Performance Benchmarks

Real performance metrics and optimization techniques to maximize the efficiency of your ETLs.

Reference Benchmarks

Base Performance by Connection Type

Connection	Records/sec	Average latency	Peak memory	Use cases
MySQL	850-1,200	45ms	128MB	OLTP, transactional
PostgreSQL	750-1,000	52ms	145MB	Analytics, reports
Fracttal API	320-480	180ms	89MB	Maintenance data
Google Sheets	45-85	1,200ms	67MB	Manual reports
HTTP REST	200-800	250ms	95MB	External integrations
Email SMTP	15-25	2,500ms	45MB	Notifications

Performance Factors

graph LR
    A[ETL Performance] --> B[Source Performance]
    A --> C[Transform Complexity]
    A --> D[Target Performance]
    B --> E[Query Optimization]
    C --> F[JSON Logic Steps]
    D --> G[Network Latency]

    classDef main fill:#e3f2fd,stroke:#1976d2,stroke-width:3px,color:#0d47a1
    classDef factor fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px,color:#4a148c
    classDef metric fill:#e8f5e8,stroke:#388e3c,stroke-width:2px,color:#1b5e20

    class A main
    class B,C,D factor
    class E,F,G metric

D --> D1[Batch processing]
D --> D2[Connection pooling]
D --> D3[Rate limiting]

```

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Real Case Studies

Case 1: Order Synchronization (DB → ERP)

Scenario: 50K daily orders from MySQL to external API

Non-Optimized

json { "benchmark": { "records": 50000, "execution_time": "18m 45s", "throughput": "44 records/sec", "memory_peak": "2.1GB", "cpu_avg": "78%", "issues": [ "N+1 queries", "No batch processing", "Full in-memory transform" ] } }

Optimized

json { "config": { "source": { "parameters": { "query_string": { "table": "orders", "where": "status IN ('completed','shipped') AND updated_at >= CURRENT_DATE - INTERVAL 1 DAY", "order_by": "id", "batch_size": 2000 } } }, "transform": [ { "batch_transform": { "chunk_size": 500, "parallel_processing": true, "max_workers": 4 } } ], "target": { "parameters": { "batch_requests": true, "max_batch_size": 100, "connection_pool": 5 } } }, "result": { "execution_time": "4m 12s", "throughput": "198 records/sec", "memory_peak": "456MB", "cpu_avg": "34%", "improvement": "4.5x faster, 78% less memory" } }

Case 2: Asset Maintenance Reports (API → Sheets)

Scenario: Daily maintenance reports from Fracttal to Google Sheets

Performance Analysis

json { "original_performance": { "records": 5000, "execution_time": "12m 30s", "api_calls": 5000, "sheets_writes": 5000, "rate_limit_hits": 47 }, "optimized_performance": { "execution_time": "2m 45s", "api_calls": 50, "sheets_writes": 25, "rate_limit_hits": 0, "strategies": [ "API pagination (100 records/call)", "Batch writing to Sheets (200 records/write)", "Intelligent retry with exponential backoff" ] } }

Case 3: Real-time Inventory Sync (DB → API → Webhook)

Scenario: Real-time inventory updates across multiple systems

Performance Metrics

json { "pipeline_performance": { "source_to_transform": "150ms", "transform_processing": "89ms", "target_delivery": "320ms", "total_latency": "559ms", "throughput": "1,789 records/sec", "success_rate": "99.7%" }, "optimization_techniques": [ "Streaming processing", "Async I/O operations", "Connection pooling", "Circuit breaker pattern" ] }

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Optimization Strategies

Database Source Optimization

Query Performance: sql -- Bad: Table scan SELECT * FROM orders WHERE customer_name LIKE '%john%' -- Good: Indexed search SELECT id, customer_id, total, status FROM orders WHERE customer_id IN (SELECT id FROM customers WHERE name_index LIKE 'john%') AND created_date >= '2024-01-01'

Batch Configuration:

{
  "source": {
    "id_type": 1,
    "form": {
      "sql": "SELECT * FROM large_table WHERE id BETWEEN ? AND ? ORDER BY id",
      "batch_size": 1000,
      "parallel_batches": 4,
      "connection_pool_size": 8
    }
  }
}

Transform Optimization

Memory-Efficient Processing:

{
  "transform": [
    {
      "streaming_transform": {
        "process_in_chunks": true,
        "chunk_size": 500,
        "memory_limit": "512MB"
      }
    },
    {
      "parallel_processing": {
        "enabled": true,
        "max_workers": 4,
        "worker_memory_limit": "128MB"
      }
    }
  ]
}

Complex Logic Optimization:

{
  "transform": [
    {
      "pre_filter": {
        "condition": {"status": {"in": ["active", "pending"]}},
        "early_exit": true
      }
    },
    {
      "cached_lookup": {
        "field": "customer_id",
        "lookup_table": "customer_cache",
        "cache_ttl": 3600
      }
    }
  ]
}

Target Optimization

Batch Writing:

{
  "target": {
    "id_type": 10,
    "form": {
      "batch_size": 1000,
      "batch_timeout": 30,
      "parallel_writes": true,
      "max_concurrent_batches": 3
    }
  }
}

Rate Limiting:

{
  "target": {
    "id_type": 2,
    "rate_limiting": {
      "requests_per_second": 10,
      "burst_size": 20,
      "backoff_strategy": "exponential"
    }
  }
}

---

Monitoring and Tuning

Performance Metrics to Track

{
  "metrics": {
    "throughput": {
      "records_per_second": "number",
      "bytes_per_second": "number",
      "target": "> 500 records/sec"
    },
    "latency": {
      "avg_record_processing_time": "milliseconds",
      "p95_processing_time": "milliseconds",
      "target": "< 100ms p95"
    },
    "resource_usage": {
      "memory_usage_mb": "number",
      "cpu_usage_percent": "number",
      "target": "< 70% CPU, < 1GB RAM"
    },
    "error_rates": {
      "connection_errors": "percentage",
      "transform_errors": "percentage",
      "target": "< 0.1% errors"
    }
  }
}

Real-time Monitoring Configuration

{
  "monitoring": {
    "enabled": true,
    "sampling_rate": 0.1,
    "metrics_interval": 30,
    "alert_thresholds": {
      "execution_time_minutes": 15,
      "error_rate_percent": 1,
      "memory_usage_gb": 2
    },
    "dashboards": {
      "grafana_endpoint": "http://monitoring:3000",
      "metrics_prefix": "etl.performance"
    }
  }
}

Performance Tuning Checklist

Before Optimization: - [ ] Establish baseline metrics - [ ] Identify bottlenecks (source, transform, or target) - [ ] Set performance targets - [ ] Plan testing strategy

Source Optimization: - [ ] Optimize queries with proper indexing - [ ] Implement batch processing - [ ] Use connection pooling - [ ] Filter data at source

Transform Optimization: - [ ] Minimize complex transformations - [ ] Use streaming processing for large datasets - [ ] Implement parallel processing - [ ] Cache frequently accessed data

Target Optimization: - [ ] Batch writes when possible - [ ] Implement proper error handling - [ ] Use connection pooling - [ ] Respect rate limits

System Optimization: - [ ] Monitor resource usage - [ ] Scale horizontally when needed - [ ] Implement circuit breakers - [ ] Use async I/O operations

---

Performance Testing

Load Testing Configuration

{
  "load_test": {
    "test_scenarios": [
      {
        "name": "normal_load",
        "records": 10000,
        "concurrent_etls": 1,
        "duration": "5m"
      },
      {
        "name": "peak_load", 
        "records": 50000,
        "concurrent_etls": 3,
        "duration": "15m"
      },
      {
        "name": "stress_test",
        "records": 100000,
        "concurrent_etls": 5,
        "duration": "30m"
      }
    ],
    "success_criteria": {
      "throughput_min": 500,
      "error_rate_max": 1,
      "memory_usage_max_gb": 2
    }
  }
}

Benchmarking Tools

# ETL Performance Profiler
python -m etl_profiler --config config.json --duration 300

# Memory Usage Analysis
python -m memory_profiler etl_script.py

# Database Query Analysis
EXPLAIN ANALYZE SELECT * FROM your_query;

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Best Practices Summary

High-Performance ETL Patterns

1. Incremental Processing: Only process changed data
2. Parallel Processing: Utilize multiple workers/threads
3. Batch Operations: Group operations for efficiency
4. Connection Pooling: Reuse connections
5. Smart Caching: Cache frequently accessed data
6. Error Recovery: Implement robust retry mechanisms

Anti-patterns to Avoid

1. Row-by-row processing: Always use batch operations
2. Full table scans: Use proper indexing and filtering
3. Synchronous I/O: Use async operations for I/O bound tasks
4. Unlimited memory usage: Implement streaming for large datasets
5. No error handling: Always implement proper error recovery
6. Hardcoded limits: Make batch sizes and timeouts configurable

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Remember: Performance optimization is iterative. Start with baseline measurements, identify bottlenecks, apply optimizations, and measure improvements. Always test under realistic load conditions.