Character filters in Elasticsearch - Mini Project: Build & Apply

Create a Custom Character Filter in Elasticsearch

📖 Scenario: You are setting up an Elasticsearch index for a book store. You want to clean up the text data by replacing certain characters before analysis.

🎯 Goal: Build an Elasticsearch index with a custom character filter that replaces the ampersand (&) with the word 'and' in book titles.

📋 What You'll Learn

Create an index called bookstore

Define a custom character filter named ampersand_filter that replaces '&' with 'and'

Add an analyzer called custom_analyzer that uses the ampersand_filter

Apply the custom_analyzer to the title field in the mapping

💡 Why This Matters

🌍 Real World

Cleaning and normalizing text data in search indexes to improve search accuracy and relevance.

💼 Career

Elasticsearch engineers and developers often create custom analyzers with character filters to handle special characters and improve search results.

1

Create the bookstore index with empty settings

Create an Elasticsearch index called bookstore with empty settings and mappings objects.

Elasticsearch

{
  "settings": {},
  "mappings": {}
}
# Your code here

Hint

Use the PUT /bookstore API with an empty settings and mappings object.

2

Add a custom character filter ampersand_filter

In the settings of the bookstore index, add a char_filter named ampersand_filter that replaces the character '&' with the string 'and'.

Elasticsearch

{
  "settings": {
    "analysis": {
      "char_filter": {
        "ampersand_filter": {
          "type": "mapping",
          "mappings": [
            "&=>and"
          ]
        }
      }
    }
  },
  "mappings": {}
}
# Your code here

Hint

Use the mapping type for the character filter and specify the mapping as &=>and.

3

Create a custom analyzer using the ampersand_filter

In the settings.analysis, add an analyzer named custom_analyzer that uses the ampersand_filter as a char_filter and the standard tokenizer.

Elasticsearch

{
  "settings": {
    "analysis": {
      "char_filter": {
        "ampersand_filter": {
          "type": "mapping",
          "mappings": [
            "&=>and"
          ]
        }
      },
      "analyzer": {
        "custom_analyzer": {
          "type": "custom",
          "char_filter": ["ampersand_filter"],
          "tokenizer": "standard"
        }
      }
    }
  },
  "mappings": {}
}
# Your code here

Hint

Define a custom analyzer with the char_filter array including ampersand_filter and use the standard tokenizer.

4

Apply the custom_analyzer to the title field mapping

In the mappings.properties, define a title field of type text that uses the custom_analyzer analyzer.

Elasticsearch

{
  "settings": {
    "analysis": {
      "char_filter": {
        "ampersand_filter": {
          "type": "mapping",
          "mappings": [
            "&=>and"
          ]
        }
      },
      "analyzer": {
        "custom_analyzer": {
          "type": "custom",
          "char_filter": ["ampersand_filter"],
          "tokenizer": "standard"
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "analyzer": "custom_analyzer"
      }
    }
  }
}
# Your code here

Hint

Under mappings.properties, define the title field as text with the custom_analyzer.

Practice

1. What is the main purpose of a character_filter in Elasticsearch analyzers?

easy

A. To split text into tokens after analysis

B. To modify text before tokenizing by replacing or removing characters

C. To store documents in the index

D. To sort search results by relevance

Solution

1. Step 1: Understand character_filter role

   Character filters act on raw text before tokenization to clean or replace characters.

2. Step 2: Differentiate from other components

   Tokenizers split text into tokens, and other parts handle indexing or sorting, not character filters.

3. Final Answer:

   To modify text before tokenizing by replacing or removing characters -> Option B

4. Quick Check:

   Character filters = modify text pre-tokenizing [OK]

Hint: Character filters act before tokenizing text [OK]

Common Mistakes:

• Confusing character filters with tokenizers
• Thinking character filters sort or store data
• Assuming character filters work after tokenizing

2. Which of the following is the correct syntax to define a mapping character filter in Elasticsearch?

easy

A. "char_filter": {"my_mapping": {"type": "mapping", "mappings": ["ph=>f", "qu=>kw"]}}

B. "char_filter": {"my_mapping": {"type": "pattern_replace", "pattern": "ph", "replacement": "f"}}

C. "char_filter": {"my_mapping": {"type": "mapping", "pattern": "ph", "replacement": "f"}}

D. "char_filter": {"my_mapping": {"type": "replace", "mappings": ["ph=>f"]}}

Solution

1. Step 1: Identify correct type and keys for mapping filter

   The mapping filter uses "type": "mapping" and a "mappings" array with string replacements.

2. Step 2: Check syntax correctness

   "char_filter": {"my_mapping": {"type": "mapping", "mappings": ["ph=>f", "qu=>kw"]}} correctly uses "type": "mapping" and "mappings" with array of strings like "ph=>f".

3. Final Answer:

   "char_filter": {"my_mapping": {"type": "mapping", "mappings": ["ph=>f", "qu=>kw"]}} -> Option A

4. Quick Check:

   Mapping filter syntax = type mapping + mappings array [OK]

Hint: Mapping filters use type 'mapping' and 'mappings' array [OK]

Common Mistakes:

• Using pattern_replace keys for mapping filter
• Wrong type name like 'replace' instead of 'mapping'
• Using 'pattern' and 'replacement' keys in mapping filter

3. Given this character filter configuration:

{
  "char_filter": {
    "my_filter": {
      "type": "pattern_replace",
      "pattern": "\\d",
      "replacement": "#"
    }
  }
}

What will be the output text after applying my_filter to the input "abc123def"?

medium

A. "abc1#3def"

B. "abc123def"

C. "abc###def"

D. "abc#23def"

Solution

1. Step 1: Understand pattern_replace with pattern '\\d'

   The pattern '\\d' matches any digit character (0-9). The replacement is '#'.

2. Step 2: Apply replacement to input text

   All digits '1', '2', '3' in "abc123def" are replaced by '#', resulting in "abc###def".

3. Final Answer:

   "abc###def" -> Option C

4. Quick Check:

   Digits replaced by '#' = "abc###def" [OK]

Hint: Pattern '\d' matches all digits, replaced by '#' [OK]

Common Mistakes:

• Replacing only first digit instead of all
• Not escaping backslash in pattern
• Confusing pattern_replace with mapping filter

4. Identify the error in this character filter definition:

{
  "char_filter": {
    "bad_filter": {
      "type": "mapping",
      "mapping": ["ph=>f"]
    }
  }
}

medium

A. Character filters cannot use arrays

B. The type 'mapping' is invalid

C. The replacement string 'ph=>f' is incorrect format

D. The key should be 'mappings' not 'mapping'

Solution

1. Step 1: Check correct key for mapping filter

   The mapping filter requires the key 'mappings' (plural) for the array of replacements.

2. Step 2: Verify other parts

   Type 'mapping' is valid, and the replacement string format "ph=>f" is correct.

3. Final Answer:

   The key should be 'mappings' not 'mapping' -> Option D

4. Quick Check:

   Key name must be 'mappings' for mapping filter [OK]

Hint: Use 'mappings' key (plural) for mapping filters [OK]

Common Mistakes:

• Using 'mapping' instead of 'mappings'
• Mistaking type name
• Wrong replacement string format

5. You want to create a character filter that replaces all occurrences of 'cat' with 'dog' and removes digits from the text before tokenizing. Which configuration correctly combines these requirements?

hard

A. {
"char_filter": {
"replace_cat": {
"type": "mapping",
"mappings": ["cat=>dog"]
},
"remove_digits": {
"type": "pattern_replace",
"pattern": "[0-9]",
"replacement": ""
}
}
}

B. {
"char_filter": {
"replace_cat": {
"type": "pattern_replace",
"pattern": "cat",
"replacement": "dog"
},
"remove_digits": {
"type": "mapping",
"mapping": ["0=>", "1=>", "2=>", "3=>", "4=>", "5=>", "6=>", "7=>", "8=>", "9=>"]
}
}
}

C. {
"char_filter": {
"replace_cat": {
"type": "mapping",
"mapping": ["cat=>dog"]
},
"remove_digits": {
"type": "pattern_replace",
"pattern": "\\d",
"replacement": ""
}
}
}

D. {
"char_filter": {
"replace_cat": {
"type": "pattern_replace",
"pattern": "cat",
"replacement": "dog"
},
"remove_digits": {
"type": "pattern_replace",
"pattern": "d",
"replacement": ""
}
}
}

Solution

1. Step 1: Check replacement of 'cat' with 'dog'

   Using a mapping filter with "cat=>dog" is correct and simpler for exact string replacement.

2. Step 2: Check digit removal filter

   Removing digits with pattern_replace using pattern "[0-9]" and empty replacement is valid and concise.

3. Step 3: Verify combined configuration

   { "char_filter": { "replace_cat": { "type": "mapping", "mappings": ["cat=>dog"] }, "remove_digits": { "type": "pattern_replace", "pattern": "[0-9]", "replacement": "" } } } correctly combines a mapping filter for 'cat' and a pattern_replace filter for digits removal.

4. Final Answer:

   { "char_filter": { "replace_cat": { "type": "mapping", "mappings": ["cat=>dog"] }, "remove_digits": { "type": "pattern_replace", "pattern": "[0-9]", "replacement": "" } } } -> Option A

5. Quick Check:

   Mapping for 'cat', pattern_replace for digits removal [OK]

Hint: Use mapping for exact words, pattern_replace for patterns [OK]

Common Mistakes:

• Using pattern_replace for simple word mapping unnecessarily
• Using mapping filter to remove digits (inefficient)
• Incorrect regex patterns for digits