Why aggregations summarize data in Elasticsearch - See It in Action

Why aggregations summarize data

📖 Scenario: You work at a bookstore that uses Elasticsearch to store sales data. You want to understand how many books were sold in each genre to make better stock decisions.

🎯 Goal: Build an Elasticsearch query that uses aggregations to summarize the total number of books sold per genre.

📋 What You'll Learn

Create an Elasticsearch index data structure with sample sales data

Add a filter to select only sales from the last month

Use a terms aggregation on the genre field to group sales

Use a sum aggregation on the copies_sold field to get total sales per genre

Print the aggregation results

💡 Why This Matters

🌍 Real World

Aggregations help businesses quickly summarize large amounts of data, like total sales per category, without manually counting each record.

💼 Career

Understanding aggregations is essential for data analysts and backend developers working with Elasticsearch to create reports and dashboards.

1

DATA SETUP: Create sample sales data

Create a variable called sales_data that contains a list of dictionaries. Each dictionary should have these exact keys and values: {"genre": "fiction", "copies_sold": 10}, {"genre": "non-fiction", "copies_sold": 5}, {"genre": "fiction", "copies_sold": 7}, {"genre": "science", "copies_sold": 3}.

Elasticsearch

# Create the sales_data list with the exact entries
# Your code here

Need a hint?

Use a list with dictionaries exactly as shown.

2

CONFIGURATION: Add a filter for recent sales

Create a variable called query that contains a dictionary with a range filter on the sale_date field to select sales from the last 30 days. Use the exact key gte with value "now-30d/d".

Elasticsearch

sales_data = [
    {"genre": "fiction", "copies_sold": 10},
    {"genre": "non-fiction", "copies_sold": 5},
    {"genre": "fiction", "copies_sold": 7},
    {"genre": "science", "copies_sold": 3}
]
# Create the query dictionary with a range filter on sale_date for last 30 days
# Your code here

Need a hint?

Use a dictionary with keys range, sale_date, and gte.

3

CORE LOGIC: Build the aggregation query

Create a variable called agg_query that contains a dictionary with a terms aggregation on the genre field named genres, and inside it a sum aggregation on the copies_sold field named total_copies.

Elasticsearch

sales_data = [
    {"genre": "fiction", "copies_sold": 10},
    {"genre": "non-fiction", "copies_sold": 5},
    {"genre": "fiction", "copies_sold": 7},
    {"genre": "science", "copies_sold": 3}
]

query = {
    "range": {
        "sale_date": {
            "gte": "now-30d/d"
        }
    }
}

# Create agg_query with terms aggregation on genre named 'genres' and sum aggregation on copies_sold named 'total_copies'
# Your code here

Need a hint?

Use nested dictionaries for terms and sum aggregations exactly as shown.

4

OUTPUT: Print the aggregation results

Write a print statement that outputs the string "Aggregation query:" followed by the agg_query variable.

Elasticsearch

Need a hint?

Use print("Aggregation query:", agg_query) exactly.