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Workflow Orchestration

OpenML Crawler provides powerful workflow orchestration capabilities for managing complex data processing pipelines. The system supports workflow definition, execution, monitoring, scheduling, and error handling across distributed environments.

Workflow Definition

Workflow DSL

Define workflows using a domain-specific language:

from openmlcrawler.orchestration import WorkflowDefinition

workflow_def = WorkflowDefinition()

# Define a data processing workflow
workflow = workflow_def.create_workflow(
    name="customer_analytics_pipeline",
    description="End-to-end customer data processing and analytics",
    version="1.0.0",
    tags=["analytics", "customer_data", "daily"]
)

# Add workflow tasks
workflow.add_task(
    name="extract_customer_data",
    type="data_ingestion",
    config={
        "source": "postgresql://db.customer_data",
        "query": "SELECT * FROM customers WHERE updated_date >= '{{ yesterday }}'",
        "output_format": "parquet"
    },
    dependencies=[]
)

workflow.add_task(
    name="validate_customer_data",
    type="data_validation",
    config={
        "input": "{{ extract_customer_data.output }}",
        "schema": "customer_schema.json",
        "validation_rules": ["not_null", "data_type", "range_check"]
    },
    dependencies=["extract_customer_data"]
)

workflow.add_task(
    name="enrich_customer_data",
    type="data_enrichment",
    config={
        "input": "{{ validate_customer_data.output }}",
        "enrichment_sources": ["geographic_data", "demographic_data"],
        "join_keys": ["postal_code", "customer_id"]
    },
    dependencies=["validate_customer_data"]
)

# Define workflow triggers
workflow.add_trigger(
    name="daily_schedule",
    type="schedule",
    config={
        "cron": "0 2 * * *",  # Daily at 2 AM
        "timezone": "UTC"
    }
)

workflow.add_trigger(
    name="manual_trigger",
    type="manual",
    config={
        "parameters": ["date_range", "customer_segment"]
    }
)

Visual Workflow Builder

Create workflows using a visual interface:

from openmlcrawler.orchestration import VisualWorkflowBuilder

builder = VisualWorkflowBuilder()

# Create workflow canvas
workflow = builder.create_canvas(
    name="complex_data_pipeline",
    layout="hierarchical"
)

# Add workflow nodes
extract_node = builder.add_node(
    name="data_extraction",
    type="source",
    position={"x": 100, "y": 100},
    config={
        "connector": "api",
        "endpoint": "https://api.example.com/data",
        "authentication": "oauth2"
    }
)

transform_node = builder.add_node(
    name="data_transformation",
    type="processor",
    position={"x": 300, "y": 100},
    config={
        "operations": ["clean", "normalize", "aggregate"],
        "output_format": "parquet"
    }
)

load_node = builder.add_node(
    name="data_loading",
    type="sink",
    position={"x": 500, "y": 100},
    config={
        "destination": "s3://data-lake/processed/",
        "partition_by": "date"
    }
)

# Connect nodes
builder.connect_nodes(
    source=extract_node,
    target=transform_node,
    condition="success"
)

builder.connect_nodes(
    source=transform_node,
    target=load_node,
    condition="data_quality_score > 0.8"
)

# Add conditional branches
quality_check = builder.add_conditional_node(
    name="quality_gate",
    position={"x": 400, "y": 200},
    conditions=[
        {
            "condition": "quality_score >= 0.9",
            "next_node": load_node
        },
        {
            "condition": "quality_score >= 0.7",
            "next_node": "data_repair"
        },
        {
            "condition": "quality_score < 0.7",
            "next_node": "alert_team"
        }
    ]
)

Template-Based Workflows

Use pre-built workflow templates:

from openmlcrawler.orchestration import WorkflowTemplates

templates = WorkflowTemplates()

# Use ETL template
etl_workflow = templates.create_from_template(
    template="etl_pipeline",
    parameters={
        "source_type": "database",
        "source_connection": "postgres://...",
        "target_type": "data_warehouse",
        "target_connection": "snowflake://...",
        "tables": ["customers", "orders", "products"],
        "incremental": True
    }
)

# Use ML pipeline template
ml_workflow = templates.create_from_template(
    template="ml_pipeline",
    parameters={
        "data_source": "s3://ml-data/",
        "model_type": "classification",
        "target_column": "churn",
        "features": ["age", "tenure", "usage"],
        "train_test_split": 0.8,
        "evaluation_metric": "accuracy"
    }
)

# Use streaming pipeline template
streaming_workflow = templates.create_from_template(
    template="streaming_pipeline",
    parameters={
        "input_stream": "kafka://topic.input",
        "processing_logic": "real_time_aggregation",
        "window_size": "5_minutes",
        "output_stream": "kafka://topic.output",
        "checkpoint_location": "s3://checkpoints/"
    }
)

Execution Engines

Local Execution

Run workflows on local infrastructure:

from openmlcrawler.orchestration import LocalExecutor

executor = LocalExecutor()

# Configure local execution
executor.configure(
    max_workers=4,
    memory_limit="8GB",
    temp_directory="/tmp/openml_workflows",
    log_level="INFO"
)

# Execute workflow locally
execution = executor.execute_workflow(
    workflow=workflow_definition,
    parameters={
        "environment": "development",
        "date_range": "2023-01-01 to 2023-12-31"
    },
    async_execution=False
)

# Monitor execution
status = executor.get_execution_status(execution_id=execution["id"])
logs = executor.get_execution_logs(execution_id=execution["id"])

Distributed Execution

Execute workflows across distributed systems:

from openmlcrawler.orchestration import DistributedExecutor

dist_executor = DistributedExecutor()

# Configure distributed execution
dist_executor.configure(
    cluster_type="kubernetes",  # or "spark", "dask", "ray"
    master_url="k8s://openml-cluster",
    worker_nodes=10,
    resource_limits={
        "cpu": "2",
        "memory": "4Gi"
    }
)

# Execute workflow distributed
dist_execution = dist_executor.execute_workflow(
    workflow=workflow_definition,
    execution_mode="parallel",  # or "sequential", "dag"
    fault_tolerance=True,
    retry_policy={
        "max_attempts": 3,
        "backoff": "exponential",
        "max_delay": "5_minutes"
    }
)

# Scale execution
dist_executor.scale_execution(
    execution_id=dist_execution["id"],
    target_workers=20,
    scaling_policy="cpu_utilization > 70%"
)

Cloud-Native Execution

Execute workflows on cloud platforms:

from openmlcrawler.orchestration import CloudExecutor

cloud_executor = CloudExecutor()

# Configure cloud execution
cloud_executor.configure(
    provider="aws",  # or "gcp", "azure"
    region="us-east-1",
    execution_service="step_functions",  # or "data_pipeline", "composer"
    resource_allocation={
        "instance_type": "m5.large",
        "spot_instances": True,
        "auto_scaling": True
    }
)

# Execute workflow on cloud
cloud_execution = cloud_executor.execute_workflow(
    workflow=workflow_definition,
    execution_environment="serverless",  # or "container", "vm"
    cost_optimization=True,
    monitoring_enabled=True
)

# Cloud-specific features
cloud_executor.enable_features([
    "spot_instance_support",
    "auto_scaling_groups",
    "cloud_watch_monitoring",
    "x_ray_tracing"
])

Scheduling and Triggers

Cron-Based Scheduling

Schedule workflows using cron expressions:

from openmlcrawler.orchestration import WorkflowScheduler

scheduler = WorkflowScheduler()

# Schedule workflow with cron
schedule_id = scheduler.schedule_workflow(
    workflow_name="daily_data_pipeline",
    cron_expression="0 6 * * 1-5",  # Monday to Friday at 6 AM
    timezone="America/New_York",
    parameters={
        "date": "{{ yesterday }}",
        "environment": "production"
    },
    enabled=True
)

# Complex scheduling
complex_schedule = scheduler.create_complex_schedule(
    workflow_name="weekly_report",
    schedule_config={
        "frequency": "weekly",
        "day_of_week": "monday",
        "time": "09:00",
        "timezone": "UTC",
        "conditions": [
            "data_available('last_week')",
            "system_load < 80%"
        ]
    }
)

Event-Based Triggers

Trigger workflows based on events:

from openmlcrawler.orchestration import EventTrigger

event_trigger = EventTrigger()

# File-based triggers
file_trigger = event_trigger.create_file_trigger(
    workflow_name="file_processing_pipeline",
    watch_path="s3://input-bucket/data/",
    file_pattern="*.json",
    event_types=["created", "modified"],
    debounce_seconds=30
)

# Database triggers
db_trigger = event_trigger.create_database_trigger(
    workflow_name="data_sync_pipeline",
    connection_string="postgres://...",
    table="customers",
    event_types=["insert", "update"],
    batch_size=100,
    polling_interval=60
)

# API-based triggers
api_trigger = event_trigger.create_api_trigger(
    workflow_name="webhook_pipeline",
    endpoint="/webhooks/data-update",
    authentication="bearer_token",
    payload_schema="data_update_schema.json"
)

# Message queue triggers
queue_trigger = event_trigger.create_queue_trigger(
    workflow_name="message_processing_pipeline",
    queue_url="sqs://data-queue",
    message_format="json",
    batch_size=10,
    visibility_timeout=300
)

Conditional Triggers

Trigger workflows based on conditions:

from openmlcrawler.orchestration import ConditionalTrigger

conditional_trigger = ConditionalTrigger()

# Data quality-based triggers
quality_trigger = conditional_trigger.create_quality_trigger(
    workflow_name="data_quality_pipeline",
    data_source="production_database",
    quality_metrics={
        "completeness": {"threshold": 0.95, "operator": "<"},
        "accuracy": {"threshold": 0.98, "operator": "<"}
    },
    check_interval=3600,  # 1 hour
    cooldown_period=1800  # 30 minutes
)

# Business logic triggers
business_trigger = conditional_trigger.create_business_trigger(
    workflow_name="business_intelligence_pipeline",
    conditions=[
        {
            "name": "sales_threshold",
            "query": "SELECT SUM(amount) > 1000000 FROM sales WHERE date = CURRENT_DATE",
            "check_interval": 1800
        },
        {
            "name": "inventory_alert",
            "query": "SELECT COUNT(*) < 100 FROM inventory WHERE status = 'low'",
            "check_interval": 3600
        }
    ]
)

# External system triggers
external_trigger = conditional_trigger.create_external_trigger(
    workflow_name="system_integration_pipeline",
    external_system="erp_system",
    trigger_condition="order_status_changed",
    parameters_mapping={
        "order_id": "external_id",
        "status": "new_status",
        "timestamp": "change_time"
    }
)

Monitoring and Observability

Workflow Monitoring

Comprehensive workflow monitoring:

from openmlcrawler.orchestration import WorkflowMonitor

monitor = WorkflowMonitor()

# Monitor workflow execution
execution_status = monitor.monitor_execution(
    workflow_name="customer_pipeline",
    execution_id="exec_12345",
    metrics=[
        "execution_time",
        "task_success_rate",
        "resource_utilization",
        "error_rate",
        "data_throughput"
    ]
)

# Real-time dashboard
dashboard = monitor.create_dashboard(
    workflow_name="customer_pipeline",
    metrics=execution_status,
    refresh_interval=30,
    alert_thresholds={
        "error_rate": 0.05,
        "execution_time": 3600  # 1 hour
    }
)

# Performance analytics
analytics = monitor.analyze_performance(
    workflow_name="customer_pipeline",
    time_range=("2023-01-01", "2023-12-31"),
    analysis_types=[
        "execution_trends",
        "bottleneck_analysis",
        "resource_optimization",
        "failure_patterns"
    ]
)

Task Monitoring

Monitor individual workflow tasks:

from openmlcrawler.orchestration import TaskMonitor

task_monitor = TaskMonitor()

# Monitor task execution
task_status = task_monitor.monitor_task(
    workflow_execution_id="exec_12345",
    task_name="data_validation",
    metrics=[
        "duration",
        "memory_usage",
        "cpu_usage",
        "input_output_size",
        "error_details"
    ]
)

# Task dependencies monitoring
dependencies = task_monitor.monitor_dependencies(
    workflow_execution_id="exec_12345",
    include_blocking_tasks=True,
    include_waiting_tasks=True
)

# Task retry monitoring
retries = task_monitor.monitor_retries(
    workflow_execution_id="exec_12345",
    include_retry_reasons=True,
    include_retry_delays=True
)

Alerting and Notifications

Set up workflow alerts and notifications:

from openmlcrawler.orchestration import WorkflowAlerting

alerting = WorkflowAlerting()

# Configure alerts
alerting.configure_alerts([
    {
        "name": "workflow_failure",
        "condition": "execution_status == 'failed'",
        "channels": ["email", "slack", "pagerduty"],
        "escalation_policy": "immediate"
    },
    {
        "name": "long_running_workflow",
        "condition": "execution_time > 7200",  # 2 hours
        "channels": ["email"],
        "escalation_policy": "delayed"
    },
    {
        "name": "data_quality_issue",
        "condition": "quality_score < 0.8",
        "channels": ["slack"],
        "escalation_policy": "notification_only"
    }
])

# Custom alert conditions
custom_alert = alerting.create_custom_alert(
    name="business_metric_alert",
    condition="""
    SELECT AVG(sales_amount) < 50000
    FROM daily_sales
    WHERE date = CURRENT_DATE
    """,
    check_interval=3600,
    notification_template="sales_drop_alert.html"
)

# Alert history and analytics
alert_history = alerting.get_alert_history(
    time_range=("2023-01-01", "2023-12-31"),
    alert_types=["failure", "performance", "quality"],
    include_resolved=True
)

Error Handling and Recovery

Retry Policies

Configure retry behavior for failed tasks:

from openmlcrawler.orchestration import RetryPolicy

retry_policy = RetryPolicy()

# Exponential backoff retry
exponential_retry = retry_policy.create_policy(
    name="exponential_backoff",
    max_attempts=5,
    initial_delay=60,  # seconds
    backoff_multiplier=2.0,
    max_delay=3600,  # 1 hour
    retry_condition="transient_error"
)

# Custom retry conditions
custom_retry = retry_policy.create_policy(
    name="custom_retry",
    max_attempts=3,
    retry_logic="""
    if error.type == 'connection_error':
        return True
    elif error.type == 'data_error' and error.severity == 'low':
        return True
    else:
        return False
    """,
    delay_strategy="fixed",
    delay_seconds=300
)

# Circuit breaker pattern
circuit_breaker = retry_policy.create_circuit_breaker(
    name="api_circuit_breaker",
    failure_threshold=5,
    recovery_timeout=600,  # 10 minutes
    monitoring_window=300  # 5 minutes
)

Error Recovery

Implement error recovery strategies:

from openmlcrawler.orchestration import ErrorRecovery

recovery = ErrorRecovery()

# Define recovery strategies
recovery_strategies = recovery.define_strategies([
    {
        "name": "data_backup_recovery",
        "trigger_condition": "data_corruption_error",
        "recovery_action": "restore_from_backup",
        "backup_location": "s3://backups/",
        "verification_step": "data_integrity_check"
    },
    {
        "name": "skip_and_continue",
        "trigger_condition": "non_critical_error",
        "recovery_action": "skip_task",
        "compensation_action": "log_warning",
        "continue_workflow": True
    },
    {
        "name": "manual_intervention",
        "trigger_condition": "business_logic_error",
        "recovery_action": "pause_workflow",
        "notification": "alert_data_team",
        "resume_condition": "manual_approval"
    }
])

# Automatic error recovery
recovery_result = recovery.execute_recovery(
    workflow_execution_id="exec_12345",
    error_details=error_info,
    strategy_selection="automatic",  # or "manual"
    max_recovery_attempts=3
)

# Recovery monitoring
recovery_status = recovery.monitor_recovery(
    recovery_execution_id=recovery_result["id"],
    monitoring_metrics=[
        "recovery_time",
        "success_rate",
        "data_loss_assessment",
        "system_impact"
    ]
)

Compensation Actions

Implement compensation for failed operations:

from openmlcrawler.orchestration import CompensationManager

compensation = CompensationManager()

# Define compensation actions
compensations = compensation.define_compensations([
    {
        "name": "undo_data_modification",
        "trigger": "data_update_task_failure",
        "compensation_query": "UPDATE table SET status = 'original' WHERE id = ?",
        "rollback_data": True
    },
    {
        "name": "delete_created_files",
        "trigger": "file_creation_task_failure",
        "compensation_action": "delete_files",
        "file_pattern": "temp_*.tmp",
        "cleanup_location": "s3://temp-files/"
    },
    {
        "name": "revert_database_changes",
        "trigger": "database_task_failure",
        "compensation_action": "execute_rollback_script",
        "script_location": "scripts/rollback.sql",
        "backup_required": True
    }
])

# Execute compensation
compensation_result = compensation.execute_compensation(
    workflow_execution_id="exec_12345",
    failed_task="data_processing",
    compensation_strategy="undo_data_modification",
    verify_compensation=True
)

Advanced Orchestration Features

Dynamic Workflows

Create workflows that adapt at runtime:

from openmlcrawler.orchestration import DynamicWorkflow

dynamic = DynamicWorkflow()

# Create dynamic workflow
workflow = dynamic.create_dynamic_workflow(
    name="adaptive_pipeline",
    base_structure={
        "extract": {"type": "data_ingestion"},
        "validate": {"type": "data_validation"},
        "process": {"type": "data_processing"}
    }
)

# Add dynamic decision points
dynamic.add_decision_point(
    workflow=workflow,
    decision_name="data_volume_check",
    condition="input_data_size > 1000000",  # 1M records
    true_branch={
        "parallel_processing": {"type": "distributed_processing", "workers": 4}
    },
    false_branch={
        "single_processing": {"type": "local_processing"}
    }
)

# Runtime adaptation
adaptation = dynamic.adapt_workflow(
    workflow_instance=workflow,
    runtime_conditions={
        "system_load": 0.8,
        "available_memory": "4GB",
        "data_characteristics": "high_variance"
    },
    adaptation_rules=[
        "scale_workers_if_high_load",
        "switch_to_memory_optimized_if_low_memory",
        "add_validation_if_high_variance"
    ]
)

Workflow Composition

Compose complex workflows from simpler ones:

from openmlcrawler.orchestration import WorkflowComposer

composer = WorkflowComposer()

# Define workflow components
components = {
    "data_ingestion": composer.create_component(
        name="data_ingestion",
        tasks=["extract_api", "extract_database", "extract_files"],
        input_schema="ingestion_input.json",
        output_schema="raw_data.json"
    ),
    "data_processing": composer.create_component(
        name="data_processing",
        tasks=["validate", "clean", "transform", "enrich"],
        input_schema="raw_data.json",
        output_schema="processed_data.json"
    ),
    "data_output": composer.create_component(
        name="data_output",
        tasks=["load_database", "load_files", "send_notifications"],
        input_schema="processed_data.json",
        output_schema="completion_status.json"
    )
}

# Compose workflow
composed_workflow = composer.compose_workflow(
    name="complete_data_pipeline",
    components=components,
    data_flow=[
        "data_ingestion -> data_processing",
        "data_processing -> data_output"
    ],
    error_handling="component_level",
    monitoring="comprehensive"
)

# Nested composition
nested_workflow = composer.create_nested_workflow(
    name="multi_stage_pipeline",
    sub_workflows=[
        "stage1_processing",
        "stage2_analytics",
        "stage3_reporting"
    ],
    coordination="event_driven"
)

Workflow Versioning

Manage workflow versions and deployments:

from openmlcrawler.orchestration import WorkflowVersioning

versioning = WorkflowVersioning()

# Create workflow version
version = versioning.create_version(
    workflow_name="customer_pipeline",
    version_number="2.1.0",
    changes={
        "added": ["new_validation_rules"],
        "modified": ["data_transformation_logic"],
        "deprecated": ["old_reporting_format"]
    },
    compatibility="backward_compatible"
)

# Version comparison
comparison = versioning.compare_versions(
    workflow_name="customer_pipeline",
    version1="2.0.0",
    version2="2.1.0",
    comparison_type="diff"  # or "compatibility", "impact"
)

# Version deployment
deployment = versioning.deploy_version(
    workflow_name="customer_pipeline",
    version="2.1.0",
    deployment_strategy="canary",  # or "blue_green", "rolling"
    traffic_distribution={
        "v2.0.0": 0.9,
        "v2.1.0": 0.1
    },
    rollback_enabled=True
)

# Version lifecycle management
lifecycle = versioning.manage_lifecycle(
    workflow_name="customer_pipeline",
    retention_policy={
        "development": "keep_last_5",
        "staging": "keep_last_10",
        "production": "keep_last_20"
    },
    archival_policy="compress_after_1_year"
)

Configuration

Orchestration Configuration

Configure orchestration settings:

orchestration:
  engine:
    type: "kubernetes"  # or "local", "distributed", "cloud"
    max_concurrent_workflows: 10
    default_timeout: 3600
    retry_policy: "exponential_backoff"

  scheduling:
    timezone: "UTC"
    max_schedule_ahead: "30_days"
    conflict_resolution: "priority_based"

  monitoring:
    enable_detailed_logging: true
    metrics_collection: true
    alert_on_failure: true
    dashboard_enabled: true

  error_handling:
    default_retry_attempts: 3
    circuit_breaker_enabled: true
    compensation_enabled: true

  security:
    workflow_encryption: true
    audit_logging: true
    access_control: "rbac"

Workflow Templates Configuration

Configure workflow templates:

workflow_templates:
  etl_pipeline:
    stages: ["extract", "transform", "load"]
    default_connectors:
      extract: "database"
      load: "data_warehouse"
    validation_rules: ["schema_check", "data_quality"]
    monitoring: "standard"

  ml_pipeline:
    stages: ["data_prep", "feature_eng", "model_train", "model_eval", "model_deploy"]
    default_algorithms: ["random_forest", "xgboost", "neural_network"]
    evaluation_metrics: ["accuracy", "precision", "recall", "f1_score"]
    deployment_targets: ["api", "batch", "streaming"]

  streaming_pipeline:
    stages: ["stream_ingest", "real_time_process", "stream_output"]
    windowing_strategies: ["tumbling", "sliding", "session"]
    state_management: "checkpointing"
    fault_tolerance: "exactly_once"

Best Practices

Workflow Design

  1. Modular Design: Break complex workflows into smaller, manageable components
  2. Error Boundaries: Implement proper error boundaries and isolation
  3. Idempotency: Design tasks to be idempotent for reliable retries
  4. Dependency Management: Clearly define and manage task dependencies
  5. Resource Optimization: Optimize resource usage and parallelization
  6. Monitoring Integration: Integrate comprehensive monitoring from the start

Execution Optimization

  1. Parallel Execution: Maximize parallel execution where possible
  2. Resource Pooling: Use resource pooling for efficient resource management
  3. Caching: Implement intelligent caching for intermediate results
  4. Batch Processing: Use batch processing for bulk operations
  5. Lazy Evaluation: Use lazy evaluation for conditional execution
  6. Incremental Processing: Implement incremental processing for efficiency

Reliability

  1. Fault Tolerance: Design for failure with proper retry and recovery mechanisms
  2. Circuit Breakers: Implement circuit breakers for external service calls
  3. Graceful Degradation: Ensure graceful degradation under load or failure
  4. Health Checks: Implement comprehensive health checks and monitoring
  5. Backup and Recovery: Maintain backup and recovery procedures
  6. Disaster Recovery: Plan for disaster recovery scenarios

Scalability

  1. Horizontal Scaling: Design for horizontal scaling across multiple nodes
  2. Load Balancing: Implement proper load balancing mechanisms
  3. Auto-Scaling: Use auto-scaling based on workload demands
  4. Queue Management: Implement efficient queue management for task distribution
  5. Data Partitioning: Use data partitioning for large-scale processing
  6. Caching Strategies: Implement multi-level caching strategies

Troubleshooting

Common Workflow Issues

Workflow Execution Failures

Issue: Workflow execution fails at specific task
Solution: Check task logs, validate input data, verify resource availability, review error handling

Performance Bottlenecks

Issue: Workflow execution is slow
Solution: Profile execution, optimize resource allocation, review parallelization, check data volumes

Dependency Conflicts

Issue: Task dependencies causing deadlocks
Solution: Review dependency graph, implement proper ordering, use conditional execution

Monitoring Issues

Missing Metrics

Issue: Workflow metrics not being collected
Solution: Verify monitoring configuration, check metric collection agents, review logging setup

Alert Fatigue

Issue: Too many alerts generated
Solution: Tune alert thresholds, implement alert aggregation, review alert conditions

Dashboard Problems

Issue: Monitoring dashboard not updating
Solution: Check data pipeline, verify metric storage, review dashboard configuration

See Also