S3Fs is a Pythonic file interface to S3. It builds on top of botocore.

The top-level class S3FileSystem holds connection information and allows typical file-system style operations like cp, mv, ls, du, glob, etc., as well as put/get of local files to/from S3.

The connection can be anonymous - in which case only publicly-available, read-only buckets are accessible - or via credentials explicitly supplied or in configuration files.

Calling open() on a S3FileSystem (typically using a context manager) provides an S3File for read or write access to a particular key. The object emulates the standard File protocol (read, write, tell, seek), such that functions expecting a file can access S3. Only binary read and write modes are implemented, with blocked caching.

S3Fs uses and is based upon fsspec.


Simple locate and read a file:

>>> import s3fs
>>> s3 = s3fs.S3FileSystem(anon=True)
>>> s3.ls('my-bucket')
>>> with s3.open('my-bucket/my-file.txt', 'rb') as f:
...     print(f.read())
b'Hello, world'

(see also walk and glob)

Reading with delimited blocks:

>>> s3.read_block(path, offset=1000, length=10, delimiter=b'\n')
b'A whole line of text\n'

Writing with blocked caching:

>>> s3 = s3fs.S3FileSystem(anon=False)  # uses default credentials
>>> with s3.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
>>> s3.du('mybucket/new-file')
{'mybucket/new-file': 4194304}

Because S3Fs faithfully copies the Python file interface it can be used smoothly with other projects that consume the file interface like gzip or pandas.

>>> with s3.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


The libraries intake, pandas and dask accept URLs with the prefix “s3://”, and will use s3fs to complete the IO operation in question. The IO functions take an argument storage_options, which will be passed to S3FileSystem, for example:

df = pd.read_excel("s3://bucket/path/file.xls",
                   storage_options={"anon": True})

This gives the chance to pass any credentials or other necessary arguments needed to s3fs.


s3fs is implemented using aiobotocore, and offers async functionality. A number of methods of S3FileSystem 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 s3fs from async code, then you should pass asynchronous=True, 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 S3 call.

async def run_program():
    s3 = S3FileSystem(..., asynchronous=True)
    session = await s3.set_session()
    ...  # perform work
    await session.close()

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.


When using Python’s multiprocessing, the start method must be set to either spawn or forkserver. fork is not safe to use because of the open sockets and async thread used by s3fs, and may lead to hard-to-find bugs and occasional deadlocks. Read more about the available start methods.


This project is meant for convenience, rather than feature completeness. The following are known current omissions:

  • file access is always binary (although readline and iterating by line are possible)

  • no permissions/access-control (i.e., no chmod/chown methods)


The logger named s3fs provides information about the operations of the file system. To quickly see all messages, you can set the environment variable S3FS_LOGGING_LEVEL=DEBUG. The presence of this environment variable will install a handler for the logger that prints messages to stderr and set the log level to the given value. More advance logging configuration is possible using Python’s standard logging framework.


The AWS key and secret may be provided explicitly when creating an S3FileSystem. A more secure way, not including the credentials directly in code, is to allow boto to establish the credentials automatically. Boto will try the following methods, in order:


  • configuration files such as ~/.aws/credentials

  • for nodes on EC2, the IAM metadata provider

You can specify a profile using s3fs.S3FileSystem(profile='PROFILE'). Otherwise sf3s will use authentication via boto environment variables.

In a distributed environment, it is not expected that raw credentials should be passed between machines. In the explicitly provided credentials case, the method S3FileSystem.get_delegated_s3pars() can be used to obtain temporary credentials. When not using explicit credentials, it should be expected that every machine also has the appropriate environment variables, config files or IAM roles available.

If none of the credential methods are available, only anonymous access will work, and anon=True must be passed to the constructor.

Furthermore, S3FileSystem.current() will return the most-recently created instance, so this method could be used in preference to the constructor in cases where the code must be agnostic of the credentials/config used.

S3 Compatible Storage

To use s3fs against an S3 compatible storage, like MinIO or Ceph Object Gateway, you’ll probably need to pass extra parameters when creating the s3fs filesystem. Here are some sample configurations:

For a self-hosted MinIO instance:

# When relying on auto discovery for credentials
>>> s3 = s3fs.S3FileSystem(
# Or passing the credentials directly
>>> s3 = s3fs.S3FileSystem(

It is also possible to set credentials through environment variables:

# export FSSPEC_S3_ENDPOINT_URL=https://...
# export FSSPEC_S3_KEY='miniokey...'
# export FSSPEC_S3_SECRET='asecretkey...'
>>> s3 = s3fs.S3FileSystem()
# or ...
>>> f = fsspec.open("s3://minio-bucket/...")

For Storj DCS via the S3-compatible Gateway:

# When relying on auto discovery for credentials
>>> s3 = s3fs.S3FileSystem(
# Or passing the credentials directly
>>> s3 = s3fs.S3FileSystem(

For a Scaleway s3-compatible storage in the fr-par zone:

>>> s3 = s3fs.S3FileSystem(
      'region_name': 'fr-par'

For an OVH s3-compatible storage in the GRA zone:

>>> s3 = s3fs.S3FileSystem(
      'region_name': 'GRA'
      'signature_version': 's3v4'

Requester Pays Buckets

Some buckets, such as the arXiv raw data, are configured so that the requester of the data pays any transfer fees. You must be authenticated to access these buckets and (because these charges maybe unexpected) amazon requires an additional key on many of the API calls. To enable RequesterPays create your file system as

>>> s3 = s3fs.S3FileSystem(anon=False, requester_pays=True)

Serverside Encryption

For some buckets/files you may want to use some of s3’s server side encryption features. s3fs supports these in a few ways

>>> s3 = s3fs.S3FileSystem(
...     s3_additional_kwargs={'ServerSideEncryption': 'AES256'})

This will create an s3 filesystem instance that will append the ServerSideEncryption argument to all s3 calls (where applicable).

The same applies for s3.open. Most of the methods on the filesystem object will also accept and forward keyword arguments to the underlying calls. The most recently specified argument is applied last in the case where both s3_additional_kwargs and a method’s **kwargs are used.

The s3.utils.SSEParams provides some convenient helpers for the serverside encryption parameters in particular. An instance can be passed instead of a regular python dictionary as the s3_additional_kwargs parameter.

Bucket Version Awareness

If your bucket has object versioning enabled then you can add version-aware support to s3fs. This ensures that if a file is opened at a particular point in time that version will be used for reading.

This mitigates the issue where more than one user is concurrently reading and writing to the same object.

>>> s3 = s3fs.S3FileSystem(version_aware=True)
# Open the file at the latest version
>>> fo = s3.open('versioned_bucket/object')
>>> versions = s3.object_version_info('versioned_bucket/object')
# Open the file at a particular version
>>> fo_old_version = s3.open('versioned_bucket/object', version_id='SOMEVERSIONID')

In order for this to function the user must have the necessary IAM permissions to perform a GetObjectVersion

Indices and tables