The Weighted Average Aggregation is a single-value metrics aggregation that computes the weighted average of numeric values extracted from the aggregated documents. It allows you to assign different weights to each value, providing more control over the average calculation compared to a simple average.
Syntax
{
"weighted_avg": {
"value": {
"field": "value_field"
},
"weight": {
"field": "weight_field"
}
}
}
For more details, refer to the official Elasticsearch documentation.
Example Usage
Consider a dataset of product reviews with ratings and helpfulness votes:
{
"aggs": {
"weighted_rating": {
"weighted_avg": {
"value": {
"field": "rating"
},
"weight": {
"field": "helpfulness_votes"
}
}
}
}
}
This aggregation calculates the average rating weighted by the number of helpfulness votes, giving more importance to ratings with higher helpfulness.
Common Issues
- Missing values: Ensure that both value and weight fields exist in your documents to avoid skewed results.
- Zero weights: Be cautious of documents with zero weights, as they can affect the calculation.
- Negative weights: Weighted Average Aggregation doesn't support negative weights.
Best Practices
- Use script-based weights for complex weighting logic.
- Combine with other aggregations for more insightful analysis.
- Consider using missing value handling options to manage documents with missing fields.
Frequently Asked Questions
Q: Can I use a script to calculate weights dynamically?
A: Yes, you can use a script in the "weight" field to calculate weights based on complex logic or multiple fields.
Q: How does the Weighted Average Aggregation handle missing values?
A: By default, documents with missing values are ignored. You can use the "missing" parameter to specify a default value for missing fields.
Q: Is it possible to use Weighted Average Aggregation with nested fields?
A: Yes, you can use it with nested fields by wrapping the weighted_avg aggregation inside a nested aggregation.
Q: Can I use Weighted Average Aggregation in combination with other aggregations?
A: Absolutely. You can combine it with bucket aggregations like terms or date histogram for more complex analyses.
Q: How does Weighted Average Aggregation perform with large datasets?
A: It generally performs well, but for very large datasets, consider using sampling or date range filtering to improve performance if needed.
