apache-spark - Spark 故障 : Caused by: org. apache.spark.shuffle.FetchFailedException:框架太大:5454002341

Question

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，即使在收到关于过大框架的错误后:

Spark 属性

--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");

错误

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)

Answer 1

使用这个 spark 配置， spark.maxRemoteBlockSizeFetchToMem < 2g

由于 > 2G 分区存在很多问题(无法 shuffle，无法缓存在磁盘上)，因此它抛出了 failedfetchedexception 太大的数据帧。