azure-datalake-store - azure-datalake-store Documentation
A pure-python interface to the Azure Data-lake Storage system, providing
pythonic file-system and file objects, seamless transition between Windows and
POSIX remote paths, high-performance up- and down-loader.
This software is under active development and not yet recommended for general
use.
Using
pip:
pip install azure-datalake-store
Manually (bleeding edge):
- •
- Download the repo from
https://github.com/Azure/azure-data-lake-store-python
- •
- checkout the dev branch
- •
- install the requirements (pip install -r
dev_requirements.txt)
- •
- install in develop mode (python setup.py
develop)
- •
- optionally: build the documentation (including this page)
by running make html in the docs directory.
Although users can generate and supply their own tokens to the base file-system
class, and there is a password-based function in the
lib module for
generating tokens, the most convenient way to supply credentials is via
environment parameters. This latter method is the one used by default in
library. The following variables are required:
- •
- azure_tenant_id
- •
- azure_username
- •
- azure_password
- •
- azure_store_name
- •
- azure_url_suffix (optional)
The
AzureDLFileSystem object is the main API for library usage of this
package. It provides typical file-system operations on the remote azure store
token = lib.auth(tenant_id, username, password)
adl = core.AzureDLFileSystem(store_name, token)
# alternatively, adl = core.AzureDLFileSystem()
# uses environment variables
print(adl.ls()) # list files in the root directory
for item in adl.ls(detail=True):
print(item) # same, but with file details as dictionaries
print(adl.walk('')) # list all files at any directory depth
print('Usage:', adl.du('', deep=True, total=True)) # total bytes usage
adl.mkdir('newdir') # create directory
adl.touch('newdir/newfile') # create empty file
adl.put('remotefile', '/home/myuser/localfile') # upload a local file
In addition, the file-system generates file objects that are compatible with the
python file interface, ensuring compatibility with libraries that work on
python files. The recommended way to use this is with a context manager
(otherwise, be sure to call
close() on the file object).
with adl.open('newfile', 'wb') as f:
f.write(b'index,a,b\n')
f.tell() # now at position 9
f.flush() # forces data upstream
f.write(b'0,1,True')
with adl.open('newfile', 'rb') as f:
print(f.readlines())
with adl.open('newfile', 'rb') as f:
df = pd.read_csv(f) # read into pandas.
To seamlessly handle remote path representations across all supported platforms,
the main API will take in numerous path types: string, Path/PurePath, and
AzureDLPath. On Windows in particular, you can pass in paths separated by
either forward slashes or backslashes.
import pathlib # only >= Python 3.4
from pathlib2 import pathlib # only <= Python 3.3
from azure.datalake.store.core import AzureDLPath
# possible remote paths to use on API
p1 = '\\foo\\bar'
p2 = '/foo/bar'
p3 = pathlib.PurePath('\\foo\\bar')
p4 = pathlib.PureWindowsPath('\\foo\\bar')
p5 = pathlib.PurePath('/foo/bar')
p6 = AzureDLPath('\\foo\\bar')
p7 = AzureDLPath('/foo/bar')
# p1, p3, and p6 only work on Windows
for p in [p1, p2, p3, p4, p5, p6, p7]:
with adl.open(p, 'rb') as f:
print(f.readlines())
Classes
ADLUploader and
ADLDownloader will chunk large files and
send many files to/from azure using multiple threads. A whole directory tree
can be transferred, files matching a specific glob-pattern or any particular
file.
# download the whole directory structure using 5 threads, 16MB chunks
ADLDownloader(adl, '', 'my_temp_dir', 5, 2**24)
TBD
TBD