Object and nested types in Elasticsearch - Mini Project: Build & Apply

Working with Object and Nested Types in Elasticsearch

📖 Scenario: You are building a search index for an online bookstore. Each book has details like title, author, and a list of reviews. Each review has a reviewer name and a rating. You want to store this data in Elasticsearch using object and nested types to keep the reviews structured.

🎯 Goal: Create an Elasticsearch index mapping that uses object type for the author details and nested type for the list of reviews. Then, add a sample document with one book, its author, and two reviews.

📋 What You'll Learn

Create an index mapping named books with author as an object type

Define reviews as a nested type with fields reviewer (text) and rating (integer)

Index a sample document with title, author details, and two reviews

Print the indexed document to verify the structure

💡 Why This Matters

🌍 Real World

Online bookstores and review platforms often store complex data with nested relationships. Using object and nested types in Elasticsearch helps keep this data organized and searchable.

💼 Career

Understanding how to model nested data in Elasticsearch is important for roles like backend developers, data engineers, and search specialists who build scalable search applications.

1

Create the index mapping with object and nested types

Create an index mapping called books with a properties field. Inside properties, define author as an object type with fields name (text) and birth_year (integer). Also, define reviews as a nested type with fields reviewer (text) and rating (integer). Write the JSON mapping exactly as shown.

Elasticsearch

{
  "mappings": {
    "properties": {
      "author": {
        "type": "object",
        "properties": {
          "name": { "type": "text" },
          "birth_year": { "type": "integer" }
        }
      },
      "reviews": {
        "type": "nested",
        "properties": {
          "reviewer": { "type": "text" },
          "rating": { "type": "integer" }
        }
      }
    }
  }
}
# Your code here

Need a hint?

Remember to nest the name and birth_year fields inside the author object, and the reviewer and rating fields inside the reviews nested type.

2

Add a sample book document with author and reviews

Create a JSON document called book_doc with these exact fields: title as "The Great Adventure", author as an object with name "John Smith" and birth_year 1975, and reviews as a list of two nested objects: first with reviewer "Alice" and rating 5, second with reviewer "Bob" and rating 4.

Elasticsearch

{
  "mappings": {
    "properties": {
      "author": {
        "type": "object",
        "properties": {
          "name": { "type": "text" },
          "birth_year": { "type": "integer" }
        }
      },
      "reviews": {
        "type": "nested",
        "properties": {
          "reviewer": { "type": "text" },
          "rating": { "type": "integer" }
        }
      }
    }
  }
}

book_doc = {
    "title": "The Great Adventure",
    "author": {
        "name": "John Smith",
        "birth_year": 1975
    },
    "reviews": [
        {"reviewer": "Alice", "rating": 5},
        {"reviewer": "Bob", "rating": 4}
    ]
}
# Your code here

Need a hint?

Make sure the author is an object with the exact fields, and reviews is a list of nested objects with the correct reviewer names and ratings.

3

Index the sample document into the books index

Write a command to index the book_doc document into the books index using the Elasticsearch Python client syntax: es.index(index="books", document=book_doc). Assign the result to a variable called response.

Elasticsearch

{
  "mappings": {
    "properties": {
      "author": {
        "type": "object",
        "properties": {
          "name": { "type": "text" },
          "birth_year": { "type": "integer" }
        }
      },
      "reviews": {
        "type": "nested",
        "properties": {
          "reviewer": { "type": "text" },
          "rating": { "type": "integer" }
        }
      }
    }
  }
}

book_doc = {
    "title": "The Great Adventure",
    "author": {
        "name": "John Smith",
        "birth_year": 1975
    },
    "reviews": [
        {"reviewer": "Alice", "rating": 5},
        {"reviewer": "Bob", "rating": 4}
    ]
}

response = es.index(index="books", document=book_doc)
# Your code here

Need a hint?

Use the es.index method with the index parameter set to "books" and the document parameter set to book_doc. Assign the result to response.

4

Print the indexed document to verify

Write a print statement to display the book_doc variable so you can see the full document structure.

Elasticsearch

{
  "mappings": {
    "properties": {
      "author": {
        "type": "object",
        "properties": {
          "name": { "type": "text" },
          "birth_year": { "type": "integer" }
        }
      },
      "reviews": {
        "type": "nested",
        "properties": {
          "reviewer": { "type": "text" },
          "rating": { "type": "integer" }
        }
      }
    }
  }
}

book_doc = {
    "title": "The Great Adventure",
    "author": {
        "name": "John Smith",
        "birth_year": 1975
    },
    "reviews": [
        {"reviewer": "Alice", "rating": 5},
        {"reviewer": "Bob", "rating": 4}
    ]
}

response = es.index(index="books", document=book_doc)

print(book_doc)
# Your code here

Need a hint?

Use print(book_doc) to display the document. The output should show the full nested structure.