A pythonic file-system interface to Google Cloud Storage.

This software is beta, use at your own risk.

Please file issues and requests on github and we welcome pull requests.

This package depends on fsspec, and inherits many useful behaviours from there, including integration with Dask, and the facility for key-value dict-like objects of the type used by zarr.


The GCSFS library can be installed using conda:

conda install -c conda-forge gcsfs

or pip:

pip install gcsfs

or by cloning the repository:

git clone https://github.com/fsspec/gcsfs/
cd gcsfs/
pip install .


Locate and read a file:

>>> import gcsfs
>>> fs = gcsfs.GCSFileSystem(project='my-google-project')
>>> fs.ls('my-bucket')
>>> with fs.open('my-bucket/my-file.txt', 'rb') as f:
...     print(f.read())
b'Hello, world'

(see also walk() and glob())

Read with delimited blocks:

>>> fs.read_block(path, offset=1000, length=10, delimiter=b'\n')
b'A whole line of text\n'

Write with blocked caching:

>>> with fs.open('mybucket/new-file', 'wb') as f:
...     f.write(2*2**20 * b'a')
...     f.write(2*2**20 * b'a') # data is flushed and file closed
>>> fs.du('mybucket/new-file')
{'mybucket/new-file': 4194304}

Because GCSFS faithfully copies the Python file interface it can be used smoothly with other projects that consume the file interface like gzip or pandas.

>>> with fs.open('mybucket/my-file.csv.gz', 'rb') as f:
...     g = gzip.GzipFile(fileobj=f)  # Decompress data with gzip
...     df = pd.read_csv(g)           # Read CSV file with Pandas


Several modes of authentication are supported:

  • if token=None (default), GCSFS will attempt to use your default gcloud credentials or, attempt to get credentials from the google metadata service, or fall back to anonymous access. This will work for most users without further action. Note that the default project may also be found, but it is often best to supply this anyway (only affects bucket- level operations).

  • if token='cloud', we assume we are running within google (compute or container engine) and fetch the credentials automatically from the metadata service.

  • you may supply a token generated by the gcloud utility; this is either a python dictionary, or the name of a file containing the JSON returned by logging in with the gcloud CLI tool (e.g., ~/.config/gcloud/application_default_credentials.json or ~/.config/gcloud/legacy_credentials/<YOUR GOOGLE USERNAME>/adc.json) or any value google Credentials object.

  • you can also generate tokens via Oauth2 in the browser using token='browser', which gcsfs then caches in a special file, ~/.gcs_tokens, and can subsequently be accessed with token='cache'.

  • anonymous only access can be selected using token='anon', e.g. to access public resources such as ‘anaconda-public-data’.

The acquired session tokens are not preserved when serializing the instances, so it is safe to pass them to worker processes on other machines if using in a distributed computation context. If credentials are given by a file path, however, then this file must exist on every machine.


The libraries intake, pandas and dask accept URLs with the prefix “gcs://”, and will use gcsfs to complete the IO operation in question. The IO functions take an argument storage_options, which will be passed to GCSFileSystem, for example:

df = pd.read_excel("gcs://bucket/path/file.xls",
                   storage_options={"token": "anon"})

This gives the chance to pass any credentials or other necessary arguments needed to gcsfs.


gcsfs is implemented using aiohttp, and offers async functionality. A number of methods of GCSFileSystem are async, for for each of these, there is also a synchronous version with the same name and lack of a “_” prefix.

If you wish to call gcsfs from async code, then you should pass asynchronous=True, loop=loop to the constructor (the latter is optional, if you wish to use both async and sync methods). You must also explicitly await the client creation before making any GCS call.

async def run_program():
    gcs = GCSFileSystem(asynchronous=True)
    print(await gcs._ls(""))

asyncio.run(run_program())  # or call from your async code

Concurrent async operations are also used internally for bulk operations such as pipe/cat, get/put, cp/mv/rm. The async calls are hidden behind a synchronisation layer, so are designed to be called from normal code. If you are not using async-style programming, you do not need to know about how this works, but you might find the implementation interesting.

For every synchronous function there is asynchronous one prefixed by _, but the open operation does not support async operation. If you need it to open some file in async manner, it’s better to asynchronously download it to temporary location and working with it from there.


gcsfs uses aiohttp for calls to the storage api, which by default ignores HTTP_PROXY/HTTPS_PROXY environment variables. To read proxy settings from the environment provide session_kwargs as follows:

fs = GCSFileSystem(project='my-google-project', session_kwargs={'trust_env': True})

For further reference check aiohttp proxy support.

Indices and tables