Customizing Text Analyzers
Create and apply custom analyzers to specific fields to control how text is processed, stemmed, and indexed for search.
Customizing Text Analyzers is a free Elasticsearch & Full Text Search Systems lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Elasticsearch & Full Text Search Systems learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Why Custom Analyzers?
Elasticsearch comes with powerful default text analyzers, but sometimes your data needs a special touch. This is where custom analyzers shine!
They allow you to precisely control how your text fields are processed for search, ensuring optimal relevancy and accuracy for your specific use case.
The Analyzer Recipe
Recall that every analyzer, custom or built-in, follows a three-step process to transform raw text into searchable tokens:
- Character Filters: Clean up the raw input string (e.g., remove HTML tags).
- Tokenizer: Breaks the processed string into individual words or tokens.
- Token Filters: Modifies, adds, or removes tokens (e.g., lowercase, remove stop words, apply stemming).
A custom analyzer lets you pick and choose these ingredients!
Defining Custom Analyzers
You define custom analyzers within an index's settings block, under analysis. This tells Elasticsearch how to process text for that index.
Here's the basic structure for creating a custom analyzer:
PUT /my_custom_index
{
"settings": {
"analysis": {
"analyzer": {
"my_custom_analyzer": {
"type": "custom",
"char_filter": [],
"tokenizer": "standard",
"filter": []
}
}
}
}
}Custom Character Filters
Character filters are the first step, acting on the raw text. They can remove or replace characters before tokenization. You can define your own or use built-in ones.
html_strip: Removes HTML tags.mapping: Replaces specified characters or strings.
Here's how to define a custom mapping filter:
PUT /my_index_with_char_filter
{
"settings": {
"analysis": {
"char_filter": {
"ampersand_to_and": {
"type": "mapping",
"mappings": ["& => and "]
}
},
"analyzer": {
"my_analyzer": {
"type": "custom",
"char_filter": ["ampersand_to_and"],
"tokenizer": "standard",
"filter": ["lowercase"]
}
}
}
}
}Selecting a Tokenizer
The tokenizer breaks the stream of characters from the character filters into individual tokens (words). Your choice here is crucial for how words are identified.
Common built-in tokenizers you can use in custom analyzers include:
standard: Good for most languages, grammar-based.whitespace: Splits text only on whitespace.keyword: Treats the entire input as a single token (useful for exact values).pattern: Splits text based on a regular expression.
Custom Token Filters
Token filters refine the tokens generated by the tokenizer. This is where most of the search logic resides, like handling synonyms or stemming.
You can define custom versions of filters or use built-in ones:
lowercase: Converts tokens to lowercase.stop: Removes common, less meaningful words (stop words).synonym: Replaces tokens with their synonyms.stemmer: Reduces words to their root form.
Let's define a custom stop word filter:
PUT /my_index_with_token_filter
{
"settings": {
"analysis": {
"filter": {
"my_custom_stop_words": {
"type": "stop",
"stopwords": ["a", "the", "is", "and", "are"]
}
},
"analyzer": {
"my_analyzer": {
"type": "custom",
"tokenizer": "standard",
"filter": ["lowercase", "my_custom_stop_words"]
}
}
}
}
}Building a Full Custom Analyzer
Now, let's combine these concepts to create a practical custom analyzer for blog post content. It will:
- Remove HTML tags.
- Tokenize standard text.
- Lowercase all tokens.
- Remove common English stop words.
This analyzer is then applied to the content field.
PUT /blog_posts_index
{
"settings": {
"analysis": {
"char_filter": {
"html_strip_char_filter": {
"type": "html_strip"
}
},
"filter": {
"english_stop_words": {
"type": "stop",
"stopwords": ["the", "a", "an", "is", "are"]
}
},
"analyzer": {
"blog_content_analyzer": {
"type": "custom",
"char_filter": ["html_strip_char_filter"],
"tokenizer": "standard",
"filter": ["lowercase", "english_stop_words"]
}
}
}
},
"mappings": {
"properties": {
"content": {
"type": "text",
"analyzer": "blog_content_analyzer"
}
}
}
}Applying to Field Mappings
Once your custom analyzer is defined in the index settings, you apply it to a text field within your index's mapping. This tells Elasticsearch to use your custom logic when indexing and searching that specific field.
You simply specify the analyzer parameter with the name of your custom analyzer:
PUT /blog_posts_index/_mapping
{
"properties": {
"content": {
"type": "text",
"analyzer": "blog_content_analyzer"
},
"title": {
"type": "text",
"analyzer": "standard"
}
}
}Testing with _analyze API
How can you be sure your custom analyzer works as expected? Use the _analyze API! It lets you simulate how text will be processed by any analyzer.
This is an indispensable tool for debugging and validating your text analysis setup.
GET /blog_posts_index/_analyze
{
"analyzer": "blog_content_analyzer",
"text": "The <b>quick</b> brown fox jumps over the lazy dog."
}Quiz Time!
You've learned about the components of a custom analyzer and how to define them. Let's test your knowledge!
Custom Analyzers: Your Search Superpower
Congratulations! You've learned how to harness the power of custom analyzers in Elasticsearch.
- You can now define custom character filters, tokenizers, and token filters.
- You know how to combine these components to create a tailor-made analyzer for your data.
- You understand how to apply this analyzer to specific fields in your mappings.
- And importantly, you know how to test your analyzer using the
_analyzeAPI.
This skill is crucial for building highly relevant and accurate search experiences!
Frequently asked questions
Is the “Customizing Text Analyzers” lesson free?
Yes — the full text of “Customizing Text Analyzers” is free to read here on the web, and the Elasticsearch & Full Text Search Systems course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Elasticsearch & Full Text Search Systems course, upgrade to CoddyKit PRO.
What will I learn in “Customizing Text Analyzers”?
Create and apply custom analyzers to specific fields to control how text is processed, stemmed, and indexed for search. You practise Elasticsearch & Full Text Search Systems with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Elasticsearch & Full Text Search Systems?
No prior experience is required. Elasticsearch & Full Text Search Systems on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Customizing Text Analyzers” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Elasticsearch & Full Text Search Systems lesson?
Yes. Every Elasticsearch & Full Text Search Systems lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Analyzers, Tokenizers, Filters
- Customizing Text Analyzers
- Boosting and Relevancy Scoring
- Synonyms and Stemming