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# Cloud Services Integration
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Cloud-specific extensions for location services, extended operations, and Google Cloud Platform integration. Provides types for multi-region services, cloud resource management, and platform-specific functionality.
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## Capabilities
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### Cloud Location Services
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Manage and query cloud service locations and regions.
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```python { .api }
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from google.cloud.location.locations_pb2 import (
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    ListLocationsRequest, ListLocationsResponse, 
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    GetLocationRequest, Location
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)
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class ListLocationsRequest(message.Message):
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    """Request to list available service locations."""
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    name: str  # Parent resource name (e.g., "projects/my-project")
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    filter: str  # Filter expression for locations
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    page_size: int  # Maximum number of locations to return
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    page_token: str  # Page token for pagination
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class ListLocationsResponse(message.Message):
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    """Response containing available locations."""
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    locations: list[Location]  # List of available locations
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    next_page_token: str  # Token for next page (if any)
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class GetLocationRequest(message.Message):
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    """Request to get details of a specific location."""
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    name: str  # Location resource name
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class Location(message.Message):
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    """Cloud service location information."""
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    name: str  # Location resource name
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    location_id: str  # Location identifier (e.g., "us-central1")
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    display_name: str  # Human-readable location name
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    labels: dict[str, str]  # Location labels and metadata
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    metadata: Any  # Location-specific metadata
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```
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### Extended Operations
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Extended operation support for complex cloud operations.
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```python { .api }
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from google.cloud.extended_operations_pb2 import ExtendedOperation
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class ExtendedOperation(message.Message):
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    """Extended operation with additional cloud-specific fields."""
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    # Inherits basic operation fields and adds:
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    # Additional cloud-specific operation metadata
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    # Enhanced status reporting
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    # Region and zone information
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    # Resource hierarchy context
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```
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### Client Generation Metadata
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Metadata for generating cloud service client libraries.
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```python { .api }
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from google.gapic.metadata.gapic_metadata_pb2 import (
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    GapicMetadata, ServiceForTransport, ServiceAsClient, MethodList
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)
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class GapicMetadata(message.Message):
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    """Metadata for GAPIC (Generated API Client) libraries."""
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    schema: str  # Metadata schema version
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    comment: str  # Additional comments
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    language: str  # Target programming language
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    proto_package: str  # Protocol buffer package name
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    library_package: str  # Generated library package name
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    services: dict[str, ServiceForTransport]  # Service transport mappings
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class ServiceForTransport(message.Message):
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    """Service configuration for different transports."""
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    clients: dict[str, ServiceAsClient]  # Client configurations
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class ServiceAsClient(message.Message):
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    """Service as client configuration."""
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    library_client: str  # Client class name
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    rpcs: dict[str, MethodList]  # RPC method mappings
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class MethodList(message.Message):
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    """List of methods for a service."""
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    methods: list[str]  # Method names
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```
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## Usage Examples
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### Listing Cloud Locations
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```python
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from google.cloud.location.locations_pb2 import ListLocationsRequest, ListLocationsResponse
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def list_all_locations(parent: str, client) -> list:
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    """List all available locations for a service."""
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    request = ListLocationsRequest()
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    request.name = parent  # e.g., "projects/my-project"
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    request.page_size = 100
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    all_locations = []
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    while True:
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        response: ListLocationsResponse = client.list_locations(request)
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        all_locations.extend(response.locations)
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        # Check if there are more pages
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        if not response.next_page_token:
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            break
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        # Set up next page request
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        request.page_token = response.next_page_token
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    return all_locations
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# Example usage
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locations = list_all_locations("projects/my-project", location_client)
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for location in locations:
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    print(f"Location: {location.location_id} ({location.display_name})")
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```
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### Filtering Locations by Region
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```python
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from google.cloud.location.locations_pb2 import ListLocationsRequest
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def list_locations_by_region(parent: str, client, region_filter: str = None):
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    """List locations with optional region filtering."""
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    request = ListLocationsRequest()
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    request.name = parent
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    if region_filter:
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        # Filter by region (e.g., "us-", "europe-", "asia-")
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        request.filter = f'labels.region:"{region_filter}*"'
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    response = client.list_locations(request)
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    return response.locations
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# Examples
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us_locations = list_locations_by_region("projects/my-project", client, "us")
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europe_locations = list_locations_by_region("projects/my-project", client, "europe")
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```
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### Getting Location Details
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```python
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from google.cloud.location.locations_pb2 import GetLocationRequest, Location
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def get_location_info(location_name: str, client) -> Location:
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    """Get detailed information about a specific location."""
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    request = GetLocationRequest()
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    request.name = location_name  # e.g., "projects/my-project/locations/us-central1"
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    location = client.get_location(request)
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    return location
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def print_location_details(location: Location):
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    """Print detailed location information."""
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    print(f"Location Name: {location.name}")
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    print(f"Location ID: {location.location_id}")
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    print(f"Display Name: {location.display_name}")
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    print("Labels:")
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    for key, value in location.labels.items():
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        print(f"  {key}: {value}")
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    if location.metadata:
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        print(f"Metadata: {location.metadata}")
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# Example usage
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location = get_location_info("projects/my-project/locations/us-central1", client)
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print_location_details(location)
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```
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### Location-Based Resource Selection
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```python
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from google.cloud.location.locations_pb2 import Location
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def select_optimal_location(locations: list[Location], preferences: dict) -> Location:
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    """Select optimal location based on preferences."""
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    scored_locations = []
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    for location in locations:
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        score = 0
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        # Score based on region preference
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        if preferences.get("preferred_region"):
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            if location.location_id.startswith(preferences["preferred_region"]):
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                score += 10
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        # Score based on labels
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        for label_key, preferred_value in preferences.get("labels", {}).items():
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            if location.labels.get(label_key) == preferred_value:
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                score += 5
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        # Score based on availability zones (if in metadata)
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        if preferences.get("multi_zone") and "zones" in location.labels:
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            zone_count = len(location.labels["zones"].split(","))
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            score += zone_count
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        scored_locations.append((location, score))
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    # Return location with highest score
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    if scored_locations:
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        return max(scored_locations, key=lambda x: x[1])[0]
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    return locations[0] if locations else None
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# Example usage
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preferences = {
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    "preferred_region": "us-central",
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    "labels": {
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        "tier": "premium"
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    },
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    "multi_zone": True
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}
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optimal_location = select_optimal_location(locations, preferences)
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print(f"Selected location: {optimal_location.location_id}")
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```
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### Working with GAPIC Metadata
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```python
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from google.gapic.metadata.gapic_metadata_pb2 import GapicMetadata
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def parse_gapic_metadata(metadata: GapicMetadata):
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    """Parse and display GAPIC metadata information."""
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    print(f"Schema: {metadata.schema}")
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    print(f"Language: {metadata.language}")
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    print(f"Proto Package: {metadata.proto_package}")
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    print(f"Library Package: {metadata.library_package}")
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    print("\nServices:")
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    for service_name, service_transport in metadata.services.items():
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        print(f"  Service: {service_name}")
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        for transport_name, client_config in service_transport.clients.items():
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            print(f"    Transport: {transport_name}")
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            print(f"    Client: {client_config.library_client}")
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            for rpc_name, method_list in client_config.rpcs.items():
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                print(f"      RPC: {rpc_name}")
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                for method in method_list.methods:
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                    print(f"        Method: {method}")
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```
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### Multi-Region Service Deployment
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```python
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from google.cloud.location.locations_pb2 import Location
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def plan_multi_region_deployment(locations: list[Location], 
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                                service_requirements: dict) -> dict:
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    """Plan multi-region deployment based on requirements."""
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    deployment_plan = {
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        "primary_regions": [],
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        "secondary_regions": [],
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        "backup_regions": []
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    }
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    # Group locations by region
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    regions = {}
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    for location in locations:
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        region = location.location_id.split('-')[0] + '-' + location.location_id.split('-')[1]
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        if region not in regions:
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            regions[region] = []
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        regions[region].append(location)
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    # Sort regions by preference
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    preferred_regions = service_requirements.get("preferred_regions", [])
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    # Select primary regions
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    for region in preferred_regions:
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        if region in regions and len(deployment_plan["primary_regions"]) < 2:
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            deployment_plan["primary_regions"].extend(regions[region][:1])
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    # Select secondary regions
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    remaining_regions = [r for r in regions.keys() 
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                        if r not in [loc.location_id for loc in deployment_plan["primary_regions"]]]
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    for region in remaining_regions[:2]:
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        deployment_plan["secondary_regions"].extend(regions[region][:1])
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    # Select backup regions
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    for region in remaining_regions[2:4]:
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        deployment_plan["backup_regions"].extend(regions[region][:1])
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    return deployment_plan
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# Example usage
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requirements = {
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    "preferred_regions": ["us-central", "us-east"],
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    "min_regions": 3,
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    "disaster_recovery": True
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}
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deployment = plan_multi_region_deployment(locations, requirements)
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print("Deployment Plan:")
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print(f"Primary: {[loc.location_id for loc in deployment['primary_regions']]}")
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print(f"Secondary: {[loc.location_id for loc in deployment['secondary_regions']]}")
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print(f"Backup: {[loc.location_id for loc in deployment['backup_regions']]}")
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```
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### Location Metadata Processing
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```python
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from google.cloud.location.locations_pb2 import Location
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from google.protobuf.struct_pb2 import Struct
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def extract_location_capabilities(location: Location) -> dict:
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    """Extract service capabilities from location metadata."""
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    capabilities = {
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        "location_id": location.location_id,
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        "display_name": location.display_name,
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        "services": [],
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        "features": {},
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        "zones": []
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    }
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    # Extract from labels
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    for key, value in location.labels.items():
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        if key.startswith("service/"):
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            service_name = key.replace("service/", "")
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            if value.lower() == "true":
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                capabilities["services"].append(service_name)
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        elif key.startswith("feature/"):
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            feature_name = key.replace("feature/", "")
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            capabilities["features"][feature_name] = value
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        elif key == "zones":
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            capabilities["zones"] = value.split(",")
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    # Extract from metadata if available
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    if location.metadata:
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        # Metadata processing would be service-specific
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        pass
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    return capabilities
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# Example usage
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for location in locations:
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    caps = extract_location_capabilities(location)
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    print(f"\nLocation: {caps['location_id']}")
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    print(f"Services: {', '.join(caps['services'])}")
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    print(f"Zones: {', '.join(caps['zones'])}")
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    if caps['features']:
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        print("Features:")
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        for feature, value in caps['features'].items():
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            print(f"  {feature}: {value}")
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```
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### Region-Based Cost Optimization
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```python
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def calculate_deployment_cost(locations: list[Location], 
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                            usage_requirements: dict) -> dict:
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    """Calculate estimated deployment costs by location."""
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    cost_estimates = {}
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    # Sample cost factors (would come from actual pricing data)
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    base_costs = {
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        "us-central": 1.0,    # Base cost multiplier
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        "us-east": 0.95,      # 5% cheaper
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        "us-west": 1.1,       # 10% more expensive
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        "europe-west": 1.2,   # 20% more expensive
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        "asia-east": 1.15     # 15% more expensive
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    }
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    for location in locations:
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        region_prefix = '-'.join(location.location_id.split('-')[:2])
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        base_multiplier = base_costs.get(region_prefix, 1.0)
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        # Calculate costs based on usage
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        compute_cost = usage_requirements.get("compute_hours", 0) * base_multiplier * 0.10
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        storage_cost = usage_requirements.get("storage_gb", 0) * base_multiplier * 0.02
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        network_cost = usage_requirements.get("network_gb", 0) * base_multiplier * 0.08
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        total_cost = compute_cost + storage_cost + network_cost
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        cost_estimates[location.location_id] = {
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            "compute": compute_cost,
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            "storage": storage_cost,
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            "network": network_cost,
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            "total": total_cost,
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            "multiplier": base_multiplier
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        }
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    return cost_estimates
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# Example usage
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usage = {
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    "compute_hours": 720,  # 1 month
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    "storage_gb": 1000,    # 1TB
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    "network_gb": 500      # 500GB transfer
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}
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costs = calculate_deployment_cost(locations, usage)
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for location_id, cost_info in sorted(costs.items(), key=lambda x: x[1]["total"]):
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    print(f"{location_id}: ${cost_info['total']:.2f}/month")
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```