使用write_table 填入功能資料表。 如需下列範例中使用的命令和參數詳細資料,請參閱功能存放區 Python API 參照。 V0.3.6 及以上 Python 複製 from databricks.feature_store import feature_table def compute_customer_features(data): ''' Feature computation code returns a DataFrame with 'custome...
// Function to upsert microBatchOutputDF into Delta table using mergedefupsertToDelta(microBatchOutputDF:DataFrame, batchId:Long) {// Set the dataframe to view namemicroBatchOutputDF.createOrReplaceTempView("updates")// Use the view name to apply MERGE//NOTE:You have to use the SparkSession th...
#read the sample data into dataframe df_flight_data = spark.read.csv("/databricks-datasets/flights/departuredelays.csv", header=True) #create the delta table to the mount point that we have created earlier dbutils.fs.rm("abfss://labdpdw@labseadpdw01.dfs.core.windows.net/mytestDB/MyFirs...
inferSchema 如果为 true,则尝试推断每个生成的 DataFrame 列的相应类型。 如果为 false,则生成的所有列均为 string 类型。 默认值:true。 XML 内置函数会忽略此选项。 读取 columnNameOfCorruptRecord 允许重命名包含由 PERMISSIVE 模式创建的格式错误的字符串的新字段。 默认:spark.sql.columnNameOfCorruptRecord。
forName(spark, "table_name")// Function to upsert microBatchOutputDF into Delta table using mergedef upsertToDelta(microBatchOutputDF: DataFrame, batchId: Long) { deltaTable.as("t") .merge( microBatchOutputDF.as("s"), "s.key = t.key") .whenMatched().updateAll() .whenNotMatched()....
可以使用实用工具com.databricks.spark.xml.util.XSDToSchema从某些 XSD 文件中提取 Spark DataFrame 架构。 它仅支持简单类型、复杂类型和序列类型,仅支持基本 XSD 功能,且处于试验阶段。 Scala importcom.databricks.spark.xml.util.XSDToSchemaimportjava.nio.file.Pathsvalschema =XSDToSchema.read(Paths.get("/pa...
创建db和table 01 02 03 04 05 06 07 08 09 %python spark.sql("create database if not exists mytestDB") #read the sample data into dataframe df_flight_data = spark.read.csv("/databricks-datasets/flights/departuredelays.csv", header=True) #create the delta table to the mount point that...
databricks表/模式部署对于aws上的databricks，aws glue catalog是一种强大的方法，可以跨所有计算和查询...
Learn how to load and transform data using the Apache Spark Python (PySpark) DataFrame API, the Apache Spark Scala DataFrame API, and the SparkR SparkDataFrame API in Databricks.
// 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 SET ...