Full-text search is one of the most powerful features of Elasticsearch, allowing you to search through large volumes of text data efficiently. This module will cover the basics of full-text search, including how to perform searches, understand the relevance scoring, and use analyzers to improve search results.

1. Full-Text Search Basics:

   • Understanding how Elasticsearch indexes and searches text.
   • The difference between structured and unstructured data.

2. Analyzers:

   • How analyzers work in Elasticsearch.
   • Built-in analyzers vs. custom analyzers.

3. Relevance Scoring:

   • How Elasticsearch scores search results.
   • Factors that influence relevance scoring.

4. Search Queries:

   • Basic match queries.
   • Advanced full-text queries.

Elasticsearch uses an inverted index to perform full-text searches. This index allows Elasticsearch to quickly find documents that contain specific terms. When you index a document, Elasticsearch breaks down the text into individual terms and stores them in the inverted index.

In this example, the document is indexed into my_index with an ID of 1. The text in the title and content fields is broken down into terms and stored in the inverted index.

Analyzers play a crucial role in full-text search. They process the text data during indexing and searching. An analyzer consists of three main components:

• Character Filters: Modify the text before tokenization.
• Tokenizer: Breaks the text into individual terms.
• Token Filters: Modify the tokens generated by the tokenizer.

Elasticsearch provides several built-in analyzers, such as:

• Standard Analyzer: The default analyzer that splits text into terms based on word boundaries.
• Whitespace Analyzer: Splits text based on whitespace.
• Keyword Analyzer: Treats the entire text as a single token.

In this example, a custom analyzer named my_custom_analyzer is created. It uses the standard tokenizer and applies lowercase and stopword filters.

Elasticsearch uses a scoring algorithm to determine the relevance of search results. The default scoring algorithm is BM25, which considers factors such as term frequency, inverse document frequency, and field length.

• Term Frequency (TF): The number of times a term appears in a document.
• Inverse Document Frequency (IDF): The importance of a term in the entire index.
• Field Length: The length of the field being searched.

The match query is the most common full-text search query. It analyzes the input text and constructs a query based on the terms generated by the analyzer.

In this example, the match query searches for documents containing the terms "distributed", "search", and "engine" in the content field.

Elasticsearch provides several advanced full-text queries, such as:

• Multi-Match Query: Searches multiple fields.
• Match Phrase Query: Searches for exact phrases.
• Common Terms Query: Optimizes search for common terms.

In this example, the multi_match query searches for the terms "Elasticsearch", "search", and "engine" in both the title and content fields.

1. Index Sample Documents:
   • Create an index named library.
   • Index three documents with fields title and description.

2. Perform a Full-Text Search:
   • Search for documents containing the term "Elasticsearch" in the description field.

The search query should return documents 1 and 3, as they contain the term "Elasticsearch" in the description field.

In this module, you learned about the basics of full-text search in Elasticsearch, including how to index documents, use analyzers, and perform search queries. You also explored relevance scoring and advanced full-text queries. By understanding these concepts, you can effectively implement full-text search in your Elasticsearch applications.

Next, we will delve into filtering and sorting search results to further refine your search capabilities.