Sådan opretter og opdaterer du en Spark-jobdefinition med Microsoft Fabric REST API

Fabric REST API'en leverer et serviceendepunkt til CRUD-operationer af Fabric-elementer. I denne tutorial gennemgår vi et end-to-end scenarie for, hvordan man opretter og opdaterer et Spark-jobdefinitionselement. Der er tre trin på højt niveau:

Opret et Spark-jobdefinitionselement med en indledende tilstand.
Upload hoveddefinitionsfilen og andre biblioteksfiler.
Opdater Spark-jobdefinitionselementet med OneLake-URL'en til hoveddefinitionsfilen og andre bibliotekfiler.

Forudsætninger

Et Microsoft Entra-token er nødvendigt for at få adgang til Fabric REST API'en. MSAL-biblioteket anbefales for at hente tokenet. Du kan få flere oplysninger under Understøttelse af godkendelsesflow i MSAL.
Der kræves et lagertoken for at få adgang til OneLake-API'en. Du kan få flere oplysninger under MSAL til Python.

Opret et Spark-jobdefinitionselement med starttilstanden

Fabric REST API'en definerer et samlet endepunkt for CRUD-operationer af Fabric-elementer. Slutpunktet er https://api.fabric.microsoft.com/v1/workspaces/{workspaceId}/items.

Vareoplysningerne er angivet i anmodningsdelen. Her er et eksempel på anmodningskroppen til oprettelse af et Spark-jobdefinitionselement:

{
    "displayName": "SJDHelloWorld",
    "type": "SparkJobDefinition",
    "definition": {
        "format": "SparkJobDefinitionV1",
        "parts": [
            {
                "path": "SparkJobDefinitionV1.json",
                "payload": "<REDACTED>",
                "payloadType": "InlineBase64"
            }
        ]
    }
}

I dette eksempel hedder Spark-jobdefinitionselementet .SJDHelloWorld Feltet payload er det base64-kodede indhold i den detaljerede opsætning. Efter dekodning er indholdet:

{
    "executableFile":null,
    "defaultLakehouseArtifactId":"",
    "mainClass":"",
    "additionalLakehouseIds":[],
    "retryPolicy":null,
    "commandLineArguments":"",
    "additionalLibraryUris":[],
    "language":"",
    "environmentArtifactId":null
}

Her er to hjælpefunktioner til at kode og afkode den detaljerede konfiguration:

import base64

def json_to_base64(json_data):
    # Serialize the JSON data to a string
    json_string = json.dumps(json_data)
    
    # Encode the JSON string as bytes
    json_bytes = json_string.encode('utf-8')
    
    # Encode the bytes as Base64
    base64_encoded = base64.b64encode(json_bytes).decode('utf-8')
    
    return base64_encoded

def base64_to_json(base64_data):
    # Decode the Base64-encoded string to bytes
    base64_bytes = base64_data.encode('utf-8')
    
    # Decode the bytes to a JSON string
    json_string = base64.b64decode(base64_bytes).decode('utf-8')
    
    # Deserialize the JSON string to a Python dictionary
    json_data = json.loads(json_string)
    
    return json_data

Her er kodeudsnittet til at oprette et Spark-jobdefinitionselement:

import requests

bearerToken = "<REDACTED>"  # Replace this token with the real AAD token

headers = {
    "Authorization": f"Bearer {bearerToken}", 
    "Content-Type": "application/json"  # Set the content type based on your request
}

payload = "<REDACTED>"

# Define the payload data for the POST request
payload_data = {
    "displayName": "SJDHelloWorld",
    "Type": "SparkJobDefinition",
    "definition": {
        "format": "SparkJobDefinitionV1",
        "parts": [
            {
                "path": "SparkJobDefinitionV1.json",
                "payload": payload,
                "payloadType": "InlineBase64"
            }
        ]
    }
}

# Make the POST request with Bearer authentication
sjdCreateUrl = f"https://api.fabric.microsoft.com//v1/workspaces/{workspaceId}/items"
response = requests.post(sjdCreateUrl, json=payload_data, headers=headers)

Upload hoveddefinitionsfilen og andre lib-filer

Der kræves et lagertoken for at uploade filen til OneLake. Her er en hjælpefunktion til at hente lagertokenet:

import msal

def getOnelakeStorageToken():
    app = msal.PublicClientApplication(
        "<REDACTED>",  # This field should be the client ID 
        authority="https://login.microsoftonline.com/microsoft.com")

    result = app.acquire_token_interactive(scopes=["https://storage.azure.com/.default"])

    print(f"Successfully acquired AAD token with storage audience:{result['access_token']}")

    return result['access_token']

Nu har vi oprettet et Spark-jobdefinitionselement. For at gøre det kørbart skal vi opsætte hoveddefinitionsfilen og de nødvendige egenskaber. Slutpunktet for overførsel af filen for dette SJD-element er https://onelake.dfs.fabric.microsoft.com/{workspaceId}/{sjditemid}. Den samme "workspaceId" som i forrige trin bør bruges. Værdien af "sjditemid" kunne findes i responskroppen for det forrige trin. Her er kodestykket til konfiguration af hoveddefinitionsfilen:

import requests

# Three steps are required: create file, append file, flush file

onelakeEndPoint = "https://onelake.dfs.fabric.microsoft.com/workspaceId/sjditemid"  # Replace the ID of workspace and item with the right one
mainExecutableFile = "main.py"  # The name of the main executable file
mainSubFolder = "Main"  # The sub folder name of the main executable file. Don't change this value


onelakeRequestMainFileCreateUrl = f"{onelakeEndPoint}/{mainSubFolder}/{mainExecutableFile}?resource=file"  # The URL for creating the main executable file via the 'file' resource type
onelakePutRequestHeaders = {
    "Authorization": f"Bearer {onelakeStorageToken}",  # The storage token can be achieved from the helper function above
}

onelakeCreateMainFileResponse = requests.put(onelakeRequestMainFileCreateUrl, headers=onelakePutRequestHeaders)
if onelakeCreateMainFileResponse.status_code == 201:
    # Request was successful
    print(f"Main File '{mainExecutableFile}' was successfully created in OneLake.")

# With the previous step, the main executable file is created in OneLake. Now we need to append the content of the main executable file

appendPosition = 0
appendAction = "append"

### Main File Append.
mainExecutableFileSizeInBytes = 83  # The size of the main executable file in bytes
onelakeRequestMainFileAppendUrl = f"{onelakeEndPoint}/{mainSubFolder}/{mainExecutableFile}?position={appendPosition}&action={appendAction}"
mainFileContents = "<REDACTED>"  # The content of the main executable file, please replace this with the real content of the main executable file
mainExecutableFileSizeInBytes = 83  # The size of the main executable file in bytes, this value should match the size of the mainFileContents

onelakePatchRequestHeaders = {
    "Authorization": f"Bearer {onelakeStorageToken}",
    "Content-Type": "text/plain"
}

onelakeAppendMainFileResponse = requests.patch(onelakeRequestMainFileAppendUrl, data = mainFileContents, headers=onelakePatchRequestHeaders)
if onelakeAppendMainFileResponse.status_code == 202:
    # Request was successful
    print(f"Successfully accepted main file '{mainExecutableFile}' append data.")

# With the previous step, the content of the main executable file is appended to the file in OneLake. Now we need to flush the file

flushAction = "flush"

### Main File flush
onelakeRequestMainFileFlushUrl = f"{onelakeEndPoint}/{mainSubFolder}/{mainExecutableFile}?position={mainExecutableFileSizeInBytes}&action={flushAction}"
print(onelakeRequestMainFileFlushUrl)
onelakeFlushMainFileResponse = requests.patch(onelakeRequestMainFileFlushUrl, headers=onelakePatchRequestHeaders)
if onelakeFlushMainFileResponse.status_code == 200:
    print(f"Successfully flushed main file '{mainExecutableFile}' contents.")
else:
    print(onelakeFlushMainFileResponse.json())

Følg den samme proces for at uploade de andre lib-filer, hvis det er nødvendigt.

Opdater Spark-jobdefinitionselementet med OneLake-URL'en til hoveddefinitionsfilen og andre bibliotekfiler

Indtil nu har vi oprettet et Spark-jobdefinitionselement med en indledende tilstand og uploadet hoveddefinitionsfilen og andre bibliotekfiler. Det sidste trin er at opdatere Spark-jobdefinitionselementet for at sætte URL-egenskaberne for hoveddefinitionsfilen og andre bibliotekfiler. Endepunktet for opdatering af Spark-jobdefinitionselementet er https://api.fabric.microsoft.com/v1/workspaces/{workspaceId}/items/{sjditemid}. De samme "workspaceId" og "sjditemid" som tidligere trin bør bruges. Her er kodeuddraget til at opdatere Spark-jobdefinitionen:

mainAbfssPath = f"abfss://{workspaceId}@onelake.dfs.fabric.microsoft.com/{sjditemid}/Main/{mainExecutableFile}"  # The workspaceId and sjditemid are the same as previous steps, the mainExecutableFile is the name of the main executable file
libsAbfssPath = f"abfss://{workspaceId}@onelake.dfs.fabric.microsoft.com/{sjditemid}/Libs/{libsFile}"  # The workspaceId and sjditemid are the same as previous steps, the libsFile is the name of the libs file
defaultLakehouseId = '<REDACTED>'  # Replace this with the real default lakehouse ID

updateRequestBodyJson = {
    "executableFile": mainAbfssPath,
    "defaultLakehouseArtifactId": defaultLakehouseId,
    "mainClass": "",
    "additionalLakehouseIds": [],
    "retryPolicy": None,
    "commandLineArguments": "",
    "additionalLibraryUris": [libsAbfssPath],
    "language": "Python",
    "environmentArtifactId": None}

# Encode the bytes as a Base64-encoded string
base64EncodedUpdateSJDPayload = json_to_base64(updateRequestBodyJson)

# Print the Base64-encoded string
print("Base64-encoded JSON payload for SJD Update:")
print(base64EncodedUpdateSJDPayload)

# Define the API URL
updateSjdUrl = f"https://api.fabric.microsoft.com//v1/workspaces/{workspaceId}/items/{sjditemid}/updateDefinition"

updatePayload = base64EncodedUpdateSJDPayload
payloadType = "InlineBase64"
path = "SparkJobDefinitionV1.json"
format = "SparkJobDefinitionV1"
Type = "SparkJobDefinition"

# Define the headers with Bearer authentication
bearerToken = "<REDACTED>"  # Replace this token with the real AAD token

headers = {
    "Authorization": f"Bearer {bearerToken}", 
    "Content-Type": "application/json"  # Set the content type based on your request
}

# Define the payload data for the POST request
payload_data = {
    "displayName": "sjdCreateTest11",
    "Type": Type,
    "definition": {
        "format": format,
        "parts": [
            {
                "path": path,
                "payload": updatePayload,
                "payloadType": payloadType
            }
        ]
    }
}


# Make the POST request with Bearer authentication
response = requests.post(updateSjdUrl, json=payload_data, headers=headers)
if response.status_code == 200:
    print("Successfully updated SJD.")
else:
    print(response.json())
    print(response.status_code)

For at opsummere hele processen er både Fabric REST API og OneLake API nødvendige for at oprette og opdatere et Spark-jobdefinitionselement. Fabric REST API'en bruges til at oprette og opdatere Spark-jobdefinitionselementet. OneLake API'en bruges til at uploade hoveddefinitionsfilen og andre biblioteksfiler. Den primære definitionsfil og andre lib-filer uploades først til OneLake. Derefter sættes URL-egenskaberne for hoveddefinitionsfilen og andre bibliotekfiler i Spark-jobdefinitionselementet.

Planlæg og kør en Apache Spark-jobdefinition

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Last updated on 2026-08-05