Building Weave: Pipeline Orchestration and Monitoring (Part 4)

# weave/orchestrators/base.py
class Orchestrator:
    """Core orchestration engine."""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.pipeline = Pipeline()
        self.monitor = Monitor()
        self.resource_manager = ResourceManager()
        
    def build_pipeline(self, steps: List[PipelineStep]) -> Pipeline:
        """Construct a pipeline from steps."""
        # Validate step dependencies
        self._validate_dependencies(steps)
        
        # Optimize step ordering
        ordered_steps = self._optimize_order(steps)
        
        # Configure monitoring
        self._setup_monitoring(ordered_steps)
        
        return Pipeline(ordered_steps)
        
    def run(self, pipeline: Pipeline, data: Any) -> Any:
        """Execute pipeline with monitoring and error handling."""
        try:
            # Initialize monitoring
            self.monitor.start_pipeline(pipeline)
            
            # Execute steps
            result = self._execute_steps(pipeline, data)
            
            # Finalize monitoring
            self.monitor.complete_pipeline(pipeline)
            
            return result
            
        except Exception as e:
            self.monitor.fail_pipeline(pipeline, e)
            raise

# weave/orchestrators/pipeline.py
class Pipeline:
    """Data generation pipeline definition."""
    
    def __init__(self, steps: List[PipelineStep] = None):
        self.steps = steps or []
        self.status = PipelineStatus()
        self.metrics = PipelineMetrics()
        
    def add_step(self, step: PipelineStep) -> None:
        """Add a step to the pipeline."""
        # Validate step compatibility
        self._validate_step(step)
        
        # Add step with metadata
        self.steps.append(self._enrich_step(step))
        
    def _enrich_step(self, step: PipelineStep) -> PipelineStep:
        """Add metadata and monitoring to step."""
        return StepWrapper(
            step=step,
            metrics=self.metrics,
            retries=self.config.retries,
            timeout=self.config.timeout
        )

# weave/orchestrators/resources.py
class ResourceManager:
    """Manage computational resources."""
    
    def __init__(self, config: Dict[str, Any]):
        self.max_memory = config.get("max_memory_gb", 4) * 1024**3
        self.max_concurrent = config.get("max_concurrent", 4)
        self.gpu_enabled = config.get("gpu_enabled", False)
        
    def allocate(self, step: PipelineStep) -> Resources:
        """Allocate resources for a step."""
        requirements = step.get_requirements()
        
        # Check resource availability
        if not self._can_allocate(requirements):
            raise ResourceError("Insufficient resources")
            
        # Allocate resources
        allocation = self._allocate_resources(requirements)
        
        # Track allocation
        self._track_allocation(allocation)
        
        return allocation

# weave/orchestrators/error_handling.py
class ErrorHandler:
    """Handle pipeline errors with retries."""
    
    def __init__(self, config: Dict[str, Any]):
        self.max_retries = config.get("max_retries", 3)
        self.retry_delay = config.get("retry_delay", 5)
        self.error_patterns = self._load_error_patterns()
        
    def handle_error(self, error: Exception, step: PipelineStep) -> Action:
        """Determine how to handle an error."""
        # Analyze error
        error_type = self._classify_error(error)
        
        # Check retry policy
        if self._should_retry(error_type, step):
            return self._create_retry_action(step)
            
        # Handle fatal error
        return self._handle_fatal_error(error, step)
        
    def _should_retry(self, error_type: str, step: PipelineStep) -> bool:
        """Determine if step should be retried."""
        return (
            error_type in self.retryable_errors and
            step.retries < self.max_retries
        )

# weave/orchestrators/monitoring.py
class Monitor:
    """Pipeline monitoring system."""
    
    def __init__(self):
        self.metrics = MetricsCollector()
        self.logger = Logger()
        self.alerts = AlertManager()
        
    def track_step(self, step: PipelineStep) -> None:
        """Track step execution metrics."""
        # Record timing
        start_time = time.time()
        
        try:
            # Execute step
            result = step.execute()
            
            # Record success metrics
            duration = time.time() - start_time
            self.metrics.record_success(step, duration)
            
        except Exception as e:
            # Record failure metrics
            self.metrics.record_failure(step, e)
            raise
            
    def get_pipeline_stats(self) -> Dict[str, Any]:
        """Get pipeline statistics."""
        return {
            "success_rate": self._calc_success_rate(),
            "avg_duration": self._calc_avg_duration(),
            "resource_usage": self._get_resource_usage(),
            "error_rates": self._get_error_rates()
        }

# weave/orchestrators/optimization.py
class PipelineOptimizer:
    """Optimize pipeline execution."""
    
    def optimize(self, pipeline: Pipeline) -> Pipeline:
        """Apply pipeline optimizations."""
        optimized = pipeline
        
        # Parallelize independent steps
        optimized = self._parallelize_steps(optimized)
        
        # Optimize resource allocation
        optimized = self._optimize_resources(optimized)
        
        # Cache intermediate results
        optimized = self._add_caching(optimized)
        
        return optimized
        
    def _parallelize_steps(self, pipeline: Pipeline) -> Pipeline:
        """Identify and parallelize independent steps."""
        dag = self._build_dependency_graph(pipeline)
        return self._schedule_parallel_execution(dag)

# Example pipeline definition
pipeline = Orchestrator().build_pipeline([
    LoadDataStep(source="raw_data.csv"),
    CleaningStep(rules=cleaning_rules),
    AugmentationStep(
        noiser=StyleTransferNoiser(style="technical")
    ),
    ValidationStep(metrics=["completeness", "coherence"]),
    ExportStep(target="processed_data.jsonl")
])

# Run with monitoring
results = pipeline.run(
    monitoring=True,
    alerts=True,
    resource_limits={"max_memory": "4GB"}
)