Error Handling in Connectors: Best Practices

Error handling in connectors can make or break your B2B SaaS integrations. Here's what you need to know:

• Error handling is crucial for smooth operations and user satisfaction
• Two main error types: permanent (client-side) and temporary (server-side)
• Common errors include data, process, authentication, and system issues

Key strategies for effective error handling:

1. Use consistent error reporting formats
2. Write clear, actionable error messages
3. Group errors into categories for easier management
4. Implement thorough error logging

Advanced techniques:

• Combine standard and custom error handling approaches
• Use smart retry methods (immediate, fixed interval, exponential backoff)
• Implement circuit breakers to prevent cascading failures
• Have backup plans (caching, alternative services, degraded functionality)

Error Handling Method	Pros	Cons
Standard	Easy to implement, consistent	May not cover all scenarios
Custom	Flexible, tailored to specific needs	More time-consuming to develop
Retry Logic	Handles temporary issues	Can delay error reporting
Circuit Breakers	Prevents system overload	May reduce availability

Remember: Good error handling keeps your system reliable, users happy, and gives developers the info they need to fix issues fast.

Related video from YouTube

Connector Errors Explained

Connector errors can mess up your integration plans. Here's what you need to know about the most common types and their causes.

Common Error Types

Connector errors come in different flavors:

Error Type	What It Means	What It Looks Like
Connectivity	Can't connect or stay connected	"HttpRequestFailure: Server returned: 500 Internal server error"
Authentication	Wrong login info or permissions	"401 Unauthorized" or "403 Forbidden"
Data Validation	Data doesn't fit the rules	Wrong date format or weird characters
System Availability	Target system is down or slow	"Service timeout"
Rate Limiting	Too many API calls	"403 Forbidden" due to API limits

Why Errors Happen

Connector errors usually come from:

1. API Changes: APIs change, docs don't keep up. Yesterday's solution might not work today.

2. Team Miscommunication: When teams don't talk, connections can fail or overload.

3. Bad Data: If your data's a mess, expect trouble.

4. Old Tech: Outdated systems struggle with modern APIs.

5. Wrong Setup: Incorrect settings or permissions block connections.

To avoid these issues:

• Check your data before sending it.
• Keep an eye on API changes.
• Add timeout handling and retry logic to your apps.

"Integration errors can and will eat into a company's profits."

This isn't just talk. A survey found 54% of companies lose over $100,000 yearly due to EDI problems. 10% lose $1 million or more.

2. Key Error Handling Principles

Error handling in connectors isn't just about catching errors. It's about managing them well. Here are the main principles:

2.1 Consistent Error Reporting

Use the same error format across your connector ecosystem. It's clearer and more efficient.

Mule 4, for example, uses a hierarchical error type system:

CONNECTIVITY (parent: MULE:CONNECTIVITY)
├── CLIENT_SECURITY (parent: CONNECTIVITY)
│   ├── BASIC_AUTHENTICATION (parent: CLIENT_SECURITY)
│   ├── OAUTH2 (parent: CLIENT_SECURITY)
└── SERVER_SECURITY (parent: CONNECTIVITY)
    ├── FORBIDDEN (parent: SERVER_SECURITY)
    └── UNAUTHORIZED (parent: SERVER_SECURITY)

This lets you handle broad error categories or specific issues.

2.2 Clear Error Messages

Make error messages easy to understand and act on. Tell users what went wrong and how to fix it.

Here's a good REST API error message:

{
  "status": "error",
  "statusCode": 404,
  "error": {
    "code": "RESOURCE_NOT_FOUND",
    "message": "The requested resource was not found.",
    "details": "The user with the ID '12345' does not exist in our records.",
    "timestamp": "2023-12-08T12:30:45Z",
    "path": "/api/v1/users/12345",
    "suggestion": "Please check if the user ID is correct or refer to our documentation at https://api.example.com/docs/errors#RESOURCE_NOT_FOUND for more information."
  },
  "requestId": "a1b2c3d4-e5f6-7890-g1h2-i3j4k5l6m7n8",
  "documentation_url": "https://api.example.com/docs/errors"
}

It gives the error type, description, details, and how to fix it.

2.3 Grouping Errors

Group errors into categories. It makes handling easier and improves response times.

You might group errors like this:

Error Category	Examples
Network Errors	Timeouts, DNS failures
Authentication Errors	Invalid credentials, expired tokens
Data Validation Errors	Invalid input, missing required fields
System Errors	Out of memory, disk full

This helps create targeted error handling strategies.

2.4 Error Logging

Good error logging is key for troubleshooting. Your logs should include:

• Error type and code
• Timestamp
• Affected system or component
• User context (if applicable)
• Stack trace (for developers)

Here's an example:

[2023-12-08 15:30:45] [ERROR] [UserService] [UserID: 12345]
Error Code: AUTH_FAILED
Message: User authentication failed
Details: Invalid password provided
Stack Trace: 
  at UserService.authenticate (UserService.js:25)
  at LoginController.handleLogin (LoginController.js:10)
  ...

This structure makes it easier to search and analyze errors.

3. Ways to Handle Errors

Let's dive into some key methods for handling errors in connectors:

3.1 Standard vs. Custom Error Handling

You've got two main options:

1. Standard: Use built-in error handling from your platform or language.
2. Custom: Create your own error handling logic for specific cases.

Approach	Pros	Cons
Standard	Easy, consistent	Might miss edge cases
Custom	Flexible, specific	Takes more time

Go custom when you need extra control or face unique API behaviors.

3.2 Retry Methods

When things fail, try again:

• Immediate retry: Give it another shot right away.
• Fixed interval: Wait a bit between tries.
• Exponential backoff: Increase wait time with each retry.

Entity Framework example: First retry is immediate, then delays increase until you hit the max retry count.

3.3 Circuit Breakers

These prevent domino-effect failures. They work in three states:

1. Closed: All good, requests go through.
2. Open: Service down, requests blocked.
3. Half-open: Testing if we're back in business.

Great for handling stubborn errors that stick around.

3.4 Backup Plans

Always have a Plan B:

• Caching: Keep recent data handy.
• Alternative services: Have a backup API ready.
• Degraded functionality: Offer basic features when full service is down.

Example: Your payment service hits a snag with fraud checks. You might:

4. Top Error Handling Tips

Let's look at some key ways to level up your error handling in SaaS integrations:

4.1 Detailed Logging

Set up solid logging to track and fix errors. Capture:

• Timestamps
• Request parameters
• Response data
• User context

Tip: Use Sentry, Loggly, or New Relic to spot API issues fast.

4.2 Standard Error Codes

Use consistent error codes to make problem-solving easier:

HTTP Status Code	Meaning	Action
400	Bad request	Skip row
401	Unauthorized	Fail mapping
403	Forbidden	Fail mapping
404	Not found	Fail mapping
429	Too many requests	Fail mapping
500	Server error	Fail mapping
503	Service unavailable	Fail mapping

4.3 Clear Error Messages

Write user-friendly error messages. For example:

{
  "status": "error",
  "statusCode": 404,
  "error": {
    "code": "RESOURCE_NOT_FOUND",
    "message": "The requested resource was not found.",
    "details": "The user with the ID '12345' does not exist in our records.",
    "suggestion": "Please check if the user ID is correct or contact support."
  }
}

This helps users understand the issue and take action.

4.4 Smart Retry Logic

Set up good retry systems for temporary errors:

1. Immediate retry for quick fixes

2. Fixed interval for slightly longer issues

3. Exponential backoff for stubborn problems

4.5 Handling Login Errors

Manage authentication failures well:

• Give clear steps to reset passwords
• Offer other login methods (like SSO)
• Use account lockouts to stop brute force attacks

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5. Advanced Error Handling

Error handling in connectors isn't just about try-catch blocks. It's about smart strategies for tough situations.

5.1 Combining and Linking Errors

Group related errors to find root causes faster. Power Automate usually stops workflows when it hits connector errors. But that's not always best.

Try this instead:

1. Group errors (auth issues, rate limits, bad data)
2. Link related errors
3. Use error codes to guide fixes

5.2 Flexible Error Handling

Adjust your approach based on the error and system state.

Error Type	What to Do
Temporary	Retry with backoff
Permanent	Log it, alert admins
Auth Issues	Ask for new login
Rate Limits	Pause, then resume

5.3 Handling Errors in Background Tasks

Background tasks are tricky. They run on their own, so errors need special care.

1. Log everything:

import logging

def background_task(a, b):
    try:
        result = a / b
    except Exception as e:
        logging.error(f"Task failed: {str(e)}")
        # More error handling here

2. Set up alerts:

Watch for tasks that run too long. Flag anything over 30 minutes.

3. Retry smart:

from tenacity import retry, stop_after_attempt, wait_exponential

@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
def retry_prone_task():
    # Task code here

4. Plan for failure:

try:
    retry_prone_task()
except Exception as e:
    logging.error(f"All retries failed: {str(e)}")
    # Backup plan goes here

6. Common Error Handling Setups

Let's dive into three key error handling setups that'll keep your connectors running smoothly.

6.1 Try-Catch Blocks

Try-catch blocks are your first line of defense. They let you run risky code and catch any errors that pop up.

Here's how to set up try-catch in Power Automate:

1. Group your actions in a "Scope"
2. Set a "SUCCESS" variable before the try scope
3. Put your main actions in the try scope
4. Add a catch scope for failures
5. Use a finally block for cleanup

For example, you might create a student record in Dataverse in the try scope. If it fails, the catch scope grabs the error message from the JSON output.

6.2 Error Limits

Error limits are like guardrails for your system. They keep things from spiraling out of control when errors hit.

Limit Type	Description	Example
Rate Limits	API request cap	200 requests/minute for support APIs
Retry Attempts	Max retries	3-5 retries for rate limit errors
Error Thresholds	Failure limit	Stop after 10 consecutive failures

Hit a rate limit? You'll see a 429 Too Many Requests error. Here's what to do:

1. Check the Retry-After header
2. Wait it out
3. Try again

6.3 Error Handling Software

Sometimes, you need the big guns. Error handling software helps you manage errors across your entire system.

Look for these features:

• Centralized error logging
• Real-time alerts
• Error categorization
• Trend analysis

For instance, SailPoint has built-in features to handle request throttling and rate limiting. This helps you dodge those HTTP 429 errors when hitting external APIs.

7. Watching for and Fixing Errors

Keeping connectors running smoothly is an ongoing task. Here's how to spot and solve problems quickly:

7.1 System Health Checks

Regular health checks are key. Here's a simple setup:

1. Pick key metrics: Choose 10 important indicators to watch.
2. Create data views: Gather the right data for monitoring.
3. Automate checks: Use tools to run checks regularly.
4. Have a plan: Know who does what when a check fails.

7.2 Error Alerts

Alerts tell you about big problems fast. Set them up like this:

Alert Type	Use Case	Example
SMS	Big issues	Server crash
Email	Daily roundup	Error trends
Slack/Teams	Team updates	New errors

Don't go overboard. Too many alerts can be overwhelming.

7.3 Using Logs to Fix Problems

Logs are treasure troves for fixing errors:

1. Centralize logs: Use one tool for all logs.
2. Make searching easy: Find what you need fast.
3. Look at context: Check logs before and after errors.
4. Spot patterns: They might point to bigger issues.

"Good error monitoring mixes the right tools with smart strategies. It's all about automating logs and alerts."

The goal? Not just fixing errors, but stopping them from happening again. Use what you learn to make your connectors better over time.

8. Testing Error Handling

Testing error handling in connectors is crucial. Here's how to do it:

8.1 Testing Single Parts

Check each part of your connector separately. Use JUnit for Java connectors:

@Test
public void testDataValidation() {
    ExampleConnector connector = new ExampleConnector();
    assertThrows(IllegalArgumentException.class, () -> {
        connector.processData(null);
    });
}

This test checks if the connector throws the right error for bad data.

8.2 Testing the Whole System

Create fake errors to test your entire connector. For web-based connectors, use Selenium:

WebDriver driver = new ChromeDriver();
driver.get("http://yourconnector.com");
WebElement errorButton = driver.findElement(By.id("trigger-error"));
errorButton.click();
String errorMessage = driver.findElement(By.id("error-display")).getText();
assertEquals("Expected error message", errorMessage);

This test triggers an error and checks for the correct message.

8.3 Stress Testing

Push your connector to its limits. Amazon's 2022 Prime Day handled over 300 million items in 48 hours without major issues.

Use Apache JMeter for stress testing:

1. Create a test plan
2. Add a Thread Group
3. Set up HTTP Requests
4. Add listeners
5. Run the test with increasing user loads

Start with 100 users, then increase. Watch for response times, error rates, and system resource use.

"Stress testing isn't just about finding breaking points. It's about understanding how your system degrades under load and ensuring it fails gracefully when pushed to the limit."

Conclusion

Error handling in connectors is crucial for data integration. As data grows and SaaS platforms become more common, so do integration errors. Companies need to step up their error handling game.

Here's what you need to know:

• Standardize error management
• Use a Hybrid Integration Platform (HIP) with solid error management
• Log details, use standard error codes, and implement smart retries

Real-world examples show why this matters:

Uber's Insurance Engineering team used non-blocking request reprocessing and Dead Letter Queues for better error handling. This helped their Driver Injury Protection program run smoothly in over 200 cities.

CrowdStrike, processing trillions of events daily with Apache Kafka, stresses the importance of Dead Letter Queues:

Dead Letter Queue Best Practices
Store error messages in the right system
Use automation for foolproof fixes
Implement proper monitoring and alerts

As connector tech evolves, companies must adapt. Focus on:

1. Detailed logging

Keep track of what's happening. It's like leaving breadcrumbs to find your way back when things go wrong.

2. Standard error codes

Speak the same language across your systems. It makes troubleshooting a lot easier.

3. Smart retry logic

Don't just try again and again blindly. Be smart about when and how you retry failed operations.

Related posts

• Error Handling Best Practices for SaaS Integrations
• API Versioning Best Practices for Backward Compatibility
• Connector Logging Setup Guide
• Integration Glossary: Key Terms Defined