In Elasticsearch, an aggregation is a way to group and summarize data in a query result. Aggregations are used to calculate metrics, generate histograms, and perform other types of statistical analysis on the data in an Elasticsearch index.
There are many different types of aggregations in Elasticsearch, including:
1. Metrics aggregations: These aggregations calculate metrics like average, min, max, sum, and count on a field or set of fields.
2. Bucket aggregations: These aggregations group documents into “buckets” based on the values in one or more fields. For example, you might use a terms aggregation to group documents by the values in a particular field, or a date histogram aggregation to group documents by date ranges.
3. Pipeline aggregations: These aggregations calculate metrics on the results of other aggregations. For example, you might use a moving average aggregation to calculate a rolling average on the results of a date histogram aggregation.
Aggregations are defined using the Elasticsearch Query DSL, and can be included in a search query using the “aggs” parameter. Here’s an example of a search query that includes a terms aggregation to group documents by the values in the “category” field:
{
"query": {
"match_all": {}
},
"aggs": {
"by_category": {
"terms": {
"field": "category"
}
}
}
}
In this example, the “aggs” parameter is used to specify a terms aggregation that groups documents by the values in the “category” field. When the search query is executed, Elasticsearch will return a response that includes a summary of the data grouped by category.
Aggregations are a powerful tool for data analysis in Elasticsearch, and can be used to generate insights and identify trends in large datasets.