Bibliotecas de Azure Data Lake Analytics para PythonAzure Data Lake Analytics libraries for python

Información generalOverview

Ejecute trabajos de análisis de macrodatos con capacidad de escalado a conjuntos de datos masivos con Azure Data Lake Analytics.Run big data analysis jobs that scale to massive data sets with Azure Data Lake Analytics.

Instalación de las bibliotecasInstall the libraries

API de administraciónManagement API

Use la API de administración para administrar cuentas, trabajos, directivas y catálogos de Data Lake Analytics.Use the management API to manage Data Lake Analytics accounts, jobs, policies, and catalogs.

pip install azure-mgmt-datalake-analytics

EjemploExample

Este es un ejemplo de cómo crear una cuenta de Data Lake Analytics y enviar un trabajo.This is an example of how to create a Data Lake Analytics account and submit a job.

## 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, DataLakeStoreAccountInfo

## 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

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

# Create the clients
resourceClient = ResourceManagementClient(credentials, subid)
adlaAcctClient = DataLakeAnalyticsAccountManagementClient(credentials, subid)
adlaJobClient = DataLakeAnalyticsJobManagementClient( credentials, 'azuredatalakeanalytics.net')

# Create resource group
armGroupResult = resourceClient.resource_groups.create_or_update(rg, ResourceGroup(___location=___location))

# Create a store account
adlaAcctResult = adlaAcctClient.account.create(
    rg,
    adla,
    DataLakeAnalyticsAccount(
        ___location=___location,
        default_data_lake_store_account=adls,
        data_lake_store_accounts=[DataLakeStoreAccountInfo(name=adls)]
    )
).wait()

# Create an ADLA account
adlaAcctResult = adlaAcctClient.account.create(
    rg,
    adla,
    DataLakeAnalyticsAccount(
        ___location=___location,
        default_data_lake_store_account=adls,
        data_lake_store_accounts=[DataLakeStoreAccountInfo(name=adls)]
    )
).wait()

# Submit a job
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)
    )
)

EjemplosSamples

Administración de Azure Data Lake AnalyticsManage Azure Data Lake Anyalytics