# Advanced Dataset Examples

This directory contains advanced-level examples that demonstrate enterprise-grade, production-ready dataset operations using fsspeckit.

## Overview

The advanced examples showcase the full power of fsspeckit's dataset utilities for complex, large-scale data processing scenarios. These examples are designed for data engineers, architects, and developers working on production systems.

## Prerequisites

Before diving into these examples, you should have completed:

- All Getting Started examples
- All Workflow examples
- Intermediate SQL, Schema, and Common utilities examples
- Strong understanding of Python and data processing concepts
- Familiarity with cloud storage concepts (AWS S3, Azure Blob, Google Cloud Storage)
- Understanding of distributed systems and parallel processing

## Available Examples

### 1. `real_time_analytics.py` - Real-Time Analytics
**Prerequisites**: All intermediate examples completed
**Time**: 45-60 minutes
**Complexity**: Advanced

**What you'll learn:**
- Real-time data ingestion and stream processing
- Sliding window analytics and time-series calculations
- Real-time dashboard data preparation
- Performance optimization for low-latency processing
- Monitoring and alerting systems for streaming data

**Key concepts:**
- Sliding window algorithms for time-based analytics
- Memory-efficient buffering strategies
- Real-time metric calculations and aggregations
- Dashboard integration patterns
- Alert detection and threshold management
- Stream processing vs. batch processing trade-offs

**Real-world applications:**
- Fraud detection systems
- Real-time monitoring dashboards
- Live analytics for web applications
- IoT data processing pipelines
- Financial trading analytics

### 2. `large_scale_processing.py` - Large-Scale Data Processing
**Prerequisites**: All intermediate examples completed
**Time**: 50-70 minutes
**Complexity**: Advanced to Expert

**What you'll learn:**
- Distributed processing patterns for terabyte-scale datasets
- Advanced memory management and resource optimization
- Parallel processing with multiprocessing and threading
- Data partitioning strategies (hash, range, size-based)
- Fault tolerance and recovery mechanisms
- Performance monitoring and profiling

**Key concepts:**
- Memory-efficient chunking and streaming
- Parallel processing optimization strategies
- Data sharding for distributed processing
- Garbage collection and resource cleanup
- Performance metrics and monitoring
- Checkpoint-based fault tolerance

**Real-world applications:**
- Big data ETL pipelines
- Data warehouse operations
- Machine learning data preparation
- Log processing and analysis
- Large-scale data migrations

### 3. `multi_cloud_operations.py` - Multi-Cloud Operations
**Prerequisites**: Cloud storage familiarity, all intermediate examples
**Time**: 40-55 minutes
**Complexity**: Advanced

**What you'll learn:**
- Multi-cloud data management and synchronization
- Cross-cloud replication strategies (active-active, primary-backup, geo-distributed)
- Cloud-agnostic data processing patterns
- Cost optimization across different providers
- Performance comparison between cloud providers
- Disaster recovery and high availability patterns

**Key concepts:**
- Multi-cloud storage provider integration
- Data consistency verification across clouds
- Replication strategy selection and implementation
- Cross-cloud querying and analytics
- Cost optimization and performance tuning
- Disaster recovery planning and testing

**Real-world applications:**
- Global data distribution strategies
- Cloud provider risk mitigation
- Multi-region data availability
- Cross-cloud analytics platforms
- Backup and disaster recovery systems

## Advanced Learning Path

### Recommended Order

1. **Real-Time Analytics** - Start here for streaming and time-series data
2. **Large-Scale Processing** - Essential for big data and performance optimization
3. **Multi-Cloud Operations** - Critical for enterprise-grade deployment

### Choosing Your Focus

**For Streaming and Real-Time Systems:**
- Focus on `real_time_analytics.py`
- Master windowing algorithms and memory management
- Understand latency optimization techniques

**For Big Data and Performance:**
- Focus on `large_scale_processing.py`
- Master parallel processing and resource management
- Learn performance profiling and optimization

**For Enterprise and Cloud Deployments:**
- Focus on `multi_cloud_operations.py`
- Master multi-cloud strategies and disaster recovery
- Understand cost optimization and compliance

## Integration Patterns

### Real-Time + Large-Scale Processing

```python
# Combine real-time processing with large-scale techniques
from fsspeckit.datasets import DuckDBParquetHandler

# Real-time ingestion with batch processing pipeline
def hybrid_analytics_system():
    # Process streaming data
    stream_processor = StreamingAnalyticsProcessor()

    # Accumulate in efficient chunks
    large_scale_processor = LargeScaleProcessor(config)

    # Periodically process accumulated data
    while stream_processor.is_active():
        if stream_processor.should_process_batch():
            batch_data = stream_processor.extract_batch()
            large_scale_processor.process_dataset_parallel(batch_data)
```

### Large-Scale + Multi-Cloud

```python
# Combine scale with cloud distribution
processor = LargeScaleProcessor(config)
cloud_manager = CloudStorageManager()

# Process and distribute to multiple clouds
for partition in dataset_partitions:
    processed_data = processor.process_partition(partition)

    # Replicate to multiple clouds simultaneously
    replication_tasks = [
        upload_to_cloud(processed_data, "aws"),
        upload_to_cloud(processed_data, "azure"),
        upload_to_cloud(processed_data, "gcp")
    ]
    await asyncio.gather(*replication_tasks)
```

### All Three: Complete Enterprise Pipeline

```python
# Enterprise-grade pipeline combining all advanced concepts
class EnterpriseDataPipeline:
    def __init__(self):
        self.real_time_processor = StreamingAnalyticsProcessor()
        self.large_scale_processor = LargeScaleProcessor(config)
        self.cloud_manager = CloudStorageManager()
        self.synchronizer = MultiCloudSynchronizer(cloud_manager)

    async def run_pipeline(self):
        # 1. Real-time ingestion
        while True:
            stream_data = await self.ingest_real_time_data()

            # 2. Batch processing for large datasets
            if self.should_batch_process():
                batch_result = await self.large_scale_processor.process_dataset_parallel(
                    stream_data, self.processing_function
                )

                # 3. Multi-cloud replication
                await self.synchronizer.replicate_to_all_clouds(
                    batch_result, replication_strategy="active_active"
                )
```

## Advanced Techniques Covered

### Real-Time Analytics Patterns

1. **Sliding Window Algorithms**
   - Time-based windows with configurable granularity
   - Memory-efficient circular buffers
   - Window overlap and gap handling
   - Late data arrival and watermarking

2. **Performance Optimization**
   - Lock-free data structures for high throughput
   - Zero-copy data transfers
   - Vectorized operations with PyArrow
   - Just-in-time compilation for hot paths

3. **Alerting and Monitoring**
   - Threshold-based alerting with hysteresis
   - Anomaly detection using statistical methods
   - Performance regression detection
   - Health check and self-healing patterns

### Large-Scale Processing Patterns

1. **Parallel Processing Strategies**
   - CPU-bound vs. I/O-bound operation optimization
   - Worker count auto-tuning based on system resources
   - Load balancing across workers
   - Deadlock prevention and resource contention handling

2. **Memory Management**
   - Streaming processing for out-of-core datasets
   - Memory pool management with PyArrow
   - Garbage collection optimization
   - Memory leak detection and prevention

3. **Fault Tolerance**
   - Checkpoint-based recovery mechanisms
   - Retry logic with exponential backoff
   - Partial failure handling and data consistency
   - Graceful degradation under resource pressure

### Multi-Cloud Patterns

1. **Replication Strategies**
   - Active-active for high availability
   - Primary-backup for consistency
   - Geo-distributed for latency optimization
   - Tiered storage for cost optimization

2. **Cross-Cloud Operations**
   - Cloud-agnostic data processing APIs
   - Provider-optimized query generation
   - Cross-cloud consistency verification
   - Automatic failover and recovery

3. **Cost and Performance Optimization**
   - Cost-aware data placement
   - Performance benchmarking across providers
   - Storage lifecycle management
   - Data transfer optimization

## Performance Benchmarks

### Expected Performance Metrics

**Real-Time Processing:**
- Latency: <100ms for simple aggregations
- Throughput: 10,000+ events/second per core
- Memory efficiency: <50MB per 1M events in sliding window
- Alert detection: <10ms threshold check time

**Large-Scale Processing:**
- Memory reduction: 50-80% (with efficient partitioning)
- Parallel efficiency: 70-90% (with optimal worker count)
- Processing speedup: 5-50x (vs. single-threaded)
- Fault tolerance: <5% overhead for checkpoints

**Multi-Cloud Operations:**
- Replication latency: 30-120s (depending on data size)
- Consistency verification: <5s for typical datasets
- Cross-cloud query latency: 100-500ms (for optimized queries)
- Cost variance: 20-40% (between providers)

## Monitoring and Observability

### Key Metrics to Track

**Real-Time Metrics:**
- Event processing latency (p50, p95, p99)
- Throughput and error rates
- Window calculation accuracy
- Memory usage patterns
- Alert trigger rates and false positives

**Large-Scale Metrics:**
- Worker utilization and efficiency
- Memory pressure and GC frequency
- Disk I/O and network usage
- Checkpoint creation and restoration time
- Task failure and retry rates

**Multi-Cloud Metrics:**
- Transfer speeds and costs per provider
- Replication lag and consistency
- Service availability and uptime
- Cross-cloud query performance
- Storage costs by provider and class

### Monitoring Setup

```python
# Comprehensive monitoring for advanced operations
class AdvancedMonitoring:
    def __init__(self):
        self.metrics_collector = MetricsCollector()
        self.alert_manager = AlertManager()
        self.performance_profiler = PerformanceProfiler()

    def monitor_real_time_system(self, processor):
        # Real-time monitoring
        self.metrics_collector.track_latency(processor.get_processing_times())
        self.metrics_collector.track_throughput(processor.get_throughput_rates())

        # Alert on anomalies
        if processor.get_error_rate() > 0.01:  # 1% error threshold
            self.alert_manager.trigger_alert("high_error_rate", processor.get_metrics())

    def monitor_large_scale_processing(self, processor):
        # Resource monitoring
        self.metrics_collector.track_memory_usage(processor.get_memory_metrics())
        self.metrics_collector.track_cpu_utilization(processor.get_cpu_metrics())

        # Performance profiling
        if processor.get_efficiency() < 0.7:  # 70% efficiency threshold
            self.performance_profiler.analyze_bottlenecks(processor)
```

## Troubleshooting

### Common Advanced Issues

**Real-Time Processing Issues:**
- Memory leaks in long-running streaming processes
- Window calculation drift over time
- Backpressure and buffer overflow
- Alert fatigue from noisy thresholds

**Large-Scale Processing Issues:**
- Worker starvation and load imbalance
- Memory fragmentation and GC overhead
- Checkpoint corruption and recovery failures
- Resource exhaustion in cluster environments

**Multi-Cloud Issues:**
- Network partition and split-brain scenarios
- Data inconsistency between providers
- Cost overruns from unexpected data transfer
- Compliance violations across jurisdictions

### Debugging Strategies

1. **Real-Time Debugging**
   ```python
   # Add detailed logging and metrics
   import logging
   logging.basicConfig(level=logging.DEBUG)

   # Enable performance profiling
   import cProfile
   cProfile.run('real_time_processor.run()', 'real_time_profile.prof')

   # Memory leak detection
   from pympler import tracker
   tr = tracker.SummaryTracker()
   tr.print_diff()
   ```

2. **Large-Scale Debugging**
   ```python
   # Profile parallel processing
   import multiprocessing as mp
   mp.log_to_stderr(logging.DEBUG)

   # Monitor resource usage
   import psutil
   process = psutil.Process()
   print(f"Memory: {process.memory_info().rss / 1024 / 1024:.1f}MB")
   print(f"CPU: {process.cpu_percent()}%")

   # Analyze task distribution
   for worker_id, metrics in processor.get_worker_metrics().items():
       print(f"Worker {worker_id}: {metrics['tasks_completed']} tasks")
   ```

3. **Multi-Cloud Debugging**
   ```python
   # Verify cross-cloud consistency
   verification_results = synchronizer.verify_replication_consistency(plan)
   for provider, result in verification_results['results'].items():
       if not result.get('consistent', False):
           print(f"Inconsistency detected in {provider}")

   # Monitor costs
   cost_analysis = storage_manager.get_cost_breakdown()
   for provider, costs in cost_analysis.items():
       if costs['monthly'] > budget_threshold:
           print(f"Cost alert: {provider} ${costs['monthly']:.2f}")
   ```

## Best Practices

### Real-Time Best Practices

1. **Design for Scalability**
   - Use appropriate window sizes for your data volume
   - Implement backpressure handling
   - Plan for horizontal scaling

2. **Monitor Continuously**
   - Track latency percentiles, not just averages
   - Monitor resource utilization trends
   - Set up automated alerting

3. **Optimize for Performance**
   - Use vectorized operations when possible
   - Minimize object allocation in hot paths
   - Profile and optimize critical sections

### Large-Scale Best Practices

1. **Resource Management**
   - Monitor memory usage and trigger cleanup
   - Use appropriate worker counts for your hardware
   - Implement graceful degradation under load

2. **Fault Tolerance**
   - Create checkpoints at appropriate intervals
   - Implement retry logic with exponential backoff
   - Design for partial failures

3. **Performance Optimization**
   - Profile before optimizing
   - Use appropriate data partitioning
   - Consider algorithmic complexity

### Multi-Cloud Best Practices

1. **Strategy First**
   - Choose replication strategy based on requirements
   - Consider data sovereignty and compliance
   - Plan for costs across all providers

2. **Consistency Matters**
   - Implement regular consistency verification
   - Use appropriate consistency models
   - Plan for reconciliation procedures

3. **Cost Management**
   - Monitor costs across all providers
   - Use appropriate storage classes
   - Implement lifecycle policies

## Next Steps

After completing these advanced examples, you'll be ready for:

### Integration Examples
- **End-to-End Pipelines**: Complete production solutions
- **Cross-Domain Workflows**: Multi-package integration
- **Production Deployments**: Operational excellence

### Real-World Applications
- **Enterprise Data Platforms**: Complete data solutions
- **Machine Learning Pipelines**: ML data preparation and serving
- **Real-Time Analytics Systems**: Production streaming analytics

### Advanced Topics
- **Custom Backend Integration**: Extending fsspeckit
- **Performance Engineering**: Advanced optimization techniques
- **Distributed Systems**: Building scalable architectures

## Dependencies

```bash
# Core dependencies
pip install fsspeckit[datasets] pyarrow duckdb

# Real-time processing
pip install asyncio psutil

# Large-scale processing
pip install multiprocessing pympler

# Multi-cloud operations
pip install boto3 azure-storage-blob google-cloud-storage

# Advanced analytics
pip install pandas polars numpy

# Monitoring and profiling
pip install psutil pympler memory-profiler

# Development and debugging
pip install jupyter matplotlib seaborn tqdm
```

## Getting Help

- **Documentation**: Complete fsspeckit API reference
- **Examples**: Additional examples in parent directories
- **Community**: GitHub discussions and issues
- **Support**: Commercial support options for enterprise deployments
- **Training**: Advanced workshops and professional services

Remember: These advanced examples demonstrate production-grade patterns. Adapt them to your specific requirements, infrastructure, and compliance needs!