Utils for streaming large files (S3, HDFS, gzip, bz2...)
Project description
What?
smart_open is a Python 2 & Python 3 library for efficient streaming of very large files from/to storages such as S3, 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.
Why?
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.
How?
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 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
Documentation
Installation
pip install smart_open
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 mock , moto and responses (pip install mock moto responses). The tests are also run automatically with Travis CI on every commit push & pull request.
Built-in help
For detailed API info, see the online help:
help('smart_open')or click here to view the help in your browser.
More examples
>>> import boto3
>>>
>>> # stream content *into* S3 (write mode) using a custom session
>>> url = 's3://smart-open-py37-benchmark-results/test.txt'
>>> lines = [b'first line\n', b'second line\n', b'third line\n']
>>> transport_params = {'session': boto3.Session(profile_name='smart_open')}
>>> with open(url, 'wb', transport_params=transport_params) as fout:
... for line in lines:
... bytes_written = fout.write(line)# 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 boto 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')Supported Compression Formats
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))
1696lzma is in the standard library in Python 3.3 and greater. For 2.7, use 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
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')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.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.
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):
>>> 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 13Specific S3 object version
The version_id transport parameter enables you to get the desired version of the object from an S3 bucket.
>>> # 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'File-like Binary Streams
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.
Migrating to the new open function
Since 1.8.1, there is a smart_open.open function that replaces smart_open.smart_open. The new function offers several advantages over the old one:
100% compatible with the built-in open function (aka io.open): it accepts all the parameters that the built-in open accepts.
The default open mode is now “r”, the same as for the built-in open. The default for the old smart_open.smart_open function used to be “rb”.
Fully documented keyword parameters (try help("smart_open.open"))
The instructions below will help you migrate to the new function painlessly.
First, update your imports:
>>> from smart_open import smart_open # before
>>> from smart_open import open # afterIn general, smart_open uses io.open directly, where possible, so if your code already uses open for local file I/O, then it will continue to work. If you want to continue using the built-in open function for e.g. debugging, then you can import smart_open and use smart_open.open.
The default read mode is now “r” (read text). If your code was implicitly relying on the default mode being “rb” (read binary), you’ll need to update it and pass “rb” explicitly.
Before:
>>> import smart_open
>>> smart_open.smart_open('s3://commoncrawl/robots.txt').read(32) # 'rb' used to be the default
b'User-Agent: *\nDisallow: /'After:
>>> import smart_open
>>> smart_open.open('s3://commoncrawl/robots.txt', 'rb').read(32)
b'User-Agent: *\nDisallow: /'The ignore_extension keyword parameter is now called ignore_ext. It behaves identically otherwise.
The most significant change is in the handling on keyword parameters for the transport layer, e.g. HTTP, S3, etc. The old function accepted these directly:
>>> url = 's3://smart-open-py37-benchmark-results/test.txt'
>>> session = boto3.Session(profile_name='smart_open')
>>> smart_open.smart_open(url, 'r', session=session).read(32)
'first line\nsecond line\nthird lin'The new function accepts a transport_params keyword argument. It’s a dict. Put your transport parameters in that dictionary.
>>> url = 's3://smart-open-py37-benchmark-results/test.txt'
>>> params = {'session': boto3.Session(profile_name='smart_open')}
>>> open(url, 'r', transport_params=params).read(32)
'first line\nsecond line\nthird lin'Renamed parameters:
s3_upload -> multipart_upload_kwargs
s3_session -> session
Removed parameters:
profile_name
The profile_name parameter has been removed. Pass an entire boto3.Session object instead.
Before:
>>> url = 's3://smart-open-py37-benchmark-results/test.txt'
>>> smart_open.smart_open(url, 'r', profile_name='smart_open').read(32)
'first line\nsecond line\nthird lin'After:
>>> url = 's3://smart-open-py37-benchmark-results/test.txt'
>>> params = {'session': boto3.Session(profile_name='smart_open')}
>>> open(url, 'r', transport_params=params).read(32)
'first line\nsecond line\nthird lin'See help("smart_open.open") for the full list of acceptable parameter names, or view the help online here.
If you pass an invalid parameter name, the smart_open.open function will warn you about it. Keep an eye on your logs for WARNING messages from smart_open.
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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.