smart_open is a Python 3 library for efficient streaming of very large files from/to storages such as S3, GCS, Azure Blob Storage, 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 1.10.1, the last version to support Python 2.
Working with large remote files, for example using Amazon's boto3 Python library, is a pain.
boto3's Object.upload_fileobj() and Object.download_fileobj() methods require gotcha-prone boilerplate to use successfully, such as constructing file-like object wrappers.
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)
74
80
78
79
>>> # 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 azure://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
smart_open supports a wide range of storage solutions, including AWS S3, Google Cloud and Azure.
Each individual solution has its own dependencies.
By default, smart_open does not install any dependencies, in order to keep the installation size small.
You can install these dependencies explicitly using:
pip install smart_open[azure] # Install Azure deps pip install smart_open[gcs] # Install GCS deps pip install smart_open[s3] # Install S3 deps
Or, if you don't mind installing a large number of third party libraries, you can install all dependencies using:
pip install smart_open[all]
Be warned that this option increases the installation size significantly, e.g. over 100MB.
If you're upgrading from smart_open versions 2.x and below, please check out the Migration Guide.
For detailed API info, see the online help:
help('smart_open')or click here to view the help in your browser.
For the sake of simplicity, the examples below assume you have all the dependencies installed, i.e. you have done:
pip install smart_open[all]
>>> 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={'client': session.client('s3')}) 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
session = boto3.Session(profile_name='digitalocean')
client = session.client('s3', endpoint_url='https://ams3.digitaloceanspaces.com')
transport_params = {'client': client}
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')
# stream from Azure Blob Storage
connect_str = os.environ['AZURE_STORAGE_CONNECTION_STRING']
transport_params = {
'client': azure.storage.blob.BlobServiceClient.from_connection_string(connect_str),
}
for line in open('azure://mycontainer/myfile.txt', transport_params=transport_params):
print(line)
# stream content *into* Azure Blob Storage (write mode):
connect_str = os.environ['AZURE_STORAGE_CONNECTION_STRING']
transport_params = {
'client': azure.storage.blob.BlobServiceClient.from_connection_string(connect_str),
}
with open('azure://mycontainer/my_file.txt', 'wb', transport_params=transport_params) as fout:
fout.write(b'hello world')The top-level compression parameter controls compression/decompression behavior when reading and writing. The supported values for this parameter are:
infer_from_extension(default behavior)disable.gz.bz2
By default, smart_open determines the compression algorithm to use based on the file extension.
>>> from smart_open import open, register_compressor
>>> with open('smart_open/tests/test_data/1984.txt.gz') as fin:
... print(fin.read(32))
It was a bright cold day in ApriYou can override this behavior to either disable compression, or explicitly specify the algorithm to use. To disable compression:
>>> from smart_open import open, register_compressor
>>> with open('smart_open/tests/test_data/1984.txt.gz', 'rb', compression='disable') as fin:
... print(fin.read(32))
b'\x1f\x8b\x08\x08\x85F\x94\\\x00\x031984.txt\x005\x8f=r\xc3@\x08\x85{\x9d\xe2\x1d@'To specify the algorithm explicitly (e.g. for non-standard file extensions):
>>> from smart_open import open, register_compressor
>>> with open('smart_open/tests/test_data/1984.txt.gzip', compression='.gz') as fin:
... print(fin.read(32))
It was a bright cold day in ApriYou can also 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/1984.txt.xz') as fin:
... print(fin.read(32))
It was a bright cold day in Aprilzma 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
- Azure Blob Storage
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(client=boto3.client('s3')))
>>> 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.Client object as a transport parameter to the open function.
You can customize the credentials when constructing the session for the client.
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,
)
client = session.client('s3', endpoint_url=..., config=...)
fin = open('s3://bucket/key', transport_params=dict(client=client))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 client 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
>>> # 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 13smart_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))smart_open uses the azure-storage-blob library to talk to Azure Blob Storage.
By default, smart_open will defer to azure-storage-blob and let it take care of the credentials.
Azure Blob Storage does not have any ways of inferring credentials therefore, passing a azure.storage.blob.BlobServiceClient
object as a transport parameter to the open function is required.
You can customize the credentials
when constructing the client. smart_open will then use the client when talking to. To follow allow with
the example below, refer to Azure's guide
to setting up authentication.
import os
from azure.storage.blob import BlobServiceClient
azure_storage_connection_string = os.environ['AZURE_STORAGE_CONNECTION_STRING']
client = BlobServiceClient.from_connection_string(azure_storage_connection_string)
fin = open('azure://my_container/my_blob.txt', transport_params=dict(client=client))If you need more credential options, refer to the Azure Storage authentication guide.
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 времяSee this document.
See this document.
smart_open comes with a comprehensive suite of unit tests.
Before you can run the test suite, install the test dependencies:
pip install -e .[test]
Now, you can run the unit tests:
pytest smart_open
The tests are also run automatically with Travis CI on every commit push & pull request.
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.