Tokenizers (standard, whitespace, pattern) in Elasticsearch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Standard Tokenizer on a Sample Text

Given the following Elasticsearch analyzer configuration using the standard tokenizer, what is the output tokens for the input text "Hello, world! This is Elasticsearch."?

Elasticsearch

{
  "analyzer": {
    "my_standard_analyzer": {
      "tokenizer": "standard"
    }
  }
}

Input text: "Hello, world! This is Elasticsearch."

A ["hello,", "world!", "this", "is", "elasticsearch."]

B ["Hello,", "world!", "This", "is", "Elasticsearch."]

C ["Hello", "world", "This", "is", "Elasticsearch"]

D ["hello", "world", "this", "is", "elasticsearch"]

💡 Hint

The standard tokenizer splits text into words by removing most punctuation and preserves case.

The standard tokenizer breaks text into words by removing most punctuation and preserves the original case of tokens.

❓ Predict Output

intermediate

Whitespace Tokenizer Output for a Given Text

What tokens does the whitespace tokenizer produce for the input text "Quick brown fox jumps over the lazy dog."?

Elasticsearch

{
  "analyzer": {
    "my_whitespace_analyzer": {
      "tokenizer": "whitespace"
    }
  }
}

Input text: "Quick brown fox jumps over the lazy dog."

A ]".god" ,"yzal" ,"eht" ,"revo" ,"spmuj" ,"xof" ,"nworb" ,"kciuQ"[

B ["Quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog."]

C ["Quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"]

D ["quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"]

💡 Hint

Whitespace tokenizer splits tokens only on spaces and does not lowercase or remove punctuation.

The whitespace tokenizer splits text only on spaces, so punctuation remains attached to tokens and casing is preserved.

❓ Predict Output

advanced

Pattern Tokenizer with Custom Regex Output

Using the pattern tokenizer with the regex "\\W+" (non-word characters as delimiters), what tokens are produced from the input text "Email me at [e-mail]!"?

Elasticsearch

{
  "analyzer": {
    "my_pattern_analyzer": {
      "filter": ["lowercase"],
      "tokenizer": {
        "type": "pattern",
        "pattern": "\\W+"
      }
    }
  }
}

Input text: "Email me at [e-mail]!"

A ["email", "me", "at", "user", "example", "com"]

B ["Email", "me", "at", "[e-mail]"]

C ["email", "me", "at", "[e-mail]"]

D ["Email", "me", "at", "user", "example", "com"]

💡 Hint

The pattern tokenizer splits on non-word characters and lowercase filter converts tokens to lowercase.

The pattern tokenizer splits on any sequence of non-word characters (like @, ., !), so '[e-mail]' splits into 'user', 'example', 'com'. The lowercase filter makes all tokens lowercase.

❓ Predict Output

advanced

Effect of Pattern Tokenizer with Complex Regex

What tokens result from using a pattern tokenizer with the regex "[ ,.!]+" on the input text "Hello, world! Welcome to Elasticsearch."?

Elasticsearch

{
  "analyzer": {
    "complex_pattern_analyzer": {
      "tokenizer": {
        "type": "pattern",
        "pattern": "[ ,.!]+"
      }
    }
  }
}

Input text: "Hello, world! Welcome to Elasticsearch."

A ["Hello", "world", "Welcome", "to", "Elasticsearch"]

B ["Hello", "world!", "Welcome", "to", "Elasticsearch."]

C ["Hello", "world", "Welcome", "to", "Elasticsearch."]

D ["Hello,", "world", "Welcome", "to", "Elasticsearch"]

💡 Hint

The pattern tokenizer splits on any sequence of spaces, commas, periods, or exclamation marks.

The regex splits tokens on spaces, commas, periods, and exclamation marks, so punctuation is removed and tokens are split cleanly.

🧠 Conceptual

expert

Choosing the Correct Tokenizer for Case-Sensitive Search

You want to create an Elasticsearch analyzer that preserves the original casing of tokens and splits text only on whitespace. Which tokenizer should you use to achieve this?

A Use the <strong>standard</strong> tokenizer with a lowercase filter.

B Use the <strong>standard</strong> tokenizer without any filters.

C Use the <strong>pattern</strong> tokenizer with pattern "\\s+" and lowercase filter.

D Use the <strong>whitespace</strong> tokenizer without any filters.

💡 Hint

Think about which tokenizer splits only on spaces and does not change case by default.

The whitespace tokenizer splits tokens only on whitespace and does not lowercase tokens by default, preserving original casing. The standard tokenizer lowercases tokens by default. Pattern tokenizer can split on whitespace but often requires filters to preserve case.