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Learn how to effectively use `aggregation` in Elasticsearch to analyze your data by running powerful queries and grouping results with camera_id and date histogram.
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Mastering Aggregation on Queries in Elasticsearch: A Step-by-Step Guide

In the vast world of data analysis, Elasticsearch shines as a powerful tool for storing, searching, and analyzing large volumes of data efficiently. One common task many users encounter is performing aggregation on queries to derive meaningful insights. If you are wondering how to run a query and then aggregate the results based on specific fields such as camera_id and date, you’re in the right place.

Understanding the Problem

Let’s break down the requirements. You have an index in Elasticsearch with a specific mapping that includes fields like date, time, frame_id, and a nested field called objects. You want to run a query that searches for specific objects (e.g., class "person") and then aggregate the results primarily by the camera_id, followed by a date histogram aggregation to observe trends over time.

Crafting the Query

To tackle this problem, you will need to construct a query that performs two key tasks:

Search for documents where the objects.class is "person".

Aggregate the results based on the camera_id and then group those results into daily buckets based on the date field.

Step 1: Writing the Base Query

The initial query you’d like to execute can be structured as follows:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Adding the Aggregation Section

Now, the next step involves incorporating an aggregation section that will allow us to group results by camera_id and then by date. Here’s how to structure the aggregation within the query:

Aggregation Structure

[[See Video to Reveal this Text or Code Snippet]]

Complete Query with Aggregation

By combining the base query with the aggregation, your complete query would look like this:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

In this guide, we learned how to write an Elasticsearch query that searches for specific object classes and aggregates results effectively. By structuring your query to include nested queries and aggregation sections, you can efficiently extract valuable insights from your data. With the structure provided above, you should be able to apply similar logic to a variety of analytical tasks using Elasticsearch.

Feel empowered to explore and customize this query further to suit your specific needs. Happy querying!