ray.data.read_binary_files#

ray.data.read_binary_files(paths: str | List[str], *, include_paths: bool = False, filesystem: pyarrow.fs.FileSystem | None = None, parallelism: int = -1, ray_remote_args: Dict[str, Any] = None, arrow_open_stream_args: Dict[str, Any] | None = None, meta_provider: BaseFileMetadataProvider | None = None, partition_filter: PathPartitionFilter | None = None, partitioning: Partitioning = None, ignore_missing_paths: bool = False, shuffle: Literal['files'] | None = None, file_extensions: List[str] | None = None, concurrency: int | None = None, override_num_blocks: int | None = None) → Dataset[source]#

Create a Dataset from binary files of arbitrary contents.

Examples

Read a file in remote storage.

>>> import ray
>>> path = "s3://anonymous@ray-example-data/pdf-sample_0.pdf"
>>> ds = ray.data.read_binary_files(path)
>>> ds.schema()
Column  Type
------  ----
bytes   binary

Read multiple local files.

>>> ray.data.read_binary_files( 
...     ["local:///path/to/file1", "local:///path/to/file2"])

Read a file with the filepaths included as a column in the dataset.

>>> path = "s3://anonymous@ray-example-data/pdf-sample_0.pdf"
>>> ds = ray.data.read_binary_files(path, include_paths=True)
>>> ds.take(1)[0]["path"]
'ray-example-data/pdf-sample_0.pdf'
Parameters:

  • paths – A single file or directory, or a list of file or directory paths. A list of paths can contain both files and directories.

  • include_paths – If True, include the path to each file. File paths are stored in the 'path' column.

  • filesystem – The PyArrow filesystem implementation to read from. These filesystems are specified in the PyArrow docs. Specify this parameter if you need to provide specific configurations to the filesystem. By default, the filesystem is automatically selected based on the scheme of the paths. For example, if the path begins with s3://, the S3FileSystem is used.

  • ray_remote_args – kwargs passed to remote() in the read tasks.

  • parallelism – This argument is deprecated. Use override_num_blocks argument.

  • arrow_open_stream_args – kwargs passed to pyarrow.fs.FileSystem.open_input_file.

  • meta_provider – A file metadata provider. Custom metadata providers may be able to resolve file metadata more quickly and/or accurately. In most cases, you do not need to set this. If None, this function uses a system-chosen implementation.

  • partition_filter – A PathPartitionFilter. Use with a custom callback to read only selected partitions of a dataset. By default, no files are filtered. By default, this does not filter out any files.

  • partitioning – A Partitioning object that describes how paths are organized. Defaults to None.

  • ignore_missing_paths – If True, ignores any file paths in paths that are not found. Defaults to False.

  • shuffle – If setting to “files”, randomly shuffle input files order before read. Defaults to not shuffle with None.

  • file_extensions – A list of file extensions to filter files by.

  • concurrency – The maximum number of Ray tasks to run concurrently. Set this to control number of tasks to run concurrently. This doesn’t change the total number of tasks run or the total number of output blocks. By default, concurrency is dynamically decided based on the available resources.

  • override_num_blocks – Override the number of output blocks from all read tasks. By default, the number of output blocks is dynamically decided based on input data size and available resources. You shouldn’t manually set this value in most cases.

Returns:

Dataset producing rows read from the specified paths.