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Manage Azure Data Lake Analytics using Python

  • Članak

Važno

Azure Data Lake Analytics retired on 29 February 2024. Learn more with this announcement.

For data analytics, your organization can use Azure Synapse Analytics or Microsoft Fabric.

This article describes how to manage Azure Data Lake Analytics accounts, data sources, users, and jobs by using Python.

Supported Python versions

  • Use a 64-bit version of Python.
  • You can use the standard Python distribution found at Python.org downloads.
  • Many developers find it convenient to use the Anaconda Python distribution.
  • This article was written using Python version 3.6 from the standard Python distribution

Install Azure Python SDK

Install the following modules:

  • The azure-mgmt-resource module includes other Azure modules for Active Directory, etc.
  • The azure-datalake-store module includes the Azure Data Lake Store filesystem operations.
  • The azure-mgmt-datalake-store module includes the Azure Data Lake Store account management operations.
  • The azure-mgmt-datalake-analytics module includes the Azure Data Lake Analytics operations.

First, ensure you have the latest pip by running the following command:

Console 
python -m pip install --upgrade pip

This document was written using pip version 9.0.1.

Use the following pip commands to install the modules from the commandline:

Console 
pip install azure-identity
pip install azure-mgmt-resource
pip install azure-datalake-store
pip install azure-mgmt-datalake-store
pip install azure-mgmt-datalake-analytics

Create a new Python script

Paste the following code into the script:

Python 
# Use this only for Azure AD service-to-service authentication
#from azure.common.credentials import ServicePrincipalCredentials

# Use this only for Azure AD end-user authentication
#from azure.common.credentials import UserPassCredentials

# Required for Azure Identity
from azure.identity import DefaultAzureCredential

# Required for Azure Resource Manager
from azure.mgmt.resource.resources import ResourceManagementClient
from azure.mgmt.resource.resources.models import ResourceGroup

# Required for Azure Data Lake Store account management
from azure.mgmt.datalake.store import DataLakeStoreAccountManagementClient
from azure.mgmt.datalake.store.models import DataLakeStoreAccount

# Required for Azure Data Lake Store filesystem management
from azure.datalake.store import core, lib, multithread

# Required for Azure Data Lake Analytics account management
from azure.mgmt.datalake.analytics.account import DataLakeAnalyticsAccountManagementClient
from azure.mgmt.datalake.analytics.account.models import DataLakeAnalyticsAccount, DataLakeStoreAccountInformation

# Required for Azure Data Lake Analytics job management
from azure.mgmt.datalake.analytics.job import DataLakeAnalyticsJobManagementClient
from azure.mgmt.datalake.analytics.job.models import JobInformation, JobState, USqlJobProperties

# Required for Azure Data Lake Analytics catalog management
from azure.mgmt.datalake.analytics.catalog import DataLakeAnalyticsCatalogManagementClient

# Required for Azure Data Lake Analytics Model
from azure.mgmt.datalake.analytics.account.models import CreateOrUpdateComputePolicyParameters

# Use these as needed for your application
import logging
import getpass
import pprint
import uuid
import time

Run this script to verify that the modules can be imported.

Authentication

Interactive user authentication with a pop-up

This method isn't supported.

Interactive user authentication with a device code

Python 
user = input(
    'Enter the user to authenticate with that has permission to subscription: ')
password = getpass.getpass()
credentials = UserPassCredentials(user, password)

Noninteractive authentication with SPI and a secret

Python 
# Acquire a credential object for the app identity. When running in the cloud,
# DefaultAzureCredential uses the app's managed identity (MSI) or user-assigned service principal.
# When run locally, DefaultAzureCredential relies on environment variables named
# AZURE_CLIENT_ID, AZURE_CLIENT_SECRET, and AZURE_TENANT_ID.

credentials = DefaultAzureCredential()

Noninteractive authentication with API and a certificate

This method isn't supported.

Common script variables

These variables are used in the samples.

Python 
subid = '<Azure Subscription ID>'
rg = '<Azure Resource Group Name>'
location = '<Location>'  # i.e. 'eastus2'
adls = '<Azure Data Lake Store Account Name>'
adla = '<Azure Data Lake Analytics Account Name>'

Create the clients

Python 
resourceClient = ResourceManagementClient(credentials, subid)
adlaAcctClient = DataLakeAnalyticsAccountManagementClient(credentials, subid)
adlaJobClient = DataLakeAnalyticsJobManagementClient(
    credentials, 'azuredatalakeanalytics.net')

Create an Azure Resource Group

Python 
armGroupResult = resourceClient.resource_groups.create_or_update(
    rg, ResourceGroup(location=location))

Create Data Lake Analytics account

First create a store account.

Python 
adlsAcctResult = adlsAcctClient.account.begin_create(
	rg,
	adls,
	DataLakeStoreAccount(
		location=location)
	)
).wait()

Then create an ADLA account that uses that store.

Python 
adlaAcctResult = adlaAcctClient.account.create(
    rg,
    adla,
    DataLakeAnalyticsAccount(
        location=location,
        default_data_lake_store_account=adls,
        data_lake_store_accounts=[DataLakeStoreAccountInformation(name=adls)]
    )
).wait()

Submit a job

Python 
script = """
@a  = 
    SELECT * FROM 
        (VALUES
            ("Contoso", 1500.0),
            ("Woodgrove", 2700.0)
        ) AS 
              D( customer, amount );
OUTPUT @a
    TO "/data.csv"
    USING Outputters.Csv();
"""

jobId = str(uuid.uuid4())
jobResult = adlaJobClient.job.create(
    adla,
    jobId,
    JobInformation(
        name='Sample Job',
        type='USql',
        properties=USqlJobProperties(script=script)
    )
)

Wait for a job to end

Python 
jobResult = adlaJobClient.job.get(adla, jobId)
while(jobResult.state != JobState.ended):
    print('Job is not yet done, waiting for 3 seconds. Current state: ' +
          jobResult.state.value)
    time.sleep(3)
    jobResult = adlaJobClient.job.get(adla, jobId)

print('Job finished with result: ' + jobResult.result.value)

List pipelines and recurrences

Depending whether your jobs have pipeline or recurrence metadata attached, you can list pipelines and recurrences.

Python 
pipelines = adlaJobClient.pipeline.list(adla)
for p in pipelines:
    print('Pipeline: ' + p.name + ' ' + p.pipelineId)

recurrences = adlaJobClient.recurrence.list(adla)
for r in recurrences:
    print('Recurrence: ' + r.name + ' ' + r.recurrenceId)

Manage compute policies

The DataLakeAnalyticsAccountManagementClient object provides methods for managing the compute policies for a Data Lake Analytics account.

List compute policies

The following code retrieves a list of compute policies for a Data Lake Analytics account.

Python 
policies = adlaAcctClient.compute_policies.list_by_account(rg, adla)
for p in policies:
    print('Name: ' + p.name + 'Type: ' + p.object_type + 'Max AUs / job: ' +
          p.max_degree_of_parallelism_per_job + 'Min priority / job: ' + p.min_priority_per_job)

Create a new compute policy

The following code creates a new compute policy for a Data Lake Analytics account, setting the maximum AUs available to the specified user to 50, and the minimum job priority to 250.

Python 
userAadObjectId = "3b097601-4912-4d41-b9d2-78672fc2acde"
newPolicyParams = CreateOrUpdateComputePolicyParameters(
    userAadObjectId, "User", 50, 250)
adlaAcctClient.compute_policies.create_or_update(
    rg, adla, "GaryMcDaniel", newPolicyParams)

Next steps