Index mappings overview in Elasticsearch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is an index mapping in Elasticsearch?

An index mapping defines how documents and their fields are stored and indexed in Elasticsearch. It tells Elasticsearch the data types and rules for each field.

beginner

Why do we need to define data types in index mappings?

Data types help Elasticsearch understand how to store, search, and sort the data correctly. For example, numbers, dates, and text are handled differently.

intermediate

What happens if you don't define a mapping for a field in Elasticsearch?

Elasticsearch tries to guess the data type automatically using dynamic mapping. But this can lead to unexpected results or inefficient searches.

intermediate

How can you change a field's mapping after the index is created?

You cannot change a field's mapping directly after creation. Instead, you create a new index with the desired mapping and reindex your data.

beginner

What is the difference between 'text' and 'keyword' data types in Elasticsearch mappings?

'text' is used for full-text search and is analyzed (broken into words). 'keyword' is for exact matches and sorting, stored as is without analysis.

What does an Elasticsearch index mapping define?

A The hardware requirements for Elasticsearch

B The network settings of Elasticsearch

C The user permissions for the index

D How documents and fields are stored and indexed

Index mapping defines how documents and their fields are stored and indexed in Elasticsearch.

Which data type should you use for full-text search in Elasticsearch?

A keyword

B integer

C text

D date

'text' type is used for full-text search because it is analyzed into words.

What happens if you add a new field without defining it in the mapping?

A Elasticsearch guesses the data type using dynamic mapping

B You must restart Elasticsearch

C The field is ignored

D Elasticsearch rejects the document

Elasticsearch uses dynamic mapping to guess the data type automatically.

Can you change a field's data type in an existing Elasticsearch index mapping?

A No, you must create a new index and reindex

B Yes, anytime

C Only if the index is empty

D Only for text fields

You cannot change a field's mapping after creation; you must create a new index and reindex data.

Which mapping type is best for sorting exact values?

A text

B keyword

C date

D boolean

'keyword' type stores exact values and is best for sorting and filtering.

Explain what an Elasticsearch index mapping is and why it is important.

Think about how Elasticsearch knows what kind of data each field holds.

Describe the difference between 'text' and 'keyword' data types in Elasticsearch mappings and when to use each.

Consider searching for words versus filtering exact values.

Practice

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

easy

A. To monitor server health

B. To create user accounts for Elasticsearch

C. To backup data automatically

D. To define how data fields are stored and searched

Solution

1. Step 1: Understand the role of index mappings

   Index mappings tell Elasticsearch how to store and interpret each field in your data.

2. Step 2: Compare options with this role

   Only To define how data fields are stored and searched matches this purpose; others relate to unrelated tasks.

3. Final Answer:

   To define how data fields are stored and searched -> Option D

4. Quick Check:

   Index mappings = data structure definition [OK]

Hint: Mappings control data structure and search behavior [OK]

Common Mistakes:

• Confusing mappings with user management
• Thinking mappings handle backups
• Assuming mappings monitor server health

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

easy

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

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

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

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

Solution

1. Step 1: Identify the correct field type for text data

   Text fields in Elasticsearch use the type "text" for full-text search.

2. Step 2: Match the option with the correct type

   "name": { "type": "text" } correctly uses "text"; others use types for keywords, numbers, or dates.

3. Final Answer:

   "name": { "type": "text" } -> Option A

4. Quick Check:

   Text fields = type "text" [OK]

Hint: Use "text" type for searchable text fields [OK]

Common Mistakes:

• Using "keyword" for full-text search fields
• Assigning numeric or date types to text data
• Missing quotes around field names

3. Given this mapping snippet:

{ "properties": { "age": { "type": "integer" }, "name": { "type": "keyword" } } }

What will happen if you try to search for documents with name containing partial words?

medium

A. Partial word matches will work because keyword fields support full-text search

B. Partial word matches will not work because keyword fields are exact values

C. Elasticsearch will throw a syntax error

D. Partial word matches will work only if age is also searched

Solution

1. Step 1: Understand keyword field behavior

   Keyword fields store exact values and do not support partial or full-text search.

2. Step 2: Analyze the search behavior for partial words

   Since name is keyword type, partial matches will fail; only exact matches work.

3. Final Answer:

   Partial word matches will not work because keyword fields are exact values -> Option B

4. Quick Check:

   Keyword = exact match only [OK]

Hint: Keyword fields do exact matches only, no partial search [OK]

Common Mistakes:

• Assuming keyword fields support partial matching
• Expecting syntax errors from valid mappings
• Confusing integer field with text search behavior

4. You defined this mapping:

{ "properties": { "date": { "type": "text" } } }

What is the problem with this mapping if you want to filter by date range?

medium

A. The field name "date" is reserved and cannot be used

B. There is no problem; text type works for date filtering

C. The type should be "date" to enable date range filtering

D. You must add "index": false to filter by date

Solution

1. Step 1: Identify the correct type for date fields

   Date range filtering requires the field to be of type "date" in Elasticsearch.

2. Step 2: Check the given mapping type

   The mapping uses "text" type, which is not suitable for date operations like range filtering.

3. Final Answer:

   The type should be "date" to enable date range filtering -> Option C

4. Quick Check:

   Date filtering needs type "date" [OK]

Hint: Use type "date" for date range filters [OK]

Common Mistakes:

• Using text type for date fields
• Thinking field names are reserved keywords
• Adding index false disables searching, not filtering

5. You want to store a user's profile with fields: username (exact match), bio (full-text search), and signup_date (date range queries). Which mapping correctly defines these fields?

hard

A. { "properties": { "username": { "type": "keyword" }, "bio": { "type": "text" }, "signup_date": { "type": "date" } } }

B. { "properties": { "username": { "type": "text" }, "bio": { "type": "keyword" }, "signup_date": { "type": "text" } } }

C. { "properties": { "username": { "type": "integer" }, "bio": { "type": "text" }, "signup_date": { "type": "date" } } }

D. { "properties": { "username": { "type": "keyword" }, "bio": { "type": "keyword" }, "signup_date": { "type": "integer" } } }

Solution

1. Step 1: Match each field to its correct type

   Username needs exact matches, so "keyword" is correct. Bio needs full-text search, so "text" is correct. Signup_date needs date range queries, so "date" is correct.

2. Step 2: Verify the options

   { "properties": { "username": { "type": "keyword" }, "bio": { "type": "text" }, "signup_date": { "type": "date" } } } matches all these requirements correctly. Others mix types incorrectly.

3. Final Answer:

   { "properties": { "username": { "type": "keyword" }, "bio": { "type": "text" }, "signup_date": { "type": "date" } } } -> Option A

4. Quick Check:

   Correct types for exact, full-text, and date fields [OK]

Hint: Use keyword for exact, text for full-text, date for dates [OK]

Common Mistakes:

• Mixing keyword and text types incorrectly
• Using integer for date fields
• Confusing full-text and exact match types