Advanced Features

This guide covers the advanced features of OpenML Crawler, including real-time monitoring, federated learning, self-healing pipelines, and distributed processing capabilities.

📊 Real-Time Monitoring & Analytics

Real-Time Data Streams

from openmlcrawler.core.monitoring import RealTimeMonitor

# Create real-time monitor
monitor = RealTimeMonitor()

# Monitor data quality in real-time
@monitor.on_data_received
def process_streaming_data(data_batch):
    """Process incoming data batches."""
    # Quality assessment
    quality_score = assess_data_quality(data_batch)

    # Alert if quality drops
    if quality_score < 0.8:
        monitor.alert("Data quality dropped below threshold", quality_score)

    # Update metrics
    monitor.update_metrics({
        "records_processed": len(data_batch),
        "quality_score": quality_score,
        "processing_time": time.time() - start_time
    })

# Start monitoring
monitor.start_stream_monitoring(
    source="kafka://data-stream:9092",
    topic="weather_data",
    batch_size=100,
    interval_seconds=30
)

Performance Monitoring

from openmlcrawler.core.monitoring import PerformanceMonitor

# Monitor system performance
perf_monitor = PerformanceMonitor()

# Track pipeline performance
with perf_monitor.track_operation("data_processing_pipeline"):
    # Your data processing code here
    df = load_dataset("weather", location="New York")
    clean_df = clean_data(df)
    result = process_data(clean_df)

# Get performance metrics
metrics = perf_monitor.get_metrics()

print("Performance Metrics:")
print(f"Total operations: {metrics['total_operations']}")
print(f"Average latency: {metrics['avg_latency_ms']:.2f}ms")
print(f"Memory usage: {metrics['memory_mb']:.2f}MB")
print(f"CPU usage: {metrics['cpu_percent']:.1f}%")

Custom Metrics & Alerts

from openmlcrawler.core.monitoring import MetricsCollector, AlertManager

# Create metrics collector
metrics = MetricsCollector()

# Define custom metrics
metrics.register_metric(
    name="data_freshness",
    type="gauge",
    description="Time since last data update"
)

metrics.register_metric(
    name="error_rate",
    type="counter",
    description="Rate of processing errors"
)

# Create alert manager
alerts = AlertManager()

# Define alerts
alerts.create_alert(
    name="high_error_rate",
    condition="error_rate > 0.05",
    severity="critical",
    channels=["email", "slack"]
)

alerts.create_alert(
    name="stale_data",
    condition="data_freshness > 3600",  # 1 hour
    severity="warning",
    channels=["dashboard"]
)

# Update metrics
metrics.set_gauge("data_freshness", time.time() - last_update)
metrics.increment_counter("error_rate", error_count)

🌐 Federated Learning

Federated Learning Setup

from openmlcrawler.core.federated import FederatedLearner, FederatedServer

# Create federated server
server = FederatedServer(
    num_clients=10,
    min_clients_per_round=5,
    rounds=100
)

# Configure federated learning
server.configure_model(
    model_type="neural_network",
    input_shape=(784,),
    num_classes=10,
    learning_rate=0.01
)

# Start federated learning
server.start_federated_training()

# Client-side participation
client = FederatedLearner(server_address="federated-server:8080")

# Join federated learning
client.join_federation(client_id="client_001")

# Train on local data
for round_num in range(100):
    # Get global model
    global_model = client.get_global_model()

    # Train on local dataset
    local_model = client.train_local_model(
        global_model,
        local_dataset,
        epochs=5
    )

    # Send updates to server
    client.send_model_update(local_model)

Privacy-Preserving Federated Learning

from openmlcrawler.core.federated import PrivateFederatedLearner

# Create privacy-preserving client
private_client = PrivateFederatedLearner(
    server_address="secure-server:8080",
    privacy_budget=1.0,
    noise_multiplier=0.1
)

# Differential privacy training
private_client.enable_differential_privacy(
    epsilon=0.5,
    delta=1e-5
)

# Secure aggregation
private_client.enable_secure_aggregation(
    key_size=2048,
    threshold=3  # Minimum clients for reconstruction
)

# Train with privacy guarantees
private_model = private_client.train_private_model(
    dataset=local_data,
    target_column="label",
    privacy_level="high"
)

Federated Analytics

from openmlcrawler.core.federated import FederatedAnalytics

# Create federated analytics engine
analytics = FederatedAnalytics()

# Distributed statistical analysis
stats = analytics.compute_federated_statistics(
    clients=["client_1", "client_2", "client_3"],
    query="SELECT AVG(temperature), COUNT(*) FROM weather_data WHERE city='NYC'",
    privacy_budget=0.1
)

print("Federated Statistics:")
print(f"Average temperature: {stats['avg_temperature']:.2f}")
print(f"Total records: {stats['total_count']}")

# Federated data visualization
chart_data = analytics.create_federated_chart(
    clients=["client_1", "client_2"],
    chart_type="histogram",
    column="temperature",
    bins=20
)

🔧 Self-Healing Pipelines

Auto-Recovery Configuration

from openmlcrawler.core.self_healing import SelfHealingPipeline

# Create self-healing pipeline
pipeline = SelfHealingPipeline()

# Configure recovery strategies
pipeline.add_recovery_strategy(
    failure_type="connection_error",
    strategy="retry",
    max_retries=3,
    backoff_seconds=5
)

pipeline.add_recovery_strategy(
    failure_type="data_quality_error",
    strategy="fallback",
    fallback_source="backup_data_source"
)

pipeline.add_recovery_strategy(
    failure_type="memory_error",
    strategy="scale_down",
    max_batch_size=1000
)

# Define pipeline steps with error handling
@pipeline.step(recovery_strategies=["retry", "fallback"])
def load_weather_data():
    return load_dataset("weather", location="New York")

@pipeline.step(recovery_strategies=["scale_down"])
def process_large_dataset(data):
    return process_data(data)

# Execute with auto-recovery
result = pipeline.execute_with_recovery()

Intelligent Error Detection

from openmlcrawler.core.self_healing import ErrorDetector

# Create error detector
detector = ErrorDetector()

# Configure error patterns
detector.add_error_pattern(
    name="api_rate_limit",
    pattern=r"rate limit exceeded",
    severity="warning",
    auto_recover=True,
    recovery_action="wait_and_retry",
    wait_seconds=60
)

detector.add_error_pattern(
    name="data_corruption",
    pattern=r"unexpected data format",
    severity="error",
    auto_recover=True,
    recovery_action="switch_source"
)

detector.add_error_pattern(
    name="memory_exhaustion",
    pattern=r"out of memory",
    severity="critical",
    auto_recover=True,
    recovery_action="reduce_batch_size"
)

# Monitor for errors
detector.start_monitoring()

# Process with error detection
try:
    result = process_data_with_monitoring(data, detector)
except Exception as e:
    recovery_action = detector.suggest_recovery(str(e))
    if recovery_action:
        print(f"Suggested recovery: {recovery_action}")
        # Execute recovery
        detector.execute_recovery(recovery_action)

Adaptive Resource Management

from openmlcrawler.core.self_healing import AdaptiveResourceManager

# Create adaptive resource manager
resource_manager = AdaptiveResourceManager()

# Configure resource limits
resource_manager.set_limits(
    max_memory_gb=8,
    max_cpu_percent=80,
    max_concurrent_tasks=10
)

# Adaptive scaling based on workload
resource_manager.enable_adaptive_scaling(
    scale_up_threshold=0.8,    # Scale up at 80% utilization
    scale_down_threshold=0.3,  # Scale down at 30% utilization
    min_workers=2,
    max_workers=20
)

# Monitor and adjust resources
resource_manager.start_resource_monitoring(interval_seconds=30)

# Process with adaptive resources
result = resource_manager.process_with_adaptation(
    data=data,
    operation="heavy_computation",
    estimated_complexity="high"
)

🚀 Distributed Processing

Ray Integration

import ray
from openmlcrawler.core.distributed import RayProcessor

# Initialize Ray
ray.init()

# Create Ray processor
processor = RayProcessor()

# Distributed data processing
@ray.remote
def process_partition(partition):
    """Process a data partition."""
    return clean_data(partition)

# Split data into partitions
partitions = split_data(df, num_partitions=10)

# Process in parallel
futures = [process_partition.remote(partition) for partition in partitions]
results = ray.get(futures)

# Combine results
final_result = combine_results(results)

Dask Integration

import dask.dataframe as dd
from openmlcrawler.core.distributed import DaskProcessor

# Create Dask processor
processor = DaskProcessor()

# Convert to Dask DataFrame
ddf = dd.from_pandas(df, npartitions=8)

# Distributed operations
clean_ddf = processor.clean_distributed(ddf)
processed_ddf = processor.process_distributed(clean_ddf)

# Compute results
result = processed_ddf.compute()

# Distributed ML training
from dask_ml.model_selection import train_test_split
from dask_ml.linear_model import LogisticRegression

X_train, X_test, y_train, y_test = train_test_split(
    processed_ddf.drop('target', axis=1),
    processed_ddf['target'],
    test_size=0.2
)

# Train distributed model
model = LogisticRegression()
model.fit(X_train, y_train)

# Predict
predictions = model.predict(X_test)

Kubernetes Integration

from openmlcrawler.core.distributed import KubernetesProcessor

# Create Kubernetes processor
k8s_processor = KubernetesProcessor(
    namespace="data-processing",
    image="openmlcrawler:latest"
)

# Deploy distributed job
job_spec = {
    "name": "data-processing-job",
    "replicas": 5,
    "resources": {
        "requests": {"cpu": "500m", "memory": "1Gi"},
        "limits": {"cpu": "1000m", "memory": "2Gi"}
    },
    "env": {
        "DATA_SOURCE": "s3://bucket/data.csv",
        "OUTPUT_PATH": "s3://bucket/processed/"
    }
}

# Deploy and monitor
job_id = k8s_processor.deploy_job(job_spec)
status = k8s_processor.monitor_job(job_id)

print(f"Job status: {status['phase']}")
print(f"Running pods: {status['running_pods']}")
print(f"Completed pods: {status['completed_pods']}")

# Get results
results = k8s_processor.get_job_results(job_id)

🔐 Advanced Security Features

Data Encryption

from openmlcrawler.core.security import DataEncryptor

# Create encryptor
encryptor = DataEncryptor(
    algorithm="AES-256-GCM",
    key_rotation_days=30
)

# Encrypt sensitive data
encrypted_df = encryptor.encrypt_dataframe(
    df,
    columns=["ssn", "credit_card", "personal_info"],
    key_id="production-key-v1"
)

# Store encrypted data
encryptor.store_encrypted_data(
    encrypted_df,
    storage_path="s3://secure-bucket/encrypted-data/",
    metadata={"encryption_key": "v1", "algorithm": "AES-256-GCM"}
)

# Decrypt when needed
decrypted_df = encryptor.decrypt_dataframe(
    encrypted_data_path="s3://secure-bucket/encrypted-data/",
    key_id="production-key-v1"
)

Access Control

from openmlcrawler.core.security import AccessController

# Create access controller
ac = AccessController()

# Define roles and permissions
ac.create_role("data_analyst", permissions=[
    "read:weather_data",
    "read:social_media_data",
    "execute:analysis_pipeline"
])

ac.create_role("data_engineer", permissions=[
    "read:*",
    "write:processed_data",
    "execute:etl_pipeline",
    "manage:pipelines"
])

# Assign roles to users
ac.assign_role("user_123", "data_analyst")
ac.assign_role("user_456", "data_engineer")

# Check permissions
if ac.has_permission("user_123", "read:weather_data"):
    weather_data = load_dataset("weather")
else:
    raise PermissionError("Access denied")

# Row-level security
ac.enable_row_level_security(
    table="customer_data",
    policy="user_id = current_user_id()"
)

Audit Logging

from openmlcrawler.core.security import AuditLogger

# Create audit logger
audit = AuditLogger(
    log_destination="cloudwatch://audit-logs",
    retention_days=365
)

# Log all data access
@audit.log_access
def access_sensitive_data(user_id, data_type):
    """Access sensitive data with audit logging."""
    audit.log_event({
        "event_type": "data_access",
        "user_id": user_id,
        "data_type": data_type,
        "timestamp": datetime.now(),
        "source_ip": get_client_ip(),
        "action": "read"
    })

    return load_dataset(data_type)

# Log pipeline executions
@audit.log_pipeline_execution
def execute_data_pipeline(pipeline_name, parameters):
    """Execute pipeline with audit logging."""
    audit.log_event({
        "event_type": "pipeline_execution",
        "pipeline_name": pipeline_name,
        "parameters": parameters,
        "user_id": get_current_user(),
        "start_time": datetime.now()
    })

    result = execute_pipeline(pipeline_name, parameters)

    audit.log_event({
        "event_type": "pipeline_completion",
        "pipeline_name": pipeline_name,
        "status": "success",
        "end_time": datetime.now(),
        "records_processed": len(result)
    })

    return result

# Query audit logs
audit_logs = audit.query_logs(
    user_id="user_123",
    date_range=("2024-01-01", "2024-01-31"),
    event_type="data_access"
)

📈 Advanced Analytics

Time Series Analysis

from openmlcrawler.core.analytics import TimeSeriesAnalyzer

# Create time series analyzer
ts_analyzer = TimeSeriesAnalyzer()

# Analyze time series data
analysis = ts_analyzer.analyze_series(
    df,
    time_column="timestamp",
    value_column="temperature",
    frequency="H"  # Hourly data
)

print("Time Series Analysis:")
print(f"Trend: {analysis['trend']}")
print(f"Seasonality: {analysis['seasonal']}")
print(f"Stationarity: {analysis['stationary']}")

# Forecast future values
forecast = ts_analyzer.forecast(
    df,
    target_column="temperature",
    periods=24,  # 24 hours ahead
    model="prophet"
)

print("24-hour Forecast:")
for i, value in enumerate(forecast['predictions'][:5]):
    print(f"Hour {i+1}: {value:.2f}")

Anomaly Detection

from openmlcrawler.core.analytics import AnomalyDetector

# Create anomaly detector
detector = AnomalyDetector()

# Detect anomalies in data
anomalies = detector.detect_anomalies(
    df,
    method="isolation_forest",
    contamination=0.1,  # Expected 10% anomalies
    features=["temperature", "humidity", "pressure"]
)

print(f"Detected {len(anomalies)} anomalies")

# Visualize anomalies
detector.plot_anomalies(
    df,
    anomalies,
    save_path="anomaly_analysis.png"
)

# Real-time anomaly detection
@detector.on_anomaly_detected
def handle_anomaly(anomaly_data):
    """Handle detected anomalies."""
    print(f"Anomaly detected: {anomaly_data}")

    # Send alert
    send_alert(
        message=f"Anomaly in {anomaly_data['feature']}: {anomaly_data['value']}",
        severity="high"
    )

# Start real-time monitoring
detector.start_realtime_monitoring(
    data_stream="kafka://sensor-data:9092",
    threshold=0.95  # 95% confidence threshold
)

Predictive Modeling

from openmlcrawler.core.analytics import PredictiveModeler

# Create predictive modeler
modeler = PredictiveModeler()

# AutoML model selection
best_model = modeler.auto_select_model(
    X_train, y_train,
    task="regression",
    time_limit=300,  # 5 minutes
    metric="rmse"
)

print(f"Best model: {best_model.__class__.__name__}")
print(f"Best score: {best_model.score(X_test, y_test):.3f}")

# Feature importance analysis
importance = modeler.analyze_feature_importance(
    best_model,
    feature_names=X_train.columns
)

print("Top 5 Important Features:")
for feature, score in importance[:5]:
    print(f"  {feature}: {score:.3f}")

# Model explainability
explanation = modeler.explain_prediction(
    model=best_model,
    instance=X_test.iloc[0],
    method="shap"
)

print("Prediction Explanation:")
print(f"Base value: {explanation['base_value']:.3f}")
print(f"Prediction: {explanation['prediction']:.3f}")

for feature, contribution in explanation['feature_contributions'][:3]:
    print(f"  {feature}: {contribution:.3f}")

🔗 API Integration

REST API Server

from openmlcrawler.core.api import APIServer

# Create API server
server = APIServer(host="0.0.0.0", port=8080)

# Register endpoints
@server.get("/health")
def health_check():
    return {"status": "healthy", "timestamp": datetime.now()}

@server.post("/datasets/{dataset_type}")
def load_dataset_endpoint(dataset_type: str, request_data: dict):
    """Load dataset via API."""
    df = load_dataset(dataset_type, **request_data)

    return {
        "dataset_type": dataset_type,
        "shape": df.shape,
        "columns": list(df.columns),
        "data": df.to_dict('records')[:10]  # First 10 rows
    }

@server.post("/process")
def process_data_endpoint(data: dict):
    """Process data via API."""
    df = pd.DataFrame(data['data'])

    # Process data
    clean_df = clean_data(df)
    result_df = prepare_for_ml(clean_df, target_column=data.get('target'))

    return {
        "processed_shape": result_df.shape,
        "quality_score": assess_data_quality(result_df),
        "data": result_df.to_dict('records')
    }

# Start server
server.start()

GraphQL API

from openmlcrawler.core.api import GraphQLServer

# Create GraphQL server
gql_server = GraphQLServer()

# Define GraphQL schema
schema = """
    type Dataset {
        id: ID!
        name: String!
        shape: String!
        columns: [String!]!
        data: [[String]]!
        quality_score: Float!
    }

    type Query {
        datasets: [Dataset!]!
        dataset(id: ID!): Dataset
    }

    type Mutation {
        loadDataset(type: String!, params: String): Dataset!
        processDataset(id: ID!, operations: [String!]!): Dataset!
    }
"""

# Implement resolvers
@gql_server.query("datasets")
def resolve_datasets():
    """Resolve all datasets query."""
    # Return list of available datasets
    return get_available_datasets()

@gql_server.query("dataset")
def resolve_dataset(id):
    """Resolve single dataset query."""
    return get_dataset_by_id(id)

@gql_server.mutation("loadDataset")
def resolve_load_dataset(type, params):
    """Resolve load dataset mutation."""
    params_dict = json.loads(params) if params else {}
    df = load_dataset(type, **params_dict)

    return {
        "id": str(uuid.uuid4()),
        "name": f"{type}_dataset",
        "shape": f"{df.shape[0]}x{df.shape[1]}",
        "columns": list(df.columns),
        "data": df.values.tolist()[:10],
        "quality_score": assess_data_quality(df)
    }

# Start GraphQL server
gql_server.start(schema, host="0.0.0.0", port=4000)

WebSocket Streaming

from openmlcrawler.core.api import WebSocketServer

# Create WebSocket server
ws_server = WebSocketServer(host="0.0.0.0", port=8081)

# Handle real-time data streaming
@ws_server.on_connect
def handle_connect(client_id):
    """Handle client connection."""
    print(f"Client {client_id} connected")

@ws_server.on_disconnect
def handle_disconnect(client_id):
    """Handle client disconnection."""
    print(f"Client {client_id} disconnected")

@ws_server.on_message("subscribe")
def handle_subscribe(client_id, data):
    """Handle data subscription."""
    dataset_type = data.get("dataset_type")
    filters = data.get("filters", {})

    # Subscribe to real-time data
    subscription_id = ws_server.subscribe_client(
        client_id,
        dataset_type,
        filters
    )

    return {"subscription_id": subscription_id}

@ws_server.on_message("unsubscribe")
def handle_unsubscribe(client_id, data):
    """Handle data unsubscription."""
    subscription_id = data.get("subscription_id")
    ws_server.unsubscribe_client(client_id, subscription_id)

    return {"status": "unsubscribed"}

# Broadcast data updates
def broadcast_data_updates():
    """Broadcast data updates to subscribed clients."""
    while True:
        # Get latest data
        new_data = get_latest_data()

        # Broadcast to subscribers
        ws_server.broadcast_to_subscribers(
            dataset_type="weather",
            data=new_data
        )

        time.sleep(30)  # Update every 30 seconds

# Start WebSocket server
ws_server.start()

# Start broadcasting in background
import threading
threading.Thread(target=broadcast_data_updates, daemon=True).start()

These advanced features make OpenML Crawler a powerful platform for enterprise-grade data processing, machine learning, and analytics workflows.