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如何在没有java堆内存错误的情况下将csv读取到pyspark中



我正试图用以下代码将csv读取到pyspark控制台中：

from pyspark.sql import SQLContext
import pyspark
sql_c = SQLContext(sc)
df = sql_c.read.csv('join_rows_no_prepended_new_line.csv')

然而，当我有144gb的空闲时，我得到了一个关于内存使用的很长的错误。此外，内存错误会在运行上述代码后立即发生，所以我不认为这实际上是内存错误。我已经安装了java 1.8、spark 2.4.0和python 3.6。我也安装了scala，但我还没有深入研究它。我没有安装hadoop(我需要它吗？)

为了纠正这个错误，我尝试增加java的堆大小，但这还没有改变错误。我用这些选项集运行了pyspark，得到了相同的结果pyspark --num-executors 5 --driver-memory 2g --executor-memory 2g

[Stage 0:>                                                          (0 + 1) / 1]2019-01-29 23:31:22 ERROR Executor:91 - Exception in task 0.0 in stage 0.0 (TID 0)
java.lang.OutOfMemoryError: Java heap space
at java.util.Arrays.copyOf(Arrays.java:3236)
at org.apache.hadoop.io.Text.setCapacity(Text.java:266)
at org.apache.hadoop.io.Text.append(Text.java:236)
at org.apache.hadoop.util.LineReader.readDefaultLine(LineReader.java:243)
at org.apache.hadoop.util.LineReader.readLine(LineReader.java:174)
at org.apache.hadoop.mapreduce.lib.input.UncompressedSplitLineReader.readLine(UncompressedSplitLineReader.java:94)
at org.apache.hadoop.mapreduce.lib.input.LineRecordReader.skipUtfByteOrderMark(LineRecordReader.java:144)
at org.apache.hadoop.mapreduce.lib.input.LineRecordReader.nextKeyValue(LineRecordReader.java:184)
at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
at org.apache.spark.sql.execution.datasources.HadoopFileLinesReader.hasNext(HadoopFileLinesReader.scala:69)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:181)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
2019-01-29 23:31:22 ERROR SparkUncaughtExceptionHandler:91 - Uncaught exception in thread Thread[Executor task launch worker for task 0,5,main]
java.lang.OutOfMemoryError: Java heap space
at java.util.Arrays.copyOf(Arrays.java:3236)
at org.apache.hadoop.io.Text.setCapacity(Text.java:266)
at org.apache.hadoop.io.Text.append(Text.java:236)
at org.apache.hadoop.util.LineReader.readDefaultLine(LineReader.java:243)
at org.apache.hadoop.util.LineReader.readLine(LineReader.java:174)
at org.apache.hadoop.mapreduce.lib.input.UncompressedSplitLineReader.readLine(UncompressedSplitLineReader.java:94)
at org.apache.hadoop.mapreduce.lib.input.LineRecordReader.skipUtfByteOrderMark(LineRecordReader.java:144)
at org.apache.hadoop.mapreduce.lib.input.LineRecordReader.nextKeyValue(LineRecordReader.java:184)
at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
at org.apache.spark.sql.execution.datasources.HadoopFileLinesReader.hasNext(HadoopFileLinesReader.scala:69)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:181)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
2019-01-29 23:31:22 WARN  TaskSetManager:66 - Lost task 0.0 in stage 0.0 (TID 0, localhost, executor driver): java.lang.OutOfMemoryError: Java heap space
at java.util.Arrays.copyOf(Arrays.java:3236)
at org.apache.hadoop.io.Text.setCapacity(Text.java:266)
at org.apache.hadoop.io.Text.append(Text.java:236)
at org.apache.hadoop.util.LineReader.readDefaultLine(LineReader.java:243)
at org.apache.hadoop.util.LineReader.readLine(LineReader.java:174)
at org.apache.hadoop.mapreduce.lib.input.UncompressedSplitLineReader.readLine(UncompressedSplitLineReader.java:94)
at org.apache.hadoop.mapreduce.lib.input.LineRecordReader.skipUtfByteOrderMark(LineRecordReader.java:144)
at org.apache.hadoop.mapreduce.lib.input.LineRecordReader.nextKeyValue(LineRecordReader.java:184)
at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
at org.apache.spark.sql.execution.datasources.HadoopFileLinesReader.hasNext(HadoopFileLinesReader.scala:69)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:181)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
2019-01-29 23:31:22 ERROR TaskSetManager:70 - Task 0 in stage 0.0 failed 1 times; aborting job
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/ec2-user/anaconda3/lib/python3.6/site-packages/pyspark/sql/readwriter.py", line 472, in csv
return self._df(self._jreader.csv(self._spark._sc._jvm.PythonUtils.toSeq(path)))
File "/home/ec2-user/anaconda3/lib/python3.6/site-packages/pyspark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
File "/home/ec2-user/anaconda3/lib/python3.6/site-packages/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/home/ec2-user/anaconda3/lib/python3.6/site-packages/pyspark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o33.csv.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost, executor driver): java.lang.OutOfMemoryError: Java heap space
at java.util.Arrays.copyOf(Arrays.java:3236)
at org.apache.hadoop.io.Text.setCapacity(Text.java:266)
at org.apache.hadoop.io.Text.append(Text.java:236)
at org.apache.hadoop.util.LineReader.readDefaultLine(LineReader.java:243)
at org.apache.hadoop.util.LineReader.readLine(LineReader.java:174)
at org.apache.hadoop.mapreduce.lib.input.UncompressedSplitLineReader.readLine(UncompressedSplitLineReader.java:94)
at org.apache.hadoop.mapreduce.lib.input.LineRecordReader.skipUtfByteOrderMark(LineRecordReader.java:144)
at org.apache.hadoop.mapreduce.lib.input.LineRecordReader.nextKeyValue(LineRecordReader.java:184)
at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
at org.apache.spark.sql.execution.datasources.HadoopFileLinesReader.hasNext(HadoopFileLinesReader.scala:69)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:181)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1887)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1875)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1874)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1874)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2108)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2057)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2046)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3384)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2545)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2545)
at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3365)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3364)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2545)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2759)
at org.apache.spark.sql.execution.datasources.csv.TextInputCSVDataSource$.infer(CSVDataSource.scala:232)
at org.apache.spark.sql.execution.datasources.csv.CSVDataSource.inferSchema(CSVDataSource.scala:68)
at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat.inferSchema(CSVFileFormat.scala:63)
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$6.apply(DataSource.scala:180)
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$6.apply(DataSource.scala:180)
at scala.Option.orElse(Option.scala:289)
at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:179)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:373)
at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:223)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:211)
at org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:617)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.OutOfMemoryError: Java heap space
at java.util.Arrays.copyOf(Arrays.java:3236)
at org.apache.hadoop.io.Text.setCapacity(Text.java:266)
at org.apache.hadoop.io.Text.append(Text.java:236)
at org.apache.hadoop.util.LineReader.readDefaultLine(LineReader.java:243)
at org.apache.hadoop.util.LineReader.readLine(LineReader.java:174)
at org.apache.hadoop.mapreduce.lib.input.UncompressedSplitLineReader.readLine(UncompressedSplitLineReader.java:94)
at org.apache.hadoop.mapreduce.lib.input.LineRecordReader.skipUtfByteOrderMark(LineRecordReader.java:144)
at org.apache.hadoop.mapreduce.lib.input.LineRecordReader.nextKeyValue(LineRecordReader.java:184)
at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
at org.apache.spark.sql.execution.datasources.HadoopFileLinesReader.hasNext(HadoopFileLinesReader.scala:69)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:181)
at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:101)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)

我相信你的问题可能来自于你提交工作的方式：

pyspark --num-executors 5 --driver-memory 2g --executor-memory 2g

如果文件如您所说是65GB，则上面提交的内容告诉spark只使用2GB的可用内存。

尝试将--driver-memory参数渐变为略大于.csv文件的大小。

例如--driver-memory 70G

解释为什么这是必要的：

如果没有带有分布式文件系统的集群，您的整个数据集都位于本地驱动器上。Spark允许您以优化的方式在集群中拆分作业，但如果没有它链接到所述独立机器集群，您的所有数据都将加载到驱动程序的内存中。因此，即使这里有更高的并行性，也需要允许作业占用与输入文件一样多或更多的空间。

编辑-在评论中回答您的问题：

当你需要为Spark工作的驱动程序分配一个完整的65G时，以及当没有必要时，有几个概念是理解的核心。

首先，Spark在JVM(Java虚拟机)上运行，JVM是代码实际执行的地方。JVM包含一个"堆空间"，可以理解为虚拟机拥有和可能使用的内存量。在上面的场景中，您没有一个单独的机器集群，您的数据也没有分布在它们之间，因此您需要为底层JVM提供足够的内存来保存您的数据，如果您打算执行任何以任何方式增加数据大小的操作，则可能更需要这样做。

现在，Spark本身是一个框架，允许您以并行和优化的方式计算计算昂贵的任务，但当您拥有像HDFS(Hadoop分布式文件系统)这样的分布式文件系统时，它会显示出其全部潜力。

在HDFS中存储数据时，您可以在每台机器上发送数据片段，Spark允许您对以这种"分块"方式存储的数据进行操作，在这种方式中，集群中每台机器的每个执行器都会在一小块上执行您的特定操作。不过，问题是，如果你想"操作"你的数据(即收集、显示、计数)，你需要将生成的数据集再次拉到一个地方——这就是我们所说的驱动程序。

这就产生了两种情况：

  1. 经过所有操作后，生成的数据很小，因此驱动程序中不需要完整的65GB。一个很好的例子是，如果您必须对原始数据进行聚合，并将数据从GB精简到MB
  2. 数据与原始数据一样大，甚至更大，这意味着你仍然需要提供足够的驱动程序内存来容纳所有数据

Spark中有很多东西需要理解和玩——我强烈建议花点时间阅读它的工作原理以及它能为你做些什么。这里还有一个教程链接，可以带你了解的每一个术语

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