Elasticsearch Mastering Elasticsearch Custom Analyzers for Enhanced Search Capabilities

By Opster Team

Updated: Nov 5, 2023

| 2 min read

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Elasticsearch offers a wide range of features that enable users to explore a variety of ways to analyze data. One of these features is the ability to create custom analyzers. Custom analyzers are a powerful tool that can be used to enhance search capabilities by tailoring the analysis process to specific data and search requirements.

Analyzers in Elasticsearch play a crucial role in the indexing process. They are responsible for breaking down the text into tokens or terms which are then indexed. Elasticsearch provides several built-in analyzers like the standard analyzer, simple analyzer, whitespace analyzer, etc. However, there might be scenarios where these built-in analyzers do not suffice. This is where custom analyzers come into play. If you want to learn more about the fundamentals of text analysis and analyzers, check out this guide.

Creating a Custom Analyzer

A custom analyzer is defined by combining a single tokenizer with zero or more token filters, and character filters. The process of creating a custom analyzer involves the following steps:

1. Define the Custom Analyzer: The first step is to define the custom analyzer in the settings of the index. This is done using the “analysis” setting.

PUT /my_index
  "settings": {
    "analysis": {
      "analyzer": {
        "my_custom_analyzer": {
          "type":      "custom", 
          "tokenizer": "standard",
          "char_filter": [
          "filter": [

In the above example, a custom analyzer named “my_custom_analyzer” is created. It uses the “standard” tokenizer and applies two filters: “lowercase” and “asciifolding”, in that order. The “html_strip” character filter is also applied which removes HTML elements from the text. In terms of execution order, the character filters are executed first, then the tokenizer, and finally the token filters.

Aside from the “standard” tokenizer, there are many other tokenizers that can help you slice and dice your text data, feel free to check them out. Similarly, the “lowercase” and “asciifolding” token filters are just two out of many more token filters that you can use to transform the text tokens coming out of the tokenizer.

2. Apply the Custom Analyzer: Once the custom analyzer is defined, it can be applied to a `text` field when creating an index.

PUT /my_index/_mapping
  "properties": {
    "my_field": {
      "type": "text",
      "analyzer": "my_custom_analyzer"

In the above example, the custom analyzer “my_custom_analyzer” is applied to the field “my_field”. It is worth noting that the mapping can also be specified at step 1 when creating the index.

Testing a Custom Analyzer

Elasticsearch provides the _analyze API to test analyzers. This API can be used to see the output tokens of an analyzer for a given text.

POST /my_index/_analyze
  "analyzer": "my_custom_analyzer",
  "text": "This is a <b>test</b>!"

In the above example, the _analyze API is used to test the “my_custom_analyzer” on the text “This is a <b>test</b>!”. The output will show the tokens produced by the analyzer, namely `this`, `is`, `a` and `test`.


Custom analyzers provide a great deal of flexibility in handling text data in Elasticsearch. They allow users to define their own analysis process tailored to their specific needs. By understanding and utilizing custom analyzers, one can significantly enhance the search capabilities of their Elasticsearch applications. 

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