REGEX

About

Regular Expressions (regex) are patterns used to match sequences of characters. In Spring applications, regex plays an important role across various modules such as:

• Web request routing

• Data validation

• Security rules

• Conditional bean creation

• Service-layer logic

Regex increases flexibility by allowing dynamic pattern matching rather than relying on hardcoded values.

1. REGEX in Controller URL Mapping

Spring allows the use of regex in path variables to restrict the format of dynamic URL segments. This is useful for building routes that only match specific patterns, such as numeric IDs, usernames, UUIDs, or custom tokens.

Syntax

In @GetMapping, @RequestMapping, or @PostMapping, regex can be added inside {variable:regex}.

Example

@GetMapping("/product/{id:[0-9]+}")
public ResponseEntity<String> getProductById(@PathVariable String id) {
    return ResponseEntity.ok("Valid product ID: " + id);
}

• This endpoint matches /product/123, but not /product/abc.

• Regex used: [0-9]+ (one or more digits).

Use Case Scenarios

• Enforce numeric-only IDs in URLs

• Match slugs (e.g., {slug:[a-z0-9-]+})

• Route based on specific formats like dates or UUIDs

2. REGEX in Bean Validation using @Pattern

Spring Boot integrates with Jakarta Bean Validation (JSR 380, formerly JSR 303), which includes @Pattern annotation to validate strings using regex. This is typically used in DTOs, forms, and REST request bodies.

Example

public class UserRequest {

    @Pattern(regexp = "^[a-zA-Z]{3,20}$", message = "Name must contain only letters and be 3 to 20 characters long")
    private String name;

    @Pattern(regexp = "^[A-Za-z0-9+_.-]+@[A-Za-z0-9.-]+$", message = "Invalid email format")
    private String email;
}

• This ensures name and email follow the required patterns.

• Annotate the controller method with @Valid or @Validated.

@PostMapping("/register")
public ResponseEntity<String> register(@Valid @RequestBody UserRequest userRequest) {
    return ResponseEntity.ok("User Registered");
}

Why Use It?

• Avoids manual validation logic

• Centralizes format rules

• Makes validation declarative and reusable

3. REGEX in Spring Security (regexMatchers)

Spring Security allows us to apply access control based on URL patterns using regex. This provides fine-grained control over access to endpoints.

Example

@Override
protected void configure(HttpSecurity http) throws Exception {
    http
        .authorizeRequests()
        .regexMatchers("/admin/[0-9]{4}").hasRole("ADMIN")
        .regexMatchers("/user/[a-z]+").hasRole("USER")
        .anyRequest().authenticated();
}

• /admin/1234 will be accessible only to users with ADMIN role.

• /user/john will be accessible only to users with USER role.

Why Use It?

• More flexible than Ant-style (/admin/**)

• Enables dynamic access rules based on format (e.g., only allow numeric IDs)

4. REGEX in Spring Expressions (@ConditionalOnExpression)

Spring Expression Language (SpEL) can use regex for conditional logic, such as loading beans only if a property matches a pattern.

Example

@ConditionalOnExpression("#{systemProperties['env'] matches 'dev|qa'}")
@Bean
public DataSource testDataSource() {
    return new HikariDataSource();
}

• This bean is only loaded when the system property env is set to dev or qa.

Benefits

• Enables environment-specific configuration using patterns

• Helps in feature toggling or staged deployment logic

5. REGEX in Property Files (External Config)

We can define regex patterns in application.properties or application.yml and inject them for use in validation or pattern matching in services.

Example

application.properties:

regex.email=^[A-Za-z0-9+_.-]+@[A-Za-z0-9.-]+$

Service:

@Value("${regex.email}")
private String emailRegex;

public boolean isValid(String email) {
    return Pattern.matches(emailRegex, email);
}

This allows updating validation rules without changing code.

6. REGEX Using java.util.regex.Pattern in Services

We can manually use regex for advanced matching in service-layer logic, especially for scenarios not covered by annotations.

Example

public boolean isPhoneNumberValid(String number) {
    Pattern pattern = Pattern.compile("^\\+91[0-9]{10}$");
    Matcher matcher = pattern.matcher(number);
    return matcher.matches();
}

This is useful when:

• Building custom validation logic

• Processing or filtering large text inputs

• Integrating regex in business rules

7. REGEX with Apache Commons RegexValidator

Apache Commons Validator provides a reusable, thread-safe way to use regex in our services.

Example

RegexValidator validator = new RegexValidator("^[0-9]{6}$");
if (validator.isValid("560001")) {
    System.out.println("Valid PIN code");
}

8. REGEX in File Parsing and Text Processing

Regular expressions (regex) are essential for extracting, filtering, transforming, and validating patterns in text. In Spring Boot (or any Java application), when reading files or processing strings, regex provides a powerful and flexible way to work with content dynamically.

Key Use Cases

• Searching for specific lines in a file (e.g., error logs)

• Extracting structured data (e.g., phone numbers, email addresses)

• Replacing or cleaning unwanted characters or formats

• Parsing logs, CSVs, and configuration files

• Validating structured inputs like dates, IDs, codes

Regex is especially helpful when:

• File formats are not standardized (e.g., free text logs)

• We need to process large text files line by line

• We want to apply complex pattern-based rules

1. Filtering Lines that Match a Pattern

We may want to process only lines that contain a specific keyword or pattern from a large file (like logs or reports).

Example – Extract lines containing "ERROR"

try (Stream<String> lines = Files.lines(Paths.get("app.log"))) {
    lines.filter(line -> line.matches(".*ERROR.*"))
         .forEach(System.out::println);
}

• .*ERROR.* matches any line containing the word "ERROR".

• Efficient for scanning and reporting.

2. Extracting Data from a Line (Pattern Matching)

Use regex groups to extract specific parts of a line. Ideal for logs, delimited text, or loosely structured content.

Example – Extract timestamp and level from a log line

Log line:

[2024-06-21 14:23:45] INFO - Payment completed

String line = "[2024-06-21 14:23:45] INFO - Payment completed";
Pattern pattern = Pattern.compile("\\[(.*?)\\]\\s+(INFO|ERROR|DEBUG)\\s+-\\s+(.*)");
Matcher matcher = pattern.matcher(line);

if (matcher.matches()) {
    String timestamp = matcher.group(1);
    String level = matcher.group(2);
    String message = matcher.group(3);

    System.out.println("Time: " + timestamp);
    System.out.println("Level: " + level);
    System.out.println("Message: " + message);
}

This approach is excellent for structured log analysis or custom reporting tools.

3. Replacing or Cleaning Text

Regex is often used to sanitize data by removing unwanted characters, trimming patterns, or replacing formats.

Example – Remove all special characters from a string

String input = "Hello@Spring#2025!";
String cleaned = input.replaceAll("[^a-zA-Z0-9 ]", "");
System.out.println(cleaned); // Output: HelloSpring2025

Example – Replace all multiple spaces with a single space

String messy = "This   is    Spring Boot";
String normalized = messy.replaceAll("\\s+", " ");

Useful for normalizing inconsistent inputs.

4. Validating Each Line in a File

Apply validation rules to each line (e.g., is it a valid email, phone number, or ID).

Example – Check if all lines are valid email addresses

Pattern emailPattern = Pattern.compile("^[A-Za-z0-9+_.-]+@[A-Za-z0-9.-]+$");

try (BufferedReader reader = Files.newBufferedReader(Paths.get("emails.txt"))) {
    String line;
    while ((line = reader.readLine()) != null) {
        if (!emailPattern.matcher(line).matches()) {
            System.out.println("Invalid email: " + line);
        }
    }
}

This technique helps validate file-based input in batch jobs, ETL processes, or user uploads.

5. Extracting All Occurrences from a Single Line

Use find() method to match multiple occurrences of a pattern in the same string.

Example – Extract all numbers from a sentence

String input = "Order 123 has 3 items and costs 450 rupees";
Pattern pattern = Pattern.compile("\\d+");
Matcher matcher = pattern.matcher(input);

while (matcher.find()) {
    System.out.println("Number: " + matcher.group());
}

Use this in analytics, reporting, or transformation scenarios.

6. Parsing Delimited Files with Optional Validation

While CSVs can be parsed with libraries (like OpenCSV), regex helps when we have inconsistent or semi-structured files.

Example – Parse and validate simple CSV manually

john,25,[email protected]
mary,abc,mary@example

Pattern linePattern = Pattern.compile("^\\w+,(\\d+),([A-Za-z0-9+_.-]+@[A-Za-z0-9.-]+)$");

try (Stream<String> lines = Files.lines(Paths.get("users.csv"))) {
    lines.forEach(line -> {
        if (linePattern.matcher(line).matches()) {
            System.out.println("Valid: " + line);
        } else {
            System.out.println("Invalid: " + line);
        }
    });
}

7. Detecting Sensitive Information in Logs

Regex can be used to detect and redact sensitive fields such as passwords, keys, tokens, or credit card numbers before logging.

Example – Mask password field in a string

String logLine = "username=admin&password=secret123&token=abc";
String masked = logLine.replaceAll("password=[^&]+", "password=****");
System.out.println(masked);

This is useful for log filtering and compliance with security standards.

8. Custom Regex Utility Method

Example – Generic reusable utility

public class RegexUtils {
    public static boolean matches(String pattern, String input) {
        return Pattern.compile(pattern).matcher(input).matches();
    }

    public static List<String> extractAll(String pattern, String input) {
        Matcher matcher = Pattern.compile(pattern).matcher(input);
        List<String> results = new ArrayList<>();
        while (matcher.find()) {
            results.add(matcher.group());
        }
        return results;
    }
}

Usage:

RegexUtils.matches("^\\d{4}$", "2025"); // true
RegexUtils.extractAll("\\d+", "Amount 300 paid on 2024-06-21");