Analytics and Export

class AnalyticsServiceClient:
    def export_analytics_metrics(self, request: ExportAnalyticsMetricsRequest) -> Operation:
        """
        Exports analytics metrics data to external destinations (long-running operation).
        
        Args:
            request: Contains catalog, output configuration, and export filters
            
        Returns:
            Operation: Resolves to ExportAnalyticsMetricsResponse with export details
            
        Raises:
            InvalidArgument: If export parameters are invalid
            PermissionDenied: If insufficient permissions for export destination
        """

class AnalyticsServiceAsyncClient:
    async def export_analytics_metrics(self, request: ExportAnalyticsMetricsRequest) -> Operation:
        """
        Exports analytics metrics data to external destinations (long-running operation).
        
        Args:
            request: Contains catalog, output configuration, and export filters
            
        Returns:
            Operation: Resolves to ExportAnalyticsMetricsResponse with export details
            
        Raises:
            InvalidArgument: If export parameters are invalid
            PermissionDenied: If insufficient permissions for export destination
        """

class ExportAnalyticsMetricsRequest:
    catalog: str  # Catalog resource name (required)
    output_config: OutputConfig  # Export destination configuration (required)
    filter: str  # Filter expression for data to export
    
class OutputConfig:
    destination: str  # Export destination type
    gcs_destination: GcsDestination  # Google Cloud Storage destination
    bigquery_destination: BigQueryDestination  # BigQuery destination

class GcsDestination:
    output_uri_prefix: str  # GCS URI prefix for output files (required)
    
class BigQueryDestination:
    project_id: str  # BigQuery project ID (required)
    dataset_id: str  # BigQuery dataset ID (required)
    table_id: str  # BigQuery table ID (required)
    table_type: str  # Table type (TABLE, VIEW)

class ExportAnalyticsMetricsResponse:
    error_samples: List[Status]  # Sample of errors encountered during export
    errors_config: ExportErrorsConfig  # Error handling configuration used
    output_result: OutputResult  # Details about exported data

class OutputResult:
    bigquery_result: List[BigQueryOutputResult]  # BigQuery export results
    gcs_result: List[GcsOutputResult]  # GCS export results

class BigQueryOutputResult:
    dataset_id: str  # BigQuery dataset ID
    table_id: str  # BigQuery table ID

class GcsOutputResult:
    output_uri: str  # GCS URI of exported file
    bytes_count: int  # Number of bytes exported

class ExportErrorsConfig:
    gcs_prefix: str  # GCS prefix for error files
    
class ExportMetadata:
    create_time: Timestamp  # Export operation start time
    update_time: Timestamp  # Last update time
    partial_failure: bool  # Whether export had partial failures
    request: ExportAnalyticsMetricsRequest  # Original export request

# Analytics metric types that can be exported
ANALYTICS_METRIC_SEARCH_ANALYTICS = "search-analytics"  # Search behavior and performance
ANALYTICS_METRIC_USER_EVENTS = "user-events"  # User interaction events
ANALYTICS_METRIC_PRODUCT_PERFORMANCE = "product-performance"  # Product view/purchase metrics  
ANALYTICS_METRIC_RECOMMENDATION_PERFORMANCE = "recommendation-performance"  # Recommendation effectiveness
ANALYTICS_METRIC_REVENUE_ANALYTICS = "revenue-analytics"  # Revenue and conversion metrics
ANALYTICS_METRIC_CATALOG_ANALYTICS = "catalog-analytics"  # Catalog usage and coverage

# Filter expressions for analytics data
# Time-based filters
FILTER_TIME_RANGE = 'event_time >= "2024-01-01T00:00:00Z" AND event_time <= "2024-01-31T23:59:59Z"'

# Event type filters  
FILTER_PURCHASE_EVENTS = 'event_type = "purchase-complete"'
FILTER_SEARCH_EVENTS = 'event_type = "search"'
FILTER_VIEW_EVENTS = 'event_type = "page-view"'

# Product filters
FILTER_PRODUCT_CATEGORY = 'product_details.product.categories: ANY("Electronics")'
FILTER_PRICE_RANGE = 'product_details.product.price_info.price >= 100 AND product_details.product.price_info.price <= 1000'

# User filters
FILTER_REGISTERED_USERS = 'user_info.user_id != ""'

from google.cloud import retail

client = retail.AnalyticsServiceClient()

# Export user events to BigQuery for analysis
output_config = retail.OutputConfig(
    bigquery_destination=retail.BigQueryDestination(
        project_id="my-analytics-project",
        dataset_id="retail_analytics",
        table_id="user_events_2024"
    )
)

request = retail.ExportAnalyticsMetricsRequest(
    catalog="projects/my-project/locations/global/catalogs/default_catalog",
    output_config=output_config,
    filter='event_time >= "2024-01-01T00:00:00Z" AND event_time <= "2024-12-31T23:59:59Z"'
)

operation = client.export_analytics_metrics(request=request)
print(f"Export operation started: {operation.name}")

# Wait for export to complete
result = operation.result()
print("Export completed successfully")

if result.output_result.bigquery_result:
    for bq_result in result.output_result.bigquery_result:
        print(f"Data exported to: {bq_result.dataset_id}.{bq_result.table_id}")

if result.error_samples:
    print(f"Export had {len(result.error_samples)} errors")
    for error in result.error_samples[:3]:  # Show first 3 errors
        print(f"- {error.message}")

# Export purchase events and revenue data to GCS
output_config = retail.OutputConfig(
    gcs_destination=retail.GcsDestination(
        output_uri_prefix="gs://my-analytics-bucket/exports/purchase-data/"
    )
)

# Filter for purchase events only
purchase_filter = 'event_type = "purchase-complete" AND event_time >= "2024-01-01T00:00:00Z"'

request = retail.ExportAnalyticsMetricsRequest(
    catalog="projects/my-project/locations/global/catalogs/default_catalog",
    output_config=output_config,
    filter=purchase_filter
)

operation = client.export_analytics_metrics(request=request)
print(f"Purchase data export operation: {operation.name}")

# Monitor operation progress
result = operation.result()
print("Purchase data export completed")

if result.output_result.gcs_result:
    for gcs_result in result.output_result.gcs_result:
        print(f"File exported: {gcs_result.output_uri}")
        print(f"Size: {gcs_result.bytes_count} bytes")

# Export product performance metrics for electronics category
product_performance_config = retail.OutputConfig(
    bigquery_destination=retail.BigQueryDestination(
        project_id="my-analytics-project",
        dataset_id="retail_analytics", 
        table_id="product_performance_electronics"
    )
)

# Filter for electronics products with detailed performance data
electronics_filter = '''
    product_details.product.categories: ANY("Electronics") AND
    event_time >= "2024-01-01T00:00:00Z" AND
    event_type: ANY("page-view", "add-to-cart", "purchase-complete")
'''

request = retail.ExportAnalyticsMetricsRequest(
    catalog="projects/my-project/locations/global/catalogs/default_catalog",
    output_config=product_performance_config,
    filter=electronics_filter
)

operation = client.export_analytics_metrics(request=request)
result = operation.result()

print("Product performance data exported")
print(f"Electronics performance data available in BigQuery")

# Export search analytics to understand search behavior and performance
search_analytics_config = retail.OutputConfig(
    bigquery_destination=retail.BigQueryDestination(
        project_id="my-analytics-project",
        dataset_id="retail_analytics",
        table_id="search_analytics_monthly"
    )
)

# Filter for search events with query and result information
search_filter = '''
    event_type = "search" AND
    event_time >= "2024-01-01T00:00:00Z" AND
    event_time <= "2024-01-31T23:59:59Z" AND
    search_query != ""
'''

request = retail.ExportAnalyticsMetricsRequest(
    catalog="projects/my-project/locations/global/catalogs/default_catalog",
    output_config=search_analytics_config,
    filter=search_filter
)

operation = client.export_analytics_metrics(request=request)
result = operation.result()

print("Search analytics exported for January 2024")

# Export recommendation performance metrics to analyze ML model effectiveness
recommendation_config = retail.OutputConfig(
    gcs_destination=retail.GcsDestination(
        output_uri_prefix="gs://my-analytics-bucket/recommendations/performance/"
    )
)

# Filter for events that have attribution tokens (came from recommendations)
recommendation_filter = '''
    attribution_token != "" AND
    event_time >= "2024-01-01T00:00:00Z" AND
    event_type: ANY("page-view", "add-to-cart", "purchase-complete")
'''

request = retail.ExportAnalyticsMetricsRequest(
    catalog="projects/my-project/locations/global/catalogs/default_catalog",
    output_config=recommendation_config,
    filter=recommendation_filter
)

operation = client.export_analytics_metrics(request=request)
result = operation.result()

print("Recommendation performance data exported")
for gcs_result in result.output_result.gcs_result:
    print(f"Recommendation metrics: {gcs_result.output_uri}")

# Export user behavior data for cohort analysis
cohort_config = retail.OutputConfig(
    bigquery_destination=retail.BigQueryDestination(
        project_id="my-analytics-project",
        dataset_id="user_analytics",
        table_id="user_cohort_data"
    )
)

# Filter for registered users with complete user journey data
user_behavior_filter = '''
    user_info.user_id != "" AND
    event_time >= "2024-01-01T00:00:00Z" AND
    event_type: ANY("page-view", "search", "add-to-cart", "purchase-complete")
'''

request = retail.ExportAnalyticsMetricsRequest(
    catalog="projects/my-project/locations/global/catalogs/default_catalog",
    output_config=cohort_config,
    filter=user_behavior_filter
)

operation = client.export_analytics_metrics(request=request)
result = operation.result()

print("User behavior data exported for cohort analysis")

# Export revenue analytics segmented by product category
revenue_config = retail.OutputConfig(
    bigquery_destination=retail.BigQueryDestination(
        project_id="my-analytics-project",
        dataset_id="revenue_analytics",
        table_id="category_revenue_2024"
    )
)

# Filter for purchase events with revenue data
revenue_filter = '''
    event_type = "purchase-complete" AND
    purchase_transaction.revenue > 0 AND
    event_time >= "2024-01-01T00:00:00Z" AND
    product_details.product.categories: ANY("Electronics", "Clothing", "Books", "Home")
'''

request = retail.ExportAnalyticsMetricsRequest(
    catalog="projects/my-project/locations/global/catalogs/default_catalog",
    output_config=revenue_config,
    filter=revenue_filter
)

operation = client.export_analytics_metrics(request=request)
result = operation.result()

print("Revenue analytics by category exported")

# After export completes, you can query the BigQuery table for insights:
print("Sample BigQuery analysis queries:")
print("""
-- Total revenue by category
SELECT 
    UNNEST(product_details.product.categories) as category,
    SUM(purchase_transaction.revenue) as total_revenue,
    COUNT(*) as transaction_count
FROM `my-analytics-project.revenue_analytics.category_revenue_2024`
GROUP BY category
ORDER BY total_revenue DESC;

-- Monthly revenue trends
SELECT 
    EXTRACT(MONTH FROM event_time) as month,
    SUM(purchase_transaction.revenue) as monthly_revenue,
    COUNT(DISTINCT user_info.user_id) as unique_customers
FROM `my-analytics-project.revenue_analytics.category_revenue_2024`
GROUP BY month
ORDER BY month;
""")

# Export with comprehensive error handling
errors_config = retail.ExportErrorsConfig(
    gcs_prefix="gs://my-analytics-bucket/export-errors/"
)

output_config = retail.OutputConfig(
    bigquery_destination=retail.BigQueryDestination(
        project_id="my-analytics-project",
        dataset_id="retail_analytics",
        table_id="user_events_with_errors"
    )
)

request = retail.ExportAnalyticsMetricsRequest(
    catalog="projects/my-project/locations/global/catalogs/default_catalog",
    output_config=output_config,
    filter='event_time >= "2024-01-01T00:00:00Z"'
)

operation = client.export_analytics_metrics(request=request)

# Monitor operation progress
import time
while not operation.done():
    print("Export in progress...")
    time.sleep(30)

result = operation.result()

# Check for errors and partial failures
if result.error_samples:
    print(f"Export completed with {len(result.error_samples)} errors:")
    for i, error in enumerate(result.error_samples[:5]):
        print(f"Error {i+1}: {error.message}")
    
    print(f"Error details saved to: {errors_config.gcs_prefix}")
else:
    print("Export completed successfully with no errors")

# Display export results
if result.output_result.bigquery_result:
    for bq_result in result.output_result.bigquery_result:
        print(f"BigQuery table created: {bq_result.dataset_id}.{bq_result.table_id}")

if result.output_result.gcs_result:
    total_bytes = sum(gcs.bytes_count for gcs in result.output_result.gcs_result)
    print(f"Total data exported: {total_bytes:,} bytes")
    for gcs_result in result.output_result.gcs_result:
        print(f"File: {gcs_result.output_uri} ({gcs_result.bytes_count:,} bytes)")