Custom analyzers in Elasticsearch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a custom analyzer named "my_analyzer" using the "standard" tokenizer.

Elasticsearch

{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "type": "custom",
          "tokenizer": "[1]"
        }
      }
    }
  }
}

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

Akeyword

Bstandard

Cwhitespace

Dsimple

Attempts:

3 left

💡 Hint

Common Mistakes

Using a filter name instead of a tokenizer name.

Choosing a tokenizer that does not split text into words.

The standard tokenizer is the default and splits text into words on word boundaries. To create a custom analyzer with this tokenizer, you specify "standard" as the tokenizer.

2fill in blank

medium

Complete the code to add a lowercase filter to the custom analyzer "my_analyzer".

Elasticsearch

{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["[1]"]
        }
      }
    }
  }
}

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

Alowercase

Buppercase

Casciifolding

Dstop

Attempts:

3 left

💡 Hint

Common Mistakes

Using "uppercase" filter instead of "lowercase".

Forgetting to add the filter array.

The lowercase filter converts all tokens to lowercase, which helps make searches case-insensitive.

3fill in blank

hard

Fix the error in the custom analyzer definition by completing the filter list to include both "lowercase" and "stop" filters.

Elasticsearch

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

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

Astop

Blowercase

Ckeyword

Dasciifolding

Attempts:

3 left

💡 Hint

Common Mistakes

Reversing the order of filters.

Using filters that do not exist or are unrelated.

The analyzer needs both the lowercase filter to normalize case and the stop filter to remove common stop words.

4fill in blank

hard

Fill both blanks to create a custom analyzer named "folding_analyzer" that uses the "standard" tokenizer and applies both "lowercase" and "asciifolding" filters.

Elasticsearch

{
  "settings": {
    "analysis": {
      "analyzer": {
        "folding_analyzer": {
          "type": "custom",
          "tokenizer": "[1]",
          "filter": ["[2]", "[3]"]
        }
      }
    }
  }
}

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

Astandard

Blowercase

Casciifolding

Dstop

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up the order of filters.

Using a tokenizer other than "standard".

This analyzer uses the standard tokenizer to split text, then applies lowercase to normalize case and asciifolding to convert accented characters to their ASCII equivalents.

5fill in blank

hard

Fill all four blanks to define a custom analyzer "custom_analyzer" with "whitespace" tokenizer and filters "lowercase", "stop", and "asciifolding" in that order.

Elasticsearch

{
  "settings": {
    "analysis": {
      "analyzer": {
        "custom_analyzer": {
          "type": "custom",
          "tokenizer": "[1]",
          "filter": ["[2]", "[3]", "[4]"]
        }
      }
    }
  }
}

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

Awhitespace

Blowercase

Cstop

Dasciifolding

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong tokenizer.

Incorrect order of filters.

Missing one of the filters.

The analyzer uses the whitespace tokenizer to split text on spaces, then applies lowercase to normalize case, stop to remove common words, and asciifolding to convert accented characters.

Practice

1. What is the main purpose of a custom analyzer in Elasticsearch?

easy

A. To control how text is broken down and processed before indexing

B. To store documents in the database

C. To create visualizations of search results

D. To manage user access permissions

Solution

1. Step 1: Understand the role of analyzers

   Analyzers in Elasticsearch process text by breaking it into tokens and applying filters.

2. Step 2: Identify the purpose of custom analyzers

   Custom analyzers let you define specific tokenizers and filters to control text processing.

3. Final Answer:

   To control how text is broken down and processed before indexing -> Option A

4. Quick Check:

   Custom analyzer = text processing control [OK]

Hint: Custom analyzers shape text before search indexing [OK]

Common Mistakes:

• Confusing analyzers with data storage
• Thinking analyzers create visualizations
• Mixing analyzers with security settings

2. Which of the following is the correct way to define a custom analyzer with a lowercase filter in Elasticsearch settings?

easy

A. "analyzer": {"my_analyzer": {"type": "custom", "tokenizer": "standard", "filter": ["lowercase"]}}

B. "analyzer": {"my_analyzer": {"type": "standard", "tokenizer": "lowercase", "filter": ["custom"]}}

C. "analyzer": {"my_analyzer": {"type": "custom", "tokenizer": "lowercase", "filter": ["standard"]}}

D. "analyzer": {"my_analyzer": {"type": "filter", "tokenizer": "standard", "filter": ["lowercase"]}}

Solution

1. Step 1: Review custom analyzer syntax

   A custom analyzer requires "type": "custom", a tokenizer, and filters as an array.

2. Step 2: Match correct tokenizer and filter

   "standard" tokenizer and "lowercase" filter are valid and correctly placed in "analyzer": {"my_analyzer": {"type": "custom", "tokenizer": "standard", "filter": ["lowercase"]}}.

3. Final Answer:

   "analyzer": {"my_analyzer": {"type": "custom", "tokenizer": "standard", "filter": ["lowercase"]}} -> Option A

4. Quick Check:

   Custom analyzer syntax = "analyzer": {"my_analyzer": {"type": "custom", "tokenizer": "standard", "filter": ["lowercase"]}} [OK]

Hint: Custom analyzer needs type, tokenizer, and filter array [OK]

Common Mistakes:

• Using wrong type like 'filter' instead of 'custom'
• Swapping tokenizer and filter names
• Missing array brackets for filters

3. Given this custom analyzer configuration:

{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase", "stop"]
        }
      },
      "filter": {
        "stop": {
          "type": "stop",
          "stopwords": ["the", "is"]
        }
      }
    }
  }
}

What is the output tokens when analyzing the text "The cat is on the mat" with my_analyzer?

medium

A. ["The", "cat", "is", "on", "the", "mat"]

B. ["cat", "is", "on", "mat"]

C. ["the", "cat", "is", "on", "the", "mat"]

D. ["cat", "on", "mat"]

Solution

1. Step 1: Apply lowercase filter

   All words become lowercase: "the", "cat", "is", "on", "the", "mat".

2. Step 2: Apply stop filter with stopwords ["the", "is"]

   Remove "the" and "is" from tokens, leaving "cat", "on", "mat".

3. Final Answer:

   ["cat", "on", "mat"] -> Option D

4. Quick Check:

   Lowercase + stop filter removes 'the' and 'is' [OK]

Hint: Lowercase then remove stopwords from tokens [OK]

Common Mistakes:

• Ignoring lowercase filter effect
• Not removing stopwords correctly
• Confusing original text with tokens

4. Identify the error in this custom analyzer definition:

{
  "analysis": {
    "analyzer": {
      "my_analyzer": {
        "type": "custom",
        "tokenizer": "standard",
        "filter": "lowercase"
      }
    }
  }
}

medium

A. Missing stopwords definition

B. The tokenizer name is invalid

C. The filter should be an array, not a string

D. The analyzer type should be "standard"

Solution

1. Step 1: Check filter syntax in custom analyzer

   Filters must be provided as an array, e.g., ["lowercase"], not a string.

2. Step 2: Verify other parts

   Tokenizer "standard" and analyzer type "custom" are correct; stopwords are optional.

3. Final Answer:

   The filter should be an array, not a string -> Option C

4. Quick Check:

   Filter must be array syntax [OK]

Hint: Filters always use array brackets [] in custom analyzers [OK]

Common Mistakes:

• Using string instead of array for filters
• Changing tokenizer to invalid types
• Assuming stopwords are mandatory

5. You want to create a custom analyzer that removes English stopwords, lowercases text, and also applies a stemmer filter named english_stemmer. Which of the following is the correct way to define this analyzer in Elasticsearch settings?

hard

A. {
"analysis": {
"analyzer": {
"custom_english": {
"type": "standard",
"tokenizer": "standard",
"filter": ["lowercase", "stop", "english_stemmer"]
}
},
"filter": {
"stop": {
"type": "stop",
"stopwords": "_english_"
},
"english_stemmer": {
"type": "stemmer",
"language": "english"
}
}
}
}

B. {
"analysis": {
"analyzer": {
"custom_english": {
"type": "custom",
"tokenizer": "standard",
"filter": ["lowercase", "stop", "english_stemmer"]
}
},
"filter": {
"stop": {
"type": "stop",
"stopwords": "_english_"
},
"english_stemmer": {
"type": "stemmer",
"language": "english"
}
}
}
}

C. {
"analysis": {
"analyzer": {
"custom_english": {
"type": "custom",
"tokenizer": "standard",
"filter": ["stop", "lowercase", "english_stemmer"]
}
},
"filter": {
"stop": {
"type": "stop",
"stopwords": "_english_"
},
"english_stemmer": {
"type": "stemmer",
"language": "english"
}
}
}
}

D. {
"analysis": {
"analyzer": {
"custom_english": {
"type": "custom",
"tokenizer": "standard",
"filter": ["lowercase", "english_stemmer", "stop"]
}
},
"filter": {
"stop": {
"type": "stop",
"stopwords": "_english_"
},
"english_stemmer": {
"type": "stemmer",
"language": "english"
}
}
}
}

Solution

1. Step 1: Confirm analyzer type and tokenizer

   The analyzer must be "custom" with "standard" tokenizer to apply filters correctly.

2. Step 2: Check filter order and definitions

   Filters should be ["lowercase", "stop", "english_stemmer"] in that order to lowercase first, remove stopwords next, then stem.

3. Step 3: Verify filter definitions

   Stop filter uses "_english_" stopwords, and stemmer filter is correctly defined with language "english".

4. Final Answer:

   { "analysis": { "analyzer": { "custom_english": { "type": "custom", "tokenizer": "standard", "filter": ["lowercase", "stop", "english_stemmer"] } }, "filter": { "stop": { "type": "stop", "stopwords": "_english_" }, "english_stemmer": { "type": "stemmer", "language": "english" } } } } -> Option B

5. Quick Check:

   Custom analyzer with correct filter order and definitions = { "analysis": { "analyzer": { "custom_english": { "type": "custom", "tokenizer": "standard", "filter": ["lowercase", "stop", "english_stemmer"] } }, "filter": { "stop": { "type": "stop", "stopwords": "_english_" }, "english_stemmer": { "type": "stemmer", "language": "english" } } } } [OK]

Hint: Filter order matters: lowercase, stop, then stemmer [OK]

Common Mistakes:

• Using wrong analyzer type like 'standard'
• Incorrect filter order causing wrong results
• Missing or wrong stopwords definition