value but it does not have any type of aggregation function (Min, max, avg) 2. Gauge widget - Contains Aggregation function to select. But not sure whether it is aggregating the field values according to the time range we select. Because everytime when the Dashboard/Widget is refreshed according to the Refresh Frequency, it is showing different values. For example, if I select the time range from March 1st to March 31st, then aggregate values should be same as it is not the real-time occurence. I tried with Graph Widget (refer the attached screenshot Avg_metric_CPU), it is providing me the proper values of that time. But I need the data to be shown as single average value, monthly-wise. Could anyone please help me on this.
I would really like to see a feature implemented that allows for easy and adjustable graph smoothing. This can be accomplished by adding a switch to the UI in the graph configuration screen. If the switch is turned to "enabled", a drop-down appears prompting for an integer between 1% and 5%. This number would represent the percentage of total datapoints that would be used to calculate the "smoothed" values. A second drop-down prompts for the position of the calculation: past, future, or both (default). For example: A graph containing 500 values - Rolling average is enabled and set to 3% (both). Each value on the graph would be recalculated to reflect the average of itself, plus the 15 (3% of 500) preceeding and proceeding datapoints. Here's what the original data might look like: (All examples below were created with TimeLion in Kibana using a similar algorithm) With 3% smoothing: And with 5% smoothing: