This project is the Python filesystem library for Azure Data Lake Store.
To install from source instead of pip (for local testing and development):
> pip install -r dev_requirements.txt
> python setup.py developTo play with the code, here is a starting point:
from azure.datalake.store import core, lib, multithread
token = lib.auth(tenant_id, username, password)
adl = core.AzureDLFileSystem(token, store_name=store_name)
# typical operations
adl.ls('')
adl.ls('tmp/', detail=True)
adl.ls('tmp/', detail=True, invalidate_cache=True)
adl.cat('littlefile')
adl.head('gdelt20150827.csv')
# file-like object
with adl.open('gdelt20150827.csv', blocksize=2**20) as f:
print(f.readline())
print(f.readline())
print(f.readline())
# could have passed f to any function requiring a file object:
# pandas.read_csv(f)
with adl.open('anewfile', 'wb') as f:
# data is written on flush/close, or when buffer is bigger than
# blocksize
f.write(b'important data')
adl.du('anewfile')
# recursively download the whole directory tree with 5 threads and
# 16MB chunks
multithread.ADLDownloader(adl, "", 'my_temp_dir', 5, 2**24)Progress can be tracked using a callback function in the form track(current, total) When passed, this will keep track of transferred bytes and be called on each complete chunk.
Here's an example using the Azure CLI progress controller as the progress_callback:
from cli.core.application import APPLICATION
def _update_progress(current, total):
hook = APPLICATION.get_progress_controller(det=True)
hook.add(message='Alive', value=current, total_val=total)
if total == current:
hook.end()
...
ADLUploader(client, destination_path, source_path, thread_count, overwrite=overwrite,
chunksize=chunk_size,
buffersize=buffer_size,
blocksize=block_size,
progress_callback=_update_progress)This will output a progress bar to the stdout:
Alive[######################### ] 40.0881%
Finished[#############################################################] 100.0000%To interact with the API at a higher-level, you can use the provided command-line interface in "samples/cli.py". You will need to set the appropriate environment variables as described above to connect to the Azure Data Lake Store. Below is a simple sample, with more details beyond.
python samples\cli.py ls -lExecute the program without arguments to access documentation.
To start the CLI in interactive mode, run "python samples/cli.py" and then type "help" to see all available commands (similiar to Unix utilities):
> python samples/cli.py
azure> help
Documented commands (type help <topic>):
========================================
cat chmod close du get help ls mv quit rmdir touch
chgrp chown df exists head info mkdir put rm tail
azure>While still in interactive mode, you can run "ls -l" to list the entries in the home directory ("help ls" will show the command's usage details). If you're not familiar with the Unix/Linux "ls" command, the columns represent 1) permissions, 2) file owner, 3) file group, 4) file size, 5-7) file's modification time, and 8) file name.
> python samples/cli.py
azure> ls -l
drwxrwx--- 0123abcd 0123abcd 0 Aug 02 12:44 azure1
-rwxrwx--- 0123abcd 0123abcd 1048576 Jul 25 18:33 abc.csv
-r-xr-xr-x 0123abcd 0123abcd 36 Jul 22 18:32 xyz.csv
drwxrwx--- 0123abcd 0123abcd 0 Aug 03 13:46 tmp
azure> ls -l --human-readable
drwxrwx--- 0123abcd 0123abcd 0B Aug 02 12:44 azure1
-rwxrwx--- 0123abcd 0123abcd 1M Jul 25 18:33 abc.csv
-r-xr-xr-x 0123abcd 0123abcd 36B Jul 22 18:32 xyz.csv
drwxrwx--- 0123abcd 0123abcd 0B Aug 03 13:46 tmp
azure>To download a remote file, run "get remote-file [local-file]". The second argument, "local-file", is optional. If not provided, the local file will be named after the remote file minus the directory path.
> python samples/cli.py
azure> ls -l
drwxrwx--- 0123abcd 0123abcd 0 Aug 02 12:44 azure1
-rwxrwx--- 0123abcd 0123abcd 1048576 Jul 25 18:33 abc.csv
-r-xr-xr-x 0123abcd 0123abcd 36 Jul 22 18:32 xyz.csv
drwxrwx--- 0123abcd 0123abcd 0 Aug 03 13:46 tmp
azure> get xyz.csv
2016-08-04 18:57:48,603 - ADLFS - DEBUG - Creating empty file xyz.csv
2016-08-04 18:57:48,604 - ADLFS - DEBUG - Fetch: xyz.csv, 0-36
2016-08-04 18:57:49,726 - ADLFS - DEBUG - Downloaded to xyz.csv, byte offset 0
2016-08-04 18:57:49,734 - ADLFS - DEBUG - File downloaded (xyz.csv -> xyz.csv)
azure>It is also possible to run in command-line mode, allowing any available command to be executed separately without remaining in the interpreter.
For example, listing the entries in the home directory:
> python samples/cli.py ls -l
drwxrwx--- 0123abcd 0123abcd 0 Aug 02 12:44 azure1
-rwxrwx--- 0123abcd 0123abcd 1048576 Jul 25 18:33 abc.csv
-r-xr-xr-x 0123abcd 0123abcd 36 Jul 22 18:32 xyz.csv
drwxrwx--- 0123abcd 0123abcd 0 Aug 03 13:46 tmp
>Also, downloading a remote file:
> python samples/cli.py get xyz.csv
2016-08-04 18:57:48,603 - ADLFS - DEBUG - Creating empty file xyz.csv
2016-08-04 18:57:48,604 - ADLFS - DEBUG - Fetch: xyz.csv, 0-36
2016-08-04 18:57:49,726 - ADLFS - DEBUG - Downloaded to xyz.csv, byte offset 0
2016-08-04 18:57:49,734 - ADLFS - DEBUG - File downloaded (xyz.csv -> xyz.csv)
>For detailed documentation about our test framework, please visit the tests folder.
Be sure to check out the Microsoft Azure Developer Forums on Stack Overflow if you have trouble with the provided code. Most questions are tagged azure and python.
If you would like to become an active contributor to this project please follow the instructions provided in Microsoft Azure Projects Contribution Guidelines. Furthermore, check out GUIDANCE.md for specific information related to this project.
If you encounter any bugs with the library please file an issue in the Issues section of the project.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.