Storage System

class Reader:
    """Storage read operations."""
    
    path: str
    original_path: str
    token: Optional[str]
    
    def get(self, path: str) -> bytes:
        """
        Get data from storage path.
        
        Parameters:
        - path: Storage path to read from
        
        Returns:
        bytes: Raw data from storage
        """
    
    def length(self, path: str) -> int:
        """
        Get length of data at storage path.
        
        Parameters:
        - path: Storage path to check
        
        Returns:
        int: Data length in bytes
        """
    
    def list(self, path: str = "") -> List[str]:
        """
        List items at storage path.
        
        Parameters:
        - path: Storage path to list (empty for root)
        
        Returns:
        List[str]: List of item names at path
        """
    
    def subdir(self, path: str) -> Reader:
        """
        Create reader for subdirectory.
        
        Parameters:
        - path: Subdirectory path
        
        Returns:
        Reader: Reader instance for subdirectory
        """

class Writer:
    """Storage write operations."""
    
    path: str
    original_path: str
    token: Optional[str]
    
    def set(self, path: str, data: bytes) -> None:
        """
        Store data at storage path.
        
        Parameters:
        - path: Storage path to write to
        - data: Raw data to store
        """
    
    def remove(self, path: str) -> None:
        """
        Remove item at storage path.
        
        Parameters:
        - path: Storage path to remove
        """
    
    def remove_directory(self, path: str) -> None:
        """
        Remove directory and all contents.
        
        Parameters:
        - path: Directory path to remove
        """
    
    def subdir(self, path: str) -> Writer:
        """
        Create writer for subdirectory.
        
        Parameters:
        - path: Subdirectory path
        
        Returns:
        Writer: Writer instance for subdirectory
        """

class ResourceMeta:
    """Storage resource metadata."""
    
    path: str
    size: int
    etag: Optional[str]
    last_modified: Optional[str]

def concurrency() -> int:
    """
    Get current storage thread count.
    
    Returns:
    int: Number of concurrent storage threads
    """

def set_concurrency(num_threads: int) -> None:
    """
    Set storage thread count for parallel operations.
    
    Parameters:
    - num_threads: Number of concurrent threads for storage operations
    """

import deeplake

# Access storage directly (usually not needed for normal usage)
# Storage operations are typically handled automatically by datasets

# Get storage configuration
current_threads = deeplake.storage.concurrency()
print(f"Current storage threads: {current_threads}")

# Optimize for high-performance systems
deeplake.storage.set_concurrency(8)
print("Increased storage concurrency for better performance")

# Create dataset on local filesystem
dataset = deeplake.create("./local_dataset")

# Deep Lake automatically handles local storage operations
dataset.add_column("data", deeplake.types.Text())
dataset.append({"data": "sample text"})
dataset.commit("Added sample data")

# Storage operations happen transparently
print(f"Dataset stored locally at: {dataset.path}")

# S3 credentials
s3_creds = {
    "aws_access_key_id": "your_access_key",
    "aws_secret_access_key": "your_secret_key",
    "aws_region": "us-east-1"
}

# Create dataset on S3
s3_dataset = deeplake.create("s3://my-bucket/my-dataset", creds=s3_creds)

# Storage operations work the same across backends
s3_dataset.add_column("images", deeplake.types.Image())
s3_dataset.add_column("labels", deeplake.types.Text())

# Batch upload to S3
batch_data = [
    {"images": f"s3://my-bucket/images/img_{i}.jpg", "labels": f"label_{i}"}
    for i in range(1000)
]

s3_dataset.extend(batch_data)
s3_dataset.commit("Uploaded batch to S3")

print(f"S3 dataset has {len(s3_dataset)} rows")

# GCS credentials (using service account key)
gcs_creds = {
    "google_application_credentials": "/path/to/service-account-key.json"
}

# Alternative: using service account JSON content
gcs_creds_json = {
    "google_application_credentials_json": {
        "type": "service_account",
        "project_id": "your-project-id",
        "private_key_id": "key-id",
        "private_key": "-----BEGIN PRIVATE KEY-----\n...\n-----END PRIVATE KEY-----\n",
        "client_email": "service-account@project.iam.gserviceaccount.com",
        "client_id": "client-id",
        "auth_uri": "https://accounts.google.com/o/oauth2/auth",
        "token_uri": "https://oauth2.googleapis.com/token"
    }
}

# Create dataset on GCS
gcs_dataset = deeplake.create("gcs://my-bucket/my-dataset", creds=gcs_creds)

# Storage operations are identical across platforms
gcs_dataset.add_column("embeddings", deeplake.types.Embedding(size=768))
gcs_dataset.append({"embeddings": [0.1] * 768})
gcs_dataset.commit("Added embeddings to GCS")

# Azure credentials
azure_creds = {
    "azure_storage_account": "mystorageaccount",
    "azure_storage_key": "your_storage_key"
}

# Alternative: using connection string
azure_creds_conn = {
    "azure_storage_connection_string": "DefaultEndpointsProtocol=https;AccountName=mystorageaccount;AccountKey=your_key;EndpointSuffix=core.windows.net"
}

# Alternative: using SAS token
azure_creds_sas = {
    "azure_storage_account": "mystorageaccount",
    "azure_storage_sas_token": "your_sas_token"
}

# Create dataset on Azure
azure_dataset = deeplake.create("azure://my-container/my-dataset", creds=azure_creds)

# Same operations across all cloud providers
azure_dataset.add_column("videos", deeplake.types.Video())
azure_dataset.append({"videos": "azure://my-container/videos/video1.mp4"})
azure_dataset.commit("Added video to Azure")

# Create datasets across multiple cloud providers
datasets = {}

# Local for development
datasets["local"] = deeplake.create("./dev_dataset")

# S3 for production
datasets["s3"] = deeplake.create("s3://prod-bucket/dataset", creds=s3_creds)

# GCS for backup
datasets["gcs"] = deeplake.create("gcs://backup-bucket/dataset", creds=gcs_creds)

# Same schema across all datasets
for name, dataset in datasets.items():
    dataset.add_column("id", deeplake.types.Int64())
    dataset.add_column("data", deeplake.types.Text())
    dataset.add_column("timestamp", deeplake.types.Int64())
    
    # Add sample data
    dataset.append({
        "id": 1,
        "data": f"Sample data in {name}",
        "timestamp": 1640995200  # Unix timestamp
    })
    
    dataset.commit(f"Initial data in {name}")
    print(f"Created {name} dataset with {len(dataset)} rows")

# Copy data between cloud providers
deeplake.copy("./dev_dataset", "s3://prod-bucket/dev-copy", dst_creds=s3_creds)
print("Copied local dataset to S3")

import time

# Measure storage performance
def benchmark_storage_operations(dataset, num_operations=100):
    start_time = time.time()
    
    # Batch operations for better performance
    batch_data = [
        {"data": f"sample_{i}", "value": i * 0.1}
        for i in range(num_operations)
    ]
    
    dataset.extend(batch_data)
    dataset.commit(f"Added {num_operations} rows")
    
    end_time = time.time()
    return end_time - start_time

# Test with different storage backends
s3_dataset = deeplake.create("s3://benchmark-bucket/s3-test", creds=s3_creds)
s3_dataset.add_column("data", deeplake.types.Text())
s3_dataset.add_column("value", deeplake.types.Float32())

# Optimize storage concurrency for benchmarking
original_concurrency = deeplake.storage.concurrency()
deeplake.storage.set_concurrency(16)  # Increase for high-throughput

s3_time = benchmark_storage_operations(s3_dataset, 1000)
print(f"S3 operations took {s3_time:.2f} seconds")

# Restore original concurrency
deeplake.storage.set_concurrency(original_concurrency)

# Robust storage operations with error handling
def safe_dataset_operation(dataset_url, creds, operation_func):
    try:
        dataset = deeplake.open(dataset_url, creds=creds)
        result = operation_func(dataset)
        return result
    
    except deeplake.StorageAccessDenied:
        print("Storage access denied - check credentials")
        return None
    
    except deeplake.StorageKeyNotFound:
        print("Dataset not found - check URL")
        return None
    
    except deeplake.StorageNetworkConnectionError:
        print("Network connection error - check connectivity")
        return None
    
    except deeplake.StorageInternalError:
        print("Storage internal error - try again later")
        return None

# Safe operations with automatic retry
def add_data_safely(dataset_url, creds, data):
    def add_data_operation(dataset):
        dataset.extend(data)
        dataset.commit("Added data safely")
        return len(dataset)
    
    result = safe_dataset_operation(dataset_url, creds, add_data_operation)
    if result:
        print(f"Successfully added data. Dataset now has {result} rows")
    else:
        print("Failed to add data")

# Example usage
sample_data = [{"text": f"sample_{i}"} for i in range(10)]
add_data_safely("s3://my-bucket/safe-dataset", s3_creds, sample_data)

# Monitor storage performance and usage
class StorageMonitor:
    def __init__(self):
        self.operations = []
    
    def time_operation(self, operation_name, operation_func):
        start_time = time.time()
        try:
            result = operation_func()
            end_time = time.time()
            duration = end_time - start_time
            
            self.operations.append({
                "operation": operation_name,
                "duration": duration,
                "success": True,
                "timestamp": start_time
            })
            
            return result
            
        except Exception as e:
            end_time = time.time()
            duration = end_time - start_time
            
            self.operations.append({
                "operation": operation_name,
                "duration": duration,
                "success": False,
                "error": str(e),
                "timestamp": start_time
            })
            
            raise
    
    def get_stats(self):
        if not self.operations:
            return {"message": "No operations recorded"}
        
        successful_ops = [op for op in self.operations if op["success"]]
        failed_ops = [op for op in self.operations if not op["success"]]
        
        avg_duration = sum(op["duration"] for op in successful_ops) / len(successful_ops) if successful_ops else 0
        
        return {
            "total_operations": len(self.operations),
            "successful": len(successful_ops),
            "failed": len(failed_ops),
            "average_duration": avg_duration,
            "success_rate": len(successful_ops) / len(self.operations) * 100
        }

# Usage
monitor = StorageMonitor()

# Monitor dataset creation
dataset = monitor.time_operation(
    "create_dataset",
    lambda: deeplake.create("s3://monitor-bucket/test-dataset", creds=s3_creds)
)

# Monitor data operations
monitor.time_operation(
    "add_column",
    lambda: dataset.add_column("data", deeplake.types.Text())
)

monitor.time_operation(
    "append_data",
    lambda: dataset.append({"data": "test data"})
)

monitor.time_operation(
    "commit",
    lambda: dataset.commit("Test commit")
)

# Get performance statistics
stats = monitor.get_stats()
print(f"Storage operations statistics: {stats}")

# Configure storage for different use cases

# High-throughput configuration
def configure_for_high_throughput():
    # Increase concurrency for parallel operations
    deeplake.storage.set_concurrency(32)
    print("Configured for high-throughput operations")

# Memory-efficient configuration  
def configure_for_memory_efficiency():
    # Reduce concurrency to save memory
    deeplake.storage.set_concurrency(2)
    print("Configured for memory efficiency")

# Balanced configuration
def configure_balanced():
    # Moderate concurrency for balanced performance
    deeplake.storage.set_concurrency(8)
    print("Configured for balanced performance")

# Apply configuration based on use case
import psutil

# Auto-configure based on system resources
available_cores = psutil.cpu_count()
available_memory_gb = psutil.virtual_memory().total / (1024**3)

if available_cores >= 16 and available_memory_gb >= 32:
    configure_for_high_throughput()
elif available_memory_gb < 8:
    configure_for_memory_efficiency()
else:
    configure_balanced()

print(f"System: {available_cores} cores, {available_memory_gb:.1f}GB RAM")
print(f"Storage concurrency: {deeplake.storage.concurrency()}")