Job cancelled because SparkContext was shut down












1















While running my spark program in jupyter notebook I got the error "Job cancelled because SparkContext was shut down".I am using spark without hadoop.The same program gave output earlier but now showing error.Any idea why would the error must have occured.



My code is :



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)


This is the point where it gives error.



    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:sparkspark-2.3.1-bin-hadoop2.7pythonpysparkmlbase.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:sparkspark-2.3.1-bin-hadoop2.7pythonpysparkmlwrapper.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:sparkspark-2.3.1-bin-hadoop2.7pythonpysparkmlwrapper.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:sparkspark-2.3.1-bin-hadoop2.7pythonlibpy4j-0.10.7-src.zippy4jjava_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:sparkspark-2.3.1-bin-hadoop2.7pythonpysparksqlutils.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:sparkspark-2.3.1-bin-hadoop2.7pythonlibpy4j-0.10.7-src.zippy4jprotocol.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)









share|improve this question























  • Could you try using SparkSession instead of SparkContext and try a pd.show() after pd=df.select(df['asin'],df['overall'],df['reviewerID'])

    – Antonio Cachuan
    Jan 2 at 20:07













  • I did as recommended by you Antonio Cachuan .But this time I got error as Py4JNetworkError: An error occurred while trying to connect to the Java server (127.0.0.1:50332)

    – Neha patel
    Jan 4 at 14:04
















1















While running my spark program in jupyter notebook I got the error "Job cancelled because SparkContext was shut down".I am using spark without hadoop.The same program gave output earlier but now showing error.Any idea why would the error must have occured.



My code is :



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)


This is the point where it gives error.



    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:sparkspark-2.3.1-bin-hadoop2.7pythonpysparkmlbase.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:sparkspark-2.3.1-bin-hadoop2.7pythonpysparkmlwrapper.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:sparkspark-2.3.1-bin-hadoop2.7pythonpysparkmlwrapper.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:sparkspark-2.3.1-bin-hadoop2.7pythonlibpy4j-0.10.7-src.zippy4jjava_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:sparkspark-2.3.1-bin-hadoop2.7pythonpysparksqlutils.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:sparkspark-2.3.1-bin-hadoop2.7pythonlibpy4j-0.10.7-src.zippy4jprotocol.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)









share|improve this question























  • Could you try using SparkSession instead of SparkContext and try a pd.show() after pd=df.select(df['asin'],df['overall'],df['reviewerID'])

    – Antonio Cachuan
    Jan 2 at 20:07













  • I did as recommended by you Antonio Cachuan .But this time I got error as Py4JNetworkError: An error occurred while trying to connect to the Java server (127.0.0.1:50332)

    – Neha patel
    Jan 4 at 14:04














1












1








1








While running my spark program in jupyter notebook I got the error "Job cancelled because SparkContext was shut down".I am using spark without hadoop.The same program gave output earlier but now showing error.Any idea why would the error must have occured.



My code is :



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)


This is the point where it gives error.



    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:sparkspark-2.3.1-bin-hadoop2.7pythonpysparkmlbase.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:sparkspark-2.3.1-bin-hadoop2.7pythonpysparkmlwrapper.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:sparkspark-2.3.1-bin-hadoop2.7pythonpysparkmlwrapper.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:sparkspark-2.3.1-bin-hadoop2.7pythonlibpy4j-0.10.7-src.zippy4jjava_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:sparkspark-2.3.1-bin-hadoop2.7pythonpysparksqlutils.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:sparkspark-2.3.1-bin-hadoop2.7pythonlibpy4j-0.10.7-src.zippy4jprotocol.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)









share|improve this question














While running my spark program in jupyter notebook I got the error "Job cancelled because SparkContext was shut down".I am using spark without hadoop.The same program gave output earlier but now showing error.Any idea why would the error must have occured.



My code is :



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)


This is the point where it gives error.



    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:sparkspark-2.3.1-bin-hadoop2.7pythonpysparkmlbase.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:sparkspark-2.3.1-bin-hadoop2.7pythonpysparkmlwrapper.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:sparkspark-2.3.1-bin-hadoop2.7pythonpysparkmlwrapper.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:sparkspark-2.3.1-bin-hadoop2.7pythonlibpy4j-0.10.7-src.zippy4jjava_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:sparkspark-2.3.1-bin-hadoop2.7pythonpysparksqlutils.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:sparkspark-2.3.1-bin-hadoop2.7pythonlibpy4j-0.10.7-src.zippy4jprotocol.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)






apache-spark pyspark






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Dec 31 '18 at 6:54









Neha patelNeha patel

611




611













  • Could you try using SparkSession instead of SparkContext and try a pd.show() after pd=df.select(df['asin'],df['overall'],df['reviewerID'])

    – Antonio Cachuan
    Jan 2 at 20:07













  • I did as recommended by you Antonio Cachuan .But this time I got error as Py4JNetworkError: An error occurred while trying to connect to the Java server (127.0.0.1:50332)

    – Neha patel
    Jan 4 at 14:04



















  • Could you try using SparkSession instead of SparkContext and try a pd.show() after pd=df.select(df['asin'],df['overall'],df['reviewerID'])

    – Antonio Cachuan
    Jan 2 at 20:07













  • I did as recommended by you Antonio Cachuan .But this time I got error as Py4JNetworkError: An error occurred while trying to connect to the Java server (127.0.0.1:50332)

    – Neha patel
    Jan 4 at 14:04

















Could you try using SparkSession instead of SparkContext and try a pd.show() after pd=df.select(df['asin'],df['overall'],df['reviewerID'])

– Antonio Cachuan
Jan 2 at 20:07







Could you try using SparkSession instead of SparkContext and try a pd.show() after pd=df.select(df['asin'],df['overall'],df['reviewerID'])

– Antonio Cachuan
Jan 2 at 20:07















I did as recommended by you Antonio Cachuan .But this time I got error as Py4JNetworkError: An error occurred while trying to connect to the Java server (127.0.0.1:50332)

– Neha patel
Jan 4 at 14:04





I did as recommended by you Antonio Cachuan .But this time I got error as Py4JNetworkError: An error occurred while trying to connect to the Java server (127.0.0.1:50332)

– Neha patel
Jan 4 at 14:04












0






active

oldest

votes











Your Answer






StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");

StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});

function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});


}
});














draft saved

draft discarded


















StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53984506%2fjob-cancelled-because-sparkcontext-was-shut-down%23new-answer', 'question_page');
}
);

Post as a guest















Required, but never shown

























0






active

oldest

votes








0






active

oldest

votes









active

oldest

votes






active

oldest

votes
















draft saved

draft discarded




















































Thanks for contributing an answer to Stack Overflow!


  • Please be sure to answer the question. Provide details and share your research!

But avoid



  • Asking for help, clarification, or responding to other answers.

  • Making statements based on opinion; back them up with references or personal experience.


To learn more, see our tips on writing great answers.




draft saved


draft discarded














StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53984506%2fjob-cancelled-because-sparkcontext-was-shut-down%23new-answer', 'question_page');
}
);

Post as a guest















Required, but never shown





















































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown

































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown







Popular posts from this blog

Monofisismo

Angular Downloading a file using contenturl with Basic Authentication

Olmecas