Standard analyzer in Elasticsearch

Introduction

The standard analyzer helps break text into words so Elasticsearch can search and match them easily.

When you want to search normal text like articles or blog posts.

When you need to split sentences into words ignoring punctuation.

When you want a simple, general way to analyze text without special rules.

When you want to make searches case-insensitive and ignore common punctuation.

Syntax

Elasticsearch

{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_standard_analyzer": {
          "type": "standard"
        }
      }
    }
  }
}

The standard analyzer splits text by spaces and punctuation.

It lowercases all words to make searching case-insensitive.

Examples

Set the default analyzer to standard for the whole index.

Elasticsearch

{
  "analyzer": {
    "default": {
      "type": "standard"
    }
  }
}

Create a custom analyzer named my_analyzer using the standard analyzer.

Elasticsearch

{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "type": "standard"
        }
      }
    }
  }
}

Sample Program

This query shows how the standard analyzer breaks the text into words.

Elasticsearch

GET /_analyze
{
  "analyzer": "standard",
  "text": "Hello, World! This is Elasticsearch."
}

Important Notes

The standard analyzer removes punctuation and lowercases words.

It is good for general English text but may not fit special languages or needs.

Summary

The standard analyzer splits text into simple lowercase words.

It ignores punctuation and spaces to help search work better.

Use it when you want a basic, general text analyzer in Elasticsearch.

Practice

1. What does the standard analyzer in Elasticsearch do by default?

easy

A. Keeps punctuation and case intact

B. Breaks text into words, ignores punctuation and case

C. Only analyzes numbers and symbols

D. Removes all spaces and joins words

Solution

1. Step 1: Understand the role of the standard analyzer

   The standard analyzer splits text into simple words by removing punctuation and ignoring case differences.

2. Step 2: Recall default behavior in Elasticsearch

   It is the default analyzer and is designed for general text processing, making text lowercase and splitting on punctuation.

3. Final Answer:

   Breaks text into words, ignores punctuation and case -> Option B

4. Quick Check:

   Standard analyzer = splits words, ignores punctuation and case [OK]

Hint: Standard analyzer splits words and lowercases text [OK]

Common Mistakes:

• Thinking it keeps punctuation
• Assuming it preserves case
• Believing it only analyzes numbers

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

easy

A. "analyzer": "whitespace"

B. "analyzer": "simple"

C. "analyzer": "keyword"

D. "analyzer": "standard"

Solution

1. Step 1: Identify the analyzer name for standard analyzer

   The standard analyzer is referenced by the string "standard" in Elasticsearch mappings.

2. Step 2: Compare with other analyzer names

   Other analyzers like "simple", "keyword", and "whitespace" are different and not the standard analyzer.

3. Final Answer:

   "analyzer": "standard" -> Option D

4. Quick Check:

   Standard analyzer name = "standard" [OK]

Hint: Use "analyzer": "standard" to apply standard analyzer [OK]

Common Mistakes:

• Using other analyzer names by mistake
• Forgetting quotes around analyzer name
• Confusing standard with simple analyzer

3. Given the text "Hello, World! Elasticsearch 7.10", what tokens will the standard analyzer produce?

medium

A. ["Hello", "World", "Elasticsearch", "7.10"]

B. ["hello,", "world!", "elasticsearch", "7.10"]

C. ["hello", "world", "elasticsearch", "7", "10"]

D. ["hello world elasticsearch 7 10"]

Solution

1. Step 1: Apply standard analyzer tokenization rules

   The standard analyzer lowercases text and splits on punctuation and whitespace, so "Hello," becomes "hello", "World!" becomes "world".

2. Step 2: Analyze numbers and punctuation

   Numbers separated by punctuation like "7.10" are split into "7" and "10" tokens.

3. Final Answer:

   ["hello", "world", "elasticsearch", "7", "10"] -> Option C

4. Quick Check:

   Standard analyzer tokens = lowercase words split on punctuation [OK]

Hint: Standard analyzer lowercases and splits on punctuation [OK]

Common Mistakes:

• Keeping punctuation attached to words
• Not splitting numbers on dots
• Preserving original case

4. You wrote this mapping snippet but your field is not analyzed as expected:

{
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "analyzer": "standard_analyzer"
      }
    }
  }
}

What is the error?

medium

A. Analyzer name should be "standard", not "standard_analyzer"

B. Field type should be "keyword" instead of "text"

C. Analyzer must be defined under "settings", not "mappings"

D. Missing "index": true property

Solution

1. Step 1: Check the analyzer name used in mapping

   The standard analyzer is named "standard". Using "standard_analyzer" is incorrect and causes the analyzer to not be found.

2. Step 2: Verify field type and analyzer placement

   Field type "text" is correct for analyzed fields, and analyzer is correctly placed under "mappings". "index": true is default and not required.

3. Final Answer:

   Analyzer name should be "standard", not "standard_analyzer" -> Option A

4. Quick Check:

   Analyzer name must be exact "standard" [OK]

Hint: Use exact analyzer name "standard" in mappings [OK]

Common Mistakes:

• Adding extra suffix to analyzer name
• Changing field type to keyword for analyzed text
• Misplacing analyzer in settings instead of mappings

5. You want to create a custom analyzer that uses the standard tokenizer but adds a lowercase filter and a stop word filter. Which of the following JSON snippets correctly defines this analyzer in Elasticsearch?

hard

A. {
"settings": {
"analysis": {
"analyzer": {
"custom_std": {
"type": "custom",
"tokenizer": "standard",
"filter": ["lowercase", "stop"]
}
}
}
}
}

B. {
"settings": {
"analysis": {
"analyzer": {
"custom_std": {
"type": "standard",
"filter": ["lowercase", "stop"]
}
}
}
}
}

C. {
"settings": {
"analysis": {
"analyzer": {
"custom_std": {
"type": "custom",
"tokenizer": "simple",
"filter": ["lowercase", "stop"]
}
}
}
}
}

D. {
"settings": {
"analysis": {
"analyzer": {
"custom_std": {
"type": "custom",
"tokenizer": "standard",
"filter": ["stop", "uppercase"]
}
}
}
}
}

Solution

1. Step 1: Define a custom analyzer with standard tokenizer

   The analyzer must have "type": "custom" and use "tokenizer": "standard" to start with the standard tokenizer.

2. Step 2: Add lowercase and stop filters in correct order

   The filters array should include "lowercase" and "stop" filters to lowercase tokens and remove stop words.

3. Step 3: Verify other options

   { "settings": { "analysis": { "analyzer": { "custom_std": { "type": "standard", "filter": ["lowercase", "stop"] } } } } } uses "type": "standard" which does not allow custom filters. { "settings": { "analysis": { "analyzer": { "custom_std": { "type": "custom", "tokenizer": "simple", "filter": ["lowercase", "stop"] } } } } } uses "simple" tokenizer, not standard. { "settings": { "analysis": { "analyzer": { "custom_std": { "type": "custom", "tokenizer": "standard", "filter": ["stop", "uppercase"] } } } } } uses "uppercase" filter which is incorrect here.

4. Final Answer:

   Custom analyzer with type custom, standard tokenizer, and filters lowercase and stop -> Option A

5. Quick Check:

   Custom analyzer = type custom + standard tokenizer + lowercase + stop filters [OK]

Hint: Custom analyzer needs type custom and tokenizer standard [OK]

Common Mistakes:

• Using type standard instead of custom for filters
• Choosing wrong tokenizer like simple
• Wrong filter order or filters like uppercase