我正在为确定父子项的表生成层次结构。
以下是使用的配置,即使在收到有关太大框架的错误后也是如此:
火花特性
--conf spark.yarn.executor.memoryOverhead=1024mb \
--conf yarn.nodemanager.resource.memory-mb=12288mb \
--driver-memory 32g \
--driver-cores 8 \
--executor-cores 32 \
--num-executors 8 \
--executor-memory 256g \
--conf spark.maxRemoteBlockSizeFetchToMem=15g
import org.apache.log4j.{Level, Logger};
import org.apache.spark.SparkContext;
import org.apache.spark.sql.{DataFrame, SparkSession};
import org.apache.spark.sql.functions._;
import org.apache.spark.sql.expressions._;
lazy val sparkSession = SparkSession.builder.enableHiveSupport().getOrCreate();
import spark.implicits._;
val hiveEmp: DataFrame = sparkSession.sql("select * from db.employee");
hiveEmp.repartition(300);
import org.apache.spark.sql.functions._;
val nestedLevel = 3;
val empHierarchy = (1 to nestedLevel).foldLeft(hiveEmp.as("wd0")) { (wDf, i) =>
val j = i - 1
wDf.join(hiveEmp.as(s"wd$i"), col(s"wd$j.parent_id".trim) === col(s"wd$i.id".trim), "left_outer")
}.select(
col("wd0.id") :: col("wd0.parent_id") ::
col("wd0.amount").as("amount") :: col("wd0.payment_id").as("payment_id") :: (
(1 to nestedLevel).toList.map(i => col(s"wd$i.amount").as(s"amount_$i")) :::
(1 to nestedLevel).toList.map(i => col(s"wd$i.payment_id").as(s"payment_id_$i"))
): _*);
empHierarchy.write.saveAsTable("employee4");
Error
Caused by: org.apache.spark.SparkException: Task failed while writing rows
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:204)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1$$anonfun$3.apply(FileFormatWriter.scala:129)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1$$anonfun$3.apply(FileFormatWriter.scala:128)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:322)
... 3 more
Caused by: org.apache.spark.shuffle.FetchFailedException: Too large frame: 5454002341
at org.apache.spark.storage.ShuffleBlockFetcherIterator.throwFetchFailedException(ShuffleBlockFetcherIterator.scala:361)
at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:336)
使用此 Spark 配置,spark.maxRemoteBlockSizeFetchToMem
由于> 2G分区存在很多问题(无法洗牌,无法在磁盘上缓存),因此它抛出failedfetchedException太大的数据帧。
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