Fuzzy matching in Elasticsearch - Mini Project: Build & Apply

Fuzzy Matching with Elasticsearch

📖 Scenario: You are building a simple search feature for a bookstore website. Users often make typos when typing book titles. To help users find books even with small spelling mistakes, you want to use fuzzy matching in Elasticsearch.

🎯 Goal: Create an Elasticsearch query that uses fuzzy matching to find books by title, allowing for small typos.

📋 What You'll Learn

Create an index called books with a title field of type text.

Add a document with the title "The Great Gatsby".

Create a variable called search_title with the value "Gret Gatsb" (a misspelled title).

Write a fuzzy match query using search_title to find the book with a small typo.

Complete the query with a fuzziness parameter set to 1.

💡 Why This Matters

🌍 Real World

Fuzzy matching helps users find information even if they make spelling mistakes or typos in search queries, improving user experience on search engines and websites.

💼 Career

Understanding fuzzy matching is useful for roles involving search engine development, data retrieval, and improving user interfaces for search functionality.

1

Create the books index with a title field

Create an Elasticsearch index called books with a mapping that defines a title field of type text.

Elasticsearch

# Create the books index with title field mapping
{
  "mappings": {
    "properties": {
      "title": {
        "type": "text"
      }
    }
  }
}

Hint

Use the mappings key with properties to define the title field as text.

2

Add a document with the title "The Great Gatsby"

Add a document to the books index with the field title set to "The Great Gatsby".

Elasticsearch

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

# Add a document with title "The Great Gatsby"
{
  "title": "The Great Gatsby"
}

Hint

Use the document JSON with the title field exactly as shown.

3

Create a variable search_title with the misspelled title

Create a variable called search_title and set it to the string "Gret Gatsb".

Elasticsearch

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

{
  "title": "The Great Gatsby"
}

# Create search_title variable
"Gret Gatsb"

Hint

Assign the string "Gret Gatsb" to the variable search_title.

4

Write a fuzzy match query using search_title with fuzziness 1

Write an Elasticsearch query that uses a match query on the title field with the value from search_title. Add the parameter fuzziness set to 1 to allow small typos.

Elasticsearch

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

{
  "title": "The Great Gatsby"
}

"search_title": "Gret Gatsb"

# Write fuzzy match query
{
  "query": {
    "match": {
      "title": {
        "query": "Gret Gatsb",
        "fuzziness": 1
      }
    }
  }
}

Hint

Use a match query with fuzziness set to 1 to allow small typos.

Practice

1. What does the fuzziness parameter control in an Elasticsearch fuzzy query?

easy

A. The maximum length of the search term

B. The number of allowed letter changes to match similar words

C. The minimum number of exact matches required

D. The number of search results returned

Solution

1. Step 1: Understand the role of fuzziness

   The fuzziness parameter sets how many letters can be changed, added, or removed to still count as a match.

2. Step 2: Compare options with definition

   Only The number of allowed letter changes to match similar words correctly describes this as controlling allowed letter changes for similar words.

3. Final Answer:

   The number of allowed letter changes to match similar words -> Option B

4. Quick Check:

   Fuzziness = allowed letter changes [OK]

Hint: Fuzziness = allowed letter changes in matching [OK]

Common Mistakes:

• Confusing fuzziness with result count
• Thinking fuzziness limits search term length
• Mixing fuzziness with prefix_length

2. Which of the following is the correct syntax to add fuzziness in an Elasticsearch query DSL?

easy

A. "match": { "field": { "query": "text", "fuzzify": 2 } }

B. "match": { "field": { "query": "text", "fuzzy": true } }

C. "match": { "field": { "query": "text", "fuzziness": "AUTO" } }

D. "match": { "field": { "query": "text", "fuzzy_level": "high" } }

Solution

1. Step 1: Recall correct fuzziness syntax

   Elasticsearch uses the fuzziness parameter inside the match query with values like "AUTO" or a number.

2. Step 2: Check each option

   Only "match": { "field": { "query": "text", "fuzziness": "AUTO" } } uses the correct key "fuzziness" and proper structure.

3. Final Answer:

   "match": { "field": { "query": "text", "fuzziness": "AUTO" } } -> Option C

4. Quick Check:

   Correct fuzziness syntax = "match": { "field": { "query": "text", "fuzziness": "AUTO" } } [OK]

Hint: Use "fuzziness" key with "AUTO" or number [OK]

Common Mistakes:

• Using incorrect keys like fuzzy or fuzzify
• Setting fuzziness outside the match query
• Using boolean true instead of fuzziness value

3. Given this Elasticsearch fuzzy query snippet:

{ "match": { "name": { "query": "roam", "fuzziness": 0, "prefix_length": 1 } } }

Which of these terms will match the query?

medium

A. "roam"

B. "foam"

C. "roams"

D. "roan"

Solution

1. Step 1: Understand fuzziness and prefix_length

   Fuzziness 0 allows no letter differences (exact match). Prefix_length 1 means first letter must match exactly.

2. Step 2: Check each option against rules

   "roam" matches exactly. "foam" differs in first letter (prefix_length=1), so no. "roams" has extra letter, no exact match. "roan" differs by one letter after prefix ('m' vs 'n'), but fuzziness=0 allows no changes, so no.

3. Final Answer:

   "roam" -> Option A

4. Quick Check:

   Exact match with prefix_length = "roam" [OK]

Hint: Prefix_length fixes first letters; fuzziness=0 requires exact match [OK]

Common Mistakes:

• Ignoring prefix_length effect on first letter
• Assuming all similar words match regardless of prefix
• Confusing fuzziness with allowed extra letters

4. You wrote this fuzzy query:

{ "match": { "title": { "query": "elastic", "fuzziness": "2", "prefix_length": 3 } } }

But it returns no results even though documents with "plastics" and "elustics" exist. What is the likely problem?

medium

A. prefix_length is too high, blocking matches with differences in first 3 letters

B. fuzziness value must be a boolean, not a number

C. query string "elastic" is misspelled

D. match query does not support fuzziness parameter

Solution

1. Step 1: Analyze prefix_length effect

   Prefix_length 3 means first 3 letters must match exactly. If documents differ in those letters, they won't match.

2. Step 2: Check fuzziness and query validity

   Fuzziness accepts numbers; "2" is valid. Query "elastic" is correct. Match query supports fuzziness.

3. Final Answer:

   prefix_length is too high, blocking matches with differences in first 3 letters -> Option A

4. Quick Check:

   High prefix_length blocks fuzzy matches = prefix_length is too high, blocking matches with differences in first 3 letters [OK]

Hint: High prefix_length blocks fuzzy matches early [OK]

Common Mistakes:

• Thinking fuzziness must be boolean
• Ignoring prefix_length effect
• Assuming match query disallows fuzziness

5. You want to find documents with names similar to "johnson" allowing up to 2 letter changes but want to speed up search by requiring the first 2 letters to match exactly. Which query is best?

hard

A. { "match": { "name": { "query": "johnson", "fuzziness": "AUTO", "prefix_length": 3 } } }

B. { "match": { "name": { "query": "johnson", "fuzziness": 1, "prefix_length": 0 } } }

C. { "match": { "name": { "query": "johnson", "fuzziness": 3, "prefix_length": 1 } } }

D. { "match": { "name": { "query": "johnson", "fuzziness": 2, "prefix_length": 2 } } }

Solution

1. Step 1: Match requirements to parameters

   Allow up to 2 letter changes means fuzziness = 2. Speed up by requiring first 2 letters exact means prefix_length = 2.

2. Step 2: Evaluate each option

   { "match": { "name": { "query": "johnson", "fuzziness": 2, "prefix_length": 2 } } } matches both requirements exactly. { "match": { "name": { "query": "johnson", "fuzziness": 1, "prefix_length": 0 } } } allows only 1 change and no prefix. { "match": { "name": { "query": "johnson", "fuzziness": "AUTO", "prefix_length": 3 } } } uses AUTO fuzziness and prefix_length 3, which is more restrictive. { "match": { "name": { "query": "johnson", "fuzziness": 3, "prefix_length": 1 } } } allows 3 changes but only 1 prefix letter.

3. Final Answer:

   { "match": { "name": { "query": "johnson", "fuzziness": 2, "prefix_length": 2 } } } -> Option D

4. Quick Check:

   Fuzziness 2 + prefix_length 2 = { "match": { "name": { "query": "johnson", "fuzziness": 2, "prefix_length": 2 } } } [OK]

Hint: Match fuzziness and prefix_length to requirements exactly [OK]

Common Mistakes:

• Using wrong fuzziness number
• Setting prefix_length too high or too low
• Confusing AUTO with exact fuzziness number