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https://issues.apache.org/jira/browse/SPARK-37598?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17456622#comment-17456622
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Apache Spark commented on SPARK-37598:
--------------------------------------
User 'masseyke' has created a pull request for this issue:
https://github.com/apache/spark/pull/34838
> Pyspark's newAPIHadoopRDD() method fails with ShortWritables
> ------------------------------------------------------------
>
> Key: SPARK-37598
> URL: https://issues.apache.org/jira/browse/SPARK-37598
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.4.8, 3.0.3, 3.1.2, 3.2.0
> Reporter: Keith Massey
> Priority: Minor
>
> If sc. newAPIHadoopRDD() is called from Pyspark using an InputFormat that has
> a ShortWritable as a field, then the call to newAPIHadoopRDD() fails. The
> reason is that shortWritable is not explicitly handled by PythonHadoopUtil
> the way that other numeric writables are (like LongWritable). The result is
> that the ShortWritable is not converted to an object that can be serialized
> by spark, and a serialization error occurs. Below is an example stack trace
> from within the pyspark shell:
> {code:java}
> >>> rdd =
> >>> sc.newAPIHadoopRDD(inputFormatClass="[org.elasticsearch.hadoop.mr|http://org.elasticsearch.hadoop.mr/].EsInputFormat";,
> ...
> keyClass="[org.apache.hadoop.io|http://org.apache.hadoop.io/].NullWritable";,
> ...
> valueClass="[org.elasticsearch.hadoop.mr|http://org.elasticsearch.hadoop.mr/].LinkedMapWritable";,
> ... conf=conf)
> 2021-12-08 14:38:40,439 ERROR scheduler.TaskSetManager: task 0.0 in stage
> 15.0 (TID 31) had a not serializable result:
> org.apache.hadoop.io.ShortWritable
> Serialization stack:
> - object not serializable (class:
> [org.apache.hadoop.io|http://org.apache.hadoop.io/].ShortWritable, value: 1)
> - writeObject data (class: java.util.HashMap)
> - object (class java.util.HashMap, \{price=1})
> - field (class: scala.Tuple2, name: _2, type: class java.lang.Object)
> - object (class scala.Tuple2, (1,\{price=1}))
> - element of array (index: 0)
> - array (class [Lscala.Tuple2;, size 1); not retrying
> Traceback (most recent call last):
> File "<stdin>", line 4, in <module>
> File "/home/hduser/spark-3.1.2-bin-hadoop3.2/python/pyspark/context.py",
> line 853, in newAPIHadoopRDD
> jconf, batchSize)
> File
> "/home/hduser/spark-3.1.2-bin-hadoop3.2/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py",
> line 1305, in __call__
> File "/home/hduser/spark-3.1.2-bin-hadoop3.2/python/pyspark/sql/utils.py",
> line 111, in deco
> return f(*a, **kw)
> File
> "/home/hduser/spark-3.1.2-bin-hadoop3.2/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py",
> line 328, in get_return_value
> py4j.protocol.Py4JJavaError: An error occurred while calling
> z:org.apache.spark.api.python.PythonRDD.newAPIHadoopRDD.
> : org.apache.spark.SparkException: Job aborted due to stage failure: task 0.0
> in stage 15.0 (TID 31) had a not serializable result:
> org.apache.hadoop.io.ShortWritable
> Serialization stack:
> - object not serializable (class:
> [org.apache.hadoop.io|http://org.apache.hadoop.io/].ShortWritable, value: 1)
> - writeObject data (class: java.util.HashMap)
> - object (class java.util.HashMap, \{price=1})
> - field (class: scala.Tuple2, name: _2, type: class java.lang.Object)
> - object (class scala.Tuple2, (1,\{price=1}))
> - element of array (index: 0)
> - array (class [Lscala.Tuple2;, size 1)
> at
> org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2258)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2207)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2206)
> at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
> at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
> at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2206)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1079)
> at
> org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1079)
> at scala.Option.foreach(Option.scala:407)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1079)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2445)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2387)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2376)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
> at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:868)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2196)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2217)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2236)
> at org.apache.spark.rdd.RDD.$anonfun$take$1(RDD.scala:1449)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:414)
> at org.apache.spark.rdd.RDD.take(RDD.scala:1422)
> at org.apache.spark.api.python.SerDeUtil$.pairRDDToPython(SerDeUtil.scala:173)
> at org.apache.spark.api.python.PythonRDD$.newAPIHadoopRDD(PythonRDD.scala:385)
> at org.apache.spark.api.python.PythonRDD.newAPIHadoopRDD(PythonRDD.scala)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> 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(Thread.java:748)
> {code}
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