azure-datalake-store

A pure-python interface to the Azure Data-lake Storage gen 1 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.

Installation

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.

Auth

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)

Pythonic Filesystem

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())

Performant up-/down-loading

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)