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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2018/10/01 22:57:00 UTC
[jira] [Updated] (SPARK-25586) toString method of
GeneralizedLinearRegressionTrainingSummary runs in infinite loop throwing
StackOverflowError
[ https://issues.apache.org/jira/browse/SPARK-25586?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen updated SPARK-25586:
------------------------------
Priority: Minor (was: Major)
Issue Type: Improvement (was: Bug)
This is not a bug; SPARK-25118 is not committed. This is an improvement that might work around a problem in the proposed implementation of that issue.
> toString method of GeneralizedLinearRegressionTrainingSummary runs in infinite loop throwing StackOverflowError
> ---------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-25586
> URL: https://issues.apache.org/jira/browse/SPARK-25586
> Project: Spark
> Issue Type: Improvement
> Components: MLlib, Spark Core
> Affects Versions: 2.3.0
> Reporter: Ankur Gupta
> Priority: Minor
>
> After the change in SPARK-25118, which enables spark-shell to run with default log level, test_glr_summary started failing with StackOverflow error.
> Cause: ClosureCleaner calls logDebug on various objects and when it is called for GeneralizedLinearRegressionTrainingSummary, it starts a spark job which runs into infinite loop and fails with the below exception.
> {code}
> ======================================================================
> ERROR: test_glr_summary (pyspark.ml.tests.TrainingSummaryTest)
> ----------------------------------------------------------------------
> Traceback (most recent call last):
> File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/ml/tests.py", line 1809, in test_glr_summary
> self.assertTrue(isinstance(s.aic, float))
> File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/ml/regression.py", line 1781, in aic
> return self._call_java("aic")
> File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/ml/wrapper.py", line 55, in _call_java
> return _java2py(sc, m(*java_args))
> File "/home/jenkins/workspace/SparkPullRequestBuilder/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
> answer, self.gateway_client, self.target_id, self.name)
> File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/sql/utils.py", line 63, in deco
> return f(*a, **kw)
> File "/home/jenkins/workspace/SparkPullRequestBuilder/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
> format(target_id, ".", name), value)
> Py4JJavaError: An error occurred while calling o31639.aic.
> : java.lang.StackOverflowError
> at java.io.UnixFileSystem.getBooleanAttributes0(Native Method)
> at java.io.UnixFileSystem.getBooleanAttributes(UnixFileSystem.java:242)
> at java.io.File.exists(File.java:819)
> at sun.misc.URLClassPath$FileLoader.getResource(URLClassPath.java:1245)
> at sun.misc.URLClassPath$FileLoader.findResource(URLClassPath.java:1212)
> at sun.misc.URLClassPath.findResource(URLClassPath.java:188)
> at java.net.URLClassLoader$2.run(URLClassLoader.java:569)
> at java.net.URLClassLoader$2.run(URLClassLoader.java:567)
> at java.security.AccessController.doPrivileged(Native Method)
> at java.net.URLClassLoader.findResource(URLClassLoader.java:566)
> at java.lang.ClassLoader.getResource(ClassLoader.java:1093)
> at java.net.URLClassLoader.getResourceAsStream(URLClassLoader.java:232)
> at java.lang.Class.getResourceAsStream(Class.java:2223)
> at org.apache.spark.util.ClosureCleaner$.getClassReader(ClosureCleaner.scala:43)
> at org.apache.spark.util.ClosureCleaner$.getInnerClosureClasses(ClosureCleaner.scala:87)
> at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:269)
> at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:162)
> at org.apache.spark.SparkContext.clean(SparkContext.scala:2342)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1.apply(RDD.scala:864)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1.apply(RDD.scala:863)
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:364)
> at org.apache.spark.rdd.RDD.mapPartitionsWithIndex(RDD.scala:863)
> at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:613)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
> at org.apache.spark.sql.execution.DeserializeToObjectExec.doExecute(objects.scala:89)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
> at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
> at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
> at org.apache.spark.sql.Dataset.rdd$lzycompute(Dataset.scala:3038)
> at org.apache.spark.sql.Dataset.rdd(Dataset.scala:3036)
> at org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary.nullDeviance$lzycompute(GeneralizedLinearRegression.scala:1342)
> at org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary.nullDeviance(GeneralizedLinearRegression.scala:1315)
> at org.apache.spark.ml.regression.GeneralizedLinearRegressionTrainingSummary.toString(GeneralizedLinearRegression.scala:1556)
> at java.lang.String.valueOf(String.java:2994)
> at java.lang.StringBuilder.append(StringBuilder.java:131)
> at scala.StringContext.standardInterpolator(StringContext.scala:125)
> at scala.StringContext.s(StringContext.scala:95)
> at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$12$$anonfun$apply$6.apply(ClosureCleaner.scala:289)
> at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$12$$anonfun$apply$6.apply(ClosureCleaner.scala:289)
> {code}
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