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Posted to issues@spark.apache.org by "Luke Chu (Jira)" <ji...@apache.org> on 2020/07/10 18:49:00 UTC
[jira] [Created] (SPARK-32275)
"None.org.apache.spark.api.java.JavaSparkContext" Issue With Spark-Mllib
Algorithm and JDBC Connectors
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-NVZjzeNo4SQ@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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