Lab - Lab Environment Setup Skill

Lab - Lab Environment Setup Skill

Arguments: $ARGUMENTS

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Design Principles

1. Technology isolation: When practicing Kafka, do not spin up ES. Each lab starts and stops independently.
2. Generated to match learning content: Instead of a pre-built generic compose, generate a configuration that matches the current learning topic.
3. Monitoring included: Every lab includes monitoring appropriate for that technology by default.
4. Incremental expansion: Start with a basic configuration and add load testing/APM as needed.

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Directory Structure

labs/
├── kafka/
│   ├── docker-compose.yml
│   ├── docker-compose.monitoring.yml   (optional)
│   ├── docker-compose.loadtest.yml     (optional)
│   └── apps/
│
├── elasticsearch/
│   ├── docker-compose.yml
│   ├── docker-compose.apm.yml          (optional)
│   └── apps/
│
├── database/
│   ├── docker-compose.yml
│   ├── docker-compose.monitoring.yml   (optional)
│   ├── data/
│   └── queries/
│
├── redis/
│   ├── docker-compose.yml
│   ├── docker-compose.monitoring.yml   (optional)
│   └── apps/
│
└── shared/
    ├── grafana/
    │   └── provisioning/
    ├── prometheus/
    │   └── prometheus.yml
    └── loadtest/
        └── k6/

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Technology-Specific Configuration Guide

Kafka Lab

Basic Configuration (docker-compose.yml)

# Components:
# - Zookeeper (1 node) or KRaft mode (without Zookeeper)
# - Kafka Broker x3 (cluster)
# - Kafka UI (for viewing topics/messages/consumer groups)
#
# Ports:
# - 9092, 9093, 9094: Kafka brokers
# - 8080: Kafka UI
#
# Network: kafka-net (bridge)

Things to verify when generating:

• KRaft vs Zookeeper: KRaft mode is recommended for Kafka 3.x and above (no Zookeeper needed)
• Memory: Limit KAFKA_HEAP_OPTS to 512MB~1GB per broker (since it runs locally)
• Volumes: Use named volumes for data persistence, or tmpfs if persistence is not needed

Adding Monitoring (docker-compose.monitoring.yml)

# Additional components:
# - JMX Exporter (attached as agent to each broker)
# - Prometheus (metrics collection)
# - Grafana (dashboards)
#   - Preset dashboards: Kafka Broker Metrics, Consumer Lag
#
# Start: docker compose -f docker-compose.yml -f docker-compose.monitoring.yml up -d

Load Testing (docker-compose.loadtest.yml)

# Additional components:
# - k6 (load generator)
#
# Example scenarios:
# - Producer throughput measurement: send N messages/sec, compare acks=0/1/all
# - Consumer lag simulation: producer speed > consumer speed

Test Apps (apps/)

• Simple Java/Kotlin producer-consumer app
• Configuration values (acks, batch.size, linger.ms, etc.) injectable via external environment variables
• Adjust app code according to the learning topic

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Elasticsearch Lab

Basic Configuration (docker-compose.yml)

# Components:
# - Elasticsearch 3-node cluster (or single node for simple practice)
# - Kibana
#
# Ports:
# - 9200: Elasticsearch
# - 5601: Kibana
#
# Notes:
# - vm.max_map_count=262144 (requires host configuration)
# - ES_JAVA_OPTS: -Xms512m -Xmx512m (limit local memory usage)
# - Adjust number of nodes to 1 or 3 depending on the practice goal

Things to verify when generating:

• Single node vs cluster: 1 node for search/analysis practice, 3 nodes for sharding/replication practice
• Security: xpack.security.enabled=false is acceptable for local practice
• Sample data: Kibana's built-in sample datasets can be used

Adding APM (docker-compose.apm.yml)

# Additional components:
# - Elastic APM Server
# - APM Agent (connected to test app)
#
# Start: docker compose -f docker-compose.yml -f docker-compose.apm.yml up -d

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Database Lab (MySQL)

Basic Configuration (docker-compose.yml)

# Components:
# - MySQL 8.x (slow_query_log enabled, long_query_time=0.5)
# - Adminer or phpMyAdmin (web-based SQL client)
#
# Ports:
# - 3306: MySQL
# - 8080: Adminer
#
# Initialization:
# - .sql files in the data/ directory are executed automatically
# - Includes scripts for generating large datasets for practice

Things to verify when generating:

• Query tuning practice: slow_query_log and performance_schema must be enabled
• EXPLAIN analysis: Data generation scripts for large datasets (1M+ rows) for index practice
• Config file: Mount my.cnf as a volume to enable parameter tuning practice

Adding Monitoring (docker-compose.monitoring.yml)

# Additional components:
# - MySQL Exporter (for Prometheus)
# - Prometheus
# - Grafana (MySQL Overview dashboard)
#
# Metrics to observe:
# - QPS, query type ratio
# - InnoDB Buffer Pool Hit Ratio
# - Connection count
# - Slow Query count

Query Practice (queries/)

• SQL files for hands-on practice of query tuning techniques from the studied chapter
• Before/After queries for EXPLAIN ANALYZE comparison

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Redis Lab

Basic Configuration (docker-compose.yml)

# Components:
# - Redis (standalone, Sentinel, or Cluster mode)
# - Redis Insight (GUI client)
#
# Ports:
# - 6379: Redis
# - 5540: Redis Insight
#
# Mode selection:
# - Standalone: basic data structures, caching practice
# - Sentinel (3 nodes): high availability, failover practice
# - Cluster (6 nodes): sharding, slot distribution practice

Things to verify when generating:

• Memory policy: Configure maxmemory + maxmemory-policy
• Persistence: Enable RDB/AOF configuration for practice
• Mode: Choose Standalone/Sentinel/Cluster based on the practice topic

Adding Monitoring (docker-compose.monitoring.yml)

# Additional components:
# - Redis Exporter (for Prometheus)
# - Prometheus + Grafana
#
# Metrics to observe:
# - Memory usage (used_memory vs maxmemory)
# - Hit Rate (keyspace_hits / keyspace_misses)
# - Connection count
# - Eviction count

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Execution Flow

Phase 1: Determine the lab environment

1. User invokes this skill (or requests a lab during a /oh-my-study-with-me:study session)
2. Identify the current learning topic.
3. Determine the configuration that fits the learning topic:
   • Is only the base needed? (compose.yml only)
   • Is monitoring needed? (+monitoring.yml)
   • Is load testing needed? (+loadtest.yml)
   • Is a cluster needed? (decide number of nodes)
4. Confirm the configuration with the user.

Phase 2: Set up the environment

1. Create the labs/[technology]/ directory.
2. Create docker-compose.yml.
   • Include settings appropriate for the learning topic.
   • Add comments explaining the meaning of each setting and the learning points.
3. Create any required config files (prometheus.yml, Grafana dashboards, etc.).
4. Start with docker compose up -d.
5. Provide the access URLs for each service.

Phase 3: Create test apps / scripts

1. Generate test code appropriate for the learning topic.
   • Kafka: producer/consumer Java app
   • ES: indexing/search scripts (curl or Java)
   • DB: SQL query files + large dataset generation scripts
   • Redis: redis-cli command scripts or Java app
2. Add comments to the test code as well, noting the learning points.

Phase 4: Practice guide

1. Explain what to observe.
   • e.g., "Run the producer with acks=1, then check the ISR in Kafka UI"
   • e.g., "Check the execution plan for this query with EXPLAIN ANALYZE"
2. Analyze the practice results together.
3. If the results differ from expectations, dig into why using First Principles.

Phase 5: Teardown

1. docker compose down (preserve data) or docker compose down -v (full cleanup)
2. Record the practice results and observations (can be used as blog material)

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Integration with /oh-my-study-with-me:study

When a request to "try it hands-on" comes up during Phase 3 (Verification) of a study session:

1. Propose a lab configuration suited to the current learning topic.
2. If the user agrees, execute Phases 2 through 4.
3. Reflect the practice results in whether the verification is passed.

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Notes

• Resource management: Always set memory limits per service, taking the local machine's memory into account.
  • Kafka 3-broker: ~2GB total
  • ES 3-node: ~2GB total
  • MySQL + monitoring: ~1GB
• Port conflicts: Since multiple labs may be running simultaneously, manage ports carefully to avoid collisions.
• Docker Desktop memory: Minimum 8GB allocation recommended (when using multiple labs simultaneously)
• .env files: Parameterize version, port, and memory settings in a per-lab .env file. However, include .env files in .gitignore and provide a .env.example instead.
• Host configuration: When using ES, sudo sysctl -w vm.max_map_count=262144 is required (on macOS, this is handled in Docker Desktop settings)