Utils for streaming large files (S3, HDFS, GCS, Azure Blob Storage, gzip, bz2...)
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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 <https://github.com/RaRe-Technologies/smart_open/releases/tag/1.10.0>
_, the last version to support Python 2.
Working with large remote files, for example using Amazon's boto3 <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
_ 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:
.. _doctools_before_examples:
.. code-block:: python
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'
.. _doctools_after_examples:
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 <MIGRATING_FROM_OLDER_VERSIONS.rst>
_.
For detailed API info, see the online help:
.. code-block:: python
help('smart_open')
or click here <https://github.com/RaRe-Technologies/smart_open/blob/master/help.txt>
__ 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]
.. code-block:: python
>>> import os, boto3
>>> from smart_open import open
>>>
>>> # 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
.. code-block:: python
# 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.
.. code-block:: python
>>> 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 Apri
You can override this behavior to either disable compression, or explicitly specify the algorithm to use. To disable compression:
.. code-block:: python
>>> 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):
.. code-block:: python
>>> 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 Apri
You can also easily add support for other file extensions and compression formats. For example, to open xz-compressed files:
.. code-block:: python
>>> 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 Apri
lzma
is in the standard library in Python 3.3 and greater.
For 2.7, use backports.lzma
_.
.. _backports.lzma: https://pypi.org/project/backports.lzma/
smart_open
supports a wide range of transport options out of the box, including:
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:
.. code-block:: python
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:
.. code-block:: python
help('smart_open.open')
smart_open
uses the boto3
library to talk to S3.
boto3
has several mechanisms <https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html>
__ 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.
.. code-block:: python
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:
.. code-block:: python
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):
.. code-block:: python
from smart_open import s3
we use workers=1 for reproducibility; you should use as many workers as you have cores
bucket = 'silo-open-data' prefix = 'Official/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)) Official/annual/monthly_rain/2010.monthly_rain.nc 13 Official/annual/monthly_rain/2011.monthly_rain.nc 13 Official/annual/monthly_rain/2012.monthly_rain.nc 13
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 <https://googleapis.dev/python/google-api-core/latest/auth.html>
__ 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 <https://googleapis.dev/python/storage/latest/client.html>
__
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 <https://cloud.google.com/storage/docs/reference/libraries#setting_up_authentication>
__
to setting up GCS authentication with a service account.
.. code-block:: python
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 <https://cloud.google.com/storage/docs/authentication#apiauth>
__.
.. code-block:: python
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 <https://docs.microsoft.com/en-us/azure/storage/common/storage-samples-python#authentication>
__
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 <https://docs.microsoft.com/en-us/azure/storage/blobs/storage-quickstart-blobs-python#copy-your-credentials-from-the-azure-portal>
__
to setting up authentication.
.. code-block:: python
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 <https://docs.microsoft.com/en-us/azure/storage/common/storage-samples-python#authentication>
__.
pathlib.Path.open
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.
.. code-block:: python
>>> 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 <howto.md>
__.
smart_open
See this document <extending.md>
__.
smart_open
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 <https://travis-ci.org/RaRe-Technologies/smart_open>
_ on every commit push & pull request.
smart_open
lives on Github <https://github.com/RaRe-Technologies/smart_open>
_. 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 <https://github.com/piskvorky/smart_open/blob/master/LICENSE>
.
Copyright (c) 2015-now Radim Řehůřek <https://radimrehurek.com>
.