我在AWS上的Scala（Zeppling/Spark-Shell）上运行Spark 2.2。
我正在尝试计算非常简单的计算：加载，过滤，缓存和计数大数据集。我的数据包含4,500 GB（4.8 TB）ORC格式，其51,317,951,565（510亿）行。
首先，我使用以下集群尝试了该过程：
1主节点-M4.Xlarge -4 CPU，16 GB MEM
150核心节点-R3.xlarge -4 CPU，29 GB MEM
150个任务节点-R3.xlarge -4 CPU，29 GB MEM
，但 OutOfMemoryError失败了。
当我查看Spark UI和Ganglia时，我看到应用程序加载超过80％的数据后，驱动程序节点在执行者停止工作时变得太忙了（CPU使用率非常低），直到崩溃为止。p>主节点和工人节点的神经节CPU使用
然后，我只是尝试将驱动程序节点增加到：
而尝试执行相同的过程1主节点-M4.2xlarge -8 CPU，31 GB MEM
它成功。
我不明白为什么驱动程序节点内存使用量直到崩溃。AFAIK只有执行者正在加载和处理任务，并且数据不应传递给主人。这可能是什么原因？
1）第二种情况的神经节主节点用法
2）Spark UI阶段
3）Spark UI DAG可视化
下面您可以找到代码：
import org.apache.spark.SparkConf
import org.apache.spark.sql.{Dataset, SaveMode, SparkSession, DataFrame}
import org.apache.spark.sql.functions.{concat_ws, expr, lit, udf}
import org.apache.spark.storage.StorageLevel
val df = spark.sql("select * from default.level_1 where date_ >= ('2017-11-08') and date_ <= ('2017-11-27')")
.drop("carrier", "city", "connection_type", "geo_country", "geo_country","geo_lat","geo_lon","geo_lon","geo_type", "ip","keywords","language","lat","lon","store_category","GEO3","GEO4")
.where("GEO4 is not null")
.withColumn("is_away", lit(0))
df.persist(StorageLevel.MEMORY_AND_DISK_SER)
df.count()
下面您可以找到错误消息 -
{"Event":"SparkListenerLogStart","Spark Version":"2.2.0"}
{"Event":"SparkListenerBlockManagerAdded","Block Manager ID":{"Executor ID":"driver","Host":"10.44.6.179","Port":44257},"Maximum Memory":6819151872,"Timestamp":1512024674827,"Maximum Onheap Memory":6819151872,"Maximum Offheap Memory":0}
{"Event":"SparkListenerEnvironmentUpdate","JVM Information":{"Java Home":"/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.141-1.b16.32.amzn1.x86_64/jre","Java Version":"1.8.0_141 (Oracle Corporation)","Scala Version":"version 2.11.8"},"Spark Properties":{"spark.sql.warehouse.dir":"hdfs:///user/spark/warehouse","spark.yarn.dist.files":"file:/etc/spark/conf/hive-site.xml","spark.executor.extraJavaOptions":"-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=70 -XX:MaxHeapFreeRatio=70 -XX:+CMSClassUnloadingEnabled -XX:OnOutOfMemoryError='kill -9 %p'","spark.driver.host":"10.44.6.179","spark.history.fs.logDirectory":"hdfs:///var/log/spark/apps","spark.eventLog.enabled":"true","spark.driver.port":"33707","spark.shuffle.service.enabled":"true","spark.driver.extraLibraryPath":"/usr/lib/hadoop/lib/native:/usr/lib/hadoop-lzo/lib/native","spark.repl.class.uri":"spark://10.44.6.179:33707/classes","spark.jars":"","spark.yarn.historyServer.address":"ip-10-44-6-179.ec2.internal:18080","spark.stage.attempt.ignoreOnDecommissionFetchFailure":"true","spark.repl.class.outputDir":"/mnt/tmp/spark-52cac1b4-614f-43a5-ab9b-5c60c6c1c5a5/repl-9389c888-603e-4988-9593-86e298d2514a","spark.app.name":"Spark shell","spark.scheduler.mode":"FIFO","spark.driver.memory":"11171M","spark.executor.instances":"200","spark.default.parallelism":"3200","spark.resourceManager.cleanupExpiredHost":"true","spark.executor.id":"driver","spark.yarn.appMasterEnv.SPARK_PUBLIC_DNS":"$(hostname -f)","spark.driver.extraJavaOptions":"-XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=70 -XX:MaxHeapFreeRatio=70 -XX:+CMSClassUnloadingEnabled -XX:OnOutOfMemoryError='kill -9 %p'","spark.submit.deployMode":"client","spark.master":"yarn","spark.ui.filters":"org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter","spark.blacklist.decommissioning.timeout":"1h","spark.executor.extraLibraryPath":"/usr/lib/hadoop/lib/native:/usr/lib/hadoop-lzo/lib/native","spark.sql.hive.metastore.sharedPrefixes":"com.amazonaws.services.dynamodbv2","spark.executor.memory":"20480M","spark.driver.extraClassPath":"/usr/lib/hadoop-lzo/lib/*:/usr/lib/hadoop/hadoop-aws.jar:/usr/share/aws/aws-java-sdk/*:/usr/share/aws/emr/emrfs/conf:/usr/share/aws/emr/emrfs/lib/*:/usr/share/aws/emr/emrfs/auxlib/*:/usr/share/aws/emr/security/conf:/usr/share/aws/emr/security/lib/*:/usr/share/aws/hmclient/lib/aws-glue-datacatalog-spark-client.jar:/usr/share/java/Hive-JSON-Serde/hive-openx-serde.jar","spark.home":"/usr/lib/spark","spark.eventLog.dir":"hdfs:///var/log/spark/apps","spark.dynamicAllocation.enabled":"true","spark.executor.extraClassPath":"/usr/lib/hadoop-lzo/lib/*:/usr/lib/hadoop/hadoop-aws.jar:/usr/share/aws/aws-java-sdk/*:/usr/share/aws/emr/emrfs/conf:/usr/share/aws/emr/emrfs/lib/*:/usr/share/aws/emr/emrfs/auxlib/*:/usr/share/aws/emr/security/conf:/usr/share/aws/emr/security/lib/*:/usr/share/aws/hmclient/lib/aws-glue-datacatalog-spark-client.jar:/usr/share/java/Hive-JSON-Serde/hive-openx-serde.jar","spark.sql.catalogImplementation":"hive","spark.executor.cores":"8","spark.history.ui.port":"18080","spark.driver.appUIAddress":"http://ip-10-44-6-179.ec2.internal:4040","spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_HOSTS":"ip-10-44-6-
注意 -
1）我试图将Storagelevel更改为cache()和DISK_ONLY，但不会影响结果。
2）我检查了"刮擦空间"的卷，我发现其中超过90％仍未使用。
谢谢！
我有一些假设，可能是由Spark SQL中的机制引起的。
简而言之，Spark SQL将收集驱动程序端的所有广播数据集，因此当您有一个大查询时，驱动程序必须具有足够的内存才能保存广播数据。
相关链接