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.msappropriately 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! 🎤
- Have you had your own Kafka horror story?
- 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. 🚀
Top comments (0)