smart_open
is a Python 3 library for efficient streaming of very large files from/to storages such as S3, GCS, HDFS, WebHDFS, HTTP, HTTPS, SFTP, or local filesystem. It supports transparent, on-the-fly (de-)compression for a variety of different formats.
smart_open
is a drop-in replacement for Python's built-in open()
: it can do anything open
can (100% compatible, falls back to native open
wherever possible), plus lots of nifty extra stuff on top.
Python 2.7 is no longer supported. If you need Python 2.7, please use smart_open 1.10.0, the last version to support Python 2.
Working with large remote files, for example using Amazon's boto and boto3 Python library, is a pain.
boto
's key.set_contents_from_string()
and key.get_contents_as_string()
methods only work for small files, because they're loaded fully into RAM, no streaming.
There are nasty hidden gotchas when using boto
's multipart upload functionality that is needed for large files, and a lot of boilerplate.
smart_open
shields you from that. It builds on boto3 and other remote storage libraries, but offers a clean unified Pythonic API. The result is less code for you to write and fewer bugs to make.
smart_open
is well-tested, well-documented, and has a simple Pythonic API:
>>> from smart_open import open
>>>
>>> # stream lines from an S3 object
>>> for line in open('s3://commoncrawl/robots.txt'):
... print(repr(line))
... break
'User-Agent: *\n'
>>> # stream from/to compressed files, with transparent (de)compression:
>>> for line in open('smart_open/tests/test_data/1984.txt.gz', encoding='utf-8'):
... print(repr(line))
'It was a bright cold day in April, and the clocks were striking thirteen.\n'
'Winston Smith, his chin nuzzled into his breast in an effort to escape the vile\n'
'wind, slipped quickly through the glass doors of Victory Mansions, though not\n'
'quickly enough to prevent a swirl of gritty dust from entering along with him.\n'
>>> # can use context managers too:
>>> with open('smart_open/tests/test_data/1984.txt.gz') as fin:
... with open('smart_open/tests/test_data/1984.txt.bz2', 'w') as fout:
... for line in fin:
... fout.write(line)
>>> # can use any IOBase operations, like seek
>>> with open('s3://commoncrawl/robots.txt', 'rb') as fin:
... for line in fin:
... print(repr(line.decode('utf-8')))
... break
... offset = fin.seek(0) # seek to the beginning
... print(fin.read(4))
'User-Agent: *\n'
b'User'
>>> # stream from HTTP
>>> for line in open('http://example.com/index.html'):
... print(repr(line))
... break
'<!doctype html>\n'
Other examples of URLs that smart_open
accepts:
s3://my_bucket/my_key s3://my_key:my_secret@my_bucket/my_key s3://my_key:my_secret@my_server:my_port@my_bucket/my_key gs://my_bucket/my_blob hdfs:///path/file hdfs://path/file webhdfs://host:port/path/file ./local/path/file ~/local/path/file local/path/file ./local/path/file.gz file:///home/user/file file:///home/user/file.bz2 [ssh|scp|sftp]://username@host//path/file [ssh|scp|sftp]://username@host/path/file [ssh|scp|sftp]://username:password@host/path/file
pip install smart_open // Install with no cloud dependencies pip install smart_open[aws] // Install AWS deps pip install smart_open[gcp] // Install GCP deps pip install smart_open[all] // Installs all cloud dependencies
Or, if you prefer to install from the source tar.gz:
python setup.py test # run unit tests python setup.py install
To run the unit tests (optional), you'll also need to install some other dependencies: see setup.py or run pip install .[test]. The tests are also run automatically with Travis CI on every commit push & pull request.
If you're upgrading from smart_open
versions 1.8.0 and below, please check out the Migration Guide.
Version 2.0
will introduce a backwards incompatible installation method with regards to the cloud dependencies. A migration path to minimize breaking
was introduced in version x.x.x
. If you want to maintain backwards compatibility (installing all dependencies) install this package via smart_open[all]
now
and once the change is made you should not have any issues. If all you care about is AWS dependencies for example you can install via smart_open[aws]
and
once the dependency change is made you will simply drop the unwanted dependencies. You can read more about the motivations here
For detailed API info, see the online help:
help('smart_open')
or click here to view the help in your browser.
>>> import os, boto3
>>>
>>> # stream content *into* S3 (write mode) using a custom session
>>> session = boto3.Session(
... aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'],
... aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY'],
... )
>>> url = 's3://smart-open-py37-benchmark-results/test.txt'
>>> with open(url, 'wb', transport_params={'session': session}) as fout:
... bytes_written = fout.write(b'hello world!')
... print(bytes_written)
12
# stream from HDFS
for line in open('hdfs://user/hadoop/my_file.txt', encoding='utf8'):
print(line)
# stream from WebHDFS
for line in open('webhdfs://host:port/user/hadoop/my_file.txt'):
print(line)
# stream content *into* HDFS (write mode):
with open('hdfs://host:port/user/hadoop/my_file.txt', 'wb') as fout:
fout.write(b'hello world')
# stream content *into* WebHDFS (write mode):
with open('webhdfs://host:port/user/hadoop/my_file.txt', 'wb') as fout:
fout.write(b'hello world')
# stream from a completely custom s3 server, like s3proxy:
for line in open('s3u://user:secret@host:port@mybucket/mykey.txt'):
print(line)
# Stream to Digital Ocean Spaces bucket providing credentials from boto3 profile
transport_params = {
'session': boto3.Session(profile_name='digitalocean'),
'resource_kwargs': {
'endpoint_url': 'https://ams3.digitaloceanspaces.com',
}
}
with open('s3://bucket/key.txt', 'wb', transport_params=transport_params) as fout:
fout.write(b'here we stand')
# stream from GCS
for line in open('gs://my_bucket/my_file.txt'):
print(line)
# stream content *into* GCS (write mode):
with open('gs://my_bucket/my_file.txt', 'wb') as fout:
fout.write(b'hello world')
smart_open
allows reading and writing gzip and bzip2 files.
They are transparently handled over HTTP, S3, and other protocols, too, based on the extension of the file being opened.
You can easily add support for other file extensions and compression formats.
For example, to open xz-compressed files:
>>> import lzma, os
>>> from smart_open import open, register_compressor
>>> def _handle_xz(file_obj, mode):
... return lzma.LZMAFile(filename=file_obj, mode=mode, format=lzma.FORMAT_XZ)
>>> register_compressor('.xz', _handle_xz)
>>> with open('smart_open/tests/test_data/crime-and-punishment.txt.xz') as fin:
... text = fin.read()
>>> print(len(text))
1696
lzma
is in the standard library in Python 3.3 and greater.
For 2.7, use backports.lzma.
smart_open
supports a wide range of transport options out of the box, including:
- S3
- HTTP, HTTPS (read-only)
- SSH, SCP and SFTP
- WebHDFS
- GCS
Each option involves setting up its own set of parameters.
For example, for accessing S3, you often need to set up authentication, like API keys or a profile name.
smart_open
's open
function accepts a keyword argument transport_params
which accepts additional parameters for the transport layer.
Here are some examples of using this parameter:
>>> import boto3
>>> fin = open('s3://commoncrawl/robots.txt', transport_params=dict(session=boto3.Session()))
>>> fin = open('s3://commoncrawl/robots.txt', transport_params=dict(buffer_size=1024))
For the full list of keyword arguments supported by each transport option, see the documentation:
help('smart_open.open')
smart_open
uses the boto3
library to talk to S3.
boto3
has several mechanisms for determining the credentials to use.
By default, smart_open
will defer to boto3
and let the latter take care of the credentials.
There are several ways to override this behavior.
The first is to pass a boto3.Session
object as a transport parameter to the open
function.
You can customize the credentials when constructing the session.
smart_open
will then use the session when talking to S3.
session = boto3.Session(
aws_access_key_id=ACCESS_KEY,
aws_secret_access_key=SECRET_KEY,
aws_session_token=SESSION_TOKEN,
)
fin = open('s3://bucket/key', transport_params=dict(session=session), ...)
Your second option is to specify the credentials within the S3 URL itself:
fin = open('s3://aws_access_key_id:aws_secret_access_key@bucket/key', ...)
Important: The two methods above are mutually exclusive. If you pass an AWS session and the URL contains credentials, smart_open
will ignore the latter.
Important: smart_open
ignores configuration files from the older boto
library.
Port your old boto
settings to boto3
in order to use them with smart_open
.
Since going over all (or select) keys in an S3 bucket is a very common operation, there's also an extra function smart_open.s3_iter_bucket()
that does this efficiently, processing the bucket keys in parallel (using multiprocessing):
>>> from smart_open import s3_iter_bucket
>>> # get data corresponding to 2010 and later under "silo-open-data/annual/monthly_rain"
>>> # we use workers=1 for reproducibility; you should use as many workers as you have cores
>>> bucket = 'silo-open-data'
>>> prefix = 'annual/monthly_rain/'
>>> for key, content in s3_iter_bucket(bucket, prefix=prefix, accept_key=lambda key: '/201' in key, workers=1, key_limit=3):
... print(key, round(len(content) / 2**20))
annual/monthly_rain/2010.monthly_rain.nc 13
annual/monthly_rain/2011.monthly_rain.nc 13
annual/monthly_rain/2012.monthly_rain.nc 13
The version_id
transport parameter enables you to get the desired version of the object from an S3 bucket.
Important
S3 disables version control by default.
Before using the version_id
parameter, you must explicitly enable version control for your S3 bucket.
Read https://docs.aws.amazon.com/AmazonS3/latest/dev/Versioning.html for details.
>>> # Read previous versions of an object in a versioned bucket
>>> bucket, key = 'smart-open-versioned', 'demo.txt'
>>> versions = [v.id for v in boto3.resource('s3').Bucket(bucket).object_versions.filter(Prefix=key)]
>>> for v in versions:
... with open('s3://%s/%s' % (bucket, key), transport_params={'version_id': v}) as fin:
... print(v, repr(fin.read()))
KiQpZPsKI5Dm2oJZy_RzskTOtl2snjBg 'second version\n'
N0GJcE3TQCKtkaS.gF.MUBZS85Gs3hzn 'first version\n'
>>> # If you don't specify a version, smart_open will read the most recent one
>>> with open('s3://%s/%s' % (bucket, key)) as fin:
... print(repr(fin.read()))
'second version\n'
smart_open
uses the google-cloud-storage
library to talk to GCS.
google-cloud-storage
uses the google-cloud
package under the hood to handle authentication.
There are several options to provide
credentials.
By default, smart_open
will defer to google-cloud-storage
and let it take care of the credentials.
To override this behavior, pass a google.cloud.storage.Client
object as a transport parameter to the open
function.
You can customize the credentials
when constructing the client. smart_open
will then use the client when talking to GCS. To follow allow with
the example below, refer to Google's guide
to setting up GCS authentication with a service account.
import os
from google.cloud.storage import Client
service_account_path = os.environ['GOOGLE_APPLICATION_CREDENTIALS']
client = Client.from_service_account_json(service_account_path)
fin = open('gs://gcp-public-data-landsat/index.csv.gz', transport_params=dict(client=client))
If you need more credential options, you can create an explicit google.auth.credentials.Credentials
object
and pass it to the Client. To create an API token for use in the example below, refer to the
GCS authentication guide.
import os
from google.auth.credentials import Credentials
from google.cloud.storage import Client
token = os.environ['GOOGLE_API_TOKEN']
credentials = Credentials(token=token)
client = Client(credentials=credentials)
fin = open('gs://gcp-public-data-landsat/index.csv.gz', transport_params=dict(client=client))
The open
function also accepts file-like objects.
This is useful when you already have a binary file open, and would like to wrap it with transparent decompression:
>>> import io, gzip
>>>
>>> # Prepare some gzipped binary data in memory, as an example.
>>> # Any binary file will do; we're using BytesIO here for simplicity.
>>> buf = io.BytesIO()
>>> with gzip.GzipFile(fileobj=buf, mode='w') as fout:
... _ = fout.write(b'this is a bytestring')
>>> _ = buf.seek(0)
>>>
>>> # Use case starts here.
>>> buf.name = 'file.gz' # add a .name attribute so smart_open knows what compressor to use
>>> import smart_open
>>> smart_open.open(buf, 'rb').read() # will gzip-decompress transparently!
b'this is a bytestring'
In this case, smart_open
relied on the .name
attribute of our binary I/O stream buf
object to determine which decompressor to use.
If your file object doesn't have one, set the .name
attribute to an appropriate value.
Furthermore, that value has to end with a known file extension (see the register_compressor
function).
Otherwise, the transparent decompression will not occur.
smart_open.open
can also be used with Path
objects.
The built-in Path.open() is not able to read text from compressed files, so use patch_pathlib
to replace it with smart_open.open() instead.
This can be helpful when e.g. working with compressed files.
>>> from pathlib import Path
>>> from smart_open.smart_open_lib import patch_pathlib
>>>
>>> _ = patch_pathlib() # replace `Path.open` with `smart_open.open`
>>>
>>> path = Path("smart_open/tests/test_data/crime-and-punishment.txt.gz")
>>>
>>> with path.open("r") as infile:
... print(infile.readline()[:41])
В начале июля, в чрезвычайно жаркое время
See this document.
smart_open
lives on Github. You can file
issues or pull requests there. Suggestions, pull requests and improvements welcome!
smart_open
is open source software released under the MIT license.
Copyright (c) 2015-now Radim Řehůřek.