When you perform a script aggregation in Elasticsearch, it executes a custom script on the fields in the index to perform a custom calculation. Here’s how it works:
1. Elasticsearch applies the input aggregation: Before performing the script aggregation, Elasticsearch first applies the specified input aggregation to the index and retrieves the set of documents that the script aggregation will be executed on. The input aggregation can be any of the supported aggregation types, such as terms, date histograms, or range aggregations.
2. Elasticsearch executes the script: Next, Elasticsearch executes the specified script on the fields in the selected documents. The script can be written in any of the scripting languages supported by Elasticsearch, such as Painless, Groovy, or JavaScript. The script can define any custom calculation or transformation on the fields in the index.
3. Elasticsearch returns the aggregated results: Once the script has been executed, Elasticsearch returns the aggregated results. The output of a script aggregation is a set of script-defined values, which can be used for further analysis or display.
For example, let’s say you have an index of customer orders, and each document has a “price” field that represents the price of the product, and a “discount” field that represents the discount applied to the price. You could perform a script aggregation to calculate the discounted price for each order, based on the values of the “price” and “discount” fields. Elasticsearch would then execute the specified script, which would calculate the discounted price for each order and return the result.
Script aggregations can be used in combination with other aggregations to perform complex analyses on your data. By performing custom calculations on the fields in the index, you can gain insights into patterns and trends in the data, and use that information to make data-driven decisions.
It’s worth noting that script aggregations can be computationally expensive and may require significant resources for large datasets. Additionally, the accuracy of the script-defined values may be impacted by factors such as scripting language used, field type, and complexity of the script.