How to make a custom file system importer

To learn how to import table data from a file to the Memgraph database, head over to the How to import table data guide.

If you want to read from a file system not currently supported by GQLAlchemy, or use a file type currently not readable, you can implement your own by extending abstract classes FileSystemHandler and DataLoader, respectively.

Info

You can also use this feature with Neo4j:

db = Neo4j(host="localhost", port="7687", username="neo4j", password="test")

Info

The features below aren’t included in the default GQLAlchemy installation. To use them, make sure to install GQLAlchemy with the relevant extras.

Implementing a new FileSystemHandler

For this guide, you will use the existing PyArrowDataLoader capable of reading CSV, Parquet, ORC and IPC/Feather/Arrow file formats. The PyArrow loader class supports fsspec-compatible file systems, so to implement an Azure Blob file system, you need to follow these steps.

1. Extend the FileSystemHandler class

This class holds the connection to the file system service and handles the path from which the DataLoader object reads files. To get a fsspec-compatible instance of an Azure Blob connection, you can use the adlfs package. We are going to pass adlfs-specific parameters such as account_name and account_key via kwargs. All that's left to do is to override the get_path method.

import adlfs

class AzureBlobFileSystemHandler(FileSystemHandler):

    def __init__(self, container_name: str, **kwargs) -> None:
        """Initializes connection and data container."""
        super().__init__(fs=adlfs.AzureBlobFileSystem(**kwargs))
        self._container_name = container_name

    def get_path(self, collection_name: str) -> str:
        """Get file path in file system."""
        return f"{self._container_name}/{collection_name}"

2. Wrap the TableToGraphImporter

Next, you are going to wrap the TableToGraphImporter class. This is optional since you can use the class directly, but it will be easier to use if we extend it with our custom importer class. Since we will be using PyArrow for data loading, you can extend the PyArrowImporter class (which extends the TableToGraphImporter) and make your own PyArrowAzureBlobImporter. This class should initialize the AzureBlobFileSystemHandler and leave the rest to the PyArrowImporter class. It should also receive a file_extension_enum argument, which defines the file type that you are going to be reading.

class PyArrowAzureBlobImporter(PyArrowImporter):
    """PyArrowImporter wrapper for use with Azure Blob File System."""

    def __init__(
        self,
        container_name: str,
        file_extension_enum: PyArrowFileTypeEnum,
        data_configuration: Dict[str, Any],
        memgraph: Optional[Memgraph] = None,
        **kwargs,
    ) -> None:
        super().__init__(
            file_system_handler=AzureBlobFileSystemHandler(        
                container_name=container_name, **kwargs
            ),
            file_extension_enum=file_extension_enum,
            data_configuration=data_configuration,
            memgraph=memgraph,
        )

3. Call translate()

Finally, to use your custom file system, initialize the Importer class and call translate()

importer = PyArrowAzureBlobImporter(
    container_name="test"
    file_extension_enum=PyArrowFileTypeEnum.Parquet,
    data_configuration=parsed_yaml,
    account_name="your_account_name",
    account_key="your_account_key",
)

importer.translate(drop_database_on_start=True)

If you want to see the full implementation of the AzureBlobFileSystem and other loader components, have a look at the code. Feel free to create a PR on the GQLAlchemy repository if you think of a new feature we could use. If you have any more questions, join our community and ping us on Discord.