Kafka perf review

Kafka Performance Configuration Review

Reviews producer and consumer configurations in both the live cluster and the codebase for performance anti-patterns. These settings are the same across all Kafka client libraries (they're Kafka protocol properties).

Target environment and path: $ARGUMENTS (defaults to src/ for codebase scan if path not specified)

Workflow

Copy this checklist and track your progress:

Performance Review Progress:
- [ ] Step 1: Inspect live cluster configs
- [ ] Step 2: Scan codebase for producer/consumer configs
- [ ] Step 3: Audit producer configs
- [ ] Step 4: Audit consumer configs
- [ ] Step 5: Cross-reference cluster and code configs
- [ ] Step 6: Generate report

1. Inspect live cluster configs via Lenses MCP
2. Scan codebase for producer/consumer config properties (see references/producer-defaults.md and references/consumer-defaults.md)
3. Audit producer configs against recommended values
4. Audit consumer configs against recommended values
5. Cross-reference cluster and code configs
6. Report findings with current values, recommended values and trade-off explanations

Step 1: Live Cluster Inspection

Use Lenses MCP tools to check cluster-side performance configs:

• get_topic - topic-level configs affecting performance (min.insync.replicas, compression.type, max.message.bytes)
• get_topic_broker_configs - broker-level configs (message.max.bytes, replica.fetch.max.bytes, num.io.threads)
• get_topic_partitions - message distribution across partitions (detect skew where one partition has significantly more bytes than others)
• get_dataset_message_metrics - message throughput over time to identify bottlenecks or capacity headroom

Expected output: Topic-level performance configs, partition distribution and throughput metrics.

Validation: If MCP calls fail, proceed with codebase-only analysis and note the limitation in the report.

Step 2: Codebase Inspection

Search the codebase for Kafka producer and consumer configuration properties. Consult references/producer-defaults.md for the full list of producer properties and references/consumer-defaults.md for consumer properties.

Also search for anti-patterns listed in references/producer-defaults.md:

• Synchronous produce calls (.get(), .result(), flush() after every send)
• Missing delivery callbacks / error handlers
• Missing graceful shutdown / rebalance listeners

Step 3: Audit Producer Configs

Compare found producer configs against the recommended values in references/producer-defaults.md. Key areas: acks, batch.size, linger.ms, compression.type, enable.idempotence and retries.

Step 4: Audit Consumer Configs

Compare found consumer configs against the recommended values in references/consumer-defaults.md. Key areas: max.poll.records, max.poll.interval.ms, auto.offset.reset, enable.auto.commit and fetch.min.bytes.

Success Criteria

Quantitative

• Triggers on 90% of performance-related queries (test with 10-20 varied phrasings)
• Completes review in under 15 tool calls (MCP + codebase search)
• 0 failed MCP calls per run

Qualitative

• Every finding shows current value, recommended value and trade-off explanation
• Anti-patterns are identified with file and line references
• Estimated throughput impact (low/medium/high) is consistently calibrated

Examples

Example 1: Routine performance review

User says: "Review Kafka performance configs for staging"

Actions:

1. Inspect cluster-side configs for all topics in staging
2. Scan src/ for producer/consumer property definitions
3. Cross-reference code configs against reference tables Result: Report with per-property findings and throughput impact estimates

Example 2: Investigating slow consumers

User says: "Why are my consumers slow? Check the performance settings."

Actions:

1. Focus on consumer config properties in the codebase
2. Check max.poll.records, fetch.min.bytes and enable.auto.commit
3. Look for anti-patterns like synchronous processing Result: Targeted report on consumer-side bottlenecks with remediation steps

Example 3: Scoped codebase review

User says: "Check Kafka configs in src/kafka/ for the production environment"

Actions:

1. Scan only src/kafka/ for producer and consumer configs
2. Cross-reference with live production cluster settings Result: Focused report on a specific directory's Kafka configurations

Troubleshooting

No Kafka config properties found in codebase

Cause: The codebase may use a framework or wrapper that hides raw Kafka properties. Solution: Search for framework-specific config patterns (e.g., Spring Boot application.yml, Django settings). Report the framework used and suggest manual review.

Lenses MCP returns no topic data

Cause: Environment name is incorrect or Lenses agent is offline. Solution: Run check_environment_health first. Verify the environment name matches what list_environments returns.

Partition skew detection is inconclusive

Cause: Topic has very low throughput so byte counts are similar across partitions. Solution: Note that skew detection requires meaningful throughput. For low-volume topics, skip the skew check and note it in the report.

Output Format

## Performance Review Report

### Cluster-Side Findings
- [topic-name] {property}: {current value}
  Recommendation: {recommended value} - {explanation}

### Codebase Findings (Producers)
- [file:line] {property} = {current value}
  Recommendation: {recommended value} - {explanation}

### Codebase Findings (Consumers)
- [file:line] {property} = {current value}
  Recommendation: {recommended value} - {explanation}

### Anti-Patterns
- [file:line] Description of the anti-pattern
  Recommendation: How to fix it

### Summary
- X producer issues found
- Y consumer issues found
- Z anti-patterns found
- Estimated throughput impact: low/medium/high