What are common pitfalls and mistakes when using Kafka?

Question

Accepted Answer

Kafka is powerful but has **common pitfalls** — misunderstandings and mistakes around partitioning, ordering, consumer behavior, delivery semantics, and operations. Understanding these helps avoid problems and use Kafka correctly.

## Design and partitioning pitfalls

```text
✗ Too FEW partitions → limits consumer parallelism (can't scale consumption beyond
  partition count); too MANY → overhead, rebalancing pain → plan partition count carefully
✗ Wrong PARTITION KEY → uneven distribution (hot partitions) or wrong ordering scope →
  choose a key for even distribution AND the ordering you need
✗ Expecting GLOBAL ordering → Kafka only orders WITHIN a partition (a common misunderstanding)
✗ Adding partitions changes key→partition mapping → breaks ordering for keys (be careful)
```

## Consumer and processing pitfalls

```text
✗ Ignoring CONSUMER LAG → consumers falling behind unnoticed → growing delay
✗ Not handling DUPLICATES → at-least-once means duplicates → need idempotent processing
  (a very common mistake — assuming exactly-once without idempotency)
✗ Slow processing blocking the consumer → causes lag/rebalances; long processing → rebalance issues
✗ Frequent REBALANCES → from slow processing/timeouts → consumption pauses (tune, optimize)
✗ Improper offset committing → reprocessing or message loss
```

## Operational and usage pitfalls

```text
✗ Using Kafka where a SIMPLE QUEUE suffices → unneeded complexity (over-engineering)
✗ Ignoring DISK/retention → disk fills up (serious failure)
✗ Under-replication / weak durability config (acks=1) → data loss on failures
✗ No monitoring; treating Kafka as simple (it's a complex distributed system)
✗ Not securing Kafka (open access)
→ understand Kafka's model to avoid these common mistakes
```

## Why it matters

Understanding common Kafka pitfalls is valuable senior-level knowledge because **recognizing and avoiding common mistakes helps use Kafka correctly**, so it's practically important for real Kafka usage.

Kafka is powerful but has many subtle pitfalls that cause real problems, and knowing them helps avoid these mistakes.

Understanding **design and partitioning pitfalls** — too few partitions (limiting parallelism) or too many (overhead), wrong partition keys (causing hot partitions or wrong ordering), expecting **global ordering** (a common misunderstanding, since Kafka only orders within a partition), and the issue that adding partitions breaks key-to-partition mapping (affecting ordering) — helps avoid the partitioning and ordering mistakes that are frequent and consequential.

Understanding **consumer and processing pitfalls** — ignoring consumer lag (consumers falling behind unnoticed), **not handling duplicates** (a very common mistake, since at-least-once delivery means duplicates that require idempotent processing — assuming exactly-once without idempotency is a frequent error), slow processing causing lag and rebalances, frequent rebalances, and improper offset committing — helps avoid the consumer-side mistakes that cause lag, duplicates, and reprocessing problems.

Understanding **operational and usage pitfalls** — using Kafka where a simple queue suffices (over-engineering), ignoring disk/retention (disk filling up), weak durability config (acks=1 risking data loss), no monitoring, and not securing Kafka — helps avoid the operational and judgment mistakes.

Knowing these pitfalls (especially the common ones around ordering misunderstanding, duplicate handling, consumer lag, and over-using Kafka) helps you use Kafka correctly and avoid the real problems that catch many users.

This awareness reflects practical Kafka experience.

Since Kafka has many subtle, common pitfalls (partitioning/ordering misunderstandings, duplicate handling, consumer lag, over-use, operational mistakes) that cause real problems, and since recognizing and avoiding them helps use Kafka correctly, understanding common Kafka pitfalls is valuable senior-level knowledge — practically important for avoiding the frequent mistakes (especially around ordering, duplicates/idempotency, lag, and appropriate use) that catch Kafka users, reflecting practical experience, and helping use Kafka correctly and reliably in real systems.