Full-Text Search is a powerful database and search engine feature that allows users to search for specific words or phrases within text-based fields. Unlike basic keyword matching, full-text search uses advanced indexing and algorithms to deliver more accurate and relevant results, even when handling large datasets. It is commonly used in applications requiring robust search functionalities, such as e-commerce websites, document management systems, and content-heavy platforms.

Key Features:
1. Relevance Ranking: Results are ranked based on their relevance to the search query. This can involve analyzing term frequency, proximity, and matching context.
2. Wildcard and Fuzzy Search: Allows partial matches and tolerance for typos or spelling variations, enhancing user experience.
3. Boolean Queries: Supports advanced logic, such as AND, OR, and NOT operators, to refine searches.
4. Stemming and Lemmatization: Matches different forms of a word, such as “run” and “running,” improving the breadth of search results.
5. Natural Language Processing (NLP): Some full-text search systems incorporate NLP to interpret search intent and semantic meaning.

Use Cases:
- E-commerce: Finding products based on descriptions, categories, or user reviews.
- Content Management Systems (CMS): Quickly locating documents, articles, or pages.
- Customer Support: Searching knowledge bases for troubleshooting information.

Popular tools supporting full-text search include Elasticsearch, Solr, and PostgreSQL's Full-Text Search extension. These tools are optimized for performance and can handle millions of records efficiently.