Examples of Elasticsearch Aggregation Syntax

Elasticsearch's aggregation syntax allows you to perform advanced data analysis and generate meaningful insights from your data. In this guide, we will explore the syntax and usage of Elasticsearch's aggregation feature.

First, let's clarify the terminology. In Elasticsearch, "aggs" and "aggregations" are used interchangeably to refer to the aggregation feature. So don't get confused if you see both terms being used.

The basic structure of an aggregation is defined using a JSON schema. Here's an example:

{
  "(agg_name)": {
    "date_histogram": {
      "field": "string",
      "calendar_interval": "string",
      "format": "string",
      "order": "#Order"
    }
  }
}

In this schema, (agg_name) represents the name of your aggregation. The date_histogram is just one type of aggregation available in Elasticsearch. It allows you to aggregate data based on a date field, grouping it into intervals defined by the calendar_interval. You can also specify the format of the date and the order in which the results should be sorted.

The "order" schema is used to define the sorting order for the aggregation results. Here's an example:

{
  "_term": "string"
}

In this schema, _term represents the field by which the results should be ordered.

When executing an aggregation query, Elasticsearch returns a response in a specific format. Here's an example of the response schema:

{
  "<aggregation_name>": {
    "<aggregation_type>": "#aggregation_body",
    "meta": "#meta_data_body",
    "aggregations": "#response"
  }
}

In this schema, <aggregation_name> represents the name of the aggregation you performed. <aggregation_type> represents the type of aggregation you used. The #aggregation_body contains the actual aggregation results, while the #meta_data_body contains any additional metadata associated with the aggregation. The #response field contains any nested aggregations if you have performed multiple aggregations.

Here's an example of the response schema for a date histogram aggregation:

{
  "(agg_name)": {
    "buckets[]": {
      "key": "integer",
      "key_as_string": "string",
      "doc_count": "integer"
    },
    "value": "float",
    "hits": "#Hits"
  }
}

In this schema, (agg_name) represents the name of the aggregation. The buckets[] field contains an array of buckets, where each bucket represents a group of documents. Each bucket has a key and key_as_string field, representing the interval for the aggregation. The doc_count field indicates the number of documents in each bucket. The value field represents the aggregated value for each bucket, and the #Hits field contains any hits associated with the aggregation.

That's a brief overview of Elasticsearch's aggregation syntax. With this powerful feature, you can perform complex data analysis and gain valuable insights from your data.