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Data · Version 1.6.0 · Reviewed 2026-08-02

Kafka Debugger

Make data systems more correct and operable for consumer lag and rebalance storms with evidence, explicit trade-offs, and a verification plan.

4 method steps 6 documented failure modes 5 diagnostic checks 7 quality gates

Diagnoses consumer lag, rebalance storms, partition skew, and delivery-guarantee problems in Kafka pipelines.

₹99 one-time

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What this skill helps you do

  • Consumer lag
  • Rebalance storms
  • Partition skew

How Kafka Debugger works

You provide

Topic config, consumer settings, and lag history

It inspects

Per-partition lag and processing time for consumer lag

It decides

A rebalance storms decision with ordering guarantees stated

You verify

Lag flattens and dead-letter volume stays bounded

What it checks first

Kafka Debugger diagnoses consumer lag, rebalance storms, partition skew, and delivery-guarantee problems in Kafka pipelines. Use it when the work involves Consumer lag, Rebalance storms, Partition skew.

  1. Consumer lag trend rather than absolute value: flat lag at any level is healthy, rising lag is not.
  2. Partition count versus consumer count, since consumers beyond the partition count are idle by definition.
  3. Whether the partition key produces even distribution, or a few keys dominate one partition.
  4. Rebalance frequency, which converts into repeated processing pauses.
  5. Whether offsets commit before or after processing, which decides between at-most-once and at-least-once.

Failure modes it recognizes

  • A poison message blocking a partition indefinitely because the consumer retries in place without dead-lettering.
  • Processing time exceeding the poll interval, causing the broker to evict the consumer and trigger a rebalance loop.
  • Committing offsets before processing, silently dropping messages on crash.
  • Producer key set to null or a timestamp, destroying ordering guarantees the consumer assumed.
  • Consumer group rebalance storms from short session timeouts on a slow consumer.
  • Unbounded retention plus a compaction misconfiguration filling disk and stopping the broker.

Answers it will reject

  • Adding consumers to reduce lag when partitions are already saturated — throughput is bounded by partitions.
  • Increasing partitions to fix lag without checking whether processing is CPU-bound downstream.
  • Treating the queue as a database by retaining everything and querying it by scan.
  • Requeueing a failed message to the tail forever, converting a bug into an infinite loop.

Decision rules it applies

  • Order is guaranteed only within a partition, so any ordering requirement must map to a partition key.
  • Choose at-least-once with idempotent consumers over attempting exactly-once across systems.
  • Dead-letter after a bounded retry count with the failure reason attached; never retry indefinitely in place.
  • Size partitions for peak throughput plus headroom, because increasing partitions later breaks key-to-partition mapping.

Evidence it asks for

  • Track consumer lag per partition, not aggregated, so a single stuck partition is visible.
  • Measure processing time per message against `max.poll.interval.ms`.
  • Alert on rebalance rate and on dead-letter volume as separate signals.

The method inside

  1. Reconstruct the symptom timeline and define what healthy behavior would look like for consumer lag.
  2. Rank hypotheses for rebalance storms by evidence, blast radius, and ability to explain every observed symptom.
  3. Run the cheapest discriminating check for partition skew; update confidence only when evidence changes.
  4. Separate immediate stabilization, confirmed cause, contributing conditions, and prevention; finish with a reproducible verification.

Deliverables

  • Consumer lag assessment
  • Rebalance storms decision and action plan
  • Partition skew verification checklist

Evidence requirements

  • Schema, access patterns, query plans, or event contracts
  • Volume, cardinality, retention, and freshness
  • Consistency, latency, and migration constraints

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

One consumer group keeps rebalancing and lag grows during peak traffic.

Expected output

Rebalance storms usually mean processing time exceeds max.poll.interval.ms. The consumer is treated as dead mid-batch, triggering a rebalance that makes lag worse...

Boundaries and compatibility

Ideal for

  • Consumer lag: produce a decision or artifact grounded in supplied evidence.
  • Rebalance storms: produce a decision or artifact grounded in supplied evidence.
  • Partition skew: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Recommending indexes without a workload
  • Treating eventual consistency as universally acceptable

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.