When Kafka "Ate" My Weekend: Lessons from the Trenches

You know that moment when you push your code to production, lean back, and wait for Slack to explode? Yeah... that was me last year, all thanks to a tiny misconfigured Kafka topic.

Long story short: Messages were flying everywhere except where they needed to go. Consumers were confused, partitions were misaligned, and I spent my Saturday night debugging with the comforting glow of system logs.

If you're working with Kafka and Spring Boot, this story is for you. Let's talk about the hard-earned lessons that textbooks won't teach you. 💬

🔧 Lesson 1: Topics Are Not "Set and Forget"

When creating Kafka topics, I used to think: "It’s just a name and some partitions, right? What could go wrong?" (Answer: Everything.)

Quick Tips:

• Always define replication factor wisely. No one likes losing messages because a single broker took a nap.
• Pre-create topics where possible. Auto-creation sounds cool until it isn't configured properly and your app fails silently.
• Naming matters — clear, consistent naming saves you (and your team) headaches six months later.

⚙️ Lesson 2: Tune Your Consumer Settings Early

Kafka consumers are like hungry toddlers: If you don't feed (configure) them properly, expect tantrums (outages).

Quick Tips:

• Set a reasonable max.poll.records — too high = memory issues, too low = bad throughput.
• Handle retries carefully. Infinite retries sound safe until you end up with a never-ending zombie message.
• Monitor lag aggressively. Lag today is downtime tomorrow.

🔒 Lesson 3: Don't Ignore Error Handling

The first time our consumer threw an exception, guess what we did? Logged it... and moved on. 🙃

(Meanwhile, thousands of broken messages piled up in the background.)

Quick Tips:

• Use a Dead Letter Topic (DLT) strategy — not optional, mandatory.
• Implement custom error handlers to gracefully process (or skip) bad messages.
• Alert on failures — not just when services die, but when patterns of failure emerge.

🧹 Lesson 4: Clean Up After Yourself

Old topics don't die; they linger... and cause confusion, increase storage costs, and attract blame during incidents.

Quick Tips:

• Set retention.ms appropriately for each topic.
• Periodically audit and delete unused topics.
• Create naming conventions that make deprecation obvious (*_deprecated, anyone?).

💬 Real Talk: What Separates Juniors from Seniors?

Juniors set up Kafka topics and celebrate when the consumer reads a message. Seniors know that production-readiness is about what happens when things go wrong.

Handling failures, monitoring lag, preparing for scaling, documenting quirks — that’s the real backend craftsmanship.

I'd love to hear from you!

• What’s the smallest mistake that caused the biggest chaos in your system?

👉 Drop your war stories or tips in the comments. Let’s help each other build better, more resilient systems. 🚀

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