Text vs keyword field types in Elasticsearch - Visual Side-by-Side Comparison

Concept Flow - Text vs keyword field types

Input Document

↓

Field Type?

↓

text

↓

Analyzed

↓

Tokenized

Documents have fields that are either 'text' or 'keyword'. Text fields are analyzed and tokenized for full-text search. Keyword fields are stored as-is for exact matching and sorting.

Execution Sample

Elasticsearch

PUT /my_index
{
  "mappings": {
    "properties": {
      "title": {"type": "text"},
      "status": {"type": "keyword"}
    }
  }
}

Defines an index with 'title' as a text field for full-text search and 'status' as a keyword field for exact matching.

Execution Table

Step	Field	Type	Analyzed?	Stored As	Use Case
1	title	text	Yes	Tokens (words)	Full-text search, relevance scoring
2	status	keyword	No	Exact value	Filtering, sorting, aggregations
3	Search 'title:quick fox'	title	Analyzed query	Matches tokens 'quick' and 'fox'	Find documents with those words
4	Filter 'status:active'	status	Exact match	Matches exact 'active'	Filter documents with status active
5	Sort by 'status'	status	Exact value	Sorts documents alphabetically by status	Ordering results
6	Exit	-	-	-	No more steps, mapping and usage clear

💡 All fields processed with their types and usage explained.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
title	undefined	text type set	analyzed tokens created	tokens matched in search	used for full-text search
status	undefined	keyword type set	stored as exact value	exact match filter applied	used for filtering and sorting

Key Moments - 3 Insights

Why is the 'text' field analyzed but the 'keyword' field is not?

Because 'text' fields are meant for full-text search, they are broken into tokens (words) to match parts of the text. 'Keyword' fields store exact values for filtering and sorting, so they are not broken down. See execution_table rows 1 and 2.

Can you use a 'keyword' field for full-text search?

No, 'keyword' fields are stored as exact values and do not support tokenization or relevance scoring. Full-text search requires 'text' fields. Refer to execution_table row 3 for how 'title' is searched.

Why do we sort on 'keyword' fields and not on 'text' fields?

'Keyword' fields store exact values, so sorting is straightforward. 'Text' fields are tokenized and analyzed, making sorting ambiguous. See execution_table row 5.

Visual Quiz - 3 Questions

Look at the execution table, what is the 'status' field stored as after step 2?

A Exact value

B Analyzed tokens

C Lowercase words

D Stemmed words

💡 Hint

Check execution_table row 2 under 'Stored As' column.

At which step does the 'title' field get tokenized for search?

A Step 1

B Step 2

C Step 3

D Step 5

💡 Hint

Look at execution_table row 3 describing search on 'title'.

If you want to filter documents by exact status, which field type should you use?

A text

B keyword

C integer

D date

💡 Hint

Refer to execution_table rows 2 and 4 about filtering on 'status'.

Concept Snapshot

Text vs Keyword fields in Elasticsearch:
- text: analyzed, tokenized for full-text search
- keyword: not analyzed, stored as exact value
- Use text for searching words inside text
- Use keyword for exact match, filtering, sorting
- Mapping defines field type for proper usage

Full Transcript

In Elasticsearch, fields can be either 'text' or 'keyword' types. Text fields are analyzed and broken into tokens to support full-text search, allowing you to find documents by words inside the text. Keyword fields are stored as exact values without analysis, making them perfect for filtering, sorting, and aggregations. For example, a 'title' field as text lets you search for words like 'quick' or 'fox', while a 'status' field as keyword lets you filter documents exactly matching 'active'. Understanding these types helps you design your index for efficient search and filtering.