Text vs keyword field types in Elasticsearch - Practice Questions

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this Elasticsearch query?

Given the following mapping and query, what will be the result count?

{
  "mappings": {
    "properties": {
      "name": { "type": "text" },
      "category": { "type": "keyword" }
    }
  }
}

POST /products/_search
{
  "query": {
    "term": { "category": "electronics" }
  }
}

A Returns no documents because 'term' query does not work on keyword fields

B Returns all documents where the category exactly matches 'electronics'

C Returns documents where the name contains the word 'electronics'

D Returns documents where the category contains the word 'electronics' anywhere

💡 Hint

Remember that keyword fields are not analyzed and match exact values.

The term query matches exact values on keyword fields. Since category is a keyword field, it matches documents where the category is exactly 'electronics'.

🧠 Conceptual

intermediate

Why use 'text' type instead of 'keyword' for a field?

Which reason best explains why you would choose text type over keyword type for a field in Elasticsearch?

A To enable full-text search with tokenization and analysis

B To store exact values for filtering and sorting

C To reduce index size by storing unanalyzed data

D To improve performance of term queries

💡 Hint

Think about when you want to search inside the text rather than match exact values.

The text type is used for full-text search. It breaks the text into tokens and applies analysis, allowing searches for words inside the text. The keyword type stores exact values without analysis.

🔧 Debug

advanced

Why does this term query return zero results?

Given this mapping and query, why does the term query return zero results?

{
  "mappings": {
    "properties": {
      "description": { "type": "text" }
    }
  }
}

POST /items/_search
{
  "query": {
    "term": { "description": "fast car" }
  }
}

A Because the term query syntax is incorrect and should use 'match' instead

B Because the index is empty and has no documents

C Because the field 'description' is missing from the mapping

D Because 'description' is a text field and term queries require exact matches, so 'fast car' as a whole does not match

💡 Hint

Think about how term queries work on text fields.

Term queries look for exact matches. Since 'description' is a text field, it is analyzed and broken into tokens like 'fast' and 'car'. The term query with 'fast car' as a whole string does not match any token exactly, so it returns zero results.

📝 Syntax

advanced

Which mapping snippet correctly defines a field for exact filtering and full-text search?

Choose the correct mapping snippet that allows a field to be searched as full text and also filtered exactly.

A { "title": { "type": "text", "fields": { "raw": { "type": "keyword" } } } }

B { "title": { "type": "keyword", "fields": { "raw": { "type": "text" } } } }

C { "title": { "type": "text" } }

D { "title": { "type": "keyword" } }

💡 Hint

Think about multi-fields and how to support both search and filtering.

Option A defines a text field for full-text search and a subfield raw of type keyword for exact filtering and sorting. This is the recommended pattern.

🚀 Application

expert

How many unique values can be efficiently filtered using a keyword field?

In Elasticsearch, what is the practical limit on the number of unique values in a keyword field before performance degrades significantly?

A Keyword fields should only have a single unique value for best performance

B There is no limit; keyword fields handle millions of unique values efficiently

C Up to a few thousand unique values is efficient; beyond that, performance may degrade

D Keyword fields are not used for filtering unique values

💡 Hint

Consider how Elasticsearch stores keyword terms and the impact on memory.

Keyword fields store terms in memory for filtering and aggregations. Having too many unique values (cardinality) can cause memory and performance issues. Usually, a few thousand unique values is manageable.

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