Why text analysis enables smart search in Elasticsearch - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a basic analyzer that lowercases text.

Elasticsearch

{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_lowercase_analyzer": {
          "type": "[1]"
        }
      }
    }
  }
}

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

Asimple

Bstandard

Ckeyword

Dcustom

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'keyword' analyzer which does not lowercase text.

Using 'standard' analyzer which does more than just lowercase.

The simple analyzer lowercases text, which is essential for case-insensitive search.

2fill in blank

medium

Complete the code to add a stopwords filter to remove common words.

Elasticsearch

{
  "settings": {
    "analysis": {
      "filter": {
        "my_stop": {
          "type": "[1]",
          "stopwords": ["the", "is", "at"]
        }
      }
    }
  }
}

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

Astop

Blowercase

Csynonym

Dstemmer

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'lowercase' filter which only changes case.

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

The stop filter removes common words like 'the', 'is', and 'at' to improve search relevance.

3fill in blank

hard

Fix the error in the analyzer definition to include both lowercase and stop filters.

Elasticsearch

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

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

Astemmer

Bkeyword

Csynonym

Dlowercase

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'stemmer' which changes word forms instead of case.

Using 'keyword' which does not change tokens.

The lowercase filter ensures tokens are lowercase before stopwords are removed.

4fill in blank

hard

Fill both blanks to create a mapping that uses the custom analyzer and enables full-text search.

Elasticsearch

{
  "mappings": {
    "properties": {
      "content": {
        "type": "[1]",
        "analyzer": "[2]"
      }
    }
  }
}

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

Atext

Bkeyword

Cmy_analyzer

Dstandard

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'keyword' type which does not analyze text.

Using 'standard' analyzer instead of the custom one.

The field type should be text to allow analyzed full-text search, and the analyzer should be the custom one my_analyzer.

5fill in blank

hard

Fill all three blanks to define a custom analyzer with a standard tokenizer, lowercase and stop filters.

Elasticsearch

{
  "settings": {
    "analysis": {
      "analyzer": {
        "custom_text_analyzer": {
          "type": "[1]",
          "tokenizer": "[2]",
          "filter": ["[3]", "stop"]
        }
      }
    }
  }
}

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

Acustom

Bstandard

Clowercase

Dkeyword

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'keyword' tokenizer which does not split text.

Omitting the lowercase filter.

The analyzer type is custom, tokenizer is standard, and filters include lowercase and stop for effective text analysis.

Practice

1. What is the main purpose of text analysis in Elasticsearch search?

easy

A. To create visual charts from search results

B. To store data in a compressed format

C. To encrypt the search queries

D. To break text into smaller parts for better matching

Solution

1. Step 1: Understand text analysis role

   Text analysis breaks down text into smaller pieces called tokens to help Elasticsearch match search terms better.

2. Step 2: Compare options

   Only To break text into smaller parts for better matching describes breaking text into parts for better matching, which is the core of text analysis.

3. Final Answer:

   To break text into smaller parts for better matching -> Option D

4. Quick Check:

   Text analysis = breaking text for better search [OK]

Hint: Text analysis splits text to improve search matches [OK]

Common Mistakes:

• Confusing text analysis with data storage
• Thinking it encrypts queries
• Assuming it creates charts

2. Which of the following is the correct way to specify an analyzer in an Elasticsearch mapping?

easy

A. { "content": { "type": "keyword", "analyzer": "standard" } }

B. { "properties": { "content": { "type": "text", "analyzer": "standard" } } }

C. { "properties": { "content": { "type": "text" } }, "analyzer": "standard" }

D. { "analyzer": "standard", "type": "text" }

Solution

1. Step 1: Review Elasticsearch mapping syntax

   Analyzers are set inside the field definition under properties, specifying type and analyzer.

2. Step 2: Identify correct JSON structure

   { "properties": { "content": { "type": "text", "analyzer": "standard" } } } correctly places analyzer inside the content field under properties with type text.

3. Final Answer:

   { "properties": { "content": { "type": "text", "analyzer": "standard" } } } -> Option B

4. Quick Check:

   Analyzer inside field properties = { "properties": { "content": { "type": "text", "analyzer": "standard" } } } [OK]

Hint: Analyzer goes inside field under properties in mapping [OK]

Common Mistakes:

• Placing analyzer outside properties
• Using keyword type with analyzer
• Incorrect JSON nesting

3. Given this Elasticsearch analyzer configuration:

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

What happens when you index the text "The Quick Brown Fox"?

medium

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

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

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

D. ["quick", "brown"]

Solution

1. Step 1: Apply tokenizer and filters

   The standard tokenizer splits text into words: ["The", "Quick", "Brown", "Fox"]. Then lowercase filter makes all lowercase: ["the", "quick", "brown", "fox"]. The stop filter removes common stop words like "the".

2. Step 2: Final tokens after filters

   After removing "the", tokens are ["quick", "brown", "fox"].

3. Final Answer:

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

4. Quick Check:

   Stop words removed, lowercase applied = ["quick", "brown", "fox"] [OK]

Hint: Stop filter removes common words like 'the' [OK]

Common Mistakes:

• Forgetting stop word removal
• Ignoring lowercase filter effect
• Assuming original casing remains

4. You wrote this analyzer in Elasticsearch:

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

But your search results are missing relevant documents. What is a likely cause?

medium

A. The stemmer filter changed words too much, causing mismatches

B. The tokenizer is incorrect and causes syntax errors

C. The lowercase filter is missing, so case mismatches occur

D. Stop filter is disabled, so too many words are indexed

Solution

1. Step 1: Understand stemmer effect

   The stemmer filter reduces words to their root form, but sometimes it changes words too aggressively, causing search terms not to match indexed terms.

2. Step 2: Analyze impact on search results

   If stemmer changes words too much, relevant documents may be missed because the search terms don't match the stemmed tokens.

3. Final Answer:

   The stemmer filter changed words too much, causing mismatches -> Option A

4. Quick Check:

   Over-stemming causes missed matches = The stemmer filter changed words too much, causing mismatches [OK]

Hint: Over-stemming can hide relevant results [OK]

Common Mistakes:

• Blaming tokenizer for syntax errors
• Ignoring stemmer impact
• Assuming lowercase filter is missing

5. You want to create a smart search that finds documents even if users type plural or different forms of words. Which analyzer setup best achieves this?

hard

A. Use a whitespace tokenizer with stop filter only

B. Use a keyword tokenizer with no filters

C. Use a standard tokenizer with lowercase and stemmer filters

D. Use a pattern tokenizer with uppercase filter

Solution

1. Step 1: Identify analyzer needs for smart search

   To match different word forms like plurals, stemming is needed. Lowercase helps ignore case differences. Standard tokenizer splits text properly.

2. Step 2: Evaluate options

   Use a standard tokenizer with lowercase and stemmer filters includes standard tokenizer, lowercase, and stemmer filters, which together enable matching different word forms smartly.

3. Final Answer:

   Use a standard tokenizer with lowercase and stemmer filters -> Option C

4. Quick Check:

   Stemming + lowercase + standard tokenizer = smart search [OK]

Hint: Combine stemmer and lowercase for flexible word matching [OK]

Common Mistakes:

• Using keyword tokenizer which disables token splitting
• Relying only on stop filter
• Using uppercase filter which doesn't help word forms