Analyzer components (tokenizer, filters) in Elasticsearch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a standard tokenizer in an Elasticsearch analyzer.

Elasticsearch

"analyzer": { "my_analyzer": { "type": "custom", "tokenizer": "[1]" } }

Drag options to blanks, or click blank then click option'

Astandard

Bkeyword

Cwhitespace

Dpattern

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'keyword' tokenizer which does not split text.

Using 'whitespace' tokenizer which only splits on spaces.

The standard tokenizer splits text into terms on word boundaries, which is the default and most common tokenizer.

2fill in blank

medium

Complete the code to add a lowercase filter to the analyzer filters list.

Elasticsearch

"analyzer": { "my_analyzer": { "type": "custom", "tokenizer": "standard", "filter": ["[1]"] } }

Drag options to blanks, or click blank then click option'

Alowercase

Basciifolding

Cuppercase

Dstop

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'stop' filter which removes common words.

Using 'uppercase' filter which converts tokens to uppercase.

The lowercase filter converts all tokens to lowercase, helping with case-insensitive search.

3fill in blank

hard

Fix the error in the filter list by choosing the correct filter name to remove stop words.

Elasticsearch

"analyzer": { "my_analyzer": { "type": "custom", "tokenizer": "standard", "filter": ["[1]"] } }

Drag options to blanks, or click blank then click option'

Aporter_stem

Blowercase

Ckeyword_marker

Dstop

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'lowercase' filter which only changes case.

Using 'porter_stem' which reduces words to their root form.

The stop filter removes common stop words like 'the', 'and', 'is' from the tokens.

4fill in blank

hard

Fill the three blanks to create an analyzer that uses the whitespace tokenizer and applies lowercase and asciifolding filters.

Elasticsearch

"analyzer": { "my_analyzer": { "type": "custom", "tokenizer": "[1]", "filter": ["[2]", "[3]"] } }

Drag options to blanks, or click blank then click option'

Awhitespace

Blowercase

Casciifolding

Dstop

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'standard' tokenizer instead of 'whitespace'.

Mixing up filter order or names.

The whitespace tokenizer splits text on spaces. The lowercase filter converts tokens to lowercase, and asciifolding removes accents from characters.

5fill in blank

hard

Fill all four blanks to define an analyzer with the standard tokenizer and filters: lowercase, stop, and porter_stem.

Elasticsearch

"analyzer": { "my_analyzer": { "type": "custom", "tokenizer": "[1]", "filter": ["[2]", "[3]", "[4]"] } }

Drag options to blanks, or click blank then click option'

Astandard

Blowercase

Cstop

Dporter_stem

Attempts:

3 left

💡 Hint

Common Mistakes

Wrong tokenizer name.

Incorrect filter order or missing filters.

This analyzer uses the standard tokenizer, then applies lowercase to normalize case, stop to remove common words, and porter_stem to reduce words to their root form.

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