Discover Elasticsearch’s architecture and capabilities. Learn about indexing and storing data, conducting precise queries, including fuzzy searches, and executing real-time data analysis effectively.

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This course explores Elasticsearch, an open-source, Java-based, full-text search and analytics engine that harnesses the power of the Lucene library. You’ll discover its capabilities as a versatile search engine for business data, offering the ability to store information, conduct searches on accurate and typo-ridden text, and perform real-time data analysis on vast datasets.

Throughout this course, you will gain comprehensive insights into the architecture of Elasticsearch and master the art of indexing and storing data within the Elasticsearch framework. You will progress to learn how to execute various types of queries, including fuzzy queries, and explore the intricacies of each operation and how it is implemented within Elasticsearch.

After completing the course, you’ll have gained a comprehensive understanding of the internal mechanisms of Elasticsearch and developed the skills to effectively use it for your data management and analysis requirements.

Elasticsearch Fundamentals: Indexing and Querying Data

# Overview

A **character filter** is a component of an analyzer that receives the original text as a stream of characters and transforms the stream by adding, removing, or changing characters. It is used to modify or clean up the input text by removing special characters, converting cases, or replacing specific characters or sequences of characters.

There are three types of character filters available in Elasticsearch: 

- The HTML strip filter

- The mapping character filter

- The pattern-replace filter



An **HTML strip** is a character filter that removes HTML elements from the text and replaces these elements with their decoded values.




## Example


The following request tests a custom analyzer that uses the `html_strip`  character filter with the `keyword` tokenizer, which returns the entire text as a single token:


GET /_analyze
{
  "tokenizer": "keyword",
  "char_filter": [
    "html_strip"
  ],
  "text": "<p>This is the <b>ElasticSearch</b> course.</p>"
}

The custom analyzer produces the following tokens:

```
["This is the Elasticsearch course."]
```

The response contains the token produced by the analyzer.

{
  "tokens": [
    {
      "token": """
This is the Elasticsearch course.
""",
      "start_offset": 0,
      "end_offset": 47,
      "type": "word",
      "position": 0
    }
  ]
}

The **mapping character filter** replaces specific characters or sequences of characters in a string with their corresponding mapped values. It can be defined as a list of keys and their associated values, and when a key is encountered in the input, it is replaced with the corresponding value. For instance,  we can use the mapping filter to replace Arabic numerals (٠‎١٢٣٤٥٦٧٨‎٩‎) with their Latin equivalents (0123456789).


The mapping character filter can be defined by one of the following:

- `mappings`: They are an array of mappings with each element having the form key $=>$ value.


- `mappings_path`: This is a path to a file containing the key $=>$ value mappings.




Explore the built-in character filters offered by Elasticsearch.

Introduction to Elasticsearch

Getting started on Elasticsearch

Text Analysis

Search on ElasticSearch

Aggregation

Conclusion

Integrate Elasticsearch in the Ruby on Rails Application

Custom Analyzers: Character filters

Overview

HTML strip

Example