#Load Scoring Data into Spark Dataframe scoreDf = spark.table({table_name}).where({required_conditions}) #Make Prediction preds = (scoreDf .withColumn('target_column_name', pyfunc_udf('Input_column1', 'Input_column2', ' Input_column3',…)) ) display(preds) 清除資源如果您想要保留 Azur...
因為 Delta Live Tables 會針對 DataFrame 定義資料集,因此您可以只使用幾行程式代碼,將使用 MLflow 的 Apache Spark 工作負載轉換成 Delta Live Tables。 如需 MLflow 的詳細資訊,請參閱使用 MLflow的 ML 生命週期管理。 如果您已經有呼叫 MLflow 模型的 Python 筆記本,您可以使用裝飾專案將此程...
url ="https://{workspace_url}/serving-endpoints/user-preferences/invocations"headers = {'Authorization':f'Bearer{DATABRICKS_TOKEN}','Content-Type':'application/json'} data = {"dataframe_records": [{"user_id": user_id}] } data_json = json.dumps(data, allow_nan=True) response = requests...
importio.delta.tables.*valdeltaTable =DeltaTable.forName(spark,"table_name")// Function to upsert microBatchOutputDF into Delta table using mergedefupsertToDelta(microBatchOutputDF:DataFrame, batchId:Long) { deltaTable.as("t") .merge( microBatchOutputDF.as("s"),"s.key = t.key") .whenMat...
See Load data using COPY INTO with temporary credentials. SELECT expression_list Selects the specified columns or expressions from the source data before copying into the Delta table. The expressions can be anything you use with SELECT statements, including window operations. You can use aggregation...
Use the URI to define a Spark UDF to load the MLflow model. Call the UDF in your table definitions to use the MLflow model. The following example shows the basic syntax for this pattern: Python %pipinstallmlflowimportdltimportmlflowrun_id="<mlflow-run-id>"model_name="<the-model-name-in...
("updates") // Use the view name to apply MERGE // NOTE: You have to use the SparkSession that has been used to define the `updates` dataframe microBatchOutputDF.sparkSession.sql(s""" MERGE INTO delta_{table_name} t USING updates s ON s.uuid = t.uuid WHEN MATCHED THEN UPDATE ...
user=username&password=pass") .option("dbtable","my_table") .option("tempdir","s3n://path/for/temp/data") .load()//Can also load data from a Redshift queryvaldf:DataFrame=sqlContext.read .format("com.databricks.spark.redshift") .option("url","jdbc:redshift://redshifthost:5439/...
user=username&password=pass") .option("dbtable", "my_table") .option("tempdir", "s3n://path/for/temp/data") .load() // Can also load data from a Redshift query val df: DataFrame = sqlContext.read .format("com.databricks.spark.redshift") .option("url", "jdbc:redshift://red...
Spark DataFrame of the requested data """ connection_url = get_sql_connection_string() return spark.read.jdbc(url=connection_url, table=query) For simplicity, in this example we do not connect to a SQL server but instead load our data from a local file o...