Error management is an essential aspect of software development since it directly impacts your application's reliability, maintainability, and user experience. As a developer, one key decision in error management is whether to throw an exception or report the problem. This option affects your code's performance, behaviour, and debugability. In this post, we'll look at the distinctions between throwing exceptions and reporting mistakes and how to best include both strategies in your error management strategy.

Understand Exceptions and Logging in Software Development

Although both exceptions and logging are essential for error management, they serve different purposes inside an application. Let's break them down.

What is an Exception?

An exception is an object that represents an error or unexpected circumstance that interrupts your program's usual flow. When an exception is thrown, the current method's execution stops, and the exception propagates up the call stack until it is caught by an exception handler, or the program fails if it is not handled. Here is an example of throwing an exception in Java.

public void divideNumbers(int a, int b) {
    if (b == 0) {
        throw new ArithmeticException("Division by zero is not allowed");
    }
    int result = a / b;
    System.out.println("Result: " + result);
}

public static void main(String[] args) {
    MyClass obj = new MyClass();
    obj.divideNumbers(10, 0); // This will trigger the exception
}

Output:

Exception in thread "main" java.lang.ArithmeticException: Division by zero is not allowed
    at MyClass.divideNumbers(MyClass.java:5)
    at MyClass.main(MyClass.java:11)

In this code:

  • If b equals zero, an ArithmeticException is thrown, preventing the division from occurring.
  • The exception disrupts the regular program flow, signalling a problem that has to be addressed.

What is logging?

Logging is the process of recording events or information about how the program is being executed, such as errors, warnings, or other relevant occurrences. Logging, unlike exceptions, has no effect on how the program is executed. Instead, it serves as a historical record that developers or system administrators can refer to later for debugging or monitoring. Here's an example utilizing Java's logging framework:

import java.util.logging.Logger;

public class MyClass {
    private static final Logger logger = Logger.getLogger(MyClass.class.getName());

    public void someMethod() {
        try {
            // Some operation that might fail
        } catch (Exception e) {
            logger.severe("An error occurred: " + e.getMessage());
        }
    }
}

In this example:

  • The program logs a SEVERE level error without interrupting the current flow.
  • This information is stored and can be reviewed later to understand the error's context.

Key differences between throwing exceptions and logging errors

  1. Program Flow: Exceptions disturb program flow and require prompt attention. Logging does not interfere with execution flow, making it handy for tracking activity without interrupting the application.
  2. Immediate Attention: Exceptions must be addressed immediately or with a fallback mechanism, although recorded data can be evaluated later.
  3. Performance Overhead: Throwing exceptions adds extra overhead, especially if done excessively, whereas logging is typically less harmful, but severe logging can also affect performance.
  4. Contextual Information: Logs can provide more complete information, covering several occurrences over time, whereas exceptions merely record the individual problem.

Impact of Each Approach on Application Flow and Performance

Both exceptions and logging have different implications for how your application runs and performs. Here's a breakdown:

  1. Flow Interruption:
    • Exceptions immediately halt the current execution path until handled. In the case of unhandled exceptions, they can even crash the entire application. This makes exceptions critical for addressing unexpected errors quickly.
    • Logging, on the other hand, records errors or other relevant information without stopping or changing the current execution. It allows the application to continue running while issues are logged for future review.
  2. Performance Considerations:
    • Exceptions are expensive operations in terms of performance, especially when thrown frequently. They involve creating new stack traces, rolling back execution, and searching for catch blocks, which consumes more resources than regular code execution.
    • Logging has a lower performance overhead compared to exceptions. However, excessive logging, especially at high verbosity levels like DEBUG or TRACE, can still slow down your application and fill up disk space quickly, making log rotation essential.
  3. Usability and Troubleshooting:
    • Exceptions are suited for scenarios where the program must respond immediately to an error and either retry, fail gracefully, or notify the user.
    • Logging is more useful for long-term monitoring, providing insights into not just errors but also warnings and general runtime behaviours that can be crucial for diagnosing performance bottlenecks, user behaviour, or subtle bugs that don’t immediately cause crashes.

When to Throw an Exception: Best Practices

Exceptions should be reserved for genuinely unusual circumstances—those that prohibit your code from performing its intended function or contract. Using exceptions efficiently may help keep code clean, legible, and dependable. The following are the recommended practices for throwing exceptions:

  1. Identify exceptional conditions: Only throw exceptions if anything unexpected occurs that cannot be handled graciously within the procedure.
  2. Use suitable exception types: Depending on the nature of the mistake, select either verified or unchecked exceptions. Checked exceptions should be used when the caller expects to be able to recover the error. Unchecked exceptions (such as 'RuntimeException') should be used to indicate code issues.
  3. Deliver meaningful exception messages: The message in an exception should explain what went wrong and give enough information to help with troubleshooting.
  4. Add custom exceptions: Built-in exceptions may not always convey the specific issue your application encounters. In such cases, creating custom exceptions can help make your code more expressive, improve readability, and facilitate debugging by clearly communicating what went wrong. A custom exception should extend from an appropriate base class, such as Exception or RuntimeException, and it should include relevant information about the error. Let’s look at an example.

Example of a Custom Exception:

// Custom exception class
public class InsufficientBalanceException extends Exception {
    public InsufficientBalanceException(String message) {
        super(message);
    }
}

// Account class with custom exception handling
public class BankAccount {
    private double balance;

    public BankAccount(double initialBalance) {
        this.balance = initialBalance;
    }

    public void withdraw(double amount) throws InsufficientBalanceException {
        if (amount > balance) {
            throw new InsufficientBalanceException("Withdrawal amount exceeds current balance");
        }
        balance -= amount;
        System.out.println("Withdrawal successful! New balance: " + balance);
    }

    public double getBalance() {
        return balance;
    }
}

public class Main {
    public static void main(String[] args) {
        BankAccount account = new BankAccount(500);

        try {
            account.withdraw(700); // This will trigger the custom exception
        } catch (InsufficientBalanceException e) {
            System.err.println("Error: " + e.getMessage());
        }
    }
}

Explanation:

  • InsufficientBalanceException is a custom exception created to handle cases where a user attempts to withdraw more money than they have in their account.
  • The withdraw method checks the current balance and throws the custom InsufficientBalanceException when the withdrawal amount exceeds the available balance.
  • This custom exception improves the clarity of the error and makes the code more maintainable, as it provides a meaningful message that can be specific to the domain (banking, in this case).

Output:

Error: Withdrawal amount exceeds current balance

Common Pitfalls in Exception Handling

When using exceptions, avoid the following common pitfalls:

  1. Overusing exceptions for control flow: Exceptions should not be used to implement ordinary program logic. For example, don't use exceptions to handle predicted results like "file not found" in a search function.
  2. Catching and rethrowing without providing value: If you catch an exception but don't provide any more context or action, consider letting it propagate.
  3. Swallowing exceptions: Do not catch exceptions without first recording or appropriately managing them. Silently swallowing exceptions makes debugging much more difficult.
  4. Throwing exceptions in performance-critical code: Excessive exception throwing can reduce performance, especially in high-frequency code pathways like loops or large-scale distributed systems.

Effective Logging Strategies for Error Management

Logging is an important tool for analyzing and debugging your application's behaviour. Effective logging procedures may significantly increase your capacity to control mistakes and track system health over time.

  1. Choose appropriate log levels:
    • DEBUG: Detailed information for diagnosing issues.
    • INFO: General operational events to track the application’s flow.
    • WARN: Potentially harmful situations that aren’t errors yet.
    • ERROR: Errors that occur during runtime but don’t stop the application.
    • FATAL: Critical errors that might cause the application to terminate.
  2. Structure log messages for clarity Include timestamps, thread IDs, log levels, and class names in your log messages. Well-structured logs may make a significant difference when debugging complicated systems.
  3. Include context in log entries: To improve logs' usefulness during troubleshooting, include important contexts such as user IDs, request parameters, or system conditions.
  4. Implement log rotation and retention policies: To minimize performance difficulties and bloated log files, use log rotation and retention policies to archive or remove older logs as needed.

Example of Structured Logging

logger.error("Failed to process order: orderId={}, userId={}, error={}",
             orderId, userId, e.getMessage());

Explanation:

  • This log entry summarizes a failed order processing attempt, including the orderId, userId, and the exact error message.
  • Including essential identifiers in the log improves debugging efficiency since the log entry provides all necessary information in one location.

Strategies for Avoiding Overloading Log Systems with Excessive Data

Too much logging can overload your system, slow down application performance, and make it more difficult to discover essential logs. Here are several techniques to avoid this:

  1. Log at appropriate levels: Ensure that you log messages at the correct level. Avoid logging too many DEBUG messages in production environments unless needed.
  2. Filter unnecessary logs: Implement filters to exclude low-priority or repetitive log entries, such as heartbeat logs in monitoring systems.
  3. Use sampling for high-volume logs: In cases where high-volume logs are generated (e.g., a log entry for each HTTP request), consider logging a sample of the data instead of every event.
  4. Log aggregation tools: Use log aggregation tools (e.g., ELK Stack, AWS CloudWatch) to centralize logs, apply filtering rules, and avoid storing redundant data.

Logging Frameworks and Tools

Choosing the correct logging framework is determined by your language, system needs, and if you operate in a distributed setting. Here are a few often-used tools:

  • Java:
    • Log4j: A powerful, flexible logging library widely used in enterprise applications.
    • SLF4J: A simple logging facade that works with various logging backends.
    • java.util.logging: The built-in logging library in the Java standard library.
  • Python:
  • JavaScript:
    • Winston: A popular, multi-transport logging tool for Node.js applications.
    • Bunyan: A JSON-based logging library designed for Node.js applications with a focus on speed.

Centralized Logging in Distributed Systems

In distributed systems, logs are produced by numerous services and computers. Centralizing these logs with technologies such as the ELK Stack (Elasticsearch, Logstash, Kibana), AWS CloudWatch Logs, or Google Stackdriver enables improved monitoring and troubleshooting. These tools allow you to search, filter, and analyze logs from all areas of the system in one place.

SigNoz is an open-source observability platform that goes beyond traditional logging tools, providing a comprehensive solution for monitoring, distributed tracing, and application metrics. It offers several advantages that make it stand out compared to tools like the ELK Stack, AWS CloudWatch Logs, and Google Stackdriver:

  • All-in-One Observability: SigNoz provides not just centralized logging but also distributed tracing and metrics collection, giving you a 360-degree view of your distributed system in one platform. This reduces the need for multiple tools, unlike ELK Stack which focuses only on logging.
  • OpenTelemetry Support: SigNoz is natively built with OpenTelemetry, an open-source standard for observability. This makes it easier to instrument applications with minimal vendor lock-in, providing more flexibility compared to proprietary solutions like AWS CloudWatch Logs and Google Stackdriver.
  • Real-Time Monitoring: SigNoz offers real-time error tracking and alerting capabilities, allowing you to quickly respond to issues as they arise. It provides better real-time insights than ELK, which can require more setup for real-time alerting.
  • Cost Efficiency: Compared to managed services like AWS CloudWatch and Google Stackdriver, which can become expensive at scale, SigNoz is open-source and self-hosted, giving you more control over costs.
  • Custom Dashboards: Like Kibana in the ELK Stack, SigNoz allows you to create custom dashboards for visualizing logs, traces, and metrics. However, SigNoz’s unified platform means you can view all these observability aspects together in one place, improving correlation and troubleshooting.

Balancing Exception Throwing and Logging

To guarantee stability and dependability, application developers have to maintain a careful balance between exception throwing and logging. Each has a specific job, and understanding when to throw exceptions or record problems may help enhance program performance and maintainability.

  1. Throw exceptions for immediate, actionable problems requiring the application to change its execution flow. These often reflect difficulties that prohibit an operation from being completed successfully (for example, authentication failure or incorrect user input).
  2. Log errors for non-critical or diagnostic purposes. Use logging to keep track of events and abnormalities that are not critical enough to cause the program to halt but still require attention (e.g., poor performance, strange user behaviour).
  3. Combine logging and exception handling to log the information of the exception before re-throwing it. This ensures you capture all relevant information for debugging while still allowing the exception to be handled appropriately by the caller.

Example: Combining Logging and Exception Handling

try {
    // Attempt a risky operation
    processTransaction(orderId);
} catch (TransactionException e) {
    logger.error("Transaction failed for orderId {}: {}", orderId, e.getMessage());
    throw new ApplicationException("Transaction could not be completed", e);
}

In this example:

  • The error is recorded before raising a custom exception (ApplicationException), providing further context. This preserves the stack trace and allows the caller to handle the new exception.

Guidelines for Balancing Exceptions and Logs.

  • Logging the same issue numerous times (for example, logging an exception at each level of the call stack) might result in excessive verbosity in your logs.
  • Frequent exceptions, especially those in loops or real-time systems, should be replaced with alternate handling techniques such as error codes or logging, as outlined in the performance section.

Error Handling in Distributed Systems

Error management in distributed systems is substantially more difficult due to the intrinsic nature of various services, network connections, and possible sources of failure. These systems require a well-structured error-handling strategy to remain reliable and deliver consistent service to consumers.

Key Strategies for Error Handling in Distributed Systems:

  1. Use correlation IDs: A correlation ID is a unique identification given to each request. It allows you to follow and trace the request as it moves through various services. By providing this ID in logs and error messages, you may track a request across several microservices, making distributed issues easier to troubleshoot.

    // Example of adding a correlation ID to logs
String correlationId = UUID.randomUUID().toString();
logger.info("Starting process with correlationId {}", correlationId);

  2. Implement circuit breakers: A circuit breaker prevents cascading failures by interrupting calls to a service that is down or under severe demand. This guarantees that dependent services are not swamped by several failed attempts.

    • Libraries such as Hystrix and Resilience4j provide circuit breaker patterns in Java.

  3. Design for failure: In distributed systems, always account for service outages, network timeouts, and unavailability. Use timeouts, retries, and fallbacks to gracefully manage errors without cascading into other services.

  4. Standardize error responses: Create a uniform error response style for your microservices, guaranteeing that when something goes wrong, all services return a standard payload. This simplifies error handling in client applications.

    {
    "error": "Service Unavailable",
    "message": "The payment service is currently down",
    "status": 503
}

Monitoring and Alerting: Beyond Logging and Exceptions

While logging and exception management are vital, they alone cannot assure an application's overall health, particularly in distributed situations. Monitoring and alerting add a proactive layer of visibility, helping teams to identify and handle problems before they develop.

Key Practices for Monitoring and Alerting

  1. Set up alerts: Alerts should be triggered depending on exceptions, error rates, or certain log patterns. This guarantees that your team is alerted of concerns in real-time, resulting in faster remedies. Establish thresholds for essential metrics, such as:
    • High mistake rates.
    • Unusually sluggish reaction times.
    • Frequently timed out or retried.
  2. Implement health checks: Use health checks to constantly monitor the status of essential services and dependencies. Services should offer health endpoints (e.g., '/health') that give status information that monitoring systems may poll.
  3. Use metrics: Gather metrics to measure key performance indicators such as response times, CPU/memory use, and error rates. These metrics give insight into how the application performs over time and assist in finding bottlenecks. Track metrics such as:
    • Latency, which measures average response times across services.
    • Error rate: The percentage of unsuccessful requests.
    • Resource usage: CPU and memory utilization by service.
  4. Integrate with incident management systems: Tools like PagerDuty, OpsGenie, or ServiceNow can help automate incident response by integrating with your monitoring tools. Alerts can be routed to the appropriate teams with clear severity levels.

Leveraging SigNoz for Advanced Error Tracking

SigNoz is an open-source observability platform built for distributed systems that provides comprehensive error tracking and monitoring capabilities. SigNoz works with applications to offer real-time visibility into their behaviour, focusing on tracing, error tracking, and performance monitoring.

For more clarity, you can check the article: Java OpenTelemetry Instrumentation

SigNoz's key features include real-time error tracking, which enables fast diagnosis and remediation.

  • It gives detailed insights into how requests move across services, allowing you to understand where issues originate and how they propagate through the system.
  • You may create custom dashboards to visually represent error patterns, performance metrics, and other crucial data points related to your services.
  • Set alerts based on metrics and log patterns to spot abnormalities early and tell your team right away.

Getting Started with SigNoz

SigNoz cloud is the easiest way to run SigNoz. Sign up for a free account and get 30 days of unlimited access to all features.

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You can also install and self-host SigNoz yourself since it is open-source. With 19,000+ GitHub stars, open-source SigNoz is loved by developers. Find the instructions to self-host SigNoz.

Best Practices for Exception and Logging Documentation

  1. Write Clear Exception Messages: Ensure exception messages explain the issue clearly and include enough details to assist with troubleshooting.
  2. Document Error Codes: Keep a centralized catalogue of error codes and their meanings to make debugging easier.
  3. Maintain Logging Guidelines: Establish and document standardized logging practices to ensure consistency across the codebase.
  4. Create an Error Catalog: Provide developers with an up-to-date reference for all common exceptions and their handling strategies.

Key Takeaways

  • Use exceptions to handle exceptional, unrecoverable circumstances, and logging to track program behavior and debug.
  • You should decide whether to throw exceptions or record problems depending on their severity, recoverability, and impact on the user experience.
  • Ensure that your application's logging strategy is consistent, including distinct log levels, suitable error messages, and clear documentation.
  • Both logging and exception handling should be used sparingly to minimize code bloat, performance concerns, and excessive complexity.
  • Tools like SigNoz may help you get useful insights by collecting logs, tracking exceptions, and monitoring the health of your application, especially in distributed systems.

FAQs

What's the difference between checked and unchecked exceptions?

  • Checked exceptions are those that must be caught or stated in the method's throws clause. They reflect circumstances that the application can foresee and recover from (such as IOException).
  • Unchecked exceptions do not need to be declared or caught, and they generally signal programming flaws or unanticipated situations from which the application cannot recover (for example, NullPointerException, ArrayIndexOutOfBoundsException).

How can I avoid the "log and throw" anti-pattern?

The "log and throw" anti-pattern occurs when an exception is logged and then re-thrown, leading to duplicate log entries. To avoid this:

  • Log once at the most appropriate level in the call stack.
  • Let the caller handle the exception without additional logging unless more context is necessary.

When should I create custom exceptions instead of using built-in ones?

Create custom exceptions for situations that require additional context. Custom exceptions can contain particular facts about your application's domain, making them easier to troubleshoot.

  • Better error categorization: Defining custom exception types allows you to more properly reflect certain error scenarios in your application.
  • Standard error handling: Custom exceptions allow you to provide consistent treatment for certain sorts of faults throughout your program.

How can I implement proper error handling in asynchronous code?

Error handling in asynchronous code (for example, utilizing futures, promises, or callbacks) differs from synchronous code:

  1. Use completion handlers to capture exceptions in asynchronous functions. For example, Java's CompletableFuture offers error handling methods such as handle and exceptionally.

    CompletableFuture.supplyAsync(() -> {
    // Do some work
    return result;
}).exceptionally(e -> {
    // Handle exception
    return fallbackResult;
});

  2. Log exceptions appropriately without disrupting the execution of other duties.

  3. Rethrow exceptions only if the fault impacts future operations. Otherwise, handle the problem using an asynchronous callback or future.

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