Pattern Matching in SQL

Question

What is Pattern Matching in SQL

Jaish Mathews · Answer

Pattern Matching in SQL Pattern matching in SQL refers to techniques and syntax used to search for specific patterns within string data in a database. It is often used to perform searches based on partial or fuzzy matches rather than exact matches. Key Methods for Pattern Matching 1. LIKE Operator The LIKE operator is used for basic pattern matching. It allows you to search for values that match a specific pattern using wildcards : % (percent sign): Matches zero one or multiple characters. _ (underscore): Matches exactly one character. Syntax SELECT column_name FROM table_name WHERE column_name LIKE 'pattern'; Examples Match any string starting with A: SELECT * FROM employees WHERE name LIKE 'A%'; Match any string ending with son: SELECT * FROM employees WHERE name LIKE '%son'; Match any string containing ing: SELECT * FROM tasks WHERE description LIKE '%ing%'; Match a five-character string starting with B: SELECT * FROM employees WHERE name LIKE 'B____'; 2. NOT LIKE Operator The NOT LIKE operator is used to exclude results that match a specific pattern. Example Exclude all names starting with A: SELECT * FROM employees WHERE name NOT LIKE 'A%'; 3. Regular Expressions (Regex) Some databases (like MySQL PostgreSQL Oracle etc.) support advanced pattern matching using regular expressions. This approach provides more complex pattern-matching capabilities than LIKE . Examples In MySQL: SELECT * FROM employees WHERE name REGEXP '^A'; -- Names starting with A In PostgreSQL: SELECT * FROM employees WHERE name ~ '^A'; -- Names starting with A Applications of Pattern Matching Search Functionality : Finding records with partial matches (e.g. find all names containing 'John'). Data Validation : Identifying records that follow or break specific naming or formatting rules. Reporting : Extracting specific subsets of data for analysis. Wildcard Searches : Searching when exact details are unavailable. Considerations Case Sensitivity : In many databases LIKE is case-insensitive but this behavior can vary (e.g. PostgreSQL is case-sensitive by default unless you use ILIKE ). Performance : Pattern matching can be slow for large datasets especially if wildcards ( % ) are used at the start of the pattern. Using indexes can help improve performance. Escaping Special Characters : If patterns include % or _ as literal characters you may need to escape them using the database's escape syntax. By understanding and utilizing pattern matching effectively you can create flexible queries to handle a wide range of search scenarios in your SQL database.

Tuhin Paul · Answer

Part -2 implementation in a .NET C# application the controller layer. [HttpGet(search)] public IActionResult SearchTransactions(string gateway string status string referencePattern) { var results = _paymentService.SearchTransactions(gateway status referencePattern); return Ok(results); } Service Layer public List SearchTransactions(string gateway string status string referencePattern) { return _paymentRepository.GetTransactions(gateway status referencePattern); } Repository Layer public List GetTransactions(string gateway string status string referencePattern) { var query = @ SELECT TransactionId Gateway Status Amount FROM Payments WHERE Gateway = @Gateway AND Status LIKE @Status AND TransactionReference LIKE @ReferencePattern; return _dbContext.Query (query new { Gateway = gateway Status = status + % ReferencePattern = referencePattern + % }).ToList(); } The pattern matching occurs in the Database Layer . SQL LIKE operators are dynamically constructed based on user input in the Service Layer .

Tuhin Paul · Answer

Part -1 Recently I have used Pattern Matching in a Payment Integration Module in my project. i will try to give you some example from there. In a payment integration module for a .NET C# application pattern matching can be used in various contexts such as: Searching Transaction Data : Filter transactions by a specific pattern (e.g. card numbers payment references). Validating User Input : Ensuring payment references or transaction IDs adhere to a specific format. Dynamic Reporting : Retrieve records based on search criteria like all payments with status starting with 'Fail'. Say a system that processes transactions through multiple payment gateways. A user wants to search for all failed transactions processed via a particular gateway ( Stripe ) and having a transaction reference starting with TXN- . SELECT TransactionId Gateway Status Amount FROM Payments WHERE Gateway = 'Stripe' AND Status LIKE 'Fail%' AND TransactionReference LIKE 'TXN-%'; In the .NET application this query can be triggered when the user enters filters in a search interface. Below I am sharing the architecture flow showing how the search filter with pattern matching is integrated into a payment module: