Autocomplete with edge n-gram in Elasticsearch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define an edge n-gram tokenizer named "autocomplete_tokenizer".

Elasticsearch

{
  "settings": {
    "analysis": {
      "tokenizer": {
        "autocomplete_tokenizer": {
          "type": "[1]",
          "min_gram": 1,
          "max_gram": 20
        }
      }
    }
  }
}

Drag options to blanks, or click blank then click option'

Awhitespace

Bkeyword

Cedge_ngram

Dstandard

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'standard' tokenizer which does not generate n-grams.

Using 'keyword' tokenizer which treats the whole input as one token.

The edge_ngram tokenizer generates tokens from the beginning of a word, which is essential for autocomplete functionality.

2fill in blank

medium

Complete the analyzer definition to use the edge n-gram tokenizer for autocomplete.

Elasticsearch

{
  "settings": {
    "analysis": {
      "analyzer": {
        "autocomplete": {
          "type": "custom",
          "tokenizer": "[1]",
          "filter": ["lowercase"]
        }
      }
    }
  }
}

Drag options to blanks, or click blank then click option'

Astandard

Bautocomplete_tokenizer

Cwhitespace

Dkeyword

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'standard' tokenizer which does not generate n-grams.

Using 'keyword' tokenizer which does not split tokens.

The analyzer uses the previously defined autocomplete_tokenizer to generate edge n-grams for autocomplete.

3fill in blank

hard

Fix the error in the mapping to use the autocomplete analyzer for the "name" field.

Elasticsearch

{
  "mappings": {
    "properties": {
      "name": {
        "type": "text",
        "analyzer": "[1]"
      }
    }
  }
}

Drag options to blanks, or click blank then click option'

Akeyword

Bstandard

Cwhitespace

Dautocomplete

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'standard' analyzer which does not support autocomplete n-grams.

Using 'keyword' analyzer which does not tokenize the field.

The autocomplete analyzer applies the edge n-gram tokenizer and lowercase filter to the "name" field for autocomplete search.

4fill in blank

hard

Fill both blanks to define a search analyzer that uses the standard tokenizer and lowercase filter.

Elasticsearch

{
  "settings": {
    "analysis": {
      "analyzer": {
        "search_analyzer": {
          "type": "custom",
          "tokenizer": "[1]",
          "filter": ["[2]"]
        }
      }
    }
  }
}

Drag options to blanks, or click blank then click option'

Astandard

Bautocomplete_tokenizer

Clowercase

Dkeyword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the edge n-gram tokenizer for search analyzer which breaks normal search.

Omitting the lowercase filter causing case-sensitive search.

The search analyzer uses the standard tokenizer and lowercase filter to process search queries properly.

5fill in blank

hard

Fill all three blanks to complete the mapping with both autocomplete and search analyzers for the "title" field.

Elasticsearch

{
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "analyzer": "[1]",
        "search_analyzer": "[2]",
        "fields": {
          "keyword": {
            "type": "keyword",
            "ignore_above": [3]
          }
        }
      }
    }
  }
}

Drag options to blanks, or click blank then click option'

Aautocomplete

Bsearch_analyzer

C256

Dstandard

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up analyzer and search_analyzer names.

Setting ignore_above to a string instead of a number.

Omitting the keyword subfield for exact matches.

The title field uses the autocomplete analyzer for indexing and the standard analyzer for searching. The keyword subfield ignores strings longer than 256 characters.

Practice

1. What is the main purpose of using an edge_ngram tokenizer in Elasticsearch autocomplete?

easy

A. To break words into prefixes for fast autocomplete suggestions

B. To sort search results alphabetically

C. To remove stop words from the search query

D. To index only complete words without partial matches

Solution

1. Step 1: Understand edge n-gram tokenizer function

   The edge n-gram tokenizer splits words into prefixes, like 'app', 'appl', 'apple' for 'apple'.

2. Step 2: Relate tokenizer to autocomplete

   This prefix splitting allows Elasticsearch to quickly suggest words as users type partial input.

3. Final Answer:

   To break words into prefixes for fast autocomplete suggestions -> Option A

4. Quick Check:

   edge_ngram tokenizer = prefix splitting [OK]

Hint: Edge n-gram creates prefixes for autocomplete speed [OK]

Common Mistakes:

• Thinking edge n-gram sorts results
• Confusing with stop word removal
• Assuming it indexes only full words

2. Which of the following is the correct way to define an edge_ngram tokenizer in Elasticsearch settings?

easy

A. {
"tokenizer": {
"my_edge_ngram": {
"type": "edge_ngram",
"min_gram": 2,
"max_gram": 5,
"token_chars": ["letter"]
}
}
}

B. {
"tokenizer": {
"my_edge_ngram": {
"type": "standard",
"min_gram": 2,
"max_gram": 5
}
}
}

C. {
"analyzer": {
"my_edge_ngram": {
"type": "edge_ngram"
}
}
}

D. {
"tokenizer": {
"my_edge_ngram": {
"type": "ngram",
"min_gram": 1,
"max_gram": 3
}
}
}

Solution

1. Step 1: Identify correct tokenizer type and parameters

   The edge_ngram tokenizer requires type 'edge_ngram' with min_gram and max_gram settings.

2. Step 2: Check token_chars usage

   { "tokenizer": { "my_edge_ngram": { "type": "edge_ngram", "min_gram": 2, "max_gram": 5, "token_chars": ["letter"] } } } correctly sets token_chars to 'letter' to include only letters in tokens.

3. Final Answer:

   Option A with type 'edge_ngram' and proper min_gram, max_gram -> Option A

4. Quick Check:

   edge_ngram tokenizer syntax = { "tokenizer": { "my_edge_ngram": { "type": "edge_ngram", "min_gram": 2, "max_gram": 5, "token_chars": ["letter"] } } } [OK]

Hint: Look for type 'edge_ngram' with min_gram and max_gram [OK]

Common Mistakes:

• Using 'standard' type instead of 'edge_ngram'
• Defining edge_ngram under analyzer instead of tokenizer
• Confusing ngram with edge_ngram tokenizer

3. Given this analyzer configuration using edge n-gram tokenizer: { "settings": { "analysis": { "tokenizer": { "autocomplete_tokenizer": { "type": "edge_ngram", "min_gram": 1, "max_gram": 3, "token_chars": ["letter"] } }, "analyzer": { "autocomplete": { "type": "custom", "tokenizer": "autocomplete_tokenizer", "filter": ["lowercase"] } } } }} What tokens will be generated for the word "cat" during indexing?

medium

A. ["cat"]

B. ["c", "a", "t"]

C. ["ca", "cat", "cat"]

D. ["c", "ca", "cat"]

Solution

1. Step 1: Understand edge n-gram tokenizer with min_gram=1 and max_gram=3

   It creates tokens from the start of the word with lengths 1 to 3 letters.

2. Step 2: Generate tokens for 'cat'

   Tokens are 'c' (1 letter), 'ca' (2 letters), and 'cat' (3 letters).

3. Final Answer:

   ["c", "ca", "cat"] -> Option D

4. Quick Check:

   edge n-gram tokens for 'cat' = ["c", "ca", "cat"] [OK]

Hint: Edge n-gram tokens start from first letter up to max_gram [OK]

Common Mistakes:

• Assuming only full word token is generated
• Including tokens from middle or end of word
• Confusing n-gram with edge n-gram tokens

4. You have this analyzer configuration for autocomplete but your search results do not return expected matches: { "settings": { "analysis": { "tokenizer": { "autocomplete_tokenizer": { "type": "edge_ngram", "min_gram": 2, "max_gram": 5 } }, "analyzer": { "autocomplete": { "type": "custom", "tokenizer": "autocomplete_tokenizer", "filter": ["lowercase"] }, "search_analyzer": { "type": "standard" } } } }} What is the likely cause of the problem?

medium

A. The tokenizer type should be 'ngram' not 'edge_ngram'

B. The search analyzer is incorrectly defined as 'standard' instead of using the same analyzer

C. The min_gram value is too low causing too many tokens

D. The filter 'lowercase' should not be used in the analyzer

Solution

1. Step 1: Understand analyzer and search_analyzer roles

   Indexing uses edge n-gram tokenizer, but searching uses standard analyzer which does not generate prefixes.

2. Step 2: Identify mismatch causing no autocomplete matches

   Because search analyzer does not tokenize input into prefixes, queries won't match edge n-gram tokens.

3. Final Answer:

   Search analyzer should match or complement edge n-gram analyzer, not standard -> Option B

4. Quick Check:

   Analyzer mismatch = The search analyzer is incorrectly defined as 'standard' instead of using the same analyzer [OK]

Hint: Search analyzer must complement edge n-gram tokenizer [OK]

Common Mistakes:

• Changing min_gram instead of fixing analyzer mismatch
• Confusing ngram and edge_ngram tokenizer types
• Removing lowercase filter unnecessarily

5. You want to implement autocomplete for a product search using edge n-gram tokenizer. Which approach correctly combines indexing and searching analyzers for best autocomplete results?

hard

A. Use edge n-gram tokenizer in the indexing analyzer and standard analyzer in the search analyzer

B. Use edge n-gram tokenizer in both indexing and search analyzers

C. Use edge n-gram tokenizer in indexing analyzer and a lowercase + standard tokenizer in search analyzer

D. Use standard tokenizer in indexing analyzer and edge n-gram tokenizer in search analyzer

Solution

1. Step 1: Understand indexing with edge n-gram tokenizer

   Indexing uses edge n-gram tokenizer to create prefix tokens for autocomplete.

2. Step 2: Understand searching with standard tokenizer plus lowercase filter

   Search analyzer uses standard tokenizer to tokenize user input normally and lowercase filter to match indexed tokens.

3. Step 3: Why not use edge n-gram tokenizer in search analyzer?

   Using edge n-gram in search analyzer can cause unexpected partial matches and performance issues.

4. Final Answer:

   Index with edge n-gram, search with standard + lowercase analyzer -> Option C

5. Quick Check:

   Index edge n-gram + search standard = Use edge n-gram tokenizer in indexing analyzer and a lowercase + standard tokenizer in search analyzer [OK]

Hint: Index with edge n-gram, search with standard + lowercase [OK]

Common Mistakes:

• Using edge n-gram tokenizer in search analyzer
• Using standard tokenizer in indexing analyzer
• Not applying lowercase filter in search analyzer