如何实现Foundry数据集间的文件复制及通过Transform合并两个含原始文件的Foundry数据集

Got it, let's break down how to handle merging two Foundry datasets into a new combined one, plus how to copy files between datasets for your API architecture update. Here's step-by-step guidance using Foundry Transforms:

Merging Two Datasets into a New Combined Dataset #

Your goal is to combine all files from Dataset A (csv1-csv5) and Dataset B (csv1-csv3) into a single new dataset. The key here is to handle duplicate filenames (like csv1-csv3 appearing in both) based on your needs—either keep one version or rename duplicates to avoid conflicts.

Python Transform Implementation #

Use Foundry's filesystem API to directly work with the raw files:

from transforms.api import transform, Input, Output
import os

@transform(
    output=Output("/your/path/to/combined-dataset"),  # Replace with your target dataset path
    dataset_a=Input("/your/path/to/dataset-a"),       # Source Dataset A path
    dataset_b=Input("/your/path/to/dataset-b")        # Source Dataset B path
)
def merge_datasets(output, dataset_a, dataset_b):
    # Fetch all files from both source datasets
    files_a = list(dataset_a.filesystem().ls())
    files_b = list(dataset_b.filesystem().ls())
    
    # Combine file lists and handle duplicates
    seen_filenames = set()
    unique_files = []
    
    for file in files_a + files_b:
        filename = os.path.basename(file.path)
        if filename not in seen_filenames:
            seen_filenames.add(filename)
            unique_files.append(file)
        # Optional: If you want to keep duplicates with a suffix (e.g., csv1 from B becomes csv1_b.csv)
        # else:
        #     base, ext = os.path.splitext(filename)
        #     new_filename = f"{base}_b{ext}"
        #     # Copy the duplicate to a renamed path in the source dataset temporarily
        #     temp_path = os.path.join(os.path.dirname(file.path), new_filename)
        #     file.copy_to(temp_path)
        #     unique_files.append(dataset_b.filesystem().get(temp_path))
    
    # Write all unique files to the output dataset
    for file in unique_files:
        output.filesystem().copy_from(file.path, os.path.basename(file.path))

Key Notes: #

• Duplicate Handling: The code above prioritizes files from Dataset A (since we process A first). If you want to prioritize Dataset B instead, swap the order of files_a + files_b to files_b + files_a.
• Raw File Support: This works for any file type (XML, CSV, etc.) since we're copying the raw files directly, not parsing their content.

Copying Files Between Foundry Datasets #

If you just need to copy specific files or all files from one dataset to another, you can simplify the transform:

Copy a Single File #

from transforms.api import transform, Input, Output

@transform(
    output=Output("/your/path/to/target-dataset"),
    source=Input("/your/path/to/source-dataset")
)
def copy_single_file(output, source):
    # Specify the exact file to copy (adjust the filename as needed)
    source_file = source.filesystem().get("csv4.csv")
    # Copy to the target dataset (keep the same name or rename it here)
    output.filesystem().copy_from(source_file.path, "csv4.csv")

Copy All Files from One Dataset to Another #

from transforms.api import transform, Input, Output
import os

@transform(
    output=Output("/your/path/to/target-dataset"),
    source=Input("/your/path/to/source-dataset")
)
def copy_all_files(output, source):
    # Iterate over all files in the source dataset and copy them to the target
    for file in source.filesystem().ls():
        output.filesystem().copy_from(file.path, os.path.basename(file.path))

How to Use These Transforms: #

1. In Foundry, create a new Python Transform.
2. Paste the relevant code, replacing the dataset paths with your actual Foundry paths.
3. Configure the input and output datasets in the Transform settings.
4. Run the Transform—once completed, your target dataset will have the merged/copied files.

内容的提问来源于stack exchange，提问作者Patrick OC