Text vs keyword field types in Elasticsearch - Quick Revision & Key Differences

Recall & Review

beginner

What is the main use of the text field type in Elasticsearch?

The text field type is used for full-text search. It breaks the content into words (tokens) so you can search for individual words or phrases inside the text.

beginner

What is the keyword field type best suited for in Elasticsearch?

The keyword field type is used for structured data like IDs, tags, or categories. It stores the whole value exactly as it is, without breaking it into words, so it is good for exact matches and sorting.

intermediate

How does Elasticsearch treat text fields differently from keyword fields during indexing?

Text fields are analyzed and broken into tokens (words) for searching, while keyword fields are not analyzed and stored as a single token for exact matching.

beginner

Can you use keyword fields for full-text search in Elasticsearch?

No, keyword fields are not suitable for full-text search because they do not break the text into words. They are meant for exact matches and aggregations.

beginner

Why might you choose a text field over a keyword field for a user comment?

Because user comments are long and need to be searched by words or phrases, a text field is better since it allows full-text search by breaking the comment into tokens.

Which Elasticsearch field type should you use to store a product ID for exact matching?

A keyword

B text

C integer

D date

Product IDs are exact values, so the keyword field type is best for exact matching.

What happens to a text field during indexing in Elasticsearch?

A It is stored as a single token without changes

B It is converted to a number

C It is ignored

D It is broken into tokens (words) for searching

Text fields are analyzed and broken into tokens to allow full-text search.

Which field type is better for sorting values alphabetically in Elasticsearch?

A text

B float

C keyword

D boolean

Keyword fields store exact values and are suitable for sorting.

Can you perform phrase searches on a keyword field?

A No, because it is not tokenized

B Yes, because it is tokenized

C Yes, but only for numbers

D No, because it is ignored

Keyword fields are not tokenized, so phrase searches are not possible.

Which field type would you use for storing a user's full name if you want to search by parts of the name?

A keyword

B text

C integer

D date

Text fields allow searching by parts of the name because they are tokenized.

Explain the difference between the text and keyword field types in Elasticsearch.

Think about how the data is stored and searched.

When would you choose a keyword field over a text field in Elasticsearch?

Consider the type of search or operation you want to perform.

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