Custom analyzers in Elasticsearch - Time & Space Complexity

Time Complexity: Custom analyzers

O(n)

Understanding Time Complexity

When using custom analyzers in Elasticsearch, it is important to understand how the processing time changes as the input size grows.

We want to know how the time to analyze text scales when using custom analyzers.

Scenario Under Consideration

Analyze the time complexity of the following Elasticsearch custom analyzer definition and usage.

PUT /my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_custom_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase", "stop"]
        }
      }
    }
  }
}

GET /my_index/_analyze
{
  "analyzer": "my_custom_analyzer",
  "text": "The Quick Brown Fox Jumps Over The Lazy Dog"
}
    

This code defines a custom analyzer that tokenizes text, converts tokens to lowercase, and removes stop words, then analyzes a sample text.

Identify Repeating Operations

Look at the steps that repeat as the input text grows.

• Primary operation: Tokenizing and filtering each word in the input text.
• How many times: Once for each token (word) in the input text.

How Execution Grows With Input

As the input text gets longer, the analyzer processes more tokens one by one.

Input Size (n tokens)	Approx. Operations
10	About 10 token processing steps
100	About 100 token processing steps
1000	About 1000 token processing steps

Pattern observation: The time grows roughly in direct proportion to the number of tokens.

Final Time Complexity

Time Complexity: O(n)

This means the time to analyze text grows linearly with the number of words in the input.

Common Mistake

[X] Wrong: "Custom analyzers process the entire text all at once, so time does not depend on input size."

[OK] Correct: Actually, analyzers work token by token, so more words mean more processing steps and longer time.

Interview Connect

Understanding how custom analyzers scale helps you explain performance when handling large text data in search systems.

Self-Check

What if we added more filters to the custom analyzer? How would that affect the time complexity?

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