Custom analyzers in Elasticsearch - Mini Project: Build & Apply

Create a Custom Analyzer in Elasticsearch

📖 Scenario: You are setting up a search engine for a small online bookstore. You want to improve search results by creating a custom analyzer that lowercases text and removes common English stopwords.

🎯 Goal: Build an Elasticsearch index with a custom analyzer that uses the standard tokenizer, lowercases all text, and removes English stopwords.

📋 What You'll Learn

Create an index called bookstore with custom analyzer settings

Define a custom analyzer named my_custom_analyzer

Use the standard tokenizer in the analyzer

Add lowercase and stop filters to the analyzer

Set the stop filter to use English stopwords

Print the analyzer settings to verify

💡 Why This Matters

🌍 Real World

Custom analyzers help improve search quality by controlling how text is broken down and filtered before searching.

💼 Career

Knowing how to create and configure custom analyzers is important for roles involving search engine setup, data indexing, and improving user search experience.

1

Create the initial index settings with an empty analyzer

Create an index called bookstore with settings that include an empty analysis object.

Elasticsearch

{
  "settings": {
    "analysis": {
      
    }
  }
}
# Your code here

Hint

Start by creating the settings key with an analysis object inside it.

2

Add a custom analyzer named my_custom_analyzer

Inside settings.analysis, add an analyzer object with a custom analyzer named my_custom_analyzer that uses the standard tokenizer and an empty filter list.

Elasticsearch

{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_custom_analyzer": {
          "tokenizer": "standard",
          "filter": []
        }
      }
    }
  }
}
# Your code here

Hint

Define the analyzer key and inside it add my_custom_analyzer with the standard tokenizer and an empty filter list.

3

Add lowercase and stop filters to the analyzer

Modify the my_custom_analyzer to include the lowercase and stop filters in the filter list. Also, add a filter object in analysis defining the stop filter with stopwords set to _english_.

Elasticsearch

{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_custom_analyzer": {
          "tokenizer": "standard",
          "filter": ["lowercase", "stop"]
        }
      },
      "filter": {
        "stop": {
          "type": "stop",
          "stopwords": "_english_"
        }
      }
    }
  }
}
# Your code here

Hint

Add the lowercase and stop filters to the analyzer's filter list. Then define the stop filter with English stopwords in the filter object.

4

Print the analyzer settings to verify

Print the complete JSON settings for the bookstore index to verify the custom analyzer configuration.

Elasticsearch

print(settings)
# Your code here

Hint

Use print(settings) to display the full JSON settings object.

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