Discover for data exploration in Elasticsearch - Mini Project: Build & Apply

Discover for data exploration

📖 Scenario: You are working with a collection of sales data stored in Elasticsearch. You want to explore this data to find useful insights like total sales per product category.

🎯 Goal: Build a simple Elasticsearch query to discover and aggregate sales data by product category.

📋 What You'll Learn

Create an Elasticsearch index with sample sales data

Define a filter to select sales from the year 2023

Write an aggregation query to sum sales amounts by product category

Print the aggregation results

💡 Why This Matters

🌍 Real World

Exploring sales data helps businesses understand which product categories perform best and make informed decisions.

💼 Career

Data analysts and developers use Elasticsearch queries to explore and summarize large datasets quickly.

1

Create sample sales data index

Create an Elasticsearch index called sales with documents containing fields product_category (string), sale_amount (number), and sale_year (number). Add exactly these three documents: {"product_category": "Books", "sale_amount": 100, "sale_year": 2023}, {"product_category": "Electronics", "sale_amount": 250, "sale_year": 2023}, {"product_category": "Books", "sale_amount": 150, "sale_year": 2022}.

Elasticsearch

# Your code here to create the sales index and add the three documents

Need a hint?

Use the PUT method to add documents to the sales index with the exact fields and values.

2

Define filter for sales in 2023

Create a variable called filter_2023 that contains a filter to select documents where sale_year is exactly 2023.

Elasticsearch

PUT /sales/_doc/1
{
  "product_category": "Books",
  "sale_amount": 100,
  "sale_year": 2023
}

PUT /sales/_doc/2
{
  "product_category": "Electronics",
  "sale_amount": 250,
  "sale_year": 2023
}

PUT /sales/_doc/3
{
  "product_category": "Books",
  "sale_amount": 150,
  "sale_year": 2022
}

# Define filter_2023 to select sale_year 2023
# Your code here

Need a hint?

Use a term filter to match sale_year exactly 2023.

3

Write aggregation query for total sales by category

Create a variable called agg_query that contains an Elasticsearch query to filter by filter_2023 and aggregate total sale_amount by product_category using a terms aggregation and a sum sub-aggregation.

Elasticsearch

PUT /sales/_doc/1
{
  "product_category": "Books",
  "sale_amount": 100,
  "sale_year": 2023
}

PUT /sales/_doc/2
{
  "product_category": "Electronics",
  "sale_amount": 250,
  "sale_year": 2023
}

PUT /sales/_doc/3
{
  "product_category": "Books",
  "sale_amount": 150,
  "sale_year": 2022
}

filter_2023 = {
  "term": {
    "sale_year": 2023
  }
}

# Create agg_query to filter by filter_2023 and aggregate sum of sale_amount by product_category
# Your code here

Need a hint?

Use a bool query with filter and a terms aggregation on product_category with a sum sub-aggregation on sale_amount.

4

Print aggregation results

Print the agg_query variable to display the full Elasticsearch query.

Elasticsearch

PUT /sales/_doc/1
{
  "product_category": "Books",
  "sale_amount": 100,
  "sale_year": 2023
}

PUT /sales/_doc/2
{
  "product_category": "Electronics",
  "sale_amount": 250,
  "sale_year": 2023
}

PUT /sales/_doc/3
{
  "product_category": "Books",
  "sale_amount": 150,
  "sale_year": 2022
}

filter_2023 = {
  "term": {
    "sale_year": 2023
  }
}

agg_query = {
  "query": {
    "bool": {
      "filter": filter_2023
    }
  },
  "aggs": {
    "sales_by_category": {
      "terms": {
        "field": "product_category"
      },
      "aggs": {
        "total_sales": {
          "sum": {
            "field": "sale_amount"
          }
        }
      }
    }
  }
}

# Print agg_query
# Your code here

Need a hint?

Use print(agg_query) to display the query.