Advanced filesystem usage

The filesystem source provides the building blocks to load data from files. This section explains how you can customize the filesystem source for your use case.

Standalone filesystem resource

You can use the standalone filesystem resource to list files in cloud storage or a local filesystem. This allows you to customize file readers or manage files using fsspec.

from dlt.sources.filesystem import filesystem

pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb")
files = filesystem(bucket_url="s3://my_bucket/data", file_glob="csv_folder/*.csv")
pipeline.run(files)

The filesystem ensures consistent file representation across bucket types and offers methods to access and read data. You can quickly build pipelines to:

• Extract text from PDFs (unstructured data source).
• Stream large file content directly from buckets.
• Copy files locally (copy files)

FileItem representation

• All dlt sources/resources that yield files follow the FileItem contract.
• File content is typically not loaded (you can control it with the extract_content parameter of the filesystem resource). Instead, full file info and methods to access content are available.
• Users can request an authenticated fsspec AbstractFileSystem instance.

FileItem fields

• file_url - complete URL of the file (e.g., s3://bucket-name/path/file). This field serves as a primary key.
• file_name - name of the file from the bucket URL.
• relative_path - set when doing glob, is a relative path to a bucket_url argument.
• mime_type - file's MIME type. It is sourced from the bucket provider or inferred from its extension.
• modification_date - file's last modification time (format: pendulum.DateTime).
• size_in_bytes - file size.
• file_content - content, provided upon request.

info

When using a nested or recursive glob pattern, relative_path will include the file's path relative to bucket_url. For instance, using the resource: filesystem("az://dlt-ci-test-bucket/standard_source/samples", file_glob="met_csv/A801/*.csv") will produce file names relative to the /standard_source/samples path, such as met_csv/A801/A881_20230920.csv. For local filesystems, POSIX paths (using "/" as separator) are returned.

File manipulation

FileItem, backed by a dictionary implementation, offers these helpers:

• read_bytes() - method, which returns the file content as bytes.
• open() - method which provides a file object when opened.
• filesystem - field, which gives access to authorized AbstractFilesystem with standard fsspec methods.

Create your own transformer

Although the filesystem resource yields the files from cloud storage or a local filesystem, you need to apply a transformer resource to retrieve the records from files. dlt natively supports three file types: CSV, Parquet, and JSONL (more details in filesystem transformer resource).

But you can easily create your own. In order to do this, you just need a function that takes as input a FileItemDict iterator and yields a list of records (recommended for performance) or individual records.

Example: read data from Excel files

The code below sets up a pipeline that reads from an Excel file using a standalone transformer:

from typing import Iterator

import dlt
from dlt.common.storages.fsspec_filesystem import FileItemDict
from dlt.common.typing import TDataItems
from dlt.sources.filesystem import filesystem

BUCKET_URL = "s3://my_bucket/data"

# Define a standalone transformer to read data from an Excel file.
@dlt.transformer(standalone=True)
def read_excel(
    items: Iterator[FileItemDict], sheet_name: str
) -> Iterator[TDataItems]:
    # Import the required pandas library.
    import pandas as pd

    # Iterate through each file item.
    for file_obj in items:
        # Open the file object.
        with file_obj.open() as file:
            # Read from the Excel file and yield its content as dictionary records.
            yield pd.read_excel(file, sheet_name).to_dict(orient="records")

# Set up the pipeline to fetch a specific Excel file from a filesystem (bucket).
example_xls = filesystem(
    bucket_url=BUCKET_URL, file_glob="../directory/example.xlsx"
) | read_excel("example_table")   # Pass the data through the transformer to read the "example_table" sheet.

pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb", dataset_name="example_xls_data")
# Execute the pipeline and load the extracted data into the "duckdb" destination.
load_info = pipeline.run(example_xls.with_name("example_xls_data"))
# Print the loading information.
print(load_info)

Example: read data from XML files

You can use any third-party library to parse an xml file (e.g., BeautifulSoup, pandas). In the following example, we will be using the xmltodict Python library.

import dlt
from dlt.common.storages.fsspec_filesystem import FileItemDict
from dlt.common.typing import TDataItems
from dlt.sources.filesystem import filesystem

BUCKET_URL = "s3://my_bucket/data"

# Define a standalone transformer to read data from an XML file.
@dlt.transformer(standalone=True)
def read_xml(items: Iterator[FileItemDict]) -> Iterator[TDataItems]:
    # Import the required xmltodict library.
    import xmltodict

    # Iterate through each file item.
    for file_obj in items:
        # Open the file object.
        with file_obj.open() as file:
            # Parse the file to dict records.
            yield xmltodict.parse(file.read())

# Set up the pipeline to fetch a specific XML file from a filesystem (bucket).
example_xml = filesystem(
    bucket_url=BUCKET_URL, file_glob="../directory/example.xml"
) | read_xml()   # Pass the data through the transformer

pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb", dataset_name="example_xml_data")
# Execute the pipeline and load the extracted data into the "duckdb" destination.
load_info = pipeline.run(example_xml.with_name("example_xml_data"))

# Print the loading information.
print(load_info)

Clean files after loading

You can get an fsspec client from the filesystem resource after it was extracted, i.e., in order to delete processed files, etc. The filesystem module contains a convenient method fsspec_from_resource that can be used as follows:

from dlt.sources.filesystem import filesystem, read_csv
from dlt.sources.filesystem.helpers import fsspec_from_resource

# Get filesystem source.
gs_resource = filesystem("gs://ci-test-bucket/")
# Extract files.
pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb")
pipeline.run(gs_resource | read_csv())
# Get fs client.
fs_client = fsspec_from_resource(gs_resource)
# Do any operation.
fs_client.ls("ci-test-bucket/standard_source/samples")

Copy files locally

To copy files locally, add a step in the filesystem resource and then load the listing to the database:

import os

import dlt
from dlt.common.storages.fsspec_filesystem import FileItemDict
from dlt.sources.filesystem import filesystem

def _copy(item: FileItemDict) -> FileItemDict:
    # Instantiate fsspec and copy file
    dest_file = os.path.join("./local_folder", item["file_name"])
    # Create destination folder
    os.makedirs(os.path.dirname(dest_file), exist_ok=True)
    # Download file
    item.fsspec.download(item["file_url"], dest_file)
    # Return file item unchanged
    return item

BUCKET_URL = "gs://ci-test-bucket/"

# Use recursive glob pattern and add file copy step
downloader = filesystem(BUCKET_URL, file_glob="**").add_map(_copy)

# NOTE: You do not need to load any data to execute extract; below, we obtain
# a list of files in a bucket and also copy them locally
listing = list(downloader)
print(listing)
# Download to table "listing"
pipeline = dlt.pipeline(pipeline_name="my_pipeline", destination="duckdb")
load_info = pipeline.run(
    downloader.with_name("listing"), write_disposition="replace"
)
# Pretty print the information on data that was loaded
print(load_info)
print(listing)
print(pipeline.last_trace.last_normalize_info)