S3Fs¶
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.
Examples¶
Simple locate and read a file:
>>> import s3fs
>>> fs = s3fs.S3FileSystem(anon=True)
>>> fs.ls('my-bucket')
['my-file.txt']
>>> with fs.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
Integration¶
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.
Async¶
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.
Multiprocessing¶
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.
Limitations¶
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)
Logging¶
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.
Credentials¶
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:
AWS_ACCESS_KEY_ID
,AWS_SECRET_ACCESS_KEY
, andAWS_SESSION_TOKEN
environment variablesconfiguration 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 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(
anon=False,
client_kwargs={
'endpoint_url': 'https://...'
}
)
# Or passing the credentials directly
>>> s3 = s3fs.S3FileSystem(
key='miniokey...',
secret='asecretkey...',
client_kwargs={
'endpoint_url': 'https://...'
}
)
For a Scaleway s3-compatible storage in the fr-par
zone:
>>> s3 = s3fs.S3FileSystem(
key='scaleway-api-key...',
secret='scaleway-secretkey...',
client_kwargs={
'endpoint_url': 'https://s3.fr-par.scw.cloud',
'region_name': 'fr-par'
}
)
For an OVH s3-compatible storage in the GRA
zone:
>>> s3 = s3fs.S3FileSystem(
key='ovh-s3-key...',
secret='ovh-s3-secretkey...',
client_kwargs={
'endpoint_url': 'https://s3.GRA.cloud.ovh.net',
'region_name': 'GRA'
},
config_kwargs={
'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