# Create MySQL instance with HA
gcloud sql instances create production-mysql \
    --database-version=MYSQL_8_0 \
    --tier=db-n1-highmem-4 \
    --region=us-central1 \
    --network=projects/PROJECT_ID/global/networks/custom-vpc \
    --no-assign-ip \
    --availability-type=REGIONAL \
    --backup-start-time=03:00 \
    --backup-location=us \
    --enable-bin-log \
    --retained-backups-count=30 \
    --retained-transaction-log-days=7 \
    --maintenance-window-day=SUN \
    --maintenance-window-hour=2 \
    --maintenance-release-channel=production \
    --enable-point-in-time-recovery \
    --insights-config-query-insights-enabled \
    --insights-config-query-string-length=1024 \
    --insights-config-record-application-tags

# Create PostgreSQL instance
gcloud sql instances create production-postgres \
    --database-version=POSTGRES_14 \
    --tier=db-custom-4-16384 \
    --region=us-central1 \
    --availability-type=REGIONAL \
    --enable-google-private-path \
    --database-flags=shared_preload_libraries=pg_stat_statements \
    --database-flags=log_statement=all \
    --database-flags=log_min_duration_statement=1000

# cloud_sql_manager.py
from google.cloud import sql_v1
from google.cloud import secretmanager
import sqlalchemy
import pymysql
import psycopg2
from contextlib import contextmanager
import time

class CloudSQLManager:
    """Manage Cloud SQL instances and connections."""

    def __init__(self, project_id):
        self.project_id = project_id
        self.sql_client = sql_v1.SqlInstancesServiceClient()
        self.backup_client = sql_v1.SqlBackupRunsServiceClient()
        self.operations_client = sql_v1.SqlOperationsServiceClient()
        self.secret_client = secretmanager.SecretManagerServiceClient()

    def create_instance_with_replicas(self, instance_id, region, database_type='MYSQL'):
        """Create Cloud SQL instance with read replicas."""
        instance = {
            "name": instance_id,
            "region": region,
            "database_version": f"{database_type}_8_0",
            "settings": {
                "tier": "db-n1-highmem-4",
                "activation_policy": "ALWAYS",
                "availability_type": "REGIONAL",
                "backup_configuration": {
                    "enabled": True,
                    "start_time": "03:00",
                    "location": region,
                    "point_in_time_recovery_enabled": True,
                    "transaction_log_retention_days": 7,
                    "retained_backups_count": 30
                },
                "ip_configuration": {
                    "ipv4_enabled": False,
                    "private_network": f"projects/{self.project_id}/global/networks/custom-vpc",
                    "enable_private_path_for_google_cloud_services": True
                },
                "database_flags": [
                    {"name": "slow_query_log", "value": "on"},
                    {"name": "long_query_time", "value": "2"},
                    {"name": "general_log", "value": "on"}
                ],
                "insights_config": {
                    "query_insights_enabled": True,
                    "query_string_length": 1024,
                    "record_application_tags": True
                }
            }
        }

        # Create primary instance
        operation = self.sql_client.insert(
            project=self.project_id,
            body=instance
        )

        # Wait for operation
        self._wait_for_operation(operation.name)

        # Create read replicas
        replica_regions = ['us-east1', 'europe-west1']
        for i, replica_region in enumerate(replica_regions):
            replica = {
                "name": f"{instance_id}-replica-{i+1}",
                "region": replica_region,
                "master_instance_name": f"projects/{self.project_id}/instances/{instance_id}",
                "replica_configuration": {
                    "failover_target": False
                }
            }

            replica_operation = self.sql_client.insert(
                project=self.project_id,
                body=replica
            )
            self._wait_for_operation(replica_operation.name)

        return instance_id

    @contextmanager
    def get_connection(self, instance_name, database_name):
        """Get database connection with automatic resource management."""
        # Get connection info
        instance = self.sql_client.get(
            project=self.project_id,
            instance=instance_name
        )

        # Get credentials from Secret Manager
        secret_name = f"projects/{self.project_id}/secrets/db-password/versions/latest"
        response = self.secret_client.access_secret_version(name=secret_name)
        password = response.payload.data.decode('UTF-8')

        # Create connection
        if 'MYSQL' in instance.database_version:
            connection = pymysql.connect(
                host=instance.ip_addresses[0].ip_address,
                user='root',
                password=password,
                database=database_name,
                cursorclass=pymysql.cursors.DictCursor
            )
        else:  # PostgreSQL
            connection = psycopg2.connect(
                host=instance.ip_addresses[0].ip_address,
                user='postgres',
                password=password,
                database=database_name
            )

        try:
            yield connection
        finally:
            connection.close()

    def setup_automatic_failover(self, instance_name):
        """Configure automatic failover for high availability."""
        # Create failover replica
        failover_replica = {
            "name": f"{instance_name}-failover",
            "master_instance_name": f"projects/{self.project_id}/instances/{instance_name}",
            "replica_configuration": {
                "failover_target": True
            }
        }

        operation = self.sql_client.insert(
            project=self.project_id,
            body=failover_replica
        )

        self._wait_for_operation(operation.name)
        print(f"Failover replica created for {instance_name}")

    def perform_backup(self, instance_name, description="Manual backup"):
        """Perform on-demand backup."""
        backup_run = {
            "instance": instance_name,
            "description": description,
            "type": "ON_DEMAND"
        }

        operation = self.backup_client.insert(
            project=self.project_id,
            instance=instance_name,
            body=backup_run
        )

        self._wait_for_operation(operation.name)
        print(f"Backup completed for {instance_name}")

        return operation

    def clone_instance(self, source_instance, target_instance, point_in_time=None):
        """Clone Cloud SQL instance."""
        clone_context = {
            "destination_instance_name": target_instance,
            "point_in_time": point_in_time  # ISO format timestamp
        }

        operation = self.sql_client.clone(
            project=self.project_id,
            instance=source_instance,
            body={"clone_context": clone_context}
        )

        self._wait_for_operation(operation.name)
        print(f"Cloned {source_instance} to {target_instance}")

    def _wait_for_operation(self, operation_name):
        """Wait for operation to complete."""
        while True:
            operation = self.operations_client.get(
                project=self.project_id,
                operation=operation_name.split('/')[-1]
            )

            if operation.status == "DONE":
                if operation.error:
                    raise Exception(f"Operation failed: {operation.error}")
                break

            time.sleep(5)

# database_migrator.py
import subprocess
from google.cloud import storage
import os

class DatabaseMigrator:
    """Migrate databases to Cloud SQL."""

    def __init__(self, project_id):
        self.project_id = project_id
        self.storage_client = storage.Client(project=project_id)

    def migrate_mysql_database(self, source_host, source_db, target_instance):
        """Migrate MySQL database to Cloud SQL."""
        # Export from source
        dump_file = f"{source_db}_dump.sql"

        export_command = [
            "mysqldump",
            "-h", source_host,
            "-u", "root",
            "-p",
            "--single-transaction",
            "--routines",
            "--triggers",
            "--events",
            "--set-gtid-purged=OFF",
            source_db,
            "-r", dump_file
        ]

        subprocess.run(export_command, check=True)

        # Upload to Cloud Storage
        bucket_name = f"{self.project_id}-sql-migration"
        bucket = self.storage_client.bucket(bucket_name)
        blob = bucket.blob(f"dumps/{dump_file}")
        blob.upload_from_filename(dump_file)

        # Import to Cloud SQL
        import_command = [
            "gcloud", "sql", "import", "sql",
            target_instance,
            f"gs://{bucket_name}/dumps/{dump_file}",
            "--database", source_db,
            "--project", self.project_id
        ]

        subprocess.run(import_command, check=True)

        # Cleanup
        os.remove(dump_file)
        blob.delete()

        print(f"Migration completed: {source_db} -> {target_instance}")

    def setup_continuous_replication(self, source_config, target_instance):
        """Setup continuous replication from external database."""
        # This would use Cloud SQL external replica feature
        replica_config = {
            "mysql_replica_configuration": {
                "master_instance_name": source_config['master_host'],
                "username": source_config['username'],
                "password": source_config['password'],
                "ssl_cipher": "TLS_AES_256_GCM_SHA384",
                "ca_certificate": source_config['ca_cert'],
                "client_certificate": source_config['client_cert'],
                "client_key": source_config['client_key']
            }
        }

        # Configure external master
        subprocess.run([
            "gcloud", "sql", "instances", "patch", target_instance,
            "--project", self.project_id,
            "--external-master-host", source_config['master_host'],
            "--external-master-port", str(source_config.get('port', 3306)),
            "--external-master-user", source_config['username'],
            "--external-master-password", source_config['password']
        ], check=True)

        print(f"Continuous replication configured for {target_instance}")

# spanner_manager.py
from google.cloud import spanner
from google.cloud.spanner_admin_database_v1 import DatabaseAdminClient
from google.cloud.spanner_admin_instance_v1 import InstanceAdminClient
import time

class SpannerManager:
    """Manage Cloud Spanner instances and databases."""

    def __init__(self, project_id):
        self.project_id = project_id
        self.spanner_client = spanner.Client(project=project_id)
        self.instance_admin = InstanceAdminClient()
        self.database_admin = DatabaseAdminClient()

    def create_multi_region_instance(self, instance_id, display_name):
        """Create multi-region Spanner instance."""
        config_name = f"projects/{self.project_id}/instanceConfigs/nam-eur-asia1"

        instance = self.spanner_client.instance(
            instance_id,
            configuration_name=config_name,
            display_name=display_name,
            node_count=3
        )

        operation = instance.create()
        operation.result()  # Wait for completion

        print(f"Created multi-region instance: {instance_id}")
        return instance

    def create_database_with_schema(self, instance_id, database_id):
        """Create database with schema."""
        instance = self.spanner_client.instance(instance_id)
        database = instance.database(database_id)

        # Define schema
        ddl_statements = [
            """CREATE TABLE Users (
                UserId STRING(36) NOT NULL,
                Email STRING(255) NOT NULL,
                Name STRING(100),
                CreatedAt TIMESTAMP NOT NULL OPTIONS (allow_commit_timestamp=true),
                UpdatedAt TIMESTAMP OPTIONS (allow_commit_timestamp=true),
                Version INT64 NOT NULL DEFAULT (0),
            ) PRIMARY KEY (UserId)""",

            """CREATE UNIQUE INDEX UsersByEmail ON Users(Email)""",

            """CREATE TABLE Orders (
                OrderId STRING(36) NOT NULL,
                UserId STRING(36) NOT NULL,
                Status STRING(20) NOT NULL,
                TotalAmount NUMERIC NOT NULL,
                Currency STRING(3) NOT NULL,
                CreatedAt TIMESTAMP NOT NULL OPTIONS (allow_commit_timestamp=true),
                CONSTRAINT FK_UserId FOREIGN KEY (UserId) REFERENCES Users (UserId),
            ) PRIMARY KEY (OrderId),
            INTERLEAVE IN PARENT Users ON DELETE CASCADE""",

            """CREATE TABLE OrderItems (
                OrderId STRING(36) NOT NULL,
                ItemId STRING(36) NOT NULL,
                ProductId STRING(36) NOT NULL,
                Quantity INT64 NOT NULL,
                Price NUMERIC NOT NULL,
                CreatedAt TIMESTAMP NOT NULL OPTIONS (allow_commit_timestamp=true),
                CONSTRAINT FK_OrderId FOREIGN KEY (OrderId) REFERENCES Orders (OrderId),
            ) PRIMARY KEY (OrderId, ItemId),
            INTERLEAVE IN PARENT Orders ON DELETE CASCADE"""
        ]

        operation = database.create(ddl_statements)
        operation.result()  # Wait for completion

        print(f"Created database with schema: {database_id}")
        return database

    def perform_distributed_transaction(self, instance_id, database_id):
        """Perform distributed transaction across tables."""
        instance = self.spanner_client.instance(instance_id)
        database = instance.database(database_id)

        def insert_order_with_items(transaction):
            # Insert user if not exists
            user_id = "user-123"
            transaction.execute_update(
                """INSERT OR UPDATE Users (UserId, Email, Name, CreatedAt)
                   VALUES (@user_id, @email, @name, PENDING_COMMIT_TIMESTAMP())""",
                params={"user_id": user_id, "email": "user@example.com", "name": "John Doe"},
                param_types={
                    "user_id": spanner.param_types.STRING,
                    "email": spanner.param_types.STRING,
                    "name": spanner.param_types.STRING
                }
            )

            # Insert order
            order_id = "order-456"
            transaction.execute_update(
                """INSERT INTO Orders (OrderId, UserId, Status, TotalAmount, Currency, CreatedAt)
                   VALUES (@order_id, @user_id, @status, @total, @currency, PENDING_COMMIT_TIMESTAMP())""",
                params={
                    "order_id": order_id,
                    "user_id": user_id,
                    "status": "PENDING",
                    "total": 99.99,
                    "currency": "USD"
                },
                param_types={
                    "order_id": spanner.param_types.STRING,
                    "user_id": spanner.param_types.STRING,
                    "status": spanner.param_types.STRING,
                    "total": spanner.param_types.NUMERIC,
                    "currency": spanner.param_types.STRING
                }
            )

            # Insert order items
            items = [
                {"item_id": "item-1", "product_id": "prod-1", "quantity": 2, "price": 29.99},
                {"item_id": "item-2", "product_id": "prod-2", "quantity": 1, "price": 40.01}
            ]

            for item in items:
                transaction.execute_update(
                    """INSERT INTO OrderItems (OrderId, ItemId, ProductId, Quantity, Price, CreatedAt)
                       VALUES (@order_id, @item_id, @product_id, @quantity, @price, PENDING_COMMIT_TIMESTAMP())""",
                    params={
                        "order_id": order_id,
                        "item_id": item["item_id"],
                        "product_id": item["product_id"],
                        "quantity": item["quantity"],
                        "price": item["price"]
                    },
                    param_types={
                        "order_id": spanner.param_types.STRING,
                        "item_id": spanner.param_types.STRING,
                        "product_id": spanner.param_types.STRING,
                        "quantity": spanner.param_types.INT64,
                        "price": spanner.param_types.NUMERIC
                    }
                )

        database.run_in_transaction(insert_order_with_items)
        print("Distributed transaction completed successfully")

    def setup_change_streams(self, instance_id, database_id):
        """Setup change streams for real-time data capture."""
        instance = self.spanner_client.instance(instance_id)
        database = instance.database(database_id)

        change_stream_ddl = [
            """CREATE CHANGE STREAM UserOrderStream
               FOR Users, Orders, OrderItems
               OPTIONS (
                   retention_period = '7d',
                   value_capture_type = 'NEW_VALUES'
               )"""
        ]

        operation = database.update_ddl(change_stream_ddl)
        operation.result()

        print("Change stream created successfully")

# firestore_advanced.py
from google.cloud import firestore
from google.cloud.firestore_v1 import aggregation
from google.cloud.firestore_v1.watch import Watch
import asyncio
from datetime import datetime, timedelta

class FirestoreAdvanced:
    """Advanced Firestore operations and patterns."""

    def __init__(self, project_id):
        self.db = firestore.Client(project=project_id)
        self.batch_size = 500

    async def bulk_import_with_batching(self, collection_name, data):
        """Bulk import data with automatic batching."""
        collection = self.db.collection(collection_name)

        # Process in batches
        for i in range(0, len(data), self.batch_size):
            batch = self.db.batch()
            batch_data = data[i:i + self.batch_size]

            for item in batch_data:
                doc_ref = collection.document(item.get('id') or None)
                batch.set(doc_ref, item)

            batch.commit()
            print(f"Imported batch {i//self.batch_size + 1}")

            # Rate limiting
            await asyncio.sleep(0.1)

    def setup_composite_indexes(self):
        """Define composite indexes for complex queries."""
        # Note: Indexes are typically defined in firestore.indexes.json
        indexes_config = {
            "indexes": [
                {
                    "collectionGroup": "orders",
                    "queryScope": "COLLECTION",
                    "fields": [
                        {"fieldPath": "userId", "order": "ASCENDING"},
                        {"fieldPath": "status", "order": "ASCENDING"},
                        {"fieldPath": "createdAt", "order": "DESCENDING"}
                    ]
                },
                {
                    "collectionGroup": "products",
                    "queryScope": "COLLECTION",
                    "fields": [
                        {"fieldPath": "category", "arrayConfig": "CONTAINS"},
                        {"fieldPath": "price", "order": "ASCENDING"},
                        {"fieldPath": "rating", "order": "DESCENDING"}
                    ]
                }
            ],
            "fieldOverrides": [
                {
                    "collectionGroup": "users",
                    "fieldPath": "email",
                    "indexes": [
                        {"queryScope": "COLLECTION", "order": "ASCENDING"},
                        {"queryScope": "COLLECTION_GROUP", "order": "ASCENDING"}
                    ]
                }
            ]
        }

        return indexes_config

    def implement_sharding_pattern(self, collection_name, document_id, data, shard_count=10):
        """Implement sharding for high-write collections."""
        import random

        # Choose random shard
        shard_id = random.randint(0, shard_count - 1)

        # Write to sharded subcollection
        shard_ref = self.db.collection(collection_name).document(document_id)\
                          .collection('shards').document(str(shard_id))

        shard_ref.set({
            **data,
            'shard_id': shard_id,
            'timestamp': firestore.SERVER_TIMESTAMP
        })

        # Update aggregate in parent document
        parent_ref = self.db.collection(collection_name).document(document_id)
        parent_ref.set({
            'shard_count': shard_count,
            'last_updated': firestore.SERVER_TIMESTAMP
        }, merge=True)

    def distributed_counter(self, counter_path, increment_value=1):
        """Implement distributed counter pattern."""
        counter_ref = self.db.document(counter_path)
        shards_ref = counter_ref.collection('shards')

        # Get or create counter document
        counter_doc = counter_ref.get()
        if not counter_doc.exists:
            counter_ref.set({
                'num_shards': 10,
                'total_count': 0
            })

            # Initialize shards
            batch = self.db.batch()
            for i in range(10):
                shard_ref = shards_ref.document(str(i))
                batch.set(shard_ref, {'count': 0})
            batch.commit()

        # Increment random shard
        import random
        shard_id = random.randint(0, 9)
        shard_ref = shards_ref.document(str(shard_id))
        shard_ref.update({'count': firestore.Increment(increment_value)})

    def get_distributed_count(self, counter_path):
        """Get total from distributed counter."""
        counter_ref = self.db.document(counter_path)
        shards_ref = counter_ref.collection('shards')

        # Sum all shards
        total = 0
        for shard in shards_ref.stream():
            total += shard.to_dict().get('count', 0)

        # Update cached total
        counter_ref.update({'total_count': total})

        return total

    def complex_query_with_pagination(self, collection_name, filters, order_by, page_size=20):
        """Perform complex query with cursor-based pagination."""
        query = self.db.collection(collection_name)

        # Apply filters
        for field, operator, value in filters:
            query = query.where(field, operator, value)

        # Apply ordering
        for field, direction in order_by:
            query = query.order_by(field, direction=direction)

        # Limit for pagination
        query = query.limit(page_size)

        # Generator for paginated results
        def paginate():
            last_doc = None
            while True:
                if last_doc:
                    paginated_query = query.start_after(last_doc)
                else:
                    paginated_query = query

                docs = list(paginated_query.stream())
                if not docs:
                    break

                yield docs
                last_doc = docs[-1]

        return paginate()

    def setup_real_time_listener(self, collection_name, callback):
        """Setup real-time listener with error handling."""
        def on_snapshot(col_snapshot, changes, read_time):
            for change in changes:
                if change.type.name == 'ADDED':
                    callback('added', change.document.id, change.document.to_dict())
                elif change.type.name == 'MODIFIED':
                    callback('modified', change.document.id, change.document.to_dict())
                elif change.type.name == 'REMOVED':
                    callback('removed', change.document.id, None)

        # Create listener
        col_ref = self.db.collection(collection_name)
        col_watch = col_ref.on_snapshot(on_snapshot)

        return col_watch  # Return for later unsubscribe

    def transaction_with_retry(self, transaction_func, max_attempts=3):
        """Execute transaction with automatic retry."""
        @firestore.transactional
        def update_in_transaction(transaction):
            return transaction_func(transaction)

        attempts = 0
        while attempts < max_attempts:
            try:
                result = update_in_transaction(self.db.transaction())
                return result
            except Exception as e:
                attempts += 1
                if attempts >= max_attempts:
                    raise
                time.sleep(2 ** attempts)  # Exponential backoff

# bigtable_manager.py
from google.cloud import bigtable
from google.cloud.bigtable import column_family
from google.cloud.bigtable import row_filters
import struct
import time

class BigtableManager:
    """Manage Bigtable instances and operations."""

    def __init__(self, project_id):
        self.project_id = project_id
        self.client = bigtable.Client(project=project_id, admin=True)

    def create_time_series_table(self, instance_id, table_id):
        """Create table optimized for time-series data."""
        instance = self.client.instance(instance_id)
        table = instance.table(table_id)

        # Create table if not exists
        if not table.exists():
            table.create()

            # Add column families
            cf_metrics = table.column_family('metrics')
            cf_metrics.create()

            cf_metadata = table.column_family('metadata')
            cf_metadata.create()

            # Set garbage collection rules
            cf_metrics.update(
                gc_rule=column_family.MaxVersionsGCRule(1) &
                column_family.MaxAgeGCRule(timedelta(days=30))
            )

            print(f"Created time-series table: {table_id}")

    def write_time_series_data(self, instance_id, table_id, device_id, metrics):
        """Write time-series data with proper row key design."""
        instance = self.client.instance(instance_id)
        table = instance.table(table_id)

        # Create row key: device_id#reverse_timestamp
        timestamp = int(time.time() * 1000)
        reverse_timestamp = 9999999999999 - timestamp
        row_key = f"{device_id}#{reverse_timestamp}".encode()

        row = table.direct_row(row_key)

        # Write metrics
        for metric_name, value in metrics.items():
            # Store as binary for efficiency
            if isinstance(value, (int, float)):
                value_bytes = struct.pack('d', float(value))
            else:
                value_bytes = str(value).encode()

            row.set_cell(
                'metrics',
                metric_name.encode(),
                value_bytes,
                timestamp=timestamp * 1000  # microseconds
            )

        # Write metadata
        row.set_cell(
            'metadata',
            b'device_id',
            device_id.encode(),
            timestamp=timestamp * 1000
        )

        row.commit()

    def query_time_range(self, instance_id, table_id, device_id, start_time, end_time):
        """Query time-series data for a specific time range."""
        instance = self.client.instance(instance_id)
        table = instance.table(table_id)

        # Convert timestamps to reverse format
        start_reverse = 9999999999999 - int(end_time * 1000)
        end_reverse = 9999999999999 - int(start_time * 1000)

        # Create row key range
        row_start = f"{device_id}#{start_reverse}".encode()
        row_end = f"{device_id}#{end_reverse}".encode()

        # Create row set
        row_set = bigtable.row_set.RowSet()
        row_set.add_row_range_from_keys(
            start_key=row_start,
            end_key=row_end
        )

        # Read rows
        rows = table.read_rows(
            row_set=row_set,
            filter_=row_filters.ColumnRangeFilter(
                'metrics',
                start_column=b'',
                end_column=b'~'
            )
        )

        # Process results
        results = []
        for row in rows:
            timestamp = 9999999999999 - int(row.row_key.decode().split('#')[1])
            metrics = {}

            for cell in row.cells['metrics'].values():
                for c in cell:
                    metric_name = c.column.decode()
                    try:
                        value = struct.unpack('d', c.value)[0]
                    except:
                        value = c.value.decode()
                    metrics[metric_name] = value

            results.append({
                'timestamp': timestamp / 1000,
                'metrics': metrics
            })

        return results

    def implement_hot_spotting_prevention(self, instance_id, table_id):
        """Implement salting to prevent hot spotting."""
        instance = self.client.instance(instance_id)
        table = instance.table(table_id)

        def salted_row_key(original_key, salt_buckets=10):
            """Add salt prefix to distribute writes."""
            import hashlib

            # Calculate salt based on key hash
            key_hash = hashlib.md5(original_key.encode()).hexdigest()
            salt = int(key_hash[:2], 16) % salt_buckets

            return f"{salt:02d}#{original_key}".encode()

        return salted_row_key

# memorystore_manager.py
import redis
from google.cloud import redis_v1
import json
import pickle
from datetime import timedelta

class MemorystoreManager:
    """Manage Memorystore Redis instances."""

    def __init__(self, project_id):
        self.project_id = project_id
        self.redis_admin = redis_v1.CloudRedisClient()
        self.redis_clients = {}

    def create_redis_instance(self, instance_id, region, tier='STANDARD_HA'):
        """Create Memorystore Redis instance."""
        parent = f"projects/{self.project_id}/locations/{region}"

        instance = {
            "name": f"{parent}/instances/{instance_id}",
            "tier": tier,
            "memory_size_gb": 5,
            "redis_version": "REDIS_6_X",
            "display_name": instance_id,
            "auth_enabled": True,
            "transit_encryption_mode": "SERVER_AUTHENTICATION",
            "persistence_config": {
                "persistence_mode": "RDB",
                "rdb_snapshot_period": "ONE_HOUR"
            }
        }

        operation = self.redis_admin.create_instance(
            parent=parent,
            instance_id=instance_id,
            instance=instance
        )

        result = operation.result()  # Wait for completion
        print(f"Created Redis instance: {instance_id}")

        return result

    def get_redis_client(self, instance_name):
        """Get Redis client for instance."""
        if instance_name not in self.redis_clients:
            # Get instance details
            instance = self.redis_admin.get_instance(name=instance_name)

            # Create Redis client
            self.redis_clients[instance_name] = redis.Redis(
                host=instance.host,
                port=instance.port,
                password=instance.auth_string if instance.auth_enabled else None,
                ssl=True,
                ssl_cert_reqs='required',
                decode_responses=True
            )

        return self.redis_clients[instance_name]

    def implement_cache_patterns(self, redis_client):
        """Implement common caching patterns."""

        # Cache-aside pattern
        def cache_aside_get(key, fetch_func, ttl=300):
            # Try to get from cache
            cached = redis_client.get(key)
            if cached:
                return json.loads(cached)

            # Fetch from source
            data = fetch_func()

            # Store in cache
            redis_client.setex(key, ttl, json.dumps(data))

            return data

        # Write-through pattern
        def write_through_set(key, value, store_func, ttl=300):
            # Store in primary storage
            store_func(value)

            # Update cache
            redis_client.setex(key, ttl, json.dumps(value))

        # Cache warming
        def warm_cache(keys_data, ttl=300):
            pipeline = redis_client.pipeline()

            for key, data in keys_data.items():
                pipeline.setex(key, ttl, json.dumps(data))

            pipeline.execute()

        return {
            'cache_aside_get': cache_aside_get,
            'write_through_set': write_through_set,
            'warm_cache': warm_cache
        }

    def setup_session_store(self, redis_client):
        """Setup Redis as session store."""

        class SessionStore:
            def __init__(self, redis_client, prefix='session:', ttl=3600):
                self.redis = redis_client
                self.prefix = prefix
                self.ttl = ttl

            def save(self, session_id, data):
                key = f"{self.prefix}{session_id}"
                self.redis.setex(key, self.ttl, pickle.dumps(data))

            def load(self, session_id):
                key = f"{self.prefix}{session_id}"
                data = self.redis.get(key)
                return pickle.loads(data) if data else None

            def delete(self, session_id):
                key = f"{self.prefix}{session_id}"
                self.redis.delete(key)

            def extend(self, session_id):
                key = f"{self.prefix}{session_id}"
                self.redis.expire(key, self.ttl)

        return SessionStore(redis_client)

    def implement_rate_limiting(self, redis_client):
        """Implement rate limiting with Redis."""

        def rate_limit(identifier, max_requests=100, window_seconds=60):
            key = f"rate_limit:{identifier}"

            try:
                current = redis_client.incr(key)

                if current == 1:
                    redis_client.expire(key, window_seconds)

                if current > max_requests:
                    return False, current

                return True, current

            except redis.RedisError:
                # Allow on Redis error (fail open)
                return True, 0

        return rate_limit

# database_optimizer.py
from sqlalchemy import create_engine, pool
from sqlalchemy.orm import sessionmaker
import threading

class DatabaseOptimizer:
    """Optimize database performance across services."""

    def __init__(self, project_id):
        self.project_id = project_id
        self.connection_pools = {}
        self.pool_lock = threading.Lock()

    def get_cloud_sql_pool(self, instance_connection_name, database):
        """Get connection pool for Cloud SQL."""
        pool_key = f"{instance_connection_name}:{database}"

        with self.pool_lock:
            if pool_key not in self.connection_pools:
                # Create SQLAlchemy engine with connection pooling
                engine = create_engine(
                    f"mysql+pymysql://root:password@/{database}",
                    connect_args={
                        "unix_socket": f"/cloudsql/{instance_connection_name}"
                    },
                    poolclass=pool.QueuePool,
                    pool_size=5,
                    max_overflow=10,
                    pool_timeout=30,
                    pool_recycle=1800,  # Recycle connections after 30 minutes
                    pool_pre_ping=True  # Verify connections before use
                )

                self.connection_pools[pool_key] = engine

        return self.connection_pools[pool_key]

    def optimize_queries(self):
        """Query optimization strategies."""

        # Example: Batch operations
        def batch_insert(engine, table_name, records, batch_size=1000):
            with engine.begin() as conn:
                for i in range(0, len(records), batch_size):
                    batch = records[i:i + batch_size]
                    conn.execute(f"INSERT INTO {table_name} VALUES", batch)

        # Example: Query result caching
        class QueryCache:
            def __init__(self, redis_client):
                self.redis = redis_client
                self.ttl = 300  # 5 minutes

            def cached_query(self, query_key, query_func):
                # Check cache
                cached = self.redis.get(f"query:{query_key}")
                if cached:
                    return json.loads(cached)

                # Execute query
                result = query_func()

                # Cache result
                self.redis.setex(
                    f"query:{query_key}",
                    self.ttl,
                    json.dumps(result, default=str)
                )

                return result

        return {
            'batch_insert': batch_insert,
            'QueryCache': QueryCache
        }

    def implement_read_write_splitting(self, primary_engine, replica_engines):
        """Implement read/write splitting."""

        class ReadWriteSplitter:
            def __init__(self, primary, replicas):
                self.primary = primary
                self.replicas = replicas
                self.current_replica = 0

            def execute_read(self, query):
                # Round-robin across replicas
                replica = self.replicas[self.current_replica]
                self.current_replica = (self.current_replica + 1) % len(self.replicas)

                with replica.connect() as conn:
                    return conn.execute(query).fetchall()

            def execute_write(self, query):
                # All writes go to primary
                with self.primary.connect() as conn:
                    return conn.execute(query)

        return ReadWriteSplitter(primary_engine, replica_engines)

# database_security.py
from google.cloud import secretmanager
from google.cloud import kms
import secrets
import hashlib

class DatabaseSecurity:
    """Implement database security best practices."""

    def __init__(self, project_id):
        self.project_id = project_id
        self.secret_client = secretmanager.SecretManagerServiceClient()
        self.kms_client = kms.KeyManagementServiceClient()

    def setup_cloud_sql_security(self, instance_name):
        """Setup comprehensive Cloud SQL security."""
        security_config = {
            # Require SSL
            "require_ssl": True,

            # Database flags for security
            "database_flags": {
                # MySQL flags
                "local_infile": "off",
                "skip_show_database": "on",
                "sql_mode": "TRADITIONAL",

                # Audit logging
                "cloudsql_mysql_audit_log": "on",
                "cloudsql_mysql_audit_log_max_file_size": "100",
                "cloudsql_mysql_audit_log_rotate": "on",

                # Performance schema for monitoring
                "performance_schema": "on"
            },

            # Backup encryption
            "backup_configuration": {
                "enabled": True,
                "point_in_time_recovery_enabled": True,
                "transaction_log_retention_days": 7,
                "location": "us",
                "backup_encryption_key_name": f"projects/{self.project_id}/locations/us/keyRings/database-keys/cryptoKeys/backup-key"
            },

            # Authorized networks (use Private IP instead)
            "authorized_networks": [],

            # Maintenance window
            "maintenance_window": {
                "hour": 3,
                "day": 0,  # Sunday
                "update_track": "stable"
            }
        }

        return security_config

    def rotate_database_credentials(self, instance_name, database_name):
        """Rotate database credentials securely."""
        # Generate new password
        new_password = secrets.token_urlsafe(32)

        # Store in Secret Manager
        secret_id = f"{instance_name}-{database_name}-password"
        parent = f"projects/{self.project_id}"

        # Create or update secret
        try:
            secret = self.secret_client.create_secret(
                parent=parent,
                secret_id=secret_id,
                secret={
                    "replication": {"automatic": {}}
                }
            )
        except:
            # Secret already exists
            secret_name = f"{parent}/secrets/{secret_id}"
            secret = self.secret_client.get_secret(name=secret_name)

        # Add new version
        self.secret_client.add_secret_version(
            parent=secret.name,
            payload={"data": new_password.encode()}
        )

        # Update database password
        # This would be done through Cloud SQL Admin API

        return secret_id

    def implement_data_encryption(self):
        """Implement application-level data encryption."""

        class FieldEncryption:
            def __init__(self, kms_key_name):
                self.kms_key = kms_key_name

            def encrypt_field(self, plaintext):
                # Generate DEK (Data Encryption Key)
                dek = secrets.token_bytes(32)

                # Encrypt DEK with KEK (Key Encryption Key) from KMS
                # This would use KMS API to encrypt the DEK

                # Encrypt data with DEK
                from cryptography.fernet import Fernet
                import base64

                key = base64.urlsafe_b64encode(dek)
                f = Fernet(key)
                ciphertext = f.encrypt(plaintext.encode())

                return {
                    'ciphertext': ciphertext,
                    'encrypted_dek': 'encrypted_dek_here'
                }

            def decrypt_field(self, encrypted_data):
                # Decrypt DEK using KMS
                # Decrypt data using DEK
                pass

        return FieldEncryption

# database_monitoring.py
from google.cloud import monitoring_v3
import time

class DatabaseMonitoring:
    """Monitor database performance and health."""

    def __init__(self, project_id):
        self.project_id = project_id
        self.monitoring_client = monitoring_v3.MetricServiceClient()
        self.alert_client = monitoring_v3.AlertPolicyServiceClient()

    def setup_cloud_sql_monitoring(self, instance_name):
        """Setup comprehensive Cloud SQL monitoring."""
        project_name = f"projects/{self.project_id}"

        # Create alert policies
        alert_policies = [
            {
                "display_name": "Cloud SQL CPU High",
                "conditions": [{
                    "display_name": "CPU utilization above 80%",
                    "condition_threshold": {
                        "filter": f'resource.type="cloudsql_database" '
                                 f'AND resource.label.database_id="{self.project_id}:{instance_name}" '
                                 f'AND metric.type="cloudsql.googleapis.com/database/cpu/utilization"',
                        "comparison": monitoring_v3.ComparisonType.COMPARISON_GT,
                        "threshold_value": 0.8,
                        "duration": {"seconds": 300},
                        "aggregations": [{
                            "alignment_period": {"seconds": 60},
                            "per_series_aligner": monitoring_v3.Aggregation.Aligner.ALIGN_MEAN
                        }]
                    }
                }]
            },
            {
                "display_name": "Cloud SQL Storage Near Limit",
                "conditions": [{
                    "display_name": "Storage usage above 90%",
                    "condition_threshold": {
                        "filter": f'resource.type="cloudsql_database" '
                                 f'AND resource.label.database_id="{self.project_id}:{instance_name}" '
                                 f'AND metric.type="cloudsql.googleapis.com/database/disk/utilization"',
                        "comparison": monitoring_v3.ComparisonType.COMPARISON_GT,
                        "threshold_value": 0.9,
                        "duration": {"seconds": 60}
                    }
                }]
            },
            {
                "display_name": "Cloud SQL Replication Lag",
                "conditions": [{
                    "display_name": "Replication lag above 30 seconds",
                    "condition_threshold": {
                        "filter": f'resource.type="cloudsql_database" '
                                 f'AND metric.type="cloudsql.googleapis.com/database/replication/replica_lag"',
                        "comparison": monitoring_v3.ComparisonType.COMPARISON_GT,
                        "threshold_value": 30,
                        "duration": {"seconds": 300}
                    }
                }]
            }
        ]

        for policy_config in alert_policies:
            policy = monitoring_v3.AlertPolicy(policy_config)
            self.alert_client.create_alert_policy(
                name=project_name,
                alert_policy=policy
            )

        print(f"Monitoring alerts created for {instance_name}")

    def create_custom_dashboard(self):
        """Create custom database monitoring dashboard."""
        dashboard_config = {
            "displayName": "Database Performance Dashboard",
            "gridLayout": {
                "widgets": [
                    {
                        "title": "Cloud SQL CPU Usage",
                        "xyChart": {
                            "dataSets": [{
                                "timeSeriesQuery": {
                                    "timeSeriesFilter": {
                                        "filter": 'resource.type="cloudsql_database" '
                                                 'AND metric.type="cloudsql.googleapis.com/database/cpu/utilization"'
                                    }
                                }
                            }]
                        }
                    },
                    {
                        "title": "Query Performance",
                        "xyChart": {
                            "dataSets": [{
                                "timeSeriesQuery": {
                                    "timeSeriesFilter": {
                                        "filter": 'resource.type="cloudsql_database" '
                                                 'AND metric.type="cloudsql.googleapis.com/database/mysql/queries"'
                                    }
                                }
                            }]
                        }
                    }
                ]
            }
        }

        return dashboard_config

def select_database_service(requirements):
    """Select appropriate database service based on requirements."""

    if requirements.get('global_consistency') and requirements.get('sql'):
        return 'Cloud Spanner'

    elif requirements.get('traditional_sql'):
        return 'Cloud SQL'

    elif requirements.get('document_store') and requirements.get('real_time'):
        return 'Firestore'

    elif requirements.get('time_series') or requirements.get('iot'):
        return 'Bigtable'

    elif requirements.get('caching'):
        return 'Memorystore'

    else:
        return 'Evaluate requirements further'