# Metric Aggregation

Metric Aggregation is used to make an aggregation of the documents on Kibana visualization
You will be using various metric aggregations while building your own visualizations. The table given below gives you a snapshot of all the aggregation functions that you would encounter on Kibana.
The aggregations in this family compute metrics based on values extracted in one way or another from the documents that are being aggregated. The values are typically extracted from the fields of the document (using the field data).
Metric aggregation mainly refers to the math calculation done on the documents
 Name Description Average Aggregation Computes the average of the field mentioned over all documents Cardinality Computes an approximate count of distinct values. Count Counts the number of values that are extracted from the aggregated documents Filter ratio Provide the percentage of the ratio of two fields Positive rate Converts the counts to rates(counts/time) Max Keeps track and returns the maximum value among the numeric values extracted from the aggregated documents. Min Keeps track and returns the minimum value among numeric values extracted from the aggregated documents. Percentile Multi-value metrics aggregation that calculates one or more percentiles over numeric values extracted from the aggregated documents Percentile Rank Multi-value metrics aggregation that calculates one or more percentile ranks over numeric values extracted from the aggregated documents. Static Value Define a static value on the visualization Std. Deviation Provides an interval of plus/minus two standard deviations from the mean. If you want a different boundary, for example, three standard deviations, you can set sigma in the request Sum Adds up numeric values that are extracted from the aggregated documents Sum of Squares Give you the measure of deviation from the mean of the values from the aggregated documents Top hit Keeps track of the most relevant document being aggregated. Intended to be used as a sub aggregator, so that the top matching documents can be aggregated. Value Count Counts the number of values that are extracted from the aggregated document Variance Measurement of the spread between values of the field in a data set
Now that you have familiarised yourself with all the basics required to work on Kibana. It is time for you to build your own first visualization on Kibana.
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