Use a single-user cluster instead, which supports RDD functionality. If you want to continue using a shared cluster, use the DataFrame API instead of the RDD API. For example, you can usespark.createDataFrameto create DataFrames. For more information on creating DataFrames, refer to the Apache Spark...
org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80) org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80) org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676) org.apache.spark.sql.DataFrameWriter$$anonfun$...
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:131) at org.apache.spark.sql.DataFrameWriter.$anonfun$runCommand$1(DataFrameWriter.scala:989) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103) ...
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:134) at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:133) at org.apache.spark.sql.DataFrameWriterV2.$anonfun$runCommand$1(DataFrameWriterV2.scala:196) at org.apache.spark.sql.catalyst...
similar functionality as GraphX except that GraphX acts on the Spark SRDD and GraphFrame works on the dataframe so GraphFrame is more user friendly (as dataframes are simpler to use). All the advantages of firing Spark SQL queries, joining datasets, filtering queries are all supported...
Let's filter the DataFrame and verify that the number of memory partitions does not change: val filteredDF = df.filter(col("person_country") === "Cuba") println(filteredDF.rdd.partitions.size) // 200 There are only 5 rows of Cuba data and 200 memory partitions, so we know that at ...
313.4.1. void RDD 回调 313.4.2. 转换 RDD 回调 313.4.3. 注解的 RDD 回调 313.5. DataFrame 作业 313.6. Hive 任务 313.7. 另请参阅 314. spark Rest 组件 spark Rest 组件 314.1. URI 格式 314.2. URI 选项 URI 选项 314.2.1. 路径名(2 参数): 314.2.2. 查询参数(11 参数):...
"Method Name": "$anonfun$createRddInternal$2", "File Name": "HoodieSparkUtils.scala", "Line Number": 137 }, { "Declaring Class": "org.apache.spark.rdd.RDD", "Method Name": "$anonfun$mapPartitions$2", "File Name": "RDD.scala", ...
(PySpark, Spark, or SparkR), executes the command, and then emits a SQL execution end event. If the execution is successful, it converts the result to a DataFrame and returns it. If an error occurs during the execution, it emits a SQL execution end event with the error details and ...
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:131) at org.apache.spark.sql.DataFrameWriter.$anonfun$runCommand$1(DataFrameWriter.scala:989) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103) ...