Python용 Azure Data Lake Analytics 라이브러리

개요

Azure Data Lake Analytics를 사용하여 대규모 데이터 집합으로 조정되는 빅 데이터 분석 작업을 실행합니다.

라이브러리 설치

관리 API

관리 API를 사용하여 Data Lake Analytics 계정, 작업, 정책 및 카탈로그를 관리합니다.

pip install azure-mgmt-datalake-analytics

예제

이 예제에서는 Data Lake Analytics 계정을 만들고 작업을 제출하는 방법을 보여 줍니다.

## 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)
    )
)

관리 API 탐색

샘플

Azure Data Lake Analytics 관리