Dynamic vs explicit mapping in Elasticsearch - Hands-On Comparison

Dynamic vs Explicit Mapping in Elasticsearch

📖 Scenario: You are setting up an Elasticsearch index to store information about books in a library. You want to understand how Elasticsearch handles data fields automatically (dynamic mapping) and how you can control the data types explicitly (explicit mapping).

🎯 Goal: Build an Elasticsearch index with dynamic mapping enabled, then create another index with explicit mapping for the title, author, and year fields. Learn how to define mappings and see the difference between letting Elasticsearch guess the data types and specifying them yourself.

📋 What You'll Learn

Create an index called library_dynamic with dynamic mapping enabled.

Create an index called library_explicit with explicit mapping for title (text), author (keyword), and year (integer).

Add a sample document to each index with the fields title, author, and year.

Verify the mappings of both indices to observe the difference.

💡 Why This Matters

🌍 Real World

In real-world applications, controlling how data is indexed in Elasticsearch is crucial for search accuracy and performance. Dynamic mapping is convenient but can lead to unexpected data types, while explicit mapping ensures data consistency.

💼 Career

Understanding dynamic and explicit mapping is essential for roles like search engineers, backend developers, and data engineers who work with Elasticsearch to build search and analytics solutions.

1

Create the library_dynamic index with dynamic mapping

Write the Elasticsearch command to create an index called library_dynamic with dynamic mapping enabled (default behavior). Use the PUT method and specify an empty mappings object.

Elasticsearch

# Your code here to create the library_dynamic index with dynamic mapping enabled

Need a hint?

Dynamic mapping is enabled by default, so you can create the index with an empty mappings object.

2

Create the library_explicit index with explicit mapping

Write the Elasticsearch command to create an index called library_explicit with explicit mapping. Define the title field as text, the author field as keyword, and the year field as integer. Use the PUT method and specify the mappings with these properties.

Elasticsearch

PUT /library_dynamic
{
  "mappings": {}
}

# Your code here to create the library_explicit index with explicit mapping

Need a hint?

Explicit mapping requires defining the properties object with each field and its data type.

3

Add a sample document to each index

Write the Elasticsearch commands to add a document to library_dynamic and library_explicit indices. Use the POST method to add a document with title as "The Great Gatsby", author as "F. Scott Fitzgerald", and year as 1925.

Elasticsearch

PUT /library_dynamic
{
  "mappings": {}
}

PUT /library_explicit
{
  "mappings": {
    "properties": {
      "title": { "type": "text" },
      "author": { "type": "keyword" },
      "year": { "type": "integer" }
    }
  }
}

# Your code here to add a document to both indices

Need a hint?

Use the POST method with /_doc to add documents to each index.

4

Verify the mappings of both indices

Write the Elasticsearch commands to get the mappings of library_dynamic and library_explicit indices. Use the GET method with /_mapping endpoint for each index.

Elasticsearch

PUT /library_dynamic
{
  "mappings": {}
}

PUT /library_explicit
{
  "mappings": {
    "properties": {
      "title": { "type": "text" },
      "author": { "type": "keyword" },
      "year": { "type": "integer" }
    }
  }
}

POST /library_dynamic/_doc
{
  "title": "The Great Gatsby",
  "author": "F. Scott Fitzgerald",
  "year": 1925
}

POST /library_explicit/_doc
{
  "title": "The Great Gatsby",
  "author": "F. Scott Fitzgerald",
  "year": 1925
}

# Your code here to get the mappings of both indices

Need a hint?

Use the GET method with /_mapping to see the field types Elasticsearch assigned or you defined.