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Weighted average
The weighted_avg
aggregation calculates the weighted average of numeric values across documents. This is useful when you want to calculate an average but weight some data points more heavily than others.
Weighted average calculation
The weighted average is calculated as (sum of value * weight) / (sum of weights)
.
Parameters
When using the weighted_avg
aggregation, you must define the following parameters:
value
: The field or script used to obtain the average numeric valuesweight
: The field or script used to obtain the weight for each value
Optionally, you can specify the following parameters:
format
: A numeric format to apply to the output valuevalue_type
: A type hint for the values when using scripts or unmapped fields
For the value or weight, you can specify the following parameters:
field
: The document field to usemissing
: A value or weight to use if the field is missing
Using the aggregation
Follow these steps to use the weighted_avg
aggregation:
1. Create an index and index some documents
PUT /products
POST /products/_doc/1
{
"name": "Product A",
"rating": 4,
"num_reviews": 100
}
POST /products/_doc/2
{
"name": "Product B",
"rating": 5,
"num_reviews": 20
}
POST /products/_doc/3
{
"name": "Product C",
"rating": 3,
"num_reviews": 50
}
2. Run the weighted_avg
aggregation
GET /products/_search
{
"size": 0,
"aggs": {
"weighted_rating": {
"weighted_avg": {
"value": {
"field": "rating"
},
"weight": {
"field": "num_reviews"
}
}
}
}
}
Handling missing values
The missing
parameter allows you to specify default values for documents missing the value
field or the weight
field instead of excluding them from the calculation.
The following is an example of this behavior. First, create an index and add sample documents. This example includes five documents with different combinations of missing values for the rating
and num_reviews
fields:
PUT /products
{
"mappings": {
"properties": {
"name": {
"type": "text"
},
"rating": {
"type": "double"
},
"num_reviews": {
"type": "integer"
}
}
}
}
POST /_bulk
{ "index": { "_index": "products" } }
{ "name": "Product A", "rating": 4.5, "num_reviews": 100 }
{ "index": { "_index": "products" } }
{ "name": "Product B", "rating": 3.8, "num_reviews": 50 }
{ "index": { "_index": "products" } }
{ "name": "Product C", "rating": null, "num_reviews": 20 }
{ "index": { "_index": "products" } }
{ "name": "Product D", "rating": 4.2, "num_reviews": null }
{ "index": { "_index": "products" } }
{ "name": "Product E", "rating": null, "num_reviews": null }
Next, run the following weighted_avg
aggregation:
GET /products/_search
{
"size": 0,
"aggs": {
"weighted_rating": {
"weighted_avg": {
"value": {
"field": "rating"
},
"weight": {
"field": "num_reviews"
}
}
}
}
}
In the response, you can see that the missing values for Product E
were completely ignored in the calculation.