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Metric aggregations
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:
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"sum_taxful_total_price": {
"sum": {
"field": "taxful_total_price"
}
}
}
}
Sample Response
...
"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:
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"unique_products": {
"cardinality": {
"field": "products.product_id"
}
}
}
}
Example response
...
"aggregations" : {
"unique_products" : {
"value" : 7033
}
}
}
Cardinality count is approximate. 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.
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.
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"number_of_values": {
"value_count": {
"field": "taxful_total_price"
}
}
}
}
Example response
...
"aggregations" : {
"number_of_values" : {
"value" : 4675
}
}
}
stats, extended_stats, matrix_stats
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:
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"stats_taxful_total_price": {
"stats": {
"field": "taxful_total_price"
}
}
}
}
Example response
...
"aggregations" : {
"stats_taxful_total_price" : {
"count" : 4675,
"min" : 6.98828125,
"max" : 2250.0,
"avg" : 75.05542864304813,
"sum" : 350884.12890625
}
}
}
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.
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"extended_stats_taxful_total_price": {
"extended_stats": {
"field": "taxful_total_price"
}
}
}
}
Sample Response
...
"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:
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:
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"matrix_stats_taxful_total_price": {
"matrix_stats": {
"fields": ["taxful_total_price", "products.base_price"]
}
}
}
}
Example response
...
"aggregations" : {
"matrix_stats_taxful_total_price" : {
"doc_count" : 4675,
"fields" : [
{
"name" : "products.base_price",
"count" : 4675,
"mean" : 34.994239430147196,
"variance" : 360.5035285833703,
"skewness" : 5.530161335032702,
"kurtosis" : 131.16306324042148,
"covariance" : {
"products.base_price" : 360.5035285833703,
"taxful_total_price" : 846.6489362233166
},
"correlation" : {
"products.base_price" : 1.0,
"taxful_total_price" : 0.8444765264325268
}
},
{
"name" : "taxful_total_price",
"count" : 4675,
"mean" : 75.05542864304839,
"variance" : 2788.1879749835402,
"skewness" : 15.812149139924037,
"kurtosis" : 619.1235507385902,
"covariance" : {
"products.base_price" : 846.6489362233166,
"taxful_total_price" : 2788.1879749835402
},
"correlation" : {
"products.base_price" : 0.8444765264325268,
"taxful_total_price" : 1.0
}
}
]
}
}
}
| Statistic | Description |
|---|---|
count | The number of samples measured. |
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. |
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:
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"percentile_taxful_total_price": {
"percentiles": {
"field": "taxful_total_price"
}
}
}
}
Example response
...
"aggregations" : {
"percentile_taxful_total_price" : {
"values" : {
"1.0" : 21.984375,
"5.0" : 27.984375,
"25.0" : 44.96875,
"50.0" : 64.22061688311689,
"75.0" : 93.0,
"95.0" : 156.0,
"99.0" : 222.0
}
}
}
}
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.
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"percentile_rank_taxful_total_price": {
"percentile_ranks": {
"field": "taxful_total_price",
"values": [
10,
15
]
}
}
}
}
Example response
...
"aggregations" : {
"percentile_rank_taxful_total_price" : {
"values" : {
"10.0" : 0.055096056411283456,
"15.0" : 0.0830092961834656
}
}
}
}
geo_bound
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:
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"geo": {
"geo_bounds": {
"field": "geoip.location"
}
}
}
}
Example response
"aggregations" : {
"geo" : {
"bounds" : {
"top_left" : {
"lat" : 52.49999997206032,
"lon" : -118.20000001229346
},
"bottom_right" : {
"lat" : 4.599999985657632,
"lon" : 55.299999956041574
}
}
}
}
}
top_hits
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:
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"top_hits_products": {
"top_hits": {
"size": 5
}
}
}
}
Example response
...
"aggregations" : {
"top_hits_products" : {
"hits" : {
"total" : {
"value" : 4675,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "opensearch_dashboards_sample_data_ecommerce",
"_type" : "_doc",
"_id" : "glMlwXcBQVLeQPrkHPtI",
"_score" : 1.0,
"_source" : {
"category" : [
"Women's Accessories",
"Women's Clothing"
],
"currency" : "EUR",
"customer_first_name" : "rania",
"customer_full_name" : "rania Evans",
"customer_gender" : "FEMALE",
"customer_id" : 24,
"customer_last_name" : "Evans",
"customer_phone" : "",
"day_of_week" : "Sunday",
"day_of_week_i" : 6,
"email" : "rania@evans-family.zzz",
"manufacturer" : [
"Tigress Enterprises"
],
"order_date" : "2021-02-28T14:16:48+00:00",
"order_id" : 583581,
"products" : [
{
"base_price" : 10.99,
"discount_percentage" : 0,
"quantity" : 1,
"manufacturer" : "Tigress Enterprises",
"tax_amount" : 0,
"product_id" : 19024,
"category" : "Women's Accessories",
"sku" : "ZO0082400824",
"taxless_price" : 10.99,
"unit_discount_amount" : 0,
"min_price" : 5.17,
"_id" : "sold_product_583581_19024",
"discount_amount" : 0,
"created_on" : "2016-12-25T14:16:48+00:00",
"product_name" : "Snood - white/grey/peach",
"price" : 10.99,
"taxful_price" : 10.99,
"base_unit_price" : 10.99
},
{
"base_price" : 32.99,
"discount_percentage" : 0,
"quantity" : 1,
"manufacturer" : "Tigress Enterprises",
"tax_amount" : 0,
"product_id" : 19260,
"category" : "Women's Clothing",
"sku" : "ZO0071900719",
"taxless_price" : 32.99,
"unit_discount_amount" : 0,
"min_price" : 17.15,
"_id" : "sold_product_583581_19260",
"discount_amount" : 0,
"created_on" : "2016-12-25T14:16:48+00:00",
"product_name" : "Cardigan - grey",
"price" : 32.99,
"taxful_price" : 32.99,
"base_unit_price" : 32.99
}
],
"sku" : [
"ZO0082400824",
"ZO0071900719"
],
"taxful_total_price" : 43.98,
"taxless_total_price" : 43.98,
"total_quantity" : 2,
"total_unique_products" : 2,
"type" : "order",
"user" : "rani",
"geoip" : {
"country_iso_code" : "EG",
"location" : {
"lon" : 31.3,
"lat" : 30.1
},
"region_name" : "Cairo Governorate",
"continent_name" : "Africa",
"city_name" : "Cairo"
},
"event" : {
"dataset" : "sample_ecommerce"
}
}
...
}
]
}
}
}
}
scripted_metric
The scripted_metric metric is a multi-value metric aggregation that returns metrics calculated from a specified script.
A script has four stages: the initial stage, the map stage, the combine stage, and the reduce stage.
init_script: (OPTIONAL) Sets the initial state and executes before any collection of documents.map_script: Checks the value of thetypefield and executes the aggregation on the collected documents.combine_script: Aggregates the state returned from every shard. The aggregated value is returned to the coordinating node.reduce_script: Provides access to the variable states; this variable combines the results from thecombine_scripton each shard into an array.
The following example aggregates the different HTTP response types in web log data:
GET opensearch_dashboards_sample_data_logs/_search
{
"size": 0,
"aggregations": {
"responses.counts": {
"scripted_metric": {
"init_script": "state.responses = ['error':0L,'success':0L,'other':0L]",
"map_script": """
def code = doc['response.keyword'].value;
if (code.startsWith('5') || code.startsWith('4')) {
state.responses.error += 1 ;
} else if(code.startsWith('2')) {
state.responses.success += 1;
} else {
state.responses.other += 1;
}
""",
"combine_script": "state.responses",
"reduce_script": """
def counts = ['error': 0L, 'success': 0L, 'other': 0L];
for (responses in states) {
counts.error += responses['error'];
counts.success += responses['success'];
counts.other += responses['other'];
}
return counts;
"""
}
}
}
}
Sample Response
...
"aggregations" : {
"responses.counts" : {
"value" : {
"other" : 0,
"success" : 12832,
"error" : 1242
}
}
}
}