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 and CLI commands.
This software is under active development and not yet recommended for general use.
pip install azure-datalake-store
Manually (bleeding edge):
- Download the repo from https://github.com/Azure/azure-data-lake-store-python
- checkout the
- install the requirememnts (
pip install -r dev_requirements.txt)
- install in develop mode (
python setup.py develop)
- optionally: build the documentation (including this page) by running
make htmlin 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 both
library and CLI usage. The following variables are required:
- azure_url_suffix (optional)
AzureDLFileSystem object is the main API for library usage of this
package. It provides typical file-system operations on the remote azure
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())
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)
Command Line Usage¶
The package provides the above functionality also from the command line (bash, powershell, etc.). Two principle modes are supported: execution of one particular file-system operation; and interactive mode in which multiple operations can be executed in series.
python cli.py ls -l
Execute the program without arguments to access documentation.