Performance Tuning Guide

# CPU-intensive workloads
instance_type: "ml.c5.2xlarge"

# Memory-intensive workloads  
instance_type: "ml.r5.xlarge"

# GPU workloads
instance_type: "ml.p3.2xlarge"

# General purpose
instance_type: "ml.m5.large"

# Use spot instances for fault-tolerant workloads
executors:
  batch_processor:
    use_spot_instances: true
    max_spot_interruptions: 3
    checkpoint_enabled: true

🗂️ Data Pipeline Optimization

Parallel Processing

# Process data in parallel chunks
modules:
  data_processing:
    executor: "${executors.spark_processor}"
    instance_count: 5  # Parallel processing
    job_parameters:
      partitions: 100
      parallel_jobs: 20

Data Compression & Formats

Use efficient data formats and compression:

• Parquet: For columnar analytics workloads
• Avro: For schema evolution support
• Gzip/Snappy: Balance between compression ratio and speed

📊 Performance Monitoring

# Check pipeline status
mk p status

# View pipeline execution details
mk p show --detailed

# Monitor service status
mk s status --detailed