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https://github.com/piskvorky/smart_open

Utils for streaming large files (S3, HDFS, gzip, bz2...)
https://github.com/piskvorky/smart_open

boto bz2 file gzip-stream hacktoberfest hdfs python s3 streaming streaming-data webhdfs

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Utils for streaming large files (S3, HDFS, gzip, bz2...)

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README 

        
======================================================

smart_open — utils for streaming large files in Python

======================================================



|License|_ |GHA|_ |Coveralls|_ |Downloads|_



.. |License| image:: https://img.shields.io/pypi/l/smart_open.svg

.. |GHA| image:: https://github.com/RaRe-Technologies/smart_open/workflows/Test/badge.svg

.. |Coveralls| image:: https://coveralls.io/repos/github/RaRe-Technologies/smart_open/badge.svg?branch=develop

.. |Downloads| image:: https://pepy.tech/badge/smart-open/month

.. _License: https://github.com/RaRe-Technologies/smart_open/blob/master/LICENSE

.. _GHA: https://github.com/RaRe-Technologies/smart_open/actions?query=workflow%3ATest

.. _Coveralls: https://coveralls.io/github/RaRe-Technologies/smart_open?branch=HEAD

.. _Downloads: https://pypi.org/project/smart-open/



What?

=====



``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.**



Why?

====



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.



How?

=====



``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

  '\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



Documentation

=============



Installation

------------



``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 `_.



Built-in help

-------------



For detailed API info, see the online help:



.. code-block:: python



    help('smart_open')



or click `here `__ to view the help in your browser.



More examples

-------------



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')



Compression Handling

--------------------



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/



Transport-specific Options

--------------------------



``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:



.. 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')



S3 Credentials

--------------



``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.



.. 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={'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``.



S3 Advanced Usage

-----------------



Additional keyword arguments can be propagated to the boto3 methods that are used by ``smart_open`` under the hood using the ``client_kwargs`` transport parameter.



For instance, to upload a blob with Metadata, ACL, StorageClass, these keyword arguments can be passed to ``create_multipart_upload`` (`docs `__).



.. code-block:: python



    kwargs = {'Metadata': {'version': 2}, 'ACL': 'authenticated-read', 'StorageClass': 'STANDARD_IA'}

    fout = open('s3://bucket/key', 'wb', transport_params={'client_kwargs': {'S3.Client.create_multipart_upload': kwargs}})



Iterating Over an S3 Bucket's Contents

--------------------------------------



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



GCS Credentials

---------------

``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.



.. 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 `__.



.. 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={'client': client})



GCS Advanced Usage

------------------



Additional keyword arguments can be propagated to the GCS open method (`docs `__), which is used by ``smart_open`` under the hood, using the ``blob_open_kwargs`` transport parameter.



Additionally keyword arguments can be propagated to the GCS ``get_blob`` method (`docs `__) when in a read-mode, using the ``get_blob_kwargs`` transport parameter.



Additional blob properties (`docs `__) can be set before an upload, as long as they are not read-only, using the ``blob_properties`` transport parameter.



.. code-block:: python



    open_kwargs = {'predefined_acl': 'authenticated-read'}

    properties = {'metadata': {'version': 2}, 'storage_class': 'COLDLINE'}

    fout = open('gs://bucket/key', 'wb', transport_params={'blob_open_kwargs': open_kwargs, 'blob_properties': properties})



Azure Credentials

-----------------



``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.



.. 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={'client': client})



If you need more credential options, refer to the

`Azure Storage authentication guide `__.



Azure Advanced Usage

--------------------



Additional keyword arguments can be propagated to the ``commit_block_list`` method (`docs `__), which is used by ``smart_open`` under the hood for uploads, using the ``blob_kwargs`` transport parameter.



.. code-block:: python



    kwargs = {'metadata': {'version': 2}}

    fout = open('azure://container/key', 'wb', transport_params={'blob_kwargs': kwargs})



Drop-in replacement of ``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



How do I ...?

=============



See `this document `__.



Extending ``smart_open``

========================



See `this document `__.



Testing ``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 `_ on every commit push & pull request.



Comments, bug reports

=====================



``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 `_.