将RDD转换为DataFrame时java.lang.StackOverFlowError

尝试为大型RDD文档计算tf-idf分数，并且每当我尝试将其转换为数据帧时，它总是崩溃。我得到的最初错误是

org.apache.spark.SparkException: Job aborted due to stage failure: Task serialization failed: java.lang.StackOverflowError

此后，重复了很多次：

        at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
        at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
        at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
        at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)

之后

at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1889)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876)
        at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
        at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
        at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
        at org.apache.spark.scheduler.DAGScheduler.submitMissingTasks(DAGScheduler.scala:1171)
        at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:1069)
        at org.apache.spark.scheduler.DAGScheduler.handleJobSubmitted(DAGScheduler.scala:1013)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2067)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
        at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
        at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
        at org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:153)
        at org.apache.spark.api.python.PythonRDD.runJob(PythonRDD.scala)
        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)

我已经做过一些研究，看来与数据帧相关的DAG（有向无环图）太大，我应该对数据进行某种缓存/检查点/持久化以解决它。仍然每次都崩溃。为了避免混淆问题，我从下面的代码中省略了那些缓存/检查点/持久化行：

from pyspark.sql import SQLContext
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('app').getOrCreate()
rdd = spark.sparkContext.parallelize([])
data = []
count = 0
for sentence in giant_list_of_sentences:
   words = sentence.split(' ')
   data.append((words,count)) #Count is the index of the document
   count += 1
   if (len(data) > 5000):
      rdd = rdd.union(spark.sparkContext.parallelize(data))
if (len(data) > 0):
   rdd = rdd.union(spark.sparkContext.parallelize(data))
df_txts = sqlContext.createDataFrame(data,["list_of_words",'index'])

始终使其到达最后一行，然后失败，除非使之仅在能正常工作的数据的一小部分上运行。

因此，解决方案实际上非常简单-事实证明，将巨型RDD转换为巨型数据帧很困难，但是将几个较小的RDD转换为几个较小的数据帧，然后将其合并，效果很好。

from pyspark.sql import SQLContext
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('app').getOrCreate()
rdds = [spark.sparkContext.parallelize([])]*6
data = []
count = 0
turn = 0
for sentence in giant_list_of_sentences:
   words = sentence.split(' ')
   data.append((words,count)) #Count is the index of the document
   count += 1
   if (len(data) > 5000):
      rdds[turn] = rdds[turn].union(spark.sparkContext.parallelize(data))
if (len(data) > 0):
   rdds[turn] = rdds[turn].union(spark.sparkContext.parallelize(data))
df_txts = rdds[0].toDF(['list_of_words','index'])
for i in range(1,len(rdds)):
   df_txts = df_txts.union(rdds[i].toDF(['list_of_words','index'])
df_txts = sqlContext.createDataFrame(data,["list_of_words",'index'])

将RDD转换为DataFrame时java.lang.StackOverFlowError

jkx_zhaobin 回答：将RDD转换为DataFrame时java.lang.StackOverFlowError

大家都在问