使用 Livy API 來提交和執行工作作業

適用於：✅Microsoft Fabric 中的資料工程和資料科學

瞭解如何使用適用於網狀架構數據工程的 Livy API 提交 Spark 工作階段作業。

必要條件

使用 Lakehouse 的 Fabric Premium 或試用版容量
像是 Visual Studio Code 的遠端用戶端，搭配 Jupyter Notebook、PySpark 和適用於 Python 的 Microsoft 驗證庫 (MSAL)。
Microsoft Entra 應用程式令牌之一。使用 Microsoft 身分識別平台來註冊應用程式
或Microsoft Entra SPN 令牌。在 Microsoft entra 中新增和管理應用程式認證
湖屋的一些數據，此範例使用 NYC計程車和豪華轎車委員會 green_tripdata_2022_08載入湖屋的 parquet 檔案

Livy API 會定義作業的統一端點。當您遵循本文中的範例時，請將佔位符 {Entra_TenantID}、{Entra_ClientID}、{Fabric_WorkspaceID}、{Fabric_LakehouseID} 取代為適當的值。

設定 Visual Studio Code來進行 Livy API 會話

在 Fabric Lakehouse 中選取 [Lakehouse 設定 ]。
導航至 Livy 端點 區段。
將工作階段作業的連接字串（圖片中的第一個紅色方塊）複製到您的程式碼中。
流覽至 Microsoft Entra 系統管理中心，並將應用程式（用戶端）識別碼和目錄（租使用者）識別碼複製到您的程式碼。

使用 Microsoft Entra 使用者令牌或 Microsoft Entra SPN 令牌驗證 Livy API 的 Spark 工作階段

使用 Microsoft Entra SPN 權杖驗證 Livy API Spark 會話

在 Visual Studio Code 中建立 .ipynb 筆記本，並插入下列程式代碼。

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)

執行筆記本儲存格。您應該會看到傳回的 Microsoft Entra 權杖。

使用 Microsoft Entra 使用者權杖驗證 Livy API Spark 會話

在 Visual Studio Code 中建立 .ipynb 筆記本，並插入下列程式代碼。

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")

執行筆記本儲存格。您應該會看到傳回的 Microsoft Entra 權杖。

建立 Livy API Spark 會話

新增另一個筆記本數據格，並插入此程序代碼。

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}")

執行筆記本的儲存格，您應該會在建立 Livy 工作階段時看到一行被列印出來。
您可以透過 [在監視中樞查看您在監視中樞的工作]（#View your jobs in the Monitoring hub），確認 Livy 會話是否已建立。

與網狀架構環境整合

根據預設，此 Livy API 會話會針對工作區的預設起始池執行。或者，您可以使用 Fabric 環境建立、設定及使用 Microsoft Fabric 中的環境，自定義 Livy API 會話用於這些 Spark 作業的 Spark 集區。若要使用 Fabric 環境，請使用此 json 載荷來更新之前的筆記本儲存格。

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

使用 Livy API 的 Spark 工作階段提交 spark.sql 語句

新增另一個筆記本數據格，並插入此程序代碼。

    # 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}")

當您執行筆記本中的單元格時，應會看到在作業提交和結果返回的過程中列印的數行增量行。

使用 Livy API Spark 會話提交第二個 spark.sql 指令

新增另一個筆記本數據格，並插入此程序代碼。

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}")

當您執行筆記本中的單元格時，應會看到在作業提交和結果返回的過程中列印的數行增量行。

結束李維會議

新增另一個筆記本數據格，並插入此程序代碼。

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}")

在監控中心檢視您的任務

您可以選取左側導覽連結中的 [監視]，來存取監視中樞以檢視各種 Apache Spark 活動。

當工作階段進行中或處於已完成狀態時，您可以流覽至 [監視] 來檢視工作階段狀態。
選取並開啟最新的活動名稱。
在此 Livy API 工作階段案例中，您可以看到先前的工作階段提交、執行詳細數據、Spark 版本和設定。請注意右上方的已停止狀態。

若要回顧整個流程，您需要遠端用戶端，例如 Visual Studio Code、Microsoft Entra 應用程式/SPN 令牌、Livy API 端點 URL、驗證您的 Lakehouse，以及 Livy API 會話。

意見反應

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Last updated on 2025-11-10

共用方式為

使用 Livy API 來提交和執行工作作業

必要條件

設定 Visual Studio Code來進行 Livy API 會話

使用 Microsoft Entra 使用者令牌或 Microsoft Entra SPN 令牌驗證 Livy API 的 Spark 工作階段

使用 Microsoft Entra SPN 權杖驗證 Livy API Spark 會話

使用 Microsoft Entra 使用者權杖驗證 Livy API Spark 會話

建立 Livy API Spark 會話

與網狀架構環境整合

使用 Livy API 的 Spark 工作階段提交 spark.sql 語句

使用 Livy API Spark 會話提交第二個 spark.sql 指令

結束李維會議

在監控中心檢視您的任務

相關內容

意見反應

其他資源