pyarrowfs-adlgen2

pyarrowfs-adlgen2 is an implementation of a pyarrow filesystem for Azure Data Lake Gen2.

It allows you to use pyarrow and pandas to read parquet datasets directly from Azure without the need to copy files to local storage first.

Installation

pip install pyarrowfs-adlgen2

Reading datasets

Example usage with pandas dataframe:

import azure.identity
import pandas as pd
import pyarrow.fs
import pyarrowfs_adlgen2

handler = pyarrowfs_adlgen2.AccountHandler.from_account_name(
    'YOUR_ACCOUNT_NAME', azure.identity.DefaultAzureCredential())
fs = pyarrow.fs.PyFileSystem(handler)
df = pd.read_parquet('container/dataset.parq', filesystem=fs)

Example usage with arrow tables:

import azure.identity
import pyarrow.dataset
import pyarrow.fs
import pyarrowfs_adlgen2

handler = pyarrowfs_adlgen2.AccountHandler.from_account_name(
    'YOUR_ACCOUNT_NAME', azure.identity.DefaultAzureCredential())
fs = pyarrow.fs.PyFileSystem(handler)
ds = pyarrow.dataset.dataset('container/dataset.parq', filesystem=fs)
table = ds.to_table()

Configuring timeouts

Timeouts are passed to azure-storage-file-datalake SDK methods. The timeout unit is in seconds.

import azure.identity
import pyarrowfs_adlgen2

handler = pyarrowfs_adlgen2.AccountHandler.from_account_name(
    'YOUR_ACCOUNT_NAME',
    azure.identity.DefaultAzureCredential(),
    timeouts=pyarrowfs_adlgen2.Timeouts(file_system_timeout=10)
)
# or mutate it:
handler.timeouts.file_client_timeout = 20

Writing datasets

With pyarrow version 3 or greater, you can write datasets from arrow tables:

import pyarrow as pa
import pyarrow.dataset

pyarrow.dataset.write_dataset(
    table,
    'name.pq',
    format='parquet',
    partitioning=pyarrow.dataset.partitioning(
        schema=pyarrow.schema([('year', pa.int32())]), flavor='hive'
    ),
    filesystem=pyarrow.fs.PyFileSystem(handler)
)

With earlier versions, files must be opened/written one at a time:

As of pyarrow version 1.0.1, pyarrow.parquet.ParquetWriter does not support pyarrow.fs.PyFileSystem, but data can be written to open files:

with fs.open_output_stream('container/out.parq') as out:
    df.to_parquet(out)

Or with arrow tables:

import pyarrow.parquet

with fs.open_output_stream('container/out.parq') as out:
    pyarrow.parquet.write_table(table, out)

Accessing only a single container/file-system

If you do not want, or can't access the whole storage account as a single filesystem, you can use pyarrowfs_adlgen2.FilesystemHandler to view a single file system within an account:

import azure.identity
import pyarrowfs_adlgen2

handler = pyarrowfs_adlgen2.FilesystemHandler.from_account_name(
   "STORAGE_ACCOUNT", "FS_NAME", azure.identity.DefaultAzureCredential())

All access is done through the file system within the storage account.

Set http headers for files for pyarrow >= 5

You can set headers for any output files by using the metadata argument to handler.open_output_stream:

import pyarrowfs_adlgen2

fs = pyarrowfs_adlgen2.AccountHandler.from_account_name("theaccount").to_fs()
metadata = {"content_type": "application/json"}
with fs.open_output_stream("container/data.json", metadata) as out:
    out.write("{}")

Note that the spelling is different than you might expect! For a list of valid keys, see ContentSettings.

You can do this for pyarrow >= 5 when using pyarrow.fs.PyFileSystem, and for any pyarrow if using the handlers from pyarrowfs_adlgen2 directly.

Running tests

To run the integration tests, you need:

• Azure Storage Account V2 with hierarchial namespace enabled (Data Lake gen2 account)
• To configure azure login (f. ex. use $ az login or set up environment variables, see azure.identity.DefaultAzureCredential)
• Install pytest, f. ex. pip install pytest

NB! All data in the storage account is deleted during testing, USE AN EMPTY ACCOUNT

AZUREARROWFS_TEST_ACT=thestorageaccount pytest