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Brug Livy-API'en til at sende og udføre sessionsjob

Gælder for:✅ Dataudvikler ing og datavidenskab i Microsoft Fabric

Få mere at vide om, hvordan du indsender Spark-sessionsjob ved hjælp af Livy-API'en til Fabric Data Engineering.

Forudsætninger

Livy-API'en definerer et samlet slutpunkt for handlinger. Erstat pladsholderne {Entra_TenantID}, {Entra_ClientID}, {Fabric_WorkspaceID}, {Fabric_LakehouseID} med de relevante værdier, når du følger eksemplerne i denne artikel.

Konfigurer Visual Studio Code for din Livy API-session

  1. Vælg Lakehouse-indstillinger i Fabric Lakehouse.

    Skærmbillede, der viser indstillingerne for Lakehouse.

  2. Gå til sektionen Livy-slutpunkt .

    skærmbillede, der viser Lakehouse Livy-slutpunktet og sessionsjobbet forbindelsesstreng.

  3. Kopiér sessionsjobbet forbindelsesstreng (første røde felt på billedet) til din kode.

  4. Gå til Microsoft Entra Administration , og kopiér både program-id'et (klient)-id'et og mappe-id'et (lejer) til din kode.

    Skærmbillede, der viser oversigt over Livy API-app i Microsoft Entra Administration.

Godkend en Livy API Spark-session ved hjælp af enten et Microsoft Entra-brugertoken eller et Microsoft Entra SPN-token

Godkend en Livy API Spark-session ved hjælp af et Microsoft Entra SPN-token

  1. Opret en .ipynb notesbog i Visual Studio Code, og indsæt 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)

  2. Kør notebook-cellen. Du bør kunne se Microsoft Entra-tokenet returneret.

    Skærmbillede, der viser Microsoft Entra SPN-tokenet, der returneres efter kørslen af cellen.

Godkend en Livy API Spark-session ved hjælp af et Microsoft Entra-brugertoken

  1. Opret en .ipynb notesbog i Visual Studio Code, og indsæt 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")

  2. Kør notebook-cellen. Du bør kunne se Microsoft Entra-tokenet returneret.

    Skærmbillede, der viser Microsoft Entra-brugertokenet, der returneres efter at have kørt celle.

Opret en Livy API Spark-session

  1. Tilføj en anden notesbogcelle, og indsæt denne kode.

    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}")

  2. Kør notesbogcellen. Du kan se én linje udskrevet, når Livy-sessionen oprettes.

    Skærmbillede, der viser resultaterne af den første udførelse af notesbogens celle.

  3. Du kan bekræfte, at Livy-sessionen er oprettet ved hjælp af [Få vist dine job i overvågningshubben](#View dine job i overvågningshubben).

Integration med Fabric-miljøer

Denne Livy API-session kører som standard i forhold til standardstartgruppen for arbejdsområdet. Du kan også bruge Fabric Environments Til at oprette, konfigurere og bruge et miljø i Microsoft Fabric til at tilpasse Spark-puljen, som Livy API-sessionen bruger til disse Spark-job. Hvis du vil bruge et Fabric Environment, skal du opdatere den tidligere notebook-celle med disse json-nyttedata.

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

Indsend en spark.sql-sætning ved hjælp af Livy API Spark-sessionen

  1. Tilføj en anden notesbogcelle, og indsæt denne kode.

        # 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}")

  2. Kør notesbogcellen. Du kan se flere trinvise linjer udskrevet, efterhånden som jobbet sendes, og resultaterne returneres.

    Skærmbillede, der viser resultaterne af den første notesbogcelle med Spark.sql udførelse.

Indsend endnu en spark.sql sætning ved hjælp af Livy API Spark-sessionen

  1. Tilføj en anden notesbogcelle, og indsæt denne kode.

    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}")

  2. Kør notesbogcellen. Du kan se flere trinvise linjer udskrevet, efterhånden som jobbet sendes, og resultaterne returneres.

    Skærmbillede, der viser resultaterne af den anden udførelse af notesbogens celle.

Afslut Livius session

  1. Tilføj en anden notesbogcelle, og indsæt denne kode.

    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}")

Få vist dine job i overvågningshubben

Du kan få adgang til overvågningshubben for at få vist forskellige Apache Spark-aktiviteter ved at vælge Overvåg i navigationslinkene til venstre.

  1. Når sessionen er i gang eller er i fuldført tilstand, kan du få vist sessionsstatussen ved at gå til Overvågning.

    Skærmbillede, der viser tidligere Livy API-indsendelser i overvågningshubben.

  2. Vælg og åbn det seneste aktivitetsnavn.

    Skærmbillede, der viser den seneste Livy API-aktivitet i overvågningshubben.

  3. I denne Livy API-session kan du se dine tidligere sessionsindsendelser, køre detaljer, Spark-versioner og konfiguration. Læg mærke til den stoppet status øverst til højre.

    Skærmbillede, der viser de seneste Livy API-aktivitetsoplysninger i overvågningshubben.

Hvis du vil opsummere hele processen, skal du have en fjernklient, f.eks . Visual Studio Code, et Microsoft Entra-app/SPN-token, URL-adressen til Livy API-slutpunktet, godkendelse mod din Lakehouse og til sidst en Session Livy-API.

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