The Average Aggregation is a single-value metrics aggregation that computes the average value of numeric fields from the documents in a bucket.
Syntax
{
"aggs": {
"avg_price": {
"avg": {
"field": "price"
}
}
}
}
For more details, refer to the official Elasticsearch documentation.
Example Usage
Here's an example of using the Average Aggregation to calculate the average price of products:
GET /products/_search
{
"size": 0,
"aggs": {
"average_price": {
"avg": {
"field": "price"
}
}
}
}
This query will return the average price across all documents in the "products" index.
Common Issues
- Null Values: By default, documents without a value for the specified field are ignored. Use
"missing"parameter to set a default value for such documents. - Non-numeric Fields: Ensure the field you're aggregating on is of a numeric type.
- Script Usage: When using scripts, be cautious of performance implications for large datasets.
Best Practices
- Use the
"missing"parameter to handle documents with null values if needed. - Consider using the
"script"parameter for more complex average calculations. - For better performance on large datasets, use the
"filter"aggregation to reduce the number of documents before calculating the average.
Frequently Asked Questions
Q: Can I use Average Aggregation on nested fields?
A: Yes, you can use Average Aggregation on nested fields by combining it with a Nested Aggregation.
Q: How does Average Aggregation handle decimal precision?
A: Elasticsearch uses double-precision floating-point format for calculations. You can control the response format using the "format" parameter.
Q: Can I exclude certain documents from the average calculation?
A: Yes, you can use a Filter Aggregation or Query DSL to exclude specific documents before applying the Average Aggregation.
Q: Is it possible to get the average of multiple fields in one query?
A: Yes, you can use multiple Average Aggregations in a single query, each targeting a different field.
Q: How does Average Aggregation perform on large datasets?
A: Average Aggregation is generally efficient, but for very large datasets, consider using sampling techniques or date histogram aggregations to improve performance.
