Fuzzy matching in Elasticsearch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is fuzzy matching in Elasticsearch?

Fuzzy matching in Elasticsearch helps find words that are similar but not exactly the same. It is useful when searching for words with typos or small differences.

beginner

Which parameter controls how many changes are allowed in a fuzzy query?

The fuzziness parameter controls how many edits (like insertions, deletions, or substitutions) are allowed between the search term and matching terms.

intermediate

What does fuzziness value 'AUTO' mean in Elasticsearch?

The value AUTO lets Elasticsearch decide the fuzziness level based on the length of the search term. It automatically adjusts how many edits are allowed.

beginner

Show a simple example of a fuzzy query in Elasticsearch JSON format.

Example:

{
  "query": {
    "fuzzy": {
      "name": {
        "value": "roam",
        "fuzziness": "AUTO"
      }
    }
  }
}

This finds words like "roam", "foam", or "roams".

beginner

Why use fuzzy matching instead of exact matching?

Fuzzy matching helps find results even if the user makes spelling mistakes or uses slightly different words. It improves search experience by being more flexible.

What does the fuzziness parameter control in Elasticsearch fuzzy queries?

A Index refresh rate

B Number of search results returned

C Number of allowed character changes

D Search timeout duration

Fuzziness controls how many character edits (insertions, deletions, substitutions) are allowed to match a term.

Which fuzziness value lets Elasticsearch decide the edit distance automatically?

A "NONE"

B "MAX"

C "MIN"

D "AUTO"

The value "AUTO" automatically adjusts fuzziness based on the length of the search term.

Fuzzy matching is useful when:

A You want to find words with typos or small differences

B You want to speed up indexing

C You want exact word matches only

D You want to delete documents

Fuzzy matching helps find words even if they have typos or small differences.

In a fuzzy query, which field specifies the word to search for?

A "value"

B "field"

C "fuzziness"

D "boost"

The "value" field holds the search term in a fuzzy query.

What type of edits does fuzziness consider?

A Only insertions

B Insertions, deletions, substitutions

C Only deletions

D Only substitutions

Fuzziness counts insertions, deletions, and substitutions as edits.

Explain how fuzzy matching improves search results in Elasticsearch.

Think about why exact matches might miss some results.

Write a simple JSON example of a fuzzy query searching for the word 'apple' with fuzziness set to 2.

Use the fuzzy query format with value and fuzziness fields.

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