When working with databases, searching against a text field is a common requirement. However, using indexes on text fields can be limited when it comes to searching in the middle or at the end of the text. This is where Microsoft full-text index comes in handy.

Microsoft full-text index is a tool that allows for fast and flexible indexing of keyword-based queries on text data stored in a Microsoft SQL Server database. Unlike the LIKE predicate, which only works on patterns, full-text queries perform linguistic searches based on words and phrases.

However, there are some limitations to using full-text search. The full-text catalogs and indexes are not stored in the SQL Server database itself, which means that if you restore the database on another server, you will need to rebuild the index. Additionally, you need to set up a job to maintain the changes to the full-text index, which means that the index may not always be up to date. Furthermore, you need to start a service called Microsoft Full-Text Engine (MSFTE SQL), which may not be suitable for all projects.

In this article, we will explore an alternative approach to improve text searching in SQL Server. We will first discuss the concept of noise words. Noise words are words that do not contribute to the search and are often considered as noise. Examples of noise words include articles (e.g. “a”, “an”, “the”) and conjunctions (e.g. “and”, “but”, “while”). It is important to identify and handle noise words accordingly.

To simulate the behavior of a full-text index, we can create a table to store noise words and a table-valued function to extract words from a string. We can then create a child table to hold keywords for the text field we want to search in. By populating this child table and using SQL queries, we can perform efficient searches based on the extracted keywords.

It is worth noting that phrasal searching, where we search for specific phrases, requires additional considerations. We need to handle white spaces and ensure that the search string matches exactly with the desired phrase. By using appropriate SQL statements and functions, we can effectively handle phrasal searching as well.

In conclusion, while Microsoft full-text index is a powerful tool for text searching, it may not always be suitable for every project. By implementing alternative approaches, such as handling noise words and simulating full-text index behavior, we can improve text searching in SQL Server and achieve efficient and accurate results.