kafka-perf-review

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Translated

Review Kafka producer and consumer performance configurations in both the live cluster (via Lenses MCP) and the codebase. Flags un-tuned defaults, anti-patterns and missing best practices. Use when user says "review Kafka performance", "check producer configs", "tune Kafka settings" or asks about throughput, batching or compression. Do NOT use for cluster sizing or capacity planning.

4installs

Sourcelensesio/agentic-engineering-for-apache-kafka

Added on2026-05-01

NPX Install

npx skill4agent add lensesio/agentic-engineering-for-apache-kafka kafka-perf-review

SKILL.md Content

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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).

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

Inspect live cluster configs via Lenses MCP

Scan codebase for producer/consumer config properties (see

references/producer-defaults.md

and

references/consumer-defaults.md

)

Audit producer configs against recommended values
Audit consumer configs against recommended values
Cross-reference cluster and code configs
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:

Inspect cluster-side configs for all topics in staging
Scan
```
src/
```
for producer/consumer property definitions
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:

Focus on consumer config properties in the codebase

Check

max.poll.records

,

fetch.min.bytes

and

enable.auto.commit

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:

Scan only
```
src/kafka/
```
for producer and consumer configs
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

kafka-perf-review

NPX Install

Tags

SKILL.md Content

Kafka Performance Configuration Review

Workflow

Step 1: Live Cluster Inspection

Step 2: Codebase Inspection

Step 3: Audit Producer Configs

Step 4: Audit Consumer Configs

Success Criteria

Quantitative

Qualitative

Examples

Example 1: Routine performance review

Example 2: Investigating slow consumers

Example 3: Scoped codebase review

Troubleshooting

No Kafka config properties found in codebase

Lenses MCP returns no topic data

Partition skew detection is inconclusive

Output Format