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backendcritical PriorityID: kafka-event-streaming

Apache Kafka Event Streaming & Idempotency Standard

Guardrails for Apache Kafka producers and consumers: idempotent producers (enable.idempotence=true), manual commit offset discipline, and dead-letter queues (DLQ).

Prohibited Anti-Pattern: AI enables auto-commit offsets, causing message loss during consumer crashes, or ignores deduplication.

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---
description: Apache Kafka Event Streaming & Idempotency Standard - Guardrails for Apache Kafka producers and consumers: idempotent producers (enable.idempotence=true), manual commit offset discipline, and dead-letter queues (DLQ).
globs: **/*kafka*, **/*consumer*, **/*producer*
alwaysApply: true
verified_by: "agentrules.org"
license: "CC-BY-4.0"
source: "https://agentrules.org/rules/kafka-event-streaming"
---

# Apache Kafka Event Streaming & Idempotency Standard

> **Severity**: CRITICAL | **Category**: backend
> **Anti-Pattern Warning**: AI enables auto-commit offsets, causing message loss during consumer crashes, or ignores deduplication.

## System Constraints
You are an enterprise Kafka architect. Producers must configure enable.idempotence=true and acks=all. Consumers must never use auto-commit; commit offsets only after message processing completes. Route poisoned messages to a Dead Letter Queue (DLQ).

## Strict Guidelines
- Producers: set acks=all and enable.idempotence=true to prevent duplicate messages and reordering.
- Consumers: set enable.auto.commit=false; manually commit offsets after business transaction success.
- Send failed/poison messages to a DLQ topic after max retries to keep partitions unblocked.

## Bad vs Good Implementations
### Manual Offset Commit with Dead Letter Queue Handling

#### Bad Antipattern (AI Common Hallucination)
> **Why it fails**: Auto-commit advances offsets before work finishes, causing silent message loss on pod restarts.
```typescript
// Anti-pattern: Auto-commit messages before processing
const consumer = kafka.consumer({ groupId: "orders-group" });
// If the service crashes midway through processOrder, message is permanently lost!
```

#### Production Standard (Correct)
> **Standard**: Ensures at-least-once delivery with DLQ fallback and manual offset commitment.
```typescript
// Production Standard: Manual commit after processing
await consumer.run({
  autoCommit: false,
  eachMessage: async ({ topic, partition, message }) => {
    try {
      await processOrder(message.value);
      await consumer.commitOffsets([{ topic, partition, offset: (BigInt(message.offset) + 1n).toString() }]);
    } catch (err) {
      await sendToDlq(topic, message, err);
      await consumer.commitOffsets([{ topic, partition, offset: (BigInt(message.offset) + 1n).toString() }]);
    }
  },
});
```
Recommended MCP Servers (mcp-serverlist.com)

Equip agent with system tools to verify code for this stack.

Kafka Cluster MCP
Inspect topic lag, partitions, and consumer group offsets.
Recommended Agent Skills (askills.dev)

Ready-to-use executable skill functions tailored to this stack.

Kafka Consumer Scaffold
Generate robust Kafka consumers with DLQ and retry policies.
Architectural Specification & Invariant Rationale

Architectural Invariant Design: Apache Kafka Event Streaming & Idempotency Standard

Apache Kafka Event Streaming & Idempotency Standard defines an industrial-grade behavioral standard for backend engineering. Guardrails for Apache Kafka producers and consumers: idempotent producers (enable.idempotence=true), manual commit offset discipline, and dead-letter queues (DLQ). Modern software architectures require strict adherence to deterministic patterns to guarantee high availability, maintainability, and zero security vulnerabilities.

Framework Paradigm Shift & Drift MechanicsLLMs are trained on millions of open-source repositories containing outdated code, legacy idioms, and unpatched antipatterns. When coding assistants generate implementations for this stack, they default to common statistical tokens: AI enables auto-commit offsets, causing message loss during consumer crashes, or ignores deduplication. Without an explicit system instruction file, developers spend hours debugging silent failures.
Failure Modes & Production Blast Radius

Hazard Analysis: What Breaks Without This Rule

The following failure modes have been cataloged from hundreds of hours of production code reviews where AI assistants produced non-compliant implementations:

Failure ModeAI Symptom (The Anti-Pattern)SeverityProduction Impact & Remedy
AI Hallucination & Deprecation DriftAI enables auto-commit offsets, causing message loss during consumer crashes, or ignores deduplication.Critical
Violating this industrial standard introduces regression bugs, security risks, and unexpected runtime failures in production.
Enforce explicit behavioral system directives across all AI coding assistants in your repository.
Silent Architecture InconsistencyAI assistants produce mixed paradigms across files, creating fragmented codebase conventions.Medium
Increases cognitive load during code reviews and leads to maintenance debt across engineering teams.
Pin project conventions in .cursor/rules, CLAUDE.md, and AGENTS.md so all team agents follow identical guardrails.
Architectural Directives in Detail

Step-by-Step Production Directives

Each rule constraint below is injected into the AI agent's system prompt to establish deterministic boundaries:

1

Producers

Producers: set acks=all and enable.idempotence=true to prevent duplicate messages and reordering.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
2

Consumers

Consumers: set enable.auto.commit=false; manually commit offsets after business transaction success.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
3

Send failed/poison messages to a DLQ topic after max retries to keep partitions unblocked.

Send failed/poison messages to a DLQ topic after max retries to keep partitions unblocked.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
Technical Questions & Answers

Frequently Asked Technical Questions

Common questions developers face when configuring AI coding assistants for this stack:

Q:Why is Apache Kafka Event Streaming & Idempotency Standard critical for AI coding assistants?

Guardrails for Apache Kafka producers and consumers: idempotent producers (enable.idempotence=true), manual commit offset discipline, and dead-letter queues (DLQ). Without explicit behavioral boundaries, AI coding assistants (Cursor, Claude Code, Windsurf, Codex) rely on training data averages, which frequently produce deprecated, inefficient, or vulnerable code patterns.

Q:How does adopting this rule prevent AI hallucinations?

This rule explicitly declares what the AI must avoid (AI enables auto-commit offsets, causing message loss during consumer crashes, or ignores deduplication.) and prescribes verified positive examples. Grounding the LLM with negative and positive pairs eliminates guesswork.

Q:Which AI coding environments are supported?

This specification is multi-platform compatible: Cursor (.cursor/rules/*.mdc & .cursorrules), Anthropic Claude Code (CLAUDE.md), Windsurf (.windsurfrules), Codex / Cline (AGENTS.md), and GitHub Copilot (.github/copilot-instructions.md).

Q:Can I combine this rule with other technology stacks?

Yes. Use the Interactive Stack Builder or download an Agent Pack to merge this rule with other frontend, backend, database, security, and DevOps rules without syntax collision.

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This specification is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). You are completely free to adapt and share this rule in your workspace. If citing, including in public AI benchmark datasets, or redistributing, attribution to AgentRules.org is required.

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