If you're looking to build a full-text search engine but don't want to rely on external tools or libraries, SQL provides a powerful set of tools to help you get the job done. In a SQL database, a full-text search engine allows you to search through all the columns stored in one or multiple tables within a single search. For instance, if you have a customer database, the full-text search engine enables you to search for emails, first names, or last names at the same time.

In this article, we'll walk you through the process of building a full-text search engine using SQL, from the basics of text searching to advanced techniques like relevance ranking and phrase searching. So whether you're a seasoned SQL developer or just getting started, you'll be able to follow along and build your own search engine in no time!

Why build a Full-Text Search Engine with SQL?

1. Efficient searching:

SQL databases are designed to efficiently manage and query large amounts of structured data, including text-based data. By building a full-text search engine within a SQL database, you can take advantage of this optimized querying to quickly and accurately search through large volumes of text data.

2. Centralized data management:

Storing text data within a SQL database can make managing and organizing your data in a centralized location easier. This can help streamline your data workflows and make updating or modifying your data easier over time.

3. Customization:

By building your own full-text search engine with SQL, you have greater control over the search algorithms and parameters used, which can allow you to fine-tune the search results and better meet the specific needs of your users.

4. Integration with other tools:

SQL databases can easily integrate with other tools and platforms, such as web applications, business intelligence tools, or data visualization software. By building a full-text search engine within a SQL database, you can more easily integrate your search functionality with these other tools and create a more seamless user experience for your audience.

Step-by-step on building a full-text search engine

In this article, I will demonstrate how to create a full-text search engine using Acho's App Builder and PostgreSQL as an example.

1. Connect to your data sources

To begin, connect to your database. Since our data is stored in PostgreSQL, click on the PostgreSQL option and follow the instructions to establish a connection to the database.

2. Create an app

After that, go to App Builder and create a new app. You can either create a blank app or start from an existing template.

3. Create a search query

In App Builder, you can drag a data node to the canvas and create a data source for your app. Data nodes are where you can write a query.

Exact Search

One straightforward way to implement full-text search in SQL involves defining search criteria for each column of interest. By specifying these criteria and joining them with the logical operator "OR," the resulting query will retrieve any row that contains the specified value in any of the relevant columns. `%{{keyword}}%` is the placeholder where you can add the search keyword.

FROM customer_database
WHERE first_name ILIKE '%{{keyword}}%'
OR last_name ILIKE '%{{keyword}}%'
OR email ILIKE '%{{keyword}}%'

OR company_name ILIKE '%{{keyword}}%'

Fuzzy Search

Another way is to use PostgreSQL's built-in full-text search functions, tsvector and to_tsquery. tsvector transforms the text into a sorted list of distinct lexemes, which are words that have been normalized to merge different variants of the same word. A tsquery value stores lexemes that are to be searched for and can combine them using the Boolean operators &, |, and !, as well as the phrase search operator <-> (FOLLOWED BY).

FROM customer_database
WHERE to_tsvector('english', first_name || ' ' || last_name || ' ' || email || ' ' || company_name) @@ to_tsquery('english', '{{keyword}}')

4. Connect your data source to the front end

Now you can go to your page and start building interactions between your front-end and data sources. First, navigate to the Create panel, where you can find all the front-end components to create a page. Drag a table element from the right-hand Create panel to the page and select the data node you just created as the data source.

Next, drag a search bar and open the bottom interaction panel. The interaction panel allows you to establish communication between your front-end components and data sources. In the panel, create a "Set SQL Parameter" action as shown below in the bottom interaction panel.

In addition to the table and search bar, you can drag and drop more elements onto your page to create a more interactive and engaging user interface.

If you’re interested in building your own full-text search engine with SQL, don’t hesitate to try! We are happy to help you learn more about it. Contact us in the chat box on the bottom right corner of this page if you have any questions!

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