Index mappings overview in Elasticsearch

Introduction

Index mappings tell Elasticsearch how to store and understand your data. They help organize data so you can search it quickly and correctly.

When you want to define how text, numbers, or dates are stored in Elasticsearch.

When you need to control how search works on your data fields.

When you want to make sure data types are correct for filtering or sorting.

When you want to add special rules like ignoring some fields or using custom analyzers.

When setting up a new Elasticsearch index for your application.

Syntax

Elasticsearch

{
  "mappings": {
    "properties": {
      "field_name": {
        "type": "data_type"
      },
      "another_field": {
        "type": "data_type"
      }
    }
  }
}

The mappings section defines the structure of your data.

Each field_name has a type like text, keyword, date, or integer.

Examples

This example defines a simple mapping with a text field, a number, and a date.

Elasticsearch

{
  "mappings": {
    "properties": {
      "name": { "type": "text" },
      "age": { "type": "integer" },
      "joined": { "type": "date" }
    }
  }
}

Here, email is a keyword (exact match), and bio is full text searchable.

Elasticsearch

{
  "mappings": {
    "properties": {
      "email": { "type": "keyword" },
      "bio": { "type": "text" }
    }
  }
}

Sample Program

This query creates a new index called users with mappings for username, age, and signup date.

Elasticsearch

PUT /users
{
  "mappings": {
    "properties": {
      "username": { "type": "keyword" },
      "age": { "type": "integer" },
      "signup_date": { "type": "date" }
    }
  }
}

Important Notes

If you don't define mappings, Elasticsearch tries to guess data types automatically.

Defining mappings helps avoid mistakes and improves search speed.

Once data is indexed, changing mappings can be difficult, so plan ahead.

Summary

Index mappings define how Elasticsearch stores and understands your data.

They help control search, filtering, and sorting behavior.

Always define mappings before adding data for best results.

Practice

1. What is the main purpose of index mappings in Elasticsearch?

easy

A. To monitor server health

B. To create user accounts for Elasticsearch

C. To backup data automatically

D. To define how data fields are stored and searched

Solution

1. Step 1: Understand the role of index mappings

   Index mappings tell Elasticsearch how to store and interpret each field in your data.

2. Step 2: Compare options with this role

   Only To define how data fields are stored and searched matches this purpose; others relate to unrelated tasks.

3. Final Answer:

   To define how data fields are stored and searched -> Option D

4. Quick Check:

   Index mappings = data structure definition [OK]

Hint: Mappings control data structure and search behavior [OK]

Common Mistakes:

• Confusing mappings with user management
• Thinking mappings handle backups
• Assuming mappings monitor server health

2. Which of the following is the correct way to define a text field in an Elasticsearch mapping?

easy

A. "name": { "type": "text" }

B. "name": { "type": "keyword" }

C. "name": { "type": "integer" }

D. "name": { "type": "date" }

Solution

1. Step 1: Identify the correct field type for text data

   Text fields in Elasticsearch use the type "text" for full-text search.

2. Step 2: Match the option with the correct type

   "name": { "type": "text" } correctly uses "text"; others use types for keywords, numbers, or dates.

3. Final Answer:

   "name": { "type": "text" } -> Option A

4. Quick Check:

   Text fields = type "text" [OK]

Hint: Use "text" type for searchable text fields [OK]

Common Mistakes:

• Using "keyword" for full-text search fields
• Assigning numeric or date types to text data
• Missing quotes around field names

3. Given this mapping snippet:

{ "properties": { "age": { "type": "integer" }, "name": { "type": "keyword" } } }

What will happen if you try to search for documents with name containing partial words?

medium

A. Partial word matches will work because keyword fields support full-text search

B. Partial word matches will not work because keyword fields are exact values

C. Elasticsearch will throw a syntax error

D. Partial word matches will work only if age is also searched

Solution

1. Step 1: Understand keyword field behavior

   Keyword fields store exact values and do not support partial or full-text search.

2. Step 2: Analyze the search behavior for partial words

   Since name is keyword type, partial matches will fail; only exact matches work.

3. Final Answer:

   Partial word matches will not work because keyword fields are exact values -> Option B

4. Quick Check:

   Keyword = exact match only [OK]

Hint: Keyword fields do exact matches only, no partial search [OK]

Common Mistakes:

• Assuming keyword fields support partial matching
• Expecting syntax errors from valid mappings
• Confusing integer field with text search behavior

4. You defined this mapping:

{ "properties": { "date": { "type": "text" } } }

What is the problem with this mapping if you want to filter by date range?

medium

A. The field name "date" is reserved and cannot be used

B. There is no problem; text type works for date filtering

C. The type should be "date" to enable date range filtering

D. You must add "index": false to filter by date

Solution

1. Step 1: Identify the correct type for date fields

   Date range filtering requires the field to be of type "date" in Elasticsearch.

2. Step 2: Check the given mapping type

   The mapping uses "text" type, which is not suitable for date operations like range filtering.

3. Final Answer:

   The type should be "date" to enable date range filtering -> Option C

4. Quick Check:

   Date filtering needs type "date" [OK]

Hint: Use type "date" for date range filters [OK]

Common Mistakes:

• Using text type for date fields
• Thinking field names are reserved keywords
• Adding index false disables searching, not filtering

5. You want to store a user's profile with fields: username (exact match), bio (full-text search), and signup_date (date range queries). Which mapping correctly defines these fields?

hard

A. { "properties": { "username": { "type": "keyword" }, "bio": { "type": "text" }, "signup_date": { "type": "date" } } }

B. { "properties": { "username": { "type": "text" }, "bio": { "type": "keyword" }, "signup_date": { "type": "text" } } }

C. { "properties": { "username": { "type": "integer" }, "bio": { "type": "text" }, "signup_date": { "type": "date" } } }

D. { "properties": { "username": { "type": "keyword" }, "bio": { "type": "keyword" }, "signup_date": { "type": "integer" } } }

Solution

1. Step 1: Match each field to its correct type

   Username needs exact matches, so "keyword" is correct. Bio needs full-text search, so "text" is correct. Signup_date needs date range queries, so "date" is correct.

2. Step 2: Verify the options

   { "properties": { "username": { "type": "keyword" }, "bio": { "type": "text" }, "signup_date": { "type": "date" } } } matches all these requirements correctly. Others mix types incorrectly.

3. Final Answer:

   { "properties": { "username": { "type": "keyword" }, "bio": { "type": "text" }, "signup_date": { "type": "date" } } } -> Option A

4. Quick Check:

   Correct types for exact, full-text, and date fields [OK]

Hint: Use keyword for exact, text for full-text, date for dates [OK]

Common Mistakes:

• Mixing keyword and text types incorrectly
• Using integer for date fields
• Confusing full-text and exact match types