FULL-TEXT-SEARCH
The full-text search feature allows you to efficiently search text stored in a PostgreSQL database. The feature includes the following:
- Configurable text search configurations
- Full-text indexing
- Full-text search functions and operators
- Full-text search queries
The full-text search feature is based on the following concepts:
- Document: A piece of text to be searched.
- Lexeme: A word that has been normalized to its base form. For example, the words "jumping", "jumps", and "jumped" are all normalized to the lexeme "jump".
- Token: A lexeme that is indexed for searching.
- Stopword: A common word that is ignored during full-text search. For example, "the" and "is" are stopwords.
- Parser: A function that converts a document into a list of tokens.
The full-text search feature is useful for searching text in the following scenarios:
- Web search engines: Search engines use full-text search to search for web pages that contain specific keywords.
- Document management systems: Document management systems use full-text search to search for documents that contain specific keywords.
- E-commerce websites: E-commerce websites use full-text search to search for products that contain specific keywords.
- Social media websites: Social media websites use full-text search to search for posts that contain specific keywords.
- Chat applications: Chat applications use full-text search to search for messages that contain specific keywords.
Technologies Used
- PostgreSQL: A powerful, open-source object-relational database system.
- pg_trgm: A PostgreSQL extension that provides functions and operators for text similarity measurement and indexing.
- Ktor: A Kotlin-based framework for building asynchronous web applications.
- Exposed: A Kotlin SQL framework that provides a type-safe SQL API.
- HikariCP: A high-performance JDBC connection pool.
- Kotlin Coroutines: A Kotlin feature that allows you to write asynchronous code in a sequential style.
Features
- Full-Text Indexing: Indexes the text columns in the database to improve search performance.
- Full-Text Search: Searches for text in the database using the full-text search feature.
- Text Similarity: Measures the similarity between two text values using the pg_trgm extension.
- Asynchronous Web Application: Uses Kotlin Coroutines to build an asynchronous web application.
- Type-Safe SQL API: Uses Exposed to write type-safe SQL queries in Kotlin.
- Connection Pooling: Uses HikariCP to manage JDBC connections efficiently.
Setup
To set up the project, you need to have Kotlin and Java installed on your machine. You also need to set the following environment variables for the database connection:
URL: The JDBC URL of your PostgreSQL database.USER: The username for your PostgreSQL database.DB_PASSWORD: The password for your PostgreSQL database.DRIVER: The JDBC driver class name for your PostgreSQL database.
Once you have these set up, you can initialize the database by calling DatabaseFactory.init(). This will set up the connection pool, create the necessary tables and indices, and set up a trigger for updating the full-text search vector.
API Endpoints
The project provides the following API endpoints:
GET /api/v1/users: Fetches users based on the provided query parameters. If an email is provided, it fetches the user with the matching email. If a search term is provided, it performs a search for users matching the term. If no parameters are provided, it fetches all users.POST /api/v1/users: Creates a new user. The request body should contain the details of the user to be created.PUT /api/v1/users/{id}: Updates the user with the provided ID. The request body should contain the updated details of the user.DELETE /api/v1/users/{id}: Deletes the user with the provided ID.
Running the Project
You can run the project using the Gradle run task. This will start the server and make the API endpoints available for use.