Text vs keyword field types in Elasticsearch - Hands-On Comparison

Text vs Keyword Field Types in Elasticsearch

📖 Scenario: You are building a simple search system for a small online bookstore. You want to store book information in Elasticsearch. Some fields need to be searchable by full text, while others need exact matching.

🎯 Goal: Create an Elasticsearch index mapping that uses text and keyword field types correctly. Then add a document and query it to see the difference.

📋 What You'll Learn

Create an index mapping with a title field of type text

Create an index mapping with an isbn field of type keyword

Index a document with a sample book title and ISBN

Query the document by full text search on title

Query the document by exact match on isbn

💡 Why This Matters

🌍 Real World

Online stores and libraries use Elasticsearch to let users search books by title (full text) and filter by ISBN or categories (exact match).

💼 Career

Understanding <code>text</code> vs <code>keyword</code> fields is essential for building efficient search applications and indexing data correctly in Elasticsearch.

1

Create index mapping with title as text and isbn as keyword

Create an index called books with a mapping where title is of type text and isbn is of type keyword. Use the Elasticsearch PUT API with the exact JSON structure shown.

Elasticsearch

{
  "mappings": {
    "properties": {
      "title": {
        "type": "text"
      },
      "isbn": {
        "type": "keyword"
      }
    }
  }
}
# Your code here to create the index with this mapping

Hint

Use the PUT method to create the index with the mapping inside the mappings key.

2

Index a document with title and isbn

Index a document into the books index with title set to "The Great Gatsby" and isbn set to "9780743273565". Use the POST API with the exact JSON body shown.

Elasticsearch

PUT /books
{
  "mappings": {
    "properties": {
      "title": {
        "type": "text"
      },
      "isbn": {
        "type": "keyword"
      }
    }
  }
}

# Your code here to index the document with title and isbn

Hint

Use POST /books/_doc to add the document with the given fields.

3

Search the document by full text on title

Write a search query using the GET /books/_search API that searches for the word "Great" in the title field using a match query.

Elasticsearch

PUT /books
{
  "mappings": {
    "properties": {
      "title": {
        "type": "text"
      },
      "isbn": {
        "type": "keyword"
      }
    }
  }
}

POST /books/_doc
{
  "title": "The Great Gatsby",
  "isbn": "9780743273565"
}

# Your code here to search for "Great" in the title field

Hint

Use a match query on the title field to find documents containing the word "Great".

4

Search the document by exact match on isbn

Write a search query using the GET /books/_search API that finds the document with isbn exactly equal to "9780743273565" using a term query.

Elasticsearch

PUT /books
{
  "mappings": {
    "properties": {
      "title": {
        "type": "text"
      },
      "isbn": {
        "type": "keyword"
      }
    }
  }
}

POST /books/_doc
{
  "title": "The Great Gatsby",
  "isbn": "9780743273565"
}

GET /books/_search
{
  "query": {
    "match": {
      "title": "Great"
    }
  }
}

# Your code here to search for the exact isbn value

Hint

Use a term query on the isbn field to find the exact match.

Practice

1. Which Elasticsearch field type should you use if you want to perform full-text search inside sentences?

easy

A. text

B. keyword

C. integer

D. date

Solution

1. Step 1: Understand the purpose of text fields

   Text fields are designed for full-text search, allowing analysis and tokenization of sentences.

2. Step 2: Compare with keyword fields

   Keyword fields are for exact matches and do not analyze the text, so they are not suitable for searching inside sentences.

3. Final Answer:

   text -> Option A

4. Quick Check:

   Full-text search = text [OK]

Hint: Full-text search needs analyzed text fields [OK]

Common Mistakes:

• Choosing keyword for full-text search
• Confusing integer or date types with text
• Thinking keyword fields tokenize text

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

easy

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

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

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

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

Solution

1. Step 1: Recall Elasticsearch field types

   Elasticsearch uses "keyword" as the type for exact match fields.

2. Step 2: Identify the correct syntax

   "keyword" is the correct type name; "text" is for analyzed fields, "string" and "fulltext" are invalid types.

3. Final Answer:

   "name": { "type": "keyword" } -> Option B

4. Quick Check:

   Keyword field syntax = "name": { "type": "keyword" } [OK]

Hint: Keyword type is exactly "keyword" in mapping [OK]

Common Mistakes:

• Using "string" instead of "keyword"
• Confusing "text" with "keyword"
• Using invalid type names like "fulltext"

3. Given this Elasticsearch mapping snippet:

{
  "properties": {
    "title": { "type": "text" },
    "category": { "type": "keyword" }
  }
}

Which query will correctly filter documents where category is exactly "books"?

medium

A. { "term": { "category": "books" } }

B. { "match": { "category": "books" } }

C. { "match": { "title": "books" } }

D. { "term": { "title": "books" } }

Solution

1. Step 1: Identify field types and query types

   "category" is a keyword field, so exact match queries use "term" query.

2. Step 2: Match query usage

   "match" queries are for full-text search on text fields, not exact matches on keyword fields.

3. Final Answer:

   { "term": { "category": "books" } } -> Option A

4. Quick Check:

   Exact filter on keyword = term query [OK]

Hint: Use term query for keyword fields exact match [OK]

Common Mistakes:

• Using match query on keyword fields
• Filtering text fields with term query expecting full-text search
• Confusing field names in queries

4. You have this mapping:

{
  "properties": {
    "username": { "type": "keyword" },
    "bio": { "type": "text" }
  }
}

A developer tries to filter users with username containing "john" using this query:

{ "match": { "username": "john" } }

Why does this query not work as expected?

medium

A. Because match query only works on numeric fields

B. Because username field should be of type text for exact matches

C. Because match query does not work on keyword fields for partial matches

D. Because bio field should be used instead of username

Solution

1. Step 1: Understand keyword field behavior

   Keyword fields are not analyzed and do not support partial matching with match query.

2. Step 2: Understand match query limitations

   Match query analyzes input and is meant for text fields, not keyword fields which require term or wildcard queries for partial matches.

3. Final Answer:

   Because match query does not work on keyword fields for partial matches -> Option C

4. Quick Check:

   Match query + keyword field = no partial match [OK]

Hint: Match query needs text fields, not keyword [OK]

Common Mistakes:

• Thinking keyword fields support partial match with match query
• Confusing field types for filtering
• Using match query on numeric or date fields

5. You want to store user tags in Elasticsearch so you can both filter by exact tag and search inside tag text. Which mapping setup is best?

hard

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

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

C. "tags": { "type": "keyword", "fields": { "analyzed": { "type": "text" } } }

D. "tags": { "type": "text", "fields": { "raw": { "type": "keyword" } } }

Solution

1. Step 1: Understand the need for both full-text search and exact filtering

   Full-text search requires text type; exact filtering requires keyword type.

2. Step 2: Use multi-fields to support both

   Mapping with "text" type and a subfield "raw" as "keyword" allows searching inside tags and filtering/sorting by exact tag.

3. Final Answer:

   "tags": { "type": "text", "fields": { "raw": { "type": "keyword" } } } -> Option D

4. Quick Check:

   Multi-fields = text + keyword for search and filter [OK]

Hint: Use multi-fields for both text search and exact filter [OK]

Common Mistakes:

• Using only text type and expecting exact filters
• Using only keyword type and expecting full-text search
• Not defining subfields for multi-purpose queries