Creating an index in Elasticsearch - Try It Yourself

Creating an index

📖 Scenario: You are setting up a simple search system for a small library. You want to create a place to store book information so you can find books easily later.

🎯 Goal: Create an Elasticsearch index called library with basic settings and mappings to store book titles and authors.

📋 What You'll Learn

Create an index named library

Add settings with number_of_shards set to 1

Add mappings for title and author fields as text

💡 Why This Matters

🌍 Real World

Creating an index is the first step in storing and searching data efficiently in Elasticsearch, useful for search engines, catalogs, and logs.

💼 Career

Knowing how to create and configure indexes is essential for roles like data engineer, backend developer, and search specialist working with Elasticsearch.

1

Create the index name

Write a JSON object to create an index named library with empty settings and mappings objects.

Elasticsearch

{
  "settings": {},
  "mappings": {}
  # Your code here
}

Hint

Start by naming the index library and add empty settings and mappings objects.

2

Add index settings

Add a settings object with number_of_shards set to 1 inside the index creation JSON.

Elasticsearch

{
  "settings": {
    "number_of_shards": 1
  },
  "mappings": {}
  # Your code here
}

Hint

Inside settings, set "number_of_shards" to 1 to keep it simple.

3

Add mappings for book fields

Add a mappings object with properties defining title and author fields as text type.

Elasticsearch

{
  "settings": {
    "number_of_shards": 1
  },
  "mappings": {
    "properties": {
      "title": { "type": "text" },
      "author": { "type": "text" }
    }
  }
  # Your code here
}

Hint

Inside mappings, add properties with title and author as text fields.

4

Complete the index creation JSON

Ensure the JSON object includes settings with number_of_shards set to 1, and mappings with title and author as text fields.

Elasticsearch

{
  "settings": {
    "number_of_shards": 1
  },
  "mappings": {
    "properties": {
      "title": { "type": "text" },
      "author": { "type": "text" }
    }
  }
  # Your code here
}

Hint

Double-check that all parts are included: settings, and mappings with correct fields.

Practice

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

easy

A. To encrypt data for security

B. To delete data permanently

C. To backup data automatically

D. To organize data for fast searching

Solution

1. Step 1: Understand the role of an index

   An index in Elasticsearch is like a folder that organizes documents to make searching fast and efficient.

2. Step 2: Compare options with index purpose

   Deleting, backing up, or encrypting data are not the main functions of an index.

3. Final Answer:

   To organize data for fast searching -> Option D

4. Quick Check:

   Index = Organize data for search [OK]

Hint: Index means organizing data for quick search [OK]

Common Mistakes:

• Confusing index with backup or encryption
• Thinking index deletes data
• Assuming index automatically secures data

2. Which is the correct syntax to create an index named products in Elasticsearch?

easy

A. PUT /products

B. POST /products

C. GET /products

D. DELETE /products

Solution

1. Step 1: Recall HTTP methods for index creation

   Elasticsearch uses the PUT method to create an index with a specific name.

2. Step 2: Match method with index name

   PUT /products correctly creates the index named 'products'. POST is used for adding documents, GET for retrieving, DELETE for removing.

3. Final Answer:

   PUT /products -> Option A

4. Quick Check:

   PUT creates index [OK]

Hint: Use PUT to create an index by name [OK]

Common Mistakes:

• Using POST instead of PUT for index creation
• Confusing GET with creation
• Trying DELETE to create index

3. Given this request to create an index:

{
  "settings": {"number_of_shards": 1},
  "mappings": {
    "properties": {
      "name": {"type": "text"},
      "price": {"type": "float"}
    }
  }
}

What will be the type of the price field in the index?

medium

A. keyword

B. float

C. integer

D. text

Solution

1. Step 1: Read the mapping for the price field

   The mapping defines "price" with "type": "float" which means it stores decimal numbers.

2. Step 2: Understand field types

   Float is a number with decimals, integer is whole number, text is for full text search, keyword is for exact matches.

3. Final Answer:

   float -> Option B

4. Quick Check:

   price type = float [OK]

Hint: Check mapping 'type' for field data type [OK]

Common Mistakes:

• Confusing float with integer
• Assuming text type for numbers
• Mixing keyword with numeric types

4. Identify the error in this index creation request:

PUT /library
{
  "settings": {
    "number_of_shards": "two"
  },
  "mappings": {
    "properties": {
      "title": {"type": "text"}
    }
  }
}

medium

A. number_of_shards should be a number, not a string

B. title field type should be keyword, not text

C. PUT method cannot be used to create index

D. Missing index name in URL

Solution

1. Step 1: Check the settings syntax

   number_of_shards must be a numeric value, but "two" is a string, causing an error.

2. Step 2: Validate other parts

   Field type 'text' is valid for title, PUT is correct method, index name 'library' is present.

3. Final Answer:

   number_of_shards should be a number, not a string -> Option A

4. Quick Check:

   number_of_shards = number [OK]

Hint: Settings values must match expected data types [OK]

Common Mistakes:

• Using string instead of number for shards
• Confusing field types for text fields
• Using wrong HTTP method for index creation

5. You want to create an index users with a field age that should only accept whole numbers and be searchable. Which mapping is correct?

hard

A. { "mappings": { "properties": { "age": { "type": "float" } } } }

B. { "mappings": { "properties": { "age": { "type": "text" } } } }

C. { "mappings": { "properties": { "age": { "type": "integer" } } } }

D. { "mappings": { "properties": { "age": { "type": "keyword" } } } }

Solution

1. Step 1: Understand the data type needed for age

   Age is a whole number, so integer type is appropriate to store numeric whole values.

2. Step 2: Check searchability and field types

   Text is for full text search, float allows decimals, keyword is for exact matches but not numeric operations.

3. Final Answer:

   { "mappings": { "properties": { "age": { "type": "integer" } } } } -> Option C

4. Quick Check:

   Whole number field = integer type [OK]

Hint: Use integer type for whole number searchable fields [OK]

Common Mistakes:

• Using text type for numeric fields
• Choosing float when decimals not needed
• Using keyword for numeric data