Autocomplete with edge n-gram in Elasticsearch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of using edge n-gram in autocomplete?

Edge n-gram helps break down words into smaller parts starting from the beginning, allowing fast prefix matching for autocomplete suggestions.

beginner

Which Elasticsearch analyzer is commonly used to create edge n-grams?

The edge_ngram tokenizer or filter is used within a custom analyzer to generate edge n-grams for autocomplete.

intermediate

What is the difference between min_gram and max_gram in edge n-gram settings?

min_gram sets the smallest size of the n-gram, and max_gram sets the largest size. For example, min_gram=2 and max_gram=5 generate n-grams of length 2 to 5.

intermediate

Why should edge n-gram be applied at index time and not at search time?

Applying edge n-gram at index time creates tokens for fast prefix matching. At search time, using a standard analyzer keeps queries simple and efficient.

advanced

Give an example of a simple Elasticsearch mapping using edge n-gram for autocomplete.

A mapping with a custom analyzer using edge n-gram filter on a text field enables autocomplete. Example:

{
  "settings": {
    "analysis": {
      "analyzer": {
        "autocomplete": {
          "tokenizer": "standard",
          "filter": ["lowercase", "autocomplete_filter"]
        }
      },
      "filter": {
        "autocomplete_filter": {
          "type": "edge_ngram",
          "min_gram": 2,
          "max_gram": 10
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "name": {
        "type": "text",
        "analyzer": "autocomplete",
        "search_analyzer": "standard"
      }
    }
  }
}

What does the edge n-gram tokenizer do in Elasticsearch?

A Breaks words into suffixes starting from the end

B Breaks words into prefixes of varying lengths starting from the beginning

C Removes stop words from the text

D Converts all text to uppercase

Edge n-gram tokenizer creates tokens that are prefixes of the original word, useful for autocomplete.

Which analyzer setting is best for searching autocomplete queries?

A Whitespace analyzer

B Edge n-gram analyzer

C Standard analyzer

D Keyword analyzer

Using the standard analyzer at search time keeps queries simple and matches the indexed edge n-grams efficiently.

If min_gram is set to 3, which of these tokens will NOT be generated for the word 'search'?

A searc

B sea

C sear

D se

With min_gram=3, tokens shorter than 3 characters like 'se' are not generated.

Why is edge n-gram indexing useful for autocomplete?

A It allows fast prefix matching of user input

B It compresses the index size

C It improves spelling correction

D It filters out irrelevant words

Edge n-gram indexing creates prefixes that match user input quickly for autocomplete suggestions.

Where should edge n-gram filters be applied in Elasticsearch for autocomplete?

A At index time analyzer

B At search time analyzer

C Both index and search time analyzers

D Neither, use keyword fields

Edge n-gram filters are applied at index time to generate tokens; search time uses a simpler analyzer.

Explain how edge n-gram helps implement autocomplete in Elasticsearch.

Think about how words are broken down and matched during search.

Describe the key settings needed to configure an edge n-gram analyzer for autocomplete.

Focus on the analyzer and filter configuration in Elasticsearch settings.