Spring Boot Logging

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

private static final Logger logger = LoggerFactory.getLogger(MyClass.class);
logger.info("Application started");

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.web.bind.annotation.*;

@RestController
class LoggingController {

  private static final Logger logger = LoggerFactory.getLogger(LoggingController.class);

  @GetMapping("/log")
  public String logExample() {

    logger.info("Info log message");
    logger.debug("Debug log message");
    logger.warn("Warning log message");
    logger.error("Error log message");

    return "Logging Example Executed";
  }
}

// Output:
// Logs printed in console based on log level

• 1 Process of recording application events.
• 2 Helps in debugging and monitoring.
• 3 Important for production systems.
• 4 Captures errors and warnings.

• 1 INFO – general information
• 2 DEBUG – debugging details
• 3 WARN – warnings
• 4 ERROR – error messages

• 1 SLF4J – logging facade
• 2 Logback – default Spring Boot logger
• 3 Log4j2 – advanced logging system

• 1 Helps debug issues.
• 2 Monitors application health.
• 3 Tracks system behavior.
• 4 Essential for production support.

• 1 Used in debugging applications.
• 2 Used in production monitoring.
• 3 Used in error tracking systems.
• 4 Used in microservices logging.
• 5 SaaS products use Spring Boot Logging in services, dashboards, background jobs, and API workflows.
• 6 ERP and banking systems apply Spring Boot Logging with validation, logging, review, and rollback plans.
• 7 E-commerce and healthcare platforms use Spring Boot Logging carefully because reliability and data correctness matter.

• 1 A Java program first evaluates the surrounding context, then applies the Spring Boot Logging rules to the current data.
• 2 The important mental model is input, transformation, result, and failure path.
• 3 In production, the same flow usually sits inside a larger layer such as a controller, service, repository, job, or UI component.

• 1 Choose the simplest implementation first, then measure real workloads.
• 2 Watch for repeated work inside loops, unnecessary allocations, and slow I/O in hot paths.
• 3 Prefer clear data structures and stable APIs before micro-optimizing syntax.

• 1 Treat external input as untrusted until it is validated.
• 2 Avoid hardcoded secrets and never print sensitive values in examples or logs.
• 3 Use established libraries for authentication, encryption, parsing, and database access.

• 1 Using System.out.println instead of logger.
• 2 Logging sensitive data.
• 3 Not configuring log levels properly.
• 4 Ignoring log files in production.
• 5 Skipping the small working example before adding framework code.
• 6 Ignoring null, empty, duplicate, and boundary inputs.
• 7 Mixing business logic, input handling, and output formatting in one place.
• 8 Using broad error handling that hides the real failure.
• 9 Forgetting to test the behavior after refactoring.
• 10 Adding clever code that future maintainers will struggle to read.

• 1 Use SLF4J with Logback.
• 2 Set proper log levels (INFO, DEBUG, ERROR).
• 3 Avoid logging sensitive information.
• 4 Use centralized logging systems.
• 5 Start with clear requirements and one minimal working example.
• 6 Use meaningful names that explain business intent.
• 7 Keep examples small enough to debug line by line.
• 8 Validate input at every trust boundary.
• 9 Handle errors explicitly and preserve useful context.
• 10 Prefer simple control flow over deeply nested logic.
• 11 Separate domain logic from I/O and framework code.
• 12 Write tests for normal, boundary, and failure cases.
• 13 Review security assumptions before production use.
• 14 Measure performance before optimizing.
• 15 Document non-obvious decisions close to the code or in project notes.
• 16 Use official documentation when behavior is version-specific.
• 17 Keep dependencies current and remove unused code.
• 18 Avoid hardcoded secrets, credentials, and environment-specific paths.
• 19 Log operational events without exposing sensitive data.
• 20 Design examples so learners can safely modify and rerun them.

• 1 Beginner: rewrite the example with different names and values.
• 2 Intermediate: add validation and handle one expected failure case.
• 3 Advanced: place Spring Boot Logging inside a small service-style design with tests.

• 1 Build a small Java console feature that demonstrates Spring Boot Logging.
• 2 Accept input, process it with the concept, print a clear result, and handle invalid input.
• 3 Add a README note explaining the design choice and two edge cases you tested.

• 1 If the program does not compile, check spelling, imports, braces, and file/class names first.
• 2 If output is unexpected, print intermediate values and verify each branch of the logic.
• 3 If the design feels complex, reduce it to the smallest working example and add pieces back one at a time.

• 1 Practice Spring Boot Logging with a second example from a business domain such as inventory, payroll, banking, or e-commerce.
• 2 Review related Java topics that cover data flow, error handling, testing, and clean design.
• 3 Compare your solution with official documentation and simplify anything you cannot explain clearly.