Tips for Exception Handling in Software Development

Global Custom Exception Handling in SAP CPI: CPI integrations fail for many reasons - API timeouts, invalid data, authentication errors. Without proper handling: Errors go unnoticed, Recovery becomes manual, Support teams get flooded.  A global exception strategy ensures: Consistent error handling across all iFlows Automatic retries & notifications Clean error logging for troubleshooting 3-Tier Exception Handling Framework 1. Local Try-Catch (Message Mapping Level) groovy: try {    // Your mapping logic } catch(Exception ex) {    def errorMsg = "Mapping failed: ${ex.message}"    throw new IllegalStateException(errorMsg) //Bubbles up to iFlow } Field-level validation errors 2. iFlow-Level Exception Subprocess xml <Exception SubProcess>  <Default Exception Handler>    <Retry>3</Retry> <!-- Auto-retry 3 times -->    <Wait>5 seconds</Wait>    <On Failure> <!-- Final fallback -->      <Data Store Write> <!-- Log error -->      <Send Alert to Slack/Email>    </On Failure>  </Default Exception Handler>    <!-- Custom exception routes -->  <Route Message when="contains(${header.SAP_ErrorCode},'AUTH_FAIL')">    <Notify Security Team>  </Route> </Exception SubProcess> 3. Global Exception Handler (Reusable) groovy // Shared Groovy Script "GlobalErrorHandler" def handleError(Message message, String context) {    def errorDetails = [        timestamp: new Date(),        iFlow: "${property.SAP_ProcessDefinition}",        error: "${property.CamelExceptionCaught}",        payload: message.getBody(String.class).take(1000) // Truncated    ]        // Write to Data Store    def ds = message.getExchange().getProperty('DataStoreWriter')    ds.storeData("Error_${UUID.randomUUID()}", errorDetails)        // Custom logic based on error type    if (context == "RETRYABLE") {        message.setHeader("SAP_RetryCount", 1)    } else {        message.setHeader("SAP_NotifyTeam", "Support")    } } Implementation Step 1: Create Reusable Components Global Error Handler Script (Groovy) Deploy Handles logging, notifications, retry logic Error Data Store Configure a dedicated Data Store named Exception Template iFlow Cloneable flow with pre-built exception subprocess Step 2: Standardize Error Payloads json {  "error_id": "{{$guid}}",  "timestamp": "{{$timestamp}}",  "iFlow": "OrderToCash_Prod",  "severity": "HIGH",  "root_cause": "SFAPI_429_TOO_MANY_REQUESTS",  "recommended_action": "Wait 5 mins then retry" } Step 3: Connect Monitoring Tools SAP Alert Notification → Email/SMS Splunk/Dynatrace Integration → Central logging Slack Webhook → Real-time alerts Advanced Patterns 1. Circuit Breaker Pattern groovy if (property.SAP_FailureCount > 5) {    // Stop processing for 1 hour    setProperty("SAP_CircuitBreaker", "OPEN")    addTimer("RESET_CIRCUIT", 3600) } 2. Dead Letter Channel xml <On Exception>  <JMS Producer Queue="CPI_DLQ">    <ErrorDetails>${property.CamelExceptionCaught}</ErrorDetails>  </JMS> </On Exception>

Level Up Your Exceptions: C# Filters That Will Blow Your Mind Tired of clunky try-catch blocks that catch too much—or worse, not enough? C# Exception Filters give you surgical precision over error handling, making your code cleaner, smarter, and more efficient. 🚨 The Problem: Traditional try-catch handling is often too broad or too messy. You either: ❌ Catch everything, leading to tangled error-handling logic ❌ Miss critical exceptions, allowing unexpected failures 💡 The Solution: Exception Filters With the when keyword, you control exactly when a catch block should execute. 𝘵𝘳𝘺{ } 𝘤𝘢𝘵𝘤𝘩 (𝘐𝘖𝘌𝘹𝘤𝘦𝘱𝘵𝘪𝘰𝘯 𝘦𝘹) 𝘸𝘩𝘦𝘯 (𝘦𝘹.𝘔𝘦𝘴𝘴𝘢𝘨𝘦.𝘊𝘰𝘯𝘵𝘢𝘪𝘯𝘴("𝘥𝘪𝘴𝘬 𝘧𝘶𝘭𝘭")) { } This catch block only triggers when the exception message contains "disk full." No more nested if statements inside catch blocks—just clean, targeted error handling. 🚀 Why Use Exception Filters? ✅ Sharper Precision – Handle exceptions only when they meet specific conditions ✅ Less Code Clutter – No need for nested logic inside catch blocks ✅ Better Logging – Log and track specific cases without affecting normal flow ✅ Fail Fast Strategy – Let unexpected errors bubble up without unnecessary catching ⚡ Best Practices: 🔹 Use with Intent – Filters should enhance handling, not hide underlying issues 🔹 Keep Conditions Simple – Complex conditions reduce readability and can impact performance 🔹 Re-throw When Necessary – If you're only logging an exception, consider re-throwing it to maintain the call stack 🚀 Take Your Exception Handling to the Next Level Exception filters change the game for C# developers, offering a level of control that makes debugging and logging much easier. Used correctly, they eliminate noise and make error handling more predictable and efficient. Have you used exception filters before? Share your thoughts below! 👇

Flow Like a Developer Always catch your errors and handle them appropriately. Unhandled Exception emails should be the exception, not the rule. We should design our automations to handle not only the happy path but also where a path may turn not so happy. When starting, we build towards our intended design. How we envision every record to be created, what the user does exactly. --- But, we all know that bugs happen, folks don't follow the process, or data happens to be...bad 🤫 So, we should handle things well, so the user doesn't get a horrible error message, and freak out, and maybe text you, call you, DM you, email you. That would never happen, right? --- As a rule of thumb we should always have error handling for: 👍 DML operations (Create, Update, Delete) - do we want to roll back? do we want to continue? 👍 Get elements - technically not needed, but I always like consistency 👍 Get element null checks - if the get resource returns nothing, will your Flow break? 👍 Actions/Callouts - if interacting with Invocable Apex or an HTTP callout, how will you handle if the action fails? Sometimes errors shouldn't stop a process, but instead take a different path. If you don't handle your errors, your entire Flow fails, versus only one part. --- Bonus tip? Be consistent in your error handling, and invest in reporting. I love Nebula Logger as an Open Source project and it has all the elements I need. Additionally, I have a Slack channel for all unhandled exceptions. That way the team can see each one, and comment on them to determine next actions or if a ticket needs to be created. #salesforce #salesforceflow #flow #salesforceadmin #flowlikeadeveloper

🚀 Building Robust Error Handling with Error Propagation in #ServiceNow 🔍YouTube Link: https://lnkd.in/eK_27X7q In my previous post, I've written briefly about error handling in ServiceNow. In this post I want to create a video on how I implemented an Error Propagation Mechanism inspired by the Chain of Responsibility pattern: 💡The Design: Each method or service generates a unique error code for easy debugging. Errors are propagated upwards through the layers, if an error occurs in Script Include 1, it’s passed to Script Include 2, then Script Include 3, and finally to the front end. 💡Intermediate layers check if an error code exists: -If yes, they pass it along without overriding. -If no, they generate a new code and attach it to the error. 💡Why This Approach? -Traceability: Unique error codes make debugging straightforward, even across complex workflows. -User-Friendly: Users can report an error code, helping developers quickly pinpoint issues. -Resilient Design: Higher layers handle errors without losing the original context. For example, if a frontend user sees Error 123, we can trace it back to the exact method in Script Include 1. No guesswork, no wasted time. 💡The Result: A clean, scalable error-handling flow that reduces troubleshooting time and enhances the user experience. How do you handle error management in your applications? Let’s share ideas and best practices. #ErrorHandling #SoftwareDesign #Debugging #DeveloperExperience #Coding