Dataset Storage Optimizer

Build a Python utility that optimizes dataset storage performance by configuring storage concurrency and implementing custom compression strategies for a Deep Lake dataset.

Capabilities

Configure storage concurrency

Set up storage concurrency to optimize data loading performance for multi-threaded operations.

• Calling configure_concurrency with a dataset path and thread_count=8 successfully configures the storage without errors. @test
• Calling configure_concurrency with thread_count=16 successfully configures the storage without errors. @test

Create dataset with optimized compression

Create a dataset with columns configured for optimal compression based on data type.

• Calling create_optimized_dataset creates a dataset with an "image" column that has JPEG compression configured. @test
• The created dataset contains a "vector" column configured for Float32 embeddings with the specified dimension. @test

Access storage metadata

Retrieve metadata information about dataset storage resources.

• Calling get_storage_metadata on a dataset returns a dictionary containing a 'size' key with an integer value representing bytes. @test

Implementation

@generates

API

"""
Dataset Storage Optimizer for Deep Lake

This module provides utilities for optimizing dataset storage performance
through concurrency configuration and compression strategies.
"""

def configure_concurrency(dataset_path: str, thread_count: int) -> None:
    """
    Configure storage concurrency for a dataset.

    Args:
        dataset_path: Path to the Deep Lake dataset
        thread_count: Number of concurrent threads to use for storage operations
    """
    pass

def create_optimized_dataset(dataset_path: str, image_quality: int = 85,
                            embedding_dim: int = 128) -> None:
    """
    Create a dataset with optimized compression settings.

    Creates a dataset with:
    - An 'image' column with JPEG compression at specified quality
    - A 'vector' column for embeddings with specified dimension

    Args:
        dataset_path: Path where the dataset will be created
        image_quality: JPEG compression quality (0-100)
        embedding_dim: Dimension of embedding vectors
    """
    pass

def get_storage_metadata(dataset_path: str) -> dict:
    """
    Retrieve storage resource metadata for a dataset.

    Args:
        dataset_path: Path to the Deep Lake dataset

    Returns:
        Dictionary containing metadata with at least:
        - 'size': Size in bytes
        - 'last_modified': Last modification timestamp (if available)
    """
    pass

Dependencies { .dependencies }

deeplake { .dependency }

Provides dataset storage and optimization capabilities including storage concurrency configuration, type system with compression options, and storage metadata access.

@satisfied-by