Metric aggregations let you perform simple calculations such as finding the minimum, maximum, and average values of a field.
## Types of metric aggregations
Metric aggregations are of two types: single-value metric aggregations and multi-value metric aggregations.
### Single-value metric aggregations
Single-value metric aggregations return a single metric. For example, `sum`, `min`, `max`, `avg`, `cardinality`, and `value_count`.
### Multi-value metric aggregations
Multi-value metric aggregations return more than one metric. For example, `stats`, `extended_stats`, `matrix_stats`, `percentile`, `percentile_ranks`, `geo_bound`, `top_hits`, and `scripted_metric`.
## sum, min, max, avg
The `sum`, `min`, `max`, and `avg` metrics are single-value metric aggregations that return the sum, minimum, maximum, and average values of a field, respectively.
The following example calculates the total sum of the `taxful_total_price` field:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"sum_taxful_total_price": {
"sum": {
"field": "taxful_total_price"
}
}
}
}
```
#### Sample Response
```json
...
"aggregations" : {
"sum_taxful_total_price" : {
"value" : 350884.12890625
}
}
}
```
In a similar fashion, you can find the minimum, maximum, and average values of a field.
## cardinality
The `cardinality` metric is a single-value metric aggregation that counts the number of unique or distinct values of a field.
The following example finds the number of unique products in an eCommerce store:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
If you have tens of thousands of products in your hypothetical store, an accurate cardinality calculation requires loading all the values into a hash set and returning its size. This approach doesn't scale well; it requires huge amounts of memory and can cause high latencies.
You can control the trade-off between memory and accuracy with the `precision_threshold` setting. This setting defines the threshold below which counts are expected to be close to accurate. Above this value, counts might become a bit less accurate. The default value of `precision_threshold` is 3,000. The maximum supported value is 40,000.
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"unique_products": {
"cardinality": {
"field": "products.product_id",
"precision_threshold": 10000
}
}
}
}
```
## value_count
The `value_count` metric is a single-value metric aggregation that calculates the number of values that an aggregation is based on.
For example, you can use the `value_count` metric with the `avg` metric to find how many numbers the aggregation uses to calculate an average value.
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
The `stats` metric is a multi-value metric aggregation that returns all basic metrics such as `min`, `max`, `sum`, `avg`, and `value_count` in one aggregation query.
The following example returns the basic stats for the `taxful_total_price` field:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
The `extended_stats` aggregation is an extended version of the `stats` aggregation. Apart from including basic stats, `extended_stats` also returns stats such as `sum_of_squares`, `variance`, and `std_deviation`.
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"extended_stats_taxful_total_price": {
"extended_stats": {
"field": "taxful_total_price"
}
}
}
}
```
#### Sample Response
```json
...
"aggregations" : {
"extended_stats_taxful_total_price" : {
"count" : 4675,
"min" : 6.98828125,
"max" : 2250.0,
"avg" : 75.05542864304813,
"sum" : 350884.12890625,
"sum_of_squares" : 3.9367749294174194E7,
"variance" : 2787.59157113862,
"variance_population" : 2787.59157113862,
"variance_sampling" : 2788.187974983536,
"std_deviation" : 52.79764740155209,
"std_deviation_population" : 52.79764740155209,
"std_deviation_sampling" : 52.80329511482722,
"std_deviation_bounds" : {
"upper" : 180.6507234461523,
"lower" : -30.53986616005605,
"upper_population" : 180.6507234461523,
"lower_population" : -30.53986616005605,
"upper_sampling" : 180.66201887270256,
"lower_sampling" : -30.551161586606312
}
}
}
}
```
The `std_deviation_bounds` object provides a visual variance of the data with an interval of plus/minus two standard deviations from the mean.
To set the standard deviation to a different value, say 3, set `sigma` to 3:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"extended_stats_taxful_total_price": {
"extended_stats": {
"field": "taxful_total_price",
"sigma": 3
}
}
}
}
```
The `matrix_stats` aggregation generates advanced stats for multiple fields in a matrix form.
The following example returns advanced stats in a matrix form for the `taxful_total_price` and `products.base_price` fields:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
`mean` | The average value of the field measured from the sample.
`variance` | How far the values of the field measured are spread out from its mean value. The larger the variance, the more it's spread from its mean value.
`skewness` | An asymmetric measure of the distribution of the field's values around the mean.
`kurtosis` | A measure of the tail heaviness of a distribution. As the tail becomes lighter, kurtosis decreases. As the tail becomes heavier, kurtosis increases. To learn about kurtosis, see [Wikipedia](https://en.wikipedia.org/wiki/Kurtosis).
`covariance` | A measure of the joint variability between two fields. A positive value means their values move in the same direction and vice versa.
`correlation` | A measure of the strength of the relationship between two fields. The valid values are between [-1, 1]. A value of -1 means that the value is negatively correlated and a value of 1 means that it's positively correlated. A value of 0 means that there's no identifiable relationship between them.
## percentile, percentile_ranks
Percentile is the percentage of the data that's at or below a certain threshold value.
The `percentile` metric is a multi-value metric aggregation that lets you find outliers in your data or figure out the distribution of your data.
Like the `cardinality` metric, the `percentile` metric is also approximate.
The following example calculates the percentile in relation to the `taxful_total_price` field:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
Percentile rank is the percentile of values at or below a threshold grouped by a specified value. For example, if a value is greater than or equal to 80% of the values, it has a percentile rank of 80.
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
The `geo_bound` metric is a multi-value metric aggregation that calculates the bounding box in terms of latitude and longitude around a `geo_point` field.
The following example returns the `geo_bound` metrics for the `geoip.location` field:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
The `top_hits` metric is a multi-value metric aggregation that ranks the matching documents based on a relevance score for the field that's being aggregated.
You can specify the following options:
-`from`: The starting position of the hit.
-`size`: The maximum size of hits to return. The default value is 3.
-`sort`: How the matching hits are sorted. By default, the hits are sorted by the relevance score of the aggregation query.
The following example returns the top 5 products in your eCommerce data:
```json
GET opensearch_dashboards_sample_data_ecommerce/_search
