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 sports a simple, Pythonic API:
>>> 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'))Working with large S3 files using Amazon's default Python library, boto and boto3, 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.
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.
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:
>>> 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:
>>> smart_open('s3://', s3_session=boto3.Session())The s3_upload argument accepts a dict of any parameters accepted by initiate_multipart_upload:
>>> 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):
>>> 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:
>>> import smart_open
>>> help(smart_open.smart_open_lib)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.