作业已取消，因为SparkContext已关闭

时间：2018-12-31 06:54:53

标签： apache-spark pyspark

在jupyter笔记本中运行我的spark程序时，出现错误“由于SparkContext已关闭，作业被取消”。我使用的是不带hadoop的spark。相同的程序可以更早地输出输出，但现在显示错误。该错误一定已经发生。

我的代码是： 

from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)

df = sqlContext.read.json("Musical_Instruments_5.json") 
pd=df.select(df['asin'],df['overall'],df['reviewerID'])

from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.ml.recommendation import ALS

from pyspark.ml.feature import StringIndexer
from pyspark.ml import Pipeline
from pyspark.sql.functions import col

indexer = [StringIndexer(inputCol=column, outputCol=column+"_index") for 
column in list(set(pd.columns)-set(['overall'])) ]

pipeline = Pipeline(stages=indexer)
transformed = pipeline.fit(pd).transform(pd)
transformed.show()

(training,test)=transformed.randomSplit([0.8, 0.2])
als=ALS(maxIter=30,regParam=0.09,rank=25,userCol="reviewerID_index",itemCol="asin_index",ratingCol="overall",coldStartStrategy="drop",nonnegative=True)
model=als.fit(training)

这就是错误所在。

    Py4JJavaError                             Traceback (most recent call last)
<ipython-input-14-2e31692d867d> in <module>()
      1 #Fit ALS model to training data
----> 2 model=als.fit(training)

C:\spark\spark-2.3.1-bin-hadoop2.7\python\pyspark\ml\base.py in fit(self, dataset, params)
    130                 return self.copy(params)._fit(dataset)
    131             else:
--> 132                 return self._fit(dataset)
    133         else:
    134             raise ValueError("Params must be either a param map or a list/tuple of param maps, "

C:\spark\spark-2.3.1-bin-hadoop2.7\python\pyspark\ml\wrapper.py in _fit(self, dataset)
    286 
    287     def _fit(self, dataset):
--> 288         java_model = self._fit_java(dataset)
    289         model = self._create_model(java_model)
    290         return self._copyValues(model)

C:\spark\spark-2.3.1-bin-hadoop2.7\python\pyspark\ml\wrapper.py in _fit_java(self, dataset)
    283         """
    284         self._transfer_params_to_java()
--> 285         return self._java_obj.fit(dataset._jdf)
    286 
    287     def _fit(self, dataset):

C:\spark\spark-2.3.1-bin-hadoop2.7\python\lib\py4j-0.10.7-src.zip\py4j\java_gateway.py in __call__(self, *args)
   1255         answer = self.gateway_client.send_command(command)
   1256         return_value = get_return_value(
-> 1257             answer, self.gateway_client, self.target_id, self.name)
   1258 
   1259         for temp_arg in temp_args:

C:\spark\spark-2.3.1-bin-hadoop2.7\python\pyspark\sql\utils.py in deco(*a, **kw)
     61     def deco(*a, **kw):
     62         try:
---> 63             return f(*a, **kw)
     64         except py4j.protocol.Py4JJavaError as e:
     65             s = e.java_exception.toString()

C:\spark\spark-2.3.1-bin-hadoop2.7\python\lib\py4j-0.10.7-src.zip\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(
    Py4JJavaError: An error occurred while calling o132.fit.
    : org.apache.spark.SparkException: Job 11 cancelled because SparkContext was shut down
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:837)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:835)
        at scala.collection.mutable.HashSet.foreach(HashSet.scala:78)
        at org.apache.spark.scheduler.DAGScheduler.cleanUpAfterSchedulerStop(DAGScheduler.scala:835)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onStop(DAGScheduler.scala:1841)
        at org.apache.spark.util.EventLoop.stop(EventLoop.scala:83)
        at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1754)
        at org.apache.spark.SparkContext$$anonfun$stop$8.apply$mcV$sp(SparkContext.scala:1931)
        at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1360)
        at org.apache.spark.SparkContext.stop(SparkContext.scala:1930)
        at org.apache.spark.SparkContext$$anonfun$2.apply$mcV$sp(SparkContext.scala:573)
        at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:216)
        at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ShutdownHookManager.scala:188)
        at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:188)
        at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:188)
        at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1991)
        at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply$mcV$sp(ShutdownHookManager.scala:188)
        at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:188)
        at org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:188)
        at scala.util.Try$.apply(Try.scala:192)
        at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188)
        at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:178)
        at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:54)
        at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2055)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2074)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2099)
        at org.apache.spark.rdd.RDD.count(RDD.scala:1162)
        at org.apache.spark.ml.recommendation.ALS$.train(ALS.scala:1030)
        at org.apache.spark.ml.recommendation.ALS.fit(ALS.scala:674)
        at org.apache.spark.ml.recommendation.ALS.fit(ALS.scala:568)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
        at java.lang.reflect.Method.invoke(Unknown Source)
        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(Unknown Source)

1 个答案:

答案 0 :(得分：1)

这个问题现在已经解决了，我必须创建一个检查点目录，因为迭代次数超过20进行训练。 用于创建检查点目录的代码是：

 public void onBindViewHolder(@NonNull FavourtieViewHolder favourtieViewHolder, int position) {
   // like this
   POJO obj=list.get(position);

// using Adapter holder class object you can also get position
 int pos=favourtieViewHolder.getAdapterPosition();

}
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