Elasticsearch Mastering Group By Queries in Elasticsearch

By Opster Team

Updated: Jul 20, 2023

| 2 min read

Introduction 

Elasticsearch, a highly scalable open-source full-text search and analytics engine, provides a robust set of aggregation capabilities that can be used to group data in various ways. One such capability is the “group by” functionality, which is similar to the SQL GROUP BY clause. This article will delve into the intricacies of using the “group by” functionality in Elasticsearch, providing examples and step-by-step instructions to help you master this feature. If you want to learn about Elasticsearch group by field: aggregations and bucketing techniques, check out this guide. 

Understanding Aggregations

Aggregations in Elasticsearch are a way to collect certain statistics over a set of documents. They provide a high-level view of the data, allowing you to summarize, analyze, and visualize the data in various ways. The “group by” functionality is achieved using the bucket aggregations provided by Elasticsearch.

Bucket Aggregations

Bucket aggregations don’t calculate metrics over fields like the metrics aggregations do, but instead, they create buckets of documents. Each bucket is associated with a criterion (depending on the aggregation type) which determines whether or not a document in the current context “falls” into it. In other words, buckets effectively define document sets. 

Terms Aggregation

The most common type of bucket aggregation is the terms aggregation. It is used to group by exact values. For example, if you have an e-commerce website and you want to know the number of orders placed by each customer, you can use the terms aggregation on the customer_id field.

Here is an example of how to use the terms aggregation:

json
GET /orders/_search
{
  "size": 0,
  "aggs": {
    "group_by_customer": {
      "terms": {
        "field": "customer_id"
      }
    }
  }
}

In this example, the `size` parameter is set to 0 because we are not interested in the actual documents, but only in the aggregation results. The `aggs` parameter defines our aggregation. We named it “group_by_customer”, but you can choose any name you like. The `terms` aggregation is used on the “customer_id” field.

The response will include the number of orders for each customer_id.

Date Histogram Aggregation

Another useful type of bucket aggregation is the date histogram aggregation. It is used to group by date intervals. For example, if you want to know the number of orders placed on your e-commerce website each day, you can use the date histogram aggregation on the order_date field.

Here is an example of how to use the date histogram aggregation:

json
GET /orders/_search
{
  "size": 0,
  "aggs": {
    "orders_over_time": {
      "date_histogram": {
        "field": "order_date",
        "calendar_interval": "day"
      }
    }
  }
}

In this example, the `date_histogram` aggregation is used on the “order_date” field, with a calendar interval of “day”. This means that the orders will be grouped by day.

The response will include the number of orders for each day.

Conclusion

The “group by” functionality in Elasticsearch, achieved using bucket aggregations, is a powerful tool for data analysis. By understanding and using the different types of bucket aggregations, you can extract valuable insights from your data. Whether you’re grouping by exact values with the terms aggregation or by date intervals with the date histogram aggregation, the possibilities are vast.