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Read GCS and local paths with the same interface, clone of tensorflow.io.gfile

Project description

blobfile

This is a standalone clone of TensorFlow's gfile, supporting both local paths and gs:// (Google Cloud Storage) paths.

The main function is BlobFile, a replacement for GFile. There are also a few additional functions, basename, dirname, and join, which mostly do the same thing as their os.path namesakes, only they also support gs:// paths.

Installation:

pip install blobfile

Usage:

import blobfile as bf

with bf.BlobFile("gs://my-bucket-name/cats", "wb") as w:
    w.write(b"meow!")

Here are the functions:

  • BlobFile - like open() but works with gs:// paths too, data can be streamed to/from the remote file. It accepts the following arguments:
    • streaming:
      • The default for streaming is True when mode is in "r", "rb" and False when mode is in "w", "wb", "a", "ab".
      • streaming=True:
        • Reading is done without downloading the entire remote file.
        • Writing is done to the remote file directly, but only in chunks of a few MB in size. flush() will not cause an early write.
        • Appending is not implemented.
      • streaming=False:
        • Reading is done by downloading the remote file to a local file during the constructor.
        • Writing is done by uploading the file on close() or during destruction.
        • Appending is done by downloading the file during construction and uploading on close().
    • buffer_size: number of bytes to buffer, this can potentially make reading more efficient.
    • cache_dir: a directory in which to cache files for reading, only valid if streaming=False and mode is in "r", "rb". You are reponsible for cleaning up the cache directory.

Some are inspired by existing os.path and shutil functions:

  • copy - copy a file from one path to another, will do a remote copy between two remote paths on the same blob storage service
  • exists - returns True if the file or directory exists
  • glob - return files matching a glob-style pattern as a generator. Globs can have surprising performance characteristics when used with blob storage. Character ranges are not supported in patterns.
  • isdir - returns True if the path is a directory
  • listdir - list contents of a directory as a generator
  • makedirs - ensure that a directory and all parent directories exist
  • remove - remove a file
  • rmdir - remove an empty directory
  • rmtree - remove a directory tree
  • stat - get the size and modification time of a file
  • walk - walk a directory tree with a generator that yields (dirpath, dirnames, filenames) tuples
  • basename - get the final component of a path
  • dirname - get the path except for the final component
  • join - join 2 or more paths together, inserting directory separators between each component

There are a few bonus functions:

  • get_url - returns a url for a path along with the expiration for that url (or None)
  • md5 - get the md5 hash for a path, for GCS this is fast, but for other backends this may be slow
  • set_log_callback - set a log callback function log(msg: string) to use instead of printing to stdout

An some exceptions:

  • Error - base class for exceptions raised by this library
  • RequestFailure - a request has failed permanently, has message:str, request:Request, and response:urllib3.HTTPResponse attributes.

Examples

Write and read a file:

import blobfile as bf

with bf.BlobFile("gs://my-bucket/file.name", "wb") as f:
    f.write(b"meow")

print("exists:", bf.exists("gs://my-bucket/file.name"))

print("contents:", bf.BlobFile("gs://my-bucket/file.name", "rb").read())

Parallel execution:

import blobfile as bf
import multiprocessing as mp
import tqdm

filenames = [f"{i}.ext" for i in range(1000)]

with mp.Pool() as pool:
    for filename, exists in tqdm.tqdm(zip(filenames, pool.imap(bf.exists, filenames)), total=len(filenames)):
        pass

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