Luke Chu created SPARK-32275:
--------------------------------

             Summary: "None.org.apache.spark.api.java.JavaSparkContext" Issue 
With Spark-Mllib Algorithm and JDBC Connectors
                 Key: SPARK-32275
                 URL: https://issues.apache.org/jira/browse/SPARK-32275
             Project: Spark
          Issue Type: Bug
          Components: PySpark
    Affects Versions: 2.4.4
         Environment: Pyspark 2.4.4

Python 3.7

Running on AWS EC2 instances with RHEL.
            Reporter: Luke Chu


While calling a spark-mllib package algorithm, specifically FPGrowth, and 
passing in a dataframe from a JDBC connector, specifically datastax's 
spark-cassandra-connector, the following is thrown at the Task level:

 
{code:java}
20/05/29 01:56:07 WARN TaskSetManager: Lost task 96.0 in stage 8.0 (TID 802, 
10.168.0.43, executor 0): org.apache.spark.api.python.PythonException: 
Traceback (most recent call last):
 File 
"/autoid/spark/spark-2.4.4-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py",
 line 366, in main
 func, profiler, deserializer, serializer = read_udfs(pickleSer, infile, 
eval_type)
 File 
"/autoid/spark/spark-2.4.4-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py",
 line 241, in read_udfs
 arg_offsets, udf = read_single_udf(pickleSer, infile, eval_type, runner_conf)
 File 
"/autoid/spark/spark-2.4.4-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py",
 line 168, in read_single_udf
 f, return_type = read_command(pickleSer, infile)
 File 
"/autoid/spark/spark-2.4.4-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py",
 line 69, in read_command
 command = serializer._read_with_length(file)
 File 
"/autoid/spark/spark-2.4.4-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/serializers.py",
 line 172, in _read_with_length
 return self.loads(obj)
 File 
"/autoid/spark/spark-2.4.4-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/serializers.py",
 line 580, in loads
 return pickle.loads(obj, encoding=encoding)
 File "build/bdist.linux-x86_64/egg/zoran_core/_init_.py", line 5, in <module>
 File "build/bdist.linux-x86_64/egg/zoran_core/config/conf.py", line 17, in 
<module>
 File "build/bdist.linux-x86_64/egg/zoran_core/utils/logger.py", line 5, in 
getSparkLogger
 File 
"/autoid/spark/spark-2.4.4-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/sql/session.py",
 line 173, in getOrCreate
 sc = SparkContext.getOrCreate(sparkConf)
 File 
"/autoid/spark/spark-2.4.4-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/context.py",
 line 367, in getOrCreate
 SparkContext(conf=conf or SparkConf())
 File 
"/autoid/spark/spark-2.4.4-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/context.py",
 line 136, in _init_
 conf, jsc, profiler_cls)
 File 
"/autoid/spark/spark-2.4.4-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/context.py",
 line 198, in _do_init
 self._jsc = jsc or self._initialize_context(self._conf._jconf)
 File 
"/autoid/spark/spark-2.4.4-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/context.py",
 line 306, in _initialize_context
 return self._jvm.JavaSparkContext(jconf)
 File 
"/autoid/spark/spark-2.4.4-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py",
 line 1525, in _call_
 answer, self._gateway_client, None, self._fqn)
 File 
"/autoid/spark/spark-2.4.4-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py",
 line 336, in get_return_value
 format(target_id, ".", name))
 py4j.protocol.Py4JError: An error occurred while calling 
None.org.apache.spark.api.java.JavaSparkContext
at 
org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456)
 at 
org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:81)
 at 
org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:64)
 at 
org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)
 at 
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
 at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
 at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
 at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
 at 
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.agg_doAggregateWithKeys_0$(Unknown
 Source)
 at 
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.processNext(Unknown
 Source)
 at 
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
 at 
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
 at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
 at 
org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
 at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
 at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
 at org.apache.spark.scheduler.Task.run(Task.scala:123)
 at 
org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
 at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
 at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
 at 
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
 at 
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
 at java.lang.Thread.run(Thread.java:748)
{code}
 

 

Note the  py4j.protocol.Py4JError: An error occurred while calling 
None.org.apache.spark.api.java.JavaSparkContext .

 

At the top level it is a WARN so execution continues and ultimately succeeds. 
This doesn't happen when the dataframe passed to the algorithm is read from 
csv. Also, I suspect this isn't unique to spark-mllib or the 
spark-cassandra-connector due to this thread:

[http://mail-archives.apache.org/mod_mbox/spark-user/201701.mbox/%3ccaohmdzfvxrwzjh6yesiann-lumz467bv3key68-nvzjzeno...@mail.gmail.com%3E]

 

Here the user ran into the same problem using GraphFrames and JDBC connectors 
it seems.

 

Just in case, this happens at the 'fit' step.
 
{code:java}
 fp_growth = FPGrowth(itemsCol='feats', \                       
minSupport=min_support, \                       minConfidence=min_confidence, \ 
                      numPartitions=num_partitions \
                      )        
model = fp_growth.fit(features_df){code}
 
where features_df is sourced from Cassandra.



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