srcd_smart_open

====================================================== smart_open — utils for streaming large files in Python

|License|_ |Travis|_

.. |License| image:: https://img.shields.io/pypi/l/smart_open.svg .. |Travis| image:: https://travis-ci.org/RaRe-Technologies/smart_open.svg?branch=master .. _Travis: https://travis-ci.org/RaRe-Technologies/smart_open .. _License: https://github.com/RaRe-Technologies/smart_open/blob/master/LICENSE

What?

smart_open is a Python 2 & Python 3 library for efficient streaming of very large files from/to S3, HDFS, WebHDFS, HTTP, or local (compressed) files. It's 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.

smart_open is well-tested, well-documented, and has a simple, Pythonic API:

.. code-block:: python

from smart_open import smart_open

stream lines from an S3 object

for line in smart_open('s3://mybucket/mykey.txt', 'rb'): ... print(line.decode('utf8'))

stream from/to compressed files, with transparent (de)compression:

for line in smart_open('./foo.txt.gz', encoding='utf8'): ... print(line)

can use context managers too:

with smart_open('/home/radim/foo.txt.bz2', 'wb') as fout: ... fout.write(u"some content\n".encode('utf8'))

with smart_open('s3://mybucket/mykey.txt', 'rb') as fin: ... for line in fin: ... print(line.decode('utf8')) ... fin.seek(0) # seek to the beginning ... b1000 = fin.read(1000) # read 1000 bytes

stream from HDFS

for line in smart_open('hdfs://user/hadoop/my_file.txt', encoding='utf8'): ... print(line)

stream from HTTP

for line in smart_open('http://example.com/index.html'): ... print(line)

stream from WebHDFS

for line in smart_open('webhdfs://host:port/user/hadoop/my_file.txt'): ... print(line)

stream content into S3 (write mode):

with smart_open('s3://mybucket/mykey.txt', 'wb') as fout: ... for line in [b'first line\n', b'second line\n', b'third line\n']: ... fout.write(line)

stream content into HDFS (write mode):

with smart_open('hdfs://host:port/user/hadoop/my_file.txt', 'wb') as fout: ... for line in [b'first line\n', b'second line\n', b'third line\n']: ... fout.write(line)

stream content into WebHDFS (write mode):

with smart_open('webhdfs://host:port/user/hadoop/my_file.txt', 'wb') as fout: ... for line in [b'first line\n', b'second line\n', b'third line\n']: ... fout.write(line)

stream using a completely custom s3 server, like s3proxy:

for line in smart_open('s3u://user:secret@host:port@mybucket/mykey.txt', 'rb'): ... print(line.decode('utf8'))

you can also use a boto.s3.key.Key instance directly:

key = boto.connect_s3().get_bucket("my_bucket").get_key("my_key") with smart_open(key, 'rb') as fin: ... for line in fin: ... print(line.decode('utf8'))

Stream to Digital Ocean Spaces bucket providing credentials from boto profile

with smart_open('s3://bucket-for-experiments/file.txt', 'wb', endpoint_url='https://ams3.digitaloceanspaces.com', profile_name='digitalocean') as fout: ... fout.write(b'here we stand')

Why?

Working with large S3 files using Amazon's default Python library, boto <http://docs.pythonboto.org/en/latest/>_ and boto3 <https://boto3.readthedocs.io/en/latest/>_, is a pain. Its key.set_contents_from_string() and key.get_contents_as_string() methods only work for small files (loaded in 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 but offers a cleaner, Pythonic API. The result is less code for you to write and fewer bugs to make.

Installation

::

pip install smart_open

Or, if you prefer to install from the source tar.gz <http://pypi.python.org/pypi/smart_open>_::

python setup.py test  # run unit tests
python setup.py install

To run the unit tests (optional), you'll also need to install mock <https://pypi.python.org/pypi/mock>_ , moto <https://github.com/spulec/moto>_ and responses <https://github.com/getsentry/responses>_ (pip install mock moto responses). The tests are also run automatically with Travis CI <https://travis-ci.org/RaRe-Technologies/smart_open>_ on every commit push & pull request.

Supported archive types

smart_open allows reading and writing gzip, bzip2 and xz files. They are transparently handled over HTTP, S3, and other protocols, too.

S3-Specific Options

The S3 reader supports gzipped content transparently, as long as the key is obviously a gzipped file (e.g. ends with ".gz").

There are a few optional keyword arguments that are useful only for S3 access.

The host and profile arguments are both passed to boto.s3_connect() as keyword arguments:

.. code-block:: python

smart_open('s3://', host='s3.amazonaws.com') smart_open('s3://', profile_name='my-profile')

The s3_session argument allows you to provide a custom boto3.Session instance for connecting to S3:

.. code-block:: python

smart_open('s3://', s3_session=boto3.Session())

The s3_upload argument accepts a dict of any parameters accepted by initiate_multipart_upload <https://boto3.readthedocs.io/en/latest/reference/services/s3.html#S3.ObjectSummary.initiate_multipart_upload/>_:

.. code-block:: python

smart_open('s3://', s3_upload={ 'ServerSideEncryption': 'AES256' })

Since going over all (or select) keys in an S3 bucket is a very common operation, there's also an extra method smart_open.s3_iter_bucket() that does this efficiently, processing the bucket keys in parallel (using multiprocessing):

.. code-block:: python

from smart_open import smart_open, s3_iter_bucket

get all JSON files under "mybucket/foo/"

bucket = boto.connect_s3().get_bucket('mybucket') for key, content in s3_iter_bucket(bucket, prefix='foo/', accept_key=lambda key: key.endswith('.json')): ... print(key, len(content))

For more info (S3 credentials in URI, minimum S3 part size...) and full method signatures, check out the API docs:

.. code-block:: python

import smart_open help(smart_open.smart_open_lib)

Comments, bug reports

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!

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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>.