When you perform a cardinality aggregation in Elasticsearch, it counts the number of unique values in a specified field across all documents in the index. Here’s how it works:
1. Elasticsearch analyzes the field: Before performing the cardinality aggregation, Elasticsearch first analyzes the specified field in all of the documents to extract the distinct values.
2. Elasticsearch counts the unique values: Next, Elasticsearch counts the number of distinct values in the field using a specialized data structure called a HyperLogLog algorithm. This algorithm allows Elasticsearch to estimate the number of unique values in a field with a high degree of accuracy while minimizing the computational resources required.
3. Elasticsearch returns the aggregated results: Once the aggregation is complete, Elasticsearch returns the aggregated results. The output of a cardinality aggregation is a single value representing the number of distinct values in the field.
For example, let’s say you have an index of customer orders, and each document has a “customer_id” field that represents the ID of the customer who placed the order. You could perform a cardinality aggregation on the “customer_id” field to count the number of unique customers who have placed orders.
Cardinality aggregations can be used in combination with other aggregations to perform complex analyses on your data. By counting the number of distinct values in a field, you can gain insights into the diversity of your data, and use that information to make data-driven decisions.
It’s worth noting that cardinality aggregations can be computationally expensive and may require significant resources for large datasets. Additionally, the accuracy of the cardinality count may be impacted by factors such as data cardinality, field type, and sampling methods.