Bruke Livy-API-en til å sende inn og kjøre øktjobber

Gjelder for:✅ Dataingeniør ing og datavitenskap i Microsoft Fabric

Lær hvordan du sender inn Spark-øktjobber ved hjelp av Livy API for Fabric Data Engineering.

Forutsetning

Fabric Premium - eller Prøvekapasitet med lakehouse
En ekstern klient som Visual Studio Code med Jupyter Notebooks, PySpark og Microsoft Authentication Library (MSAL) for Python
Enten et Microsoft Entra-apptoken. Registrere et program med Microsofts identitetsplattform
Eller et Microsoft Entra SPN-token. Legge til og behandle programlegitimasjon i Microsoft Entra
Noen data i lakehouse, dette eksemplet bruker NYC Taxi &Limousine Commission green_tripdata_2022_08 en parkett fil lastet til lakehouse

Livy-API-en definerer et enhetlig endepunkt for operasjoner. Erstatt plassholderne {Entra_TenantID}, {Entra_ClientID}, {Fabric_WorkspaceID}, {Fabric_LakehouseID} med de riktige verdiene når du følger eksemplene i denne artikkelen.

Konfigurer Visual Studio Code for Livy API-økten

Velg Lakehouse-innstillinger i Fabric Lakehouse.
Gå til livy-endepunktinndelingen.
Kopier øktjobben tilkoblingsstreng (første røde boks i bildet) til koden.
Gå til administrasjonssenteret for Microsoft Entra, og kopier både program-ID-en (klient)- og katalog-ID-en (leier) til koden.

Godkjenne en Livy API Spark-økt ved hjelp av enten et Microsoft Entra-brukertoken eller et Microsoft Entra SPN-token

Godkjenne en Livy API Spark-økt ved hjelp av et Microsoft Entra SPN-token

Opprett en .ipynb notatblokk i Visual Studio Code, og sett inn følgende kode.

import sys
from msal import ConfidentialClientApplication

# Configuration - Replace with your actual values
tenant_id = "Entra_TenantID"  # Microsoft Entra tenant ID
client_id = "Entra_ClientID"  # Service Principal Application ID

# Certificate paths - Update these paths to your certificate files
certificate_path = "PATH_TO_YOUR_CERTIFICATE.pem"      # Public certificate file
private_key_path = "PATH_TO_YOUR_PRIVATE_KEY.pem"      # Private key file
certificate_thumbprint = "YOUR_CERTIFICATE_THUMBPRINT" # Certificate thumbprint

# OAuth settings
audience = "https://analysis.windows.net/powerbi/api/.default"
authority = f"https://login.windows.net/{tenant_id}"

def get_access_token(client_id, audience, authority, certificate_path, private_key_path, certificate_thumbprint=None):
    """
    Get an app-only access token for a Service Principal using OAuth 2.0 client credentials flow.

    This function uses certificate-based authentication which is more secure than client secrets.

    Args:
        client_id (str): The Service Principal's client ID  
        audience (str): The audience for the token (resource scope)
        authority (str): The OAuth authority URL
        certificate_path (str): Path to the certificate file (.pem format)
        private_key_path (str): Path to the private key file (.pem format)
        certificate_thumbprint (str): Certificate thumbprint (optional but recommended)

    Returns:
        str: The access token for API authentication

    Raises:
        Exception: If token acquisition fails
    """
    try:
        # Read the certificate from PEM file
        with open(certificate_path, "r", encoding="utf-8") as f:
            certificate_pem = f.read()

        # Read the private key from PEM file
        with open(private_key_path, "r", encoding="utf-8") as f:
            private_key_pem = f.read()

        # Create the confidential client application
        app = ConfidentialClientApplication(
            client_id=client_id,
            authority=authority,
            client_credential={
                "private_key": private_key_pem,
                "thumbprint": certificate_thumbprint,
                "certificate": certificate_pem
            }
        )

        # Acquire token using client credentials flow
        token_response = app.acquire_token_for_client(scopes=[audience])

        if "access_token" in token_response:
            print("Successfully acquired access token")
            return token_response["access_token"]
        else:
            raise Exception(f"Failed to retrieve token: {token_response.get('error_description', 'Unknown error')}")

    except FileNotFoundError as e:
        print(f"Certificate file not found: {e}")
        sys.exit(1)
    except Exception as e:
        print(f"Error retrieving token: {e}", file=sys.stderr)
        sys.exit(1)

# Get the access token
token = get_access_token(client_id, audience, authority, certificate_path, private_key_path, certificate_thumbprint)

Kjør notatblokkcellen. Du skal se Microsoft Entra-tokenet returnert.

Godkjenne en Livy API Spark-økt ved hjelp av et Microsoft Entra-brukertoken

Opprett en .ipynb notatblokk i Visual Studio Code, og sett inn følgende kode.

from msal import PublicClientApplication
import requests
import time

# Configuration - Replace with your actual values
tenant_id = "Entra_TenantID"  # Microsoft Entra tenant ID
client_id = "Entra_ClientID"  # Application ID (can be the same as above or different)

# Required scopes for Microsoft Fabric API access
scopes = [
    "https://api.fabric.microsoft.com/Lakehouse.Execute.All",      # Execute operations in lakehouses
    "https://api.fabric.microsoft.com/Lakehouse.Read.All",        # Read lakehouse metadata
    "https://api.fabric.microsoft.com/Item.ReadWrite.All",        # Read/write fabric items
    "https://api.fabric.microsoft.com/Workspace.ReadWrite.All",   # Access workspace operations
    "https://api.fabric.microsoft.com/Code.AccessStorage.All",    # Access storage from code
    "https://api.fabric.microsoft.com/Code.AccessAzureKeyvault.All",     # Access Azure Key Vault
    "https://api.fabric.microsoft.com/Code.AccessAzureDataExplorer.All", # Access Azure Data Explorer
    "https://api.fabric.microsoft.com/Code.AccessAzureDataLake.All",     # Access Azure Data Lake
    "https://api.fabric.microsoft.com/Code.AccessFabric.All"             # General Fabric access
]

def get_access_token(tenant_id, client_id, scopes):
    """
    Get an access token using interactive authentication.

    This method will open a browser window for user authentication.

    Args:
        tenant_id (str): The Microsoft Entra tenant ID
        client_id (str): The application client ID
        scopes (list): List of required permission scopes

    Returns:
        str: The access token, or None if authentication fails
    """
    app = PublicClientApplication(
        client_id,
        authority=f"https://login.microsoftonline.com/{tenant_id}"
    )

    print("Opening browser for interactive authentication...")
    token_response = app.acquire_token_interactive(scopes=scopes)

    if "access_token" in token_response:
        print("Successfully authenticated")
        return token_response["access_token"]
    else:
        print(f"Authentication failed: {token_response.get('error_description', 'Unknown error')}")
        return None

# Uncomment the lines below to use interactive authentication
token = get_access_token(tenant_id, client_id, scopes)
print("Access token acquired via interactive login")

Kjør notatblokkcellen. Du skal se Microsoft Entra-tokenet returnert.

Opprette en Livy API Spark-økt

Legg til en annen notatblokkcelle, og sett inn denne koden.

import json
import requests

api_base_url = "https://api.fabric.microsoft.com/"  # Base URL for Fabric APIs

# Fabric Resource IDs - Replace with your workspace and lakehouse IDs
workspace_id = "Fabric_WorkspaceID"
lakehouse_id = "Fabric_LakehouseID"

# Construct the Livy API session URL
# URL pattern: {base_url}/v1/workspaces/{workspace_id}/lakehouses/{lakehouse_id}/livyapi/versions/{api_version}/sessions
livy_api_session_url = (f"{api_base_url}v1/workspaces/{workspace_id}/lakehouses/{lakehouse_id}/"
                       f"livyapi/versions/2023-12-01/sessions")

# Set up authentication headers
headers = {"Authorization": f"Bearer {token}"}

print(f"Livy API URL: {livy_api_session_url}")
print("Creating Livy session...")

try:
    # Create a new Livy session with default configuration
    create_livy_session = requests.post(livy_api_session_url, headers=headers, json={})

    # Check if the request was successful
    if create_livy_session.status_code == 200:
        session_info = create_livy_session.json()
        print('Livy session creation request submitted successfully')
        print(f'Session Info: {json.dumps(session_info, indent=2)}')

        # Extract session ID for future operations
        livy_session_id = session_info['id']
        livy_session_url = f"{livy_api_session_url}/{livy_session_id}"

        print(f"Session ID: {livy_session_id}")
        print(f"Session URL: {livy_session_url}")

    else:
        print(f"Failed to create session. Status code: {create_livy_session.status_code}")
        print(f"Response: {create_livy_session.text}")

except requests.exceptions.RequestException as e:
    print(f"Network error occurred: {e}")
except json.JSONDecodeError as e:
    print(f"JSON decode error: {e}")
    print(f"Response text: {create_livy_session.text}")
except Exception as e:
    print(f"Unexpected error: {e}")

Kjør notatblokkcellen, du bør se én linje som skrives ut når Livy-økten opprettes.
Du kan bekrefte at Livy-økten er opprettet ved hjelp av [Vis jobbene dine i overvåkingshuben](#View jobbene dine i overvåkingshuben).

Integrering med stoffmiljøer

Som standard kjører denne Livy API-økten mot standard startutvalg for arbeidsområdet. Alternativt kan du bruke Fabric Environments Create, configure, and use an environment in Microsoft Fabric to customize the Spark pool that the Livy API session uses for these Spark jobs. Hvis du vil bruke et strukturmiljø, oppdaterer du den forrige notatblokkcellen med denne json-nyttelasten.

create_livy_session = requests.post(livy_base_url, headers = headers, json = {
    "conf" : {
        "spark.fabric.environmentDetails" : "{\"id\" : \""EnvironmentID""}"}
        }
)

Send inn en spark.sql-setning ved hjelp av Livy API Spark-økten

Legg til en annen notatblokkcelle, og sett inn denne koden.

    # call get session API
import time

table_name = "green_tripdata_2022"

print("Checking session status...")

# Get current session status
get_session_response = requests.get(livy_session_url, headers=headers)
session_status = get_session_response.json()
print(f"Current session state: {session_status['state']}")

# Wait for session to become idle (ready to accept statements)
print("Waiting for session to become idle...")
while session_status["state"] != "idle":
    print(f"   Session state: {session_status['state']} - waiting 5 seconds...")
    time.sleep(5)
    get_session_response = requests.get(livy_session_url, headers=headers)
    session_status = get_session_response.json()

print("Session is now idle and ready to accept statements")

# Execute a Spark SQL statement
execute_statement_url = f"{livy_session_url}/statements"

# Define your Spark SQL query - Replace with your actual table and query
payload_data = {
    "code": "spark.sql(\"SELECT * FROM {table_name} WHERE column_name = 'some_value' LIMIT 10\").show()",
    "kind": "spark"  # Type of code (spark, pyspark, sql, etc.)
}

print("Submitting Spark SQL statement...")
print(f"Query: {payload_data['code']}")

try:
    # Submit the statement for execution
    execute_statement_response = requests.post(execute_statement_url, headers=headers, json=payload_data)

    if execute_statement_response.status_code == 200:
        statement_info = execute_statement_response.json()
        print('Statement submitted successfully')
        print(f"Statement Info: {json.dumps(statement_info, indent=2)}")

        # Get statement ID for monitoring
        statement_id = str(statement_info['id'])
        get_statement_url = f"{livy_session_url}/statements/{statement_id}"

        print(f"Statement ID: {statement_id}")

        # Monitor statement execution
        print("Monitoring statement execution...")
        get_statement_response = requests.get(get_statement_url, headers=headers)
        statement_status = get_statement_response.json()

        while statement_status["state"] != "available":
            print(f"   Statement state: {statement_status['state']} - waiting 5 seconds...")
            time.sleep(5)
            get_statement_response = requests.get(get_statement_url, headers=headers)
            statement_status = get_statement_response.json()

        # Retrieve and display results
        print("Statement execution completed!")
        if 'output' in statement_status and 'data' in statement_status['output']:
            results = statement_status['output']['data']['text/plain']
            print("Query Results:")
            print(results)
        else:
            print("No output data available")

    else:
        print(f"Failed to submit statement. Status code: {execute_statement_response.status_code}")
        print(f"Response: {execute_statement_response.text}")

except Exception as e:
    print(f"Error executing statement: {e}")

Kjør notatblokkcellen, du bør se flere trinnvise linjer skrevet ut når jobben sendes inn, og resultatene returneres.

Send inn en annen spark.sql-setning ved hjelp av Livy API Spark-økten

Legg til en annen notatblokkcelle, og sett inn denne koden.

print("Executing additional Spark SQL statement...")

# Wait for session to be idle again
get_session_response = requests.get(livy_session_url, headers=headers)
session_status = get_session_response.json()

while session_status["state"] != "idle":
    print(f"   Waiting for session to be idle... Current state: {session_status['state']}")
    time.sleep(5)
    get_session_response = requests.get(livy_session_url, headers=headers)
    session_status = get_session_response.json()

# Execute another statement - Replace with your actual query
payload_data = {
    "code": f"spark.sql(\"SELECT COUNT(*) as total_records FROM {table_name}\").show()",
    "kind": "spark"
}

print(f"Executing query: {payload_data['code']}")

try:
    # Submit the second statement
    execute_statement_response = requests.post(execute_statement_url, headers=headers, json=payload_data)

    if execute_statement_response.status_code == 200:
        statement_info = execute_statement_response.json()
        print('Second statement submitted successfully')

        statement_id = str(statement_info['id'])
        get_statement_url = f"{livy_session_url}/statements/{statement_id}"

        # Monitor execution
        print("Monitoring statement execution...")
        get_statement_response = requests.get(get_statement_url, headers=headers)
        statement_status = get_statement_response.json()

        while statement_status["state"] != "available":
            print(f"   Statement state: {statement_status['state']} - waiting 5 seconds...")
            time.sleep(5)
            get_statement_response = requests.get(get_statement_url, headers=headers)
            statement_status = get_statement_response.json()

        # Display results
        print("Second statement execution completed!")
        if 'output' in statement_status and 'data' in statement_status['output']:
            results = statement_status['output']['data']['text/plain']
            print("Query Results:")
            print(results)
        else:
            print("No output data available")

    else:
        print(f"Failed to submit second statement. Status code: {execute_statement_response.status_code}")

except Exception as e:
    print(f"Error executing second statement: {e}")

Kjør notatblokkcellen, du bør se flere trinnvise linjer skrevet ut når jobben sendes inn, og resultatene returneres.

Avslutt Livius-sesjonen

Legg til en annen notatblokkcelle, og sett inn denne koden.

print("Cleaning up Livy session...")

try:
    # Check current session status before deletion
    get_session_response = requests.get(livy_session_url, headers=headers)
    if get_session_response.status_code == 200:
        session_info = get_session_response.json()
        print(f"Session state before deletion: {session_info.get('state', 'unknown')}")

    print(f"Deleting session at: {livy_session_url}")

    # Delete the session
    delete_response = requests.delete(livy_session_url, headers=headers)

    if delete_response.status_code == 200:
        print("Session deleted successfully")
    elif delete_response.status_code == 404:
        print("Session was already deleted or not found")
    else:
        print(f"Delete request completed with status code: {delete_response.status_code}")
        print(f"Response: {delete_response.text}")

    print(f"Delete response details: {delete_response}")

except requests.exceptions.RequestException as e:
    print(f"Network error during session deletion: {e}")
except Exception as e:
    print(f"Error during session cleanup: {e}")

Vise jobbene dine i overvåkingshuben

Du kan få tilgang til overvåkingshuben for å vise ulike Apache Spark-aktiviteter ved å velge Monitor i navigasjonskoblingene til venstre.

Når økten pågår eller er i fullført tilstand, kan du vise øktstatusen ved å navigere til Monitor.
Velg og åpne det nyeste aktivitetsnavnet.
I dette livy API-økttilfellet kan du se tidligere øktinnsendinger, kjøre detaljer, Spark-versjoner og konfigurasjon. Legg merke til stoppet status øverst til høyre.

Hvis du vil oppsummere hele prosessen, trenger du en ekstern klient, for eksempel Visual Studio Code, en Microsoft Entra-app/SPN-token, Livy API-endepunkt-NETTADRESSE, godkjenning mot Lakehouse og til slutt en Session Livy API.

Tilbakemeldinger

Var denne siden nyttig?

Last updated on 2025-11-10

Del via

Bruke Livy-API-en til å sende inn og kjøre øktjobber

Forutsetning

Konfigurer Visual Studio Code for Livy API-økten

Godkjenne en Livy API Spark-økt ved hjelp av enten et Microsoft Entra-brukertoken eller et Microsoft Entra SPN-token

Godkjenne en Livy API Spark-økt ved hjelp av et Microsoft Entra SPN-token

Godkjenne en Livy API Spark-økt ved hjelp av et Microsoft Entra-brukertoken

Opprette en Livy API Spark-økt

Integrering med stoffmiljøer

Send inn en spark.sql-setning ved hjelp av Livy API Spark-økten

Send inn en annen spark.sql-setning ved hjelp av Livy API Spark-økten

Avslutt Livius-sesjonen

Vise jobbene dine i overvåkingshuben

Relatert innhold

Tilbakemeldinger

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