Search-as-you-type field in Elasticsearch - Mini Project: Build & Apply

Search-as-you-type field

📖 Scenario: You are building a search feature for a website that helps users find products quickly as they type. To make the search fast and helpful, you will create a special field in Elasticsearch called a search-as-you-type field. This field lets Elasticsearch find matches even if the user types only part of a word.

🎯 Goal: Create an Elasticsearch index with a search_as_you_type field called product_name. Then add some sample products. Finally, write a query that finds products as the user types part of the name.

📋 What You'll Learn

Create an index called products with a product_name field of type search_as_you_type

Add three products with exact names: Apple iPhone, Apple Watch, and Samsung Galaxy

Write a search query that uses multi_match with type set to bool_prefix to find products by partial input

Print the names of matching products from the search results

💡 Why This Matters

🌍 Real World

Search-as-you-type fields are used in online stores, libraries, and apps to help users find items quickly by typing only part of the name.

💼 Career

Knowing how to set up and query search-as-you-type fields is useful for backend developers, search engineers, and anyone working with Elasticsearch to build fast and user-friendly search features.

1

Create the Elasticsearch index with a search-as-you-type field

Create an index called products with a mapping that defines a field named product_name of type search_as_you_type. Use the Elasticsearch PUT request with the mapping inside the mappings key.

Elasticsearch

# Create the index 'products' with 'product_name' as search_as_you_type
# Your code here

Hint

Use the PUT method to create the index. Inside mappings, define properties with product_name as search_as_you_type.

2

Add sample products to the index

Add three documents to the products index with the exact product_name values: Apple iPhone, Apple Watch, and Samsung Galaxy. Use the POST method to add each document with an id of your choice.

Elasticsearch

PUT /products
{
  "mappings": {
    "properties": {
      "product_name": {
        "type": "search_as_you_type"
      }
    }
  }
}

# Add document with id 1 and product_name 'Apple iPhone'
# Your code here

Hint

Use POST /products/_doc/<id> to add each product. The product_name field must match exactly.

3

Write a search query using multi_match with bool_prefix

Write a search query using POST /products/_search that uses multi_match with type set to bool_prefix. Search the product_name field for the partial input Apple. This will find products starting with 'Apple'.

Elasticsearch

PUT /products
{
  "mappings": {
    "properties": {
      "product_name": {
        "type": "search_as_you_type"
      }
    }
  }
}

POST /products/_doc/1
{
  "product_name": "Apple iPhone"
}

POST /products/_doc/2
{
  "product_name": "Apple Watch"
}

POST /products/_doc/3
{
  "product_name": "Samsung Galaxy"
}

# Write a search query to find products starting with 'Apple'
# Your code here

Hint

Use multi_match with type set to bool_prefix. Include product_name and its _2gram and _3gram subfields in fields.

4

Print the names of matching products from the search results

Print the product_name values from the search results of the previous query. Show only the names of the products that matched the partial input Apple.

Elasticsearch

PUT /products
{
  "mappings": {
    "properties": {
      "product_name": {
        "type": "search_as_you_type"
      }
    }
  }
}

POST /products/_doc/1
{
  "product_name": "Apple iPhone"
}

POST /products/_doc/2
{
  "product_name": "Apple Watch"
}

POST /products/_doc/3
{
  "product_name": "Samsung Galaxy"
}

POST /products/_search
{
  "query": {
    "multi_match": {
      "query": "Apple",
      "type": "bool_prefix",
      "fields": ["product_name", "product_name._2gram", "product_name._3gram"]
    }
  }
}

# Print the product_name values from the search hits
# Your code here

Hint

Extract the product_name from each hit in the search results and print it. The output should list the matching product names line by line.

Practice

1. What is the main purpose of a search_as_you_type field in Elasticsearch?

easy

A. To provide instant search results as the user types each character

B. To store large binary files efficiently

C. To perform complex aggregations on numeric data

D. To index documents only after full text is entered

Solution

1. Step 1: Understand the purpose of search_as_you_type

   The search_as_you_type field is designed to help users get search results instantly as they type, improving user experience.

2. Step 2: Compare with other options

   Options B, C, and D describe unrelated Elasticsearch features like binary storage, aggregations, or delayed indexing, which are not related to search_as_you_type.

3. Final Answer:

   To provide instant search results as the user types each character -> Option A

4. Quick Check:

   search_as_you_type = instant search while typing [OK]

Hint: Remember: search_as_you_type means live search suggestions [OK]

Common Mistakes:

• Confusing search_as_you_type with binary or numeric fields
• Thinking it delays indexing until full input
• Assuming it is for aggregations

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

easy

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

B. "title": { "type": "completion" }

C. "title": { "type": "keyword" }

D. "title": { "type": "search_as_you_type" }

Solution

1. Step 1: Identify the correct field type for search-as-you-type

   The search_as_you_type field type must be explicitly set as "type": "search_as_you_type" in the mapping.

2. Step 2: Eliminate other field types

   "title": { "type": "text" } uses text, which is for full text search but not optimized for search-as-you-type. "title": { "type": "keyword" } uses keyword, which is for exact matches. "title": { "type": "completion" } uses completion, which is for autocomplete but different from search_as_you_type.

3. Final Answer:

   "title": { "type": "search_as_you_type" } -> Option D

4. Quick Check:

   Mapping type for search-as-you-type = search_as_you_type [OK]

Hint: Use type "search_as_you_type" exactly in mapping [OK]

Common Mistakes:

• Using "text" instead of "search_as_you_type"
• Confusing with "completion" type
• Omitting the type field

3. Given this mapping snippet:

{
  "mappings": {
    "properties": {
      "name": { "type": "search_as_you_type" }
    }
  }
}

Which query will correctly search for documents where the name field starts with "mic"?

medium

A. { "match": { "name": "mic" } }

B. { "multi_match": { "query": "mic", "type": "bool_prefix", "fields": ["name", "name._2gram", "name._3gram"] } }

C. { "term": { "name": "mic" } }

D. { "prefix": { "name": "mic" } }

Solution

1. Step 1: Understand how to query search_as_you_type fields

   Search-as-you-type fields require a multi_match query with type: bool_prefix and fields including the base field and its n-gram subfields.

2. Step 2: Analyze each query option

   { "match": { "name": "mic" } } uses simple match, which does not support prefix matching well. { "term": { "name": "mic" } } uses term query, which requires exact match. { "prefix": { "name": "mic" } } uses prefix query, which is not optimized for search_as_you_type. { "multi_match": { "query": "mic", "type": "bool_prefix", "fields": ["name", "name._2gram", "name._3gram"] } } correctly uses multi_match with bool_prefix and the required fields.

3. Final Answer:

   { "multi_match": { "query": "mic", "type": "bool_prefix", "fields": ["name", "name._2gram", "name._3gram"] } } -> Option B

4. Quick Check:

   Use multi_match + bool_prefix + ngram fields [OK]

Hint: Use multi_match with bool_prefix and ngram fields [OK]

Common Mistakes:

• Using term or prefix queries alone
• Not including ngram subfields in fields list
• Using simple match query for prefix search

4. You wrote this query to search a search_as_you_type field but get no results:

{ "multi_match": { "query": "app", "type": "bool_prefix", "fields": ["title"] } }

What is the likely problem?

medium

A. The field 'title' must be of type 'keyword' for this query

B. The 'bool_prefix' type is invalid for multi_match queries

C. The query should include ngram subfields like 'title._2gram' and 'title._3gram'

D. The query string 'app' is too short to match anything

Solution

1. Step 1: Check query fields for search_as_you_type

   Search-as-you-type fields generate subfields with ngrams like '_2gram' and '_3gram' that must be included in the query fields for prefix matching.

2. Step 2: Identify missing ngram subfields in the query

   The query only searches 'title' field, missing 'title._2gram' and 'title._3gram', so it won't match prefixes properly.

3. Final Answer:

   The query should include ngram subfields like 'title._2gram' and 'title._3gram' -> Option C

4. Quick Check:

   Include ngram subfields in fields list for prefix search [OK]

Hint: Always add _2gram and _3gram subfields in multi_match fields [OK]

Common Mistakes:

• Omitting ngram subfields in fields list
• Thinking bool_prefix is invalid
• Assuming keyword type is needed
• Blaming query length

5. You want to implement a search-as-you-type feature for a product catalog with a name field. Which of the following mapping and query combinations will correctly support prefix matching as the user types?

hard

A. Mapping: { "name": { "type": "search_as_you_type" } }Query: { "multi_match": { "query": "lap", "type": "bool_prefix", "fields": ["name", "name._2gram", "name._3gram"] } }

B. Mapping: { "name": { "type": "text" } }Query: { "match": { "name": "lap" } }

C. Mapping: { "name": { "type": "keyword" } }Query: { "prefix": { "name": "lap" } }

D. Mapping: { "name": { "type": "completion" } }Query: { "term": { "name": "lap" } }

Solution

1. Step 1: Choose the correct mapping for search-as-you-type

   The search_as_you_type type is designed for prefix matching and instant search as the user types.

2. Step 2: Use the correct query for prefix matching

   The multi_match query with bool_prefix type and fields including the base field and its ngram subfields is required for effective prefix matching.

3. Step 3: Evaluate other options

   Mapping: { "name": { "type": "text" } }
   Query: { "match": { "name": "lap" } } uses text with simple match, which does not support prefix search well. Mapping: { "name": { "type": "keyword" } }
   Query: { "prefix": { "name": "lap" } } uses keyword with prefix query, which is less flexible. Mapping: { "name": { "type": "completion" } }
   Query: { "term": { "name": "lap" } } uses completion with term query, which is incorrect usage.

4. Final Answer:

   Mapping: { "name": { "type": "search_as_you_type" } } and Query: { "multi_match": { "query": "lap", "type": "bool_prefix", "fields": ["name", "name._2gram", "name._3gram"] } } -> Option A

5. Quick Check:

   search_as_you_type + multi_match bool_prefix + ngram fields = correct [OK]

Hint: Combine search_as_you_type mapping with multi_match bool_prefix query [OK]

Common Mistakes:

• Using text type with match query for prefix search
• Using keyword type with prefix query only
• Confusing completion type with search_as_you_type
• Using term query for prefix matching