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.

Let's Begin!