Analyzer components (tokenizer, filters) in Elasticsearch - Mini Project: Build & Apply

Build a Custom Elasticsearch Analyzer with Tokenizer and Filters

📖 Scenario: You are setting up a search engine for a small online bookstore. You want to create a custom analyzer in Elasticsearch that breaks text into words and processes them to improve search results.

🎯 Goal: Create a custom Elasticsearch analyzer using a tokenizer and filters. You will define the tokenizer and filters, then combine them into an analyzer, and finally test the analyzer to see the processed tokens.

📋 What You'll Learn

Create an index with a custom analyzer named my_custom_analyzer

Use the standard tokenizer in the analyzer

Add a lowercase filter named lowercase

Add a stop filter named stop with stop words the, and, is

Test the analyzer with the text "The quick Brown fox jumps over the lazy Dog and is happy"

💡 Why This Matters

🌍 Real World

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

💼 Career

Understanding analyzers is key for roles in search engineering, backend development, and data engineering working with Elasticsearch.

1

Create the index with a custom analyzer using the standard tokenizer

Write the JSON to create an Elasticsearch index named books with a custom analyzer called my_custom_analyzer that uses the standard tokenizer.

Elasticsearch

{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_custom_analyzer": {
          "tokenizer": "standard"
          # Your code here
        }
      }
    }
  }
}

Hint

Define the my_custom_analyzer inside analysis.analyzer with the standard tokenizer.

2

Add lowercase and stop filters to the analyzer

Add the filters lowercase and stop to the my_custom_analyzer. Define the stop filter with stop words the, and, and is inside analysis.filter.

Elasticsearch

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

Hint

Define the stop inside analysis.filter and add both lowercase and stop to the analyzer's filter list.

3

Test the custom analyzer with sample text

Write the JSON query to analyze the text "The quick Brown fox jumps over the lazy Dog and is happy" using the my_custom_analyzer on the books index.

Elasticsearch

{
  "analyzer": "my_custom_analyzer",
  "text": "The quick Brown fox jumps over the lazy Dog and is happy"
  # Your code here
}

Hint

Use the _analyze API with the analyzer set to my_custom_analyzer and provide the sample text.

4

Display the tokens produced by the analyzer

Print the tokens returned by the analyzer for the text "The quick Brown fox jumps over the lazy Dog and is happy". The output should list tokens without the stop words and all in lowercase.

Elasticsearch

# Your code here to display the tokens

Hint

The tokens should be lowercase and exclude the, and, is.

Practice

1. What is the main role of a tokenizer in an Elasticsearch analyzer?

easy

A. To remove stop words from the text

B. To split text into smaller pieces called tokens

C. To convert tokens to lowercase

D. To index the tokens for searching

Solution

1. Step 1: Understand the tokenizer's function

   A tokenizer breaks the input text into smaller parts called tokens, which are the basic units for searching.

2. Step 2: Differentiate tokenizer from filters

   Filters modify tokens after tokenization, such as lowercasing or removing stop words, but tokenizers do the initial splitting.

3. Final Answer:

   To split text into smaller pieces called tokens -> Option B

4. Quick Check:

   Tokenizer = splits text [OK]

Hint: Tokenizer always splits text into tokens first [OK]

Common Mistakes:

• Confusing tokenizer with filters like lowercase or stop word removal
• Thinking tokenizer indexes tokens
• Assuming tokenizer modifies tokens after splitting

2. Which of the following is the correct syntax to define a standard tokenizer in an Elasticsearch analyzer settings?

easy

A. "tokenizer": standard

B. "tokenizer": ["standard"]

C. "tokenizer": { "type": "standard" }

D. "tokenizer": { "name": "standard" }

Solution

1. Step 1: Recall Elasticsearch tokenizer syntax

   Tokenizers are defined as objects with a type field specifying the tokenizer type.

2. Step 2: Compare options

   "tokenizer": { "type": "standard" } correctly uses an object with type key, while others use incorrect formats like strings or arrays.

3. Final Answer:

   "tokenizer": { "type": "standard" } -> Option C

4. Quick Check:

   Tokenizer syntax uses type key in object [OK]

Hint: Tokenizer must be an object with type key [OK]

Common Mistakes:

• Using string directly instead of object with type
• Using array syntax for tokenizer
• Using incorrect key names like name instead of type

3. Given this analyzer configuration snippet:

{
  "tokenizer": "whitespace",
  "filter": ["lowercase", "stop"]
}

What will be the output tokens for the input text: "The Quick Brown Fox"?

medium

A. ["quick", "brown", "fox"]

B. ["The", "Quick", "Brown", "Fox"]

C. ["quick", "brown"]

D. ["the", "quick", "brown", "fox"]

Solution

1. Step 1: Apply the whitespace tokenizer

   The whitespace tokenizer splits text by spaces, so tokens are ["The", "Quick", "Brown", "Fox"].

2. Step 2: Apply lowercase and stop filters

   The lowercase filter converts all tokens to lowercase: ["the", "quick", "brown", "fox"]. The stop filter removes common stop words like "the", leaving ["quick", "brown", "fox"].

3. Final Answer:

   ["quick", "brown", "fox"] -> Option A

4. Quick Check:

   Lowercase + stop filter removes "the" [OK]

Hint: Stop filter removes common words after lowercase [OK]

Common Mistakes:

• Ignoring stop word removal
• Not applying lowercase filter before stop filter
• Assuming tokenizer changes case

4. Identify the error in this analyzer filter configuration:

{
  "filter": [
    { "type": "lowercase" },
    { "type": "stop", "stopwords": "_english_" },
    { "type": "unknown_filter" }
  ]
}

medium

A. Filters cannot be defined as objects

B. The lowercase filter must be a string, not an object

C. The stop filter syntax is incorrect

D. The filter list contains an invalid filter type

Solution

1. Step 1: Review each filter type

   Lowercase and stop filters are valid and correctly defined. The stop filter uses a valid stopwords parameter.

2. Step 2: Identify invalid filter

   The filter with type "unknown_filter" is not a valid Elasticsearch filter type and will cause an error.

3. Final Answer:

   The filter list contains an invalid filter type -> Option D

4. Quick Check:

   Invalid filter type causes error [OK]

Hint: Check filter types are valid Elasticsearch filters [OK]

Common Mistakes:

• Assuming all filter types are valid
• Thinking lowercase filter must be string
• Ignoring unknown filter errors

5. You want to create a custom analyzer that splits text on whitespace, lowercases tokens, and removes English stop words. Which of the following analyzer settings is correct?

hard

A. { "tokenizer": { "type": "whitespace" }, "filter": ["lowercase", { "type": "stop", "stopwords": "_english_" }] }

B. { "tokenizer": "whitespace", "filter": ["lowercase", stop] }

C. { "tokenizer": "standard", "filter": ["lowercase", "stop"] }

D. { "tokenizer": { "type": "whitespace" }, "filter": ["stop", "lowercase"] }

Solution

1. Step 1: Confirm tokenizer type

   The tokenizer must be whitespace type, correctly defined as an object with type "whitespace".

2. Step 2: Confirm filter order and definition

   Filters should lowercase first, then remove stop words. { "tokenizer": { "type": "whitespace" }, "filter": ["lowercase", { "type": "stop", "stopwords": "_english_" }] } correctly defines lowercase as string and stop filter with explicit stopwords parameter.

3. Step 3: Check other options

   { "tokenizer": "whitespace", "filter": ["lowercase", "stop"] } uses tokenizer as string which is allowed but less explicit; { "tokenizer": "standard", "filter": ["lowercase", "stop"] } uses standard tokenizer which is incorrect; { "tokenizer": { "type": "whitespace" }, "filter": ["stop", "lowercase"] } reverses filter order which may affect results.

4. Final Answer:

   { "tokenizer": { "type": "whitespace" }, "filter": ["lowercase", { "type": "stop", "stopwords": "_english_" }] } -> Option A

5. Quick Check:

   Explicit tokenizer and correct filter order [OK]

Hint: Use object for tokenizer and correct filter order [OK]

Common Mistakes:

• Using wrong tokenizer type
• Reversing filter order
• Not specifying stopwords explicitly