[
https://issues.apache.org/jira/browse/SPARK-40706?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Bruce Robbins resolved SPARK-40706.
-----------------------------------
Resolution: Duplicate
> IllegalStateException when querying array values inside a nested struct
> -----------------------------------------------------------------------
>
> Key: SPARK-40706
> URL: https://issues.apache.org/jira/browse/SPARK-40706
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 3.2.0
> Reporter: Rohan Barman
> Priority: Major
>
> We are in the process of migrating our PySpark applications from Spark
> version 3.1.2 to Spark version 3.2.0.
> This bug is present in version 3.2.0. We do not see this issue in version
> 3.1.2.
>
> *Minimal example to reproduce bug*
> Below is a minimal example that generates hardcoded data and queries. The
> data has several nested structs and arrays.
> Our real use case reads data from avro files and has more complex queries,
> but this is sufficient to reproduce the error.
>
> {code:java}
> # Generate data
> data = [
> ('1',{
> 'timestamp': '09/07/2022',
> 'message': 'm1',
> 'data':{
> 'items': {
> 'id':1,
> 'attempt':[
> {'risk':[
> {'score':[1,2,3]},
> {'indicator':[
> {'code':'c1','value':'abc'},
> {'code':'c2','value':'def'}
> ]}
> ]}
> ]
> }
> }
> })
> ]
> from pyspark.sql.types import *
> schema = StructType([
> StructField('id', StringType(), True),
> StructField('response', StructType([
> StructField('timestamp', StringType(), True),
> StructField('message',StringType(), True),
> StructField('data', StructType([
> StructField('items', StructType([
> StructField('id', StringType(), True),
> StructField("attempt", ArrayType(StructType([
> StructField("risk", ArrayType(StructType([
> StructField('score', ArrayType(StringType()), True),
> StructField('indicator', ArrayType(StructType([
> StructField('code', StringType(), True),
> StructField('value', StringType(), True),
> ])))
> ])))
> ])))
> ]))
> ]))
> ])),
> ])
> df = spark.createDataFrame(data=data, schema=schema)
> df.printSchema()
> df.createOrReplaceTempView("tbl")
> # Execute query
> query = """
> SELECT
> response.message as message,
> response.timestamp as timestamp,
> score as risk_score,
> model.value as model_type
> FROM tbl
> LATERAL VIEW OUTER explode(response.data.items.attempt)
> AS Attempt
> LATERAL VIEW OUTER explode(response.data.items.attempt.risk)
> AS RiskModels
> LATERAL VIEW OUTER explode(RiskModels)
> AS RiskModel
> LATERAL VIEW OUTER explode(RiskModel.indicator)
> AS Model
> LATERAL VIEW OUTER explode(RiskModel.Score)
> AS Score
> """
> result = spark.sql(query)
> print(result.count())
> print(result.head(10)) {code}
>
> *Post execution*
> The above code thows an IllegalStateException. The entire error log is posted
> at the end of this ticket.
> {code:java}
> java.lang.IllegalStateException: Couldn't find _extract_timestamp#44 in
> [_extract_message#50,RiskModel#12]{code}
>
> The error seems to indicate that the _timestamp_ column is not available.
> However we see _timestamp_ if we print the schema of the source dataframe.
> {code:java}
> # df.printSchema()
> root
> |-- id: string (nullable = true)
> |-- response: struct (nullable = true)
> | |-- timestamp: string (nullable = true)
> | |-- message: string (nullable = true)
> | |-- data: struct (nullable = true)
> | | |-- items: struct (nullable = true)
> | | | |-- id: string (nullable = true)
> | | | |-- attempt: array (nullable = true)
> | | | | |-- element: struct (containsNull = true)
> | | | | | |-- risk: array (nullable = true)
> | | | | | | |-- element: struct (containsNull = true)
> | | | | | | | |-- score: array (nullable = true)
> | | | | | | | | |-- element: string (containsNull =
> true)
> | | | | | | | |-- indicator: array (nullable = true)
> | | | | | | | | |-- element: struct (containsNull =
> true)
> | | | | | | | | | |-- code: string (nullable =
> true)
> | | | | | | | | | |-- value: string (nullable =
> true) {code}
> *Extra observations*
> We noticed that several query modifications can resolve the error.
> 1) Error goes away if we also explicitly select all columns:
> {code:java}
> SELECT
> *,
> response.message as message,
> response.timestamp as timestamp,
> score as risk_score,
> model.value as model_type
> FROM tbl
> LATERAL VIEW OUTER explode(response.data.items.attempt)
> AS Attempt
> LATERAL VIEW OUTER explode(response.data.items.attempt.risk)
> AS RiskModels
> LATERAL VIEW OUTER explode(RiskModels)
> AS RiskModel
> LATERAL VIEW OUTER explode(RiskModel.indicator)
> AS Model
> LATERAL VIEW OUTER explode(RiskModel.Score)
> AS Score {code}
>
> 2) Error goes away if we only query from one of the nested arrays
> {code:java}
> SELECT
> response.message as message,
> response.timestamp as timestamp,
> --score as risk_score,
> model.value as model_type
> FROM tbl
> LATERAL VIEW OUTER explode(response.data.items.attempt)
> AS Attempt
> LATERAL VIEW OUTER explode(response.data.items.attempt.risk)
> AS RiskModels
> LATERAL VIEW OUTER explode(RiskModels)
> AS RiskModel
> LATERAL VIEW OUTER explode(RiskModel.indicator)
> AS Model
> --LATERAL VIEW OUTER explode(RiskModel.Score)
> AS Score {code}
>
> We also noticed that the IllegalStateException refers to the second column in
> the SELECT query.
> For example if _timestamp_ is second, the exception refers to _timestamp_
> {code:java}
> SELECT
> response.message as message,
> response.timestamp as timestamp,
> ..... {code}
> {code:java}
> java.lang.IllegalStateException: Couldn't find _extract_timestamp#53 in
> [_extract_message#59,RiskModel#21]{code}
> If we swap the order so that _message_ is second, the exception refers to
> _message._
> {code:java}
> SELECT
> response.timestamp as timestamp,
> response.message as message,
> ..... {code}
> {code:java}
> java.lang.IllegalStateException: Couldn't find _extract_message#53 in
> [_extract_timestamp#59,RiskModel#21]{code}
>
> *---*
> Are there any spark settings that can resolve this? Or an alternate way to
> query deeply nested structs?
>
> *Full error:*
>
> {code:java}
> py4j.protocol.Py4JJavaError: An error occurred while calling
> o40.collectToPython.
> : java.lang.IllegalStateException: Couldn't find _extract_timestamp#44 in
> [_extract_message#50,RiskModel#12]
> at
> org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:80)
> at
> org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:73)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:584)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:176)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:584)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:560)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:528)
> at
> org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:73)
> at
> org.apache.spark.sql.catalyst.expressions.BindReferences$.$anonfun$bindReferences$1(BoundAttribute.scala:94)
> at scala.collection.immutable.List.map(List.scala:297)
> at
> org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReferences(BoundAttribute.scala:94)
> at
> org.apache.spark.sql.execution.ProjectExec.doConsume(basicPhysicalOperators.scala:69)
> at
> org.apache.spark.sql.execution.CodegenSupport.consume(WholeStageCodegenExec.scala:196)
> at
> org.apache.spark.sql.execution.CodegenSupport.consume$(WholeStageCodegenExec.scala:151)
> at
> org.apache.spark.sql.execution.GenerateExec.consume(GenerateExec.scala:58)
> at
> org.apache.spark.sql.execution.GenerateExec.codeGenCollection(GenerateExec.scala:232)
> at
> org.apache.spark.sql.execution.GenerateExec.doConsume(GenerateExec.scala:145)
> at
> org.apache.spark.sql.execution.CodegenSupport.constructDoConsumeFunction(WholeStageCodegenExec.scala:223)
> at
> org.apache.spark.sql.execution.CodegenSupport.consume(WholeStageCodegenExec.scala:194)
> at
> org.apache.spark.sql.execution.CodegenSupport.consume$(WholeStageCodegenExec.scala:151)
> at
> org.apache.spark.sql.execution.GenerateExec.consume(GenerateExec.scala:58)
> at
> org.apache.spark.sql.execution.GenerateExec.codeGenCollection(GenerateExec.scala:232)
> at
> org.apache.spark.sql.execution.GenerateExec.doConsume(GenerateExec.scala:145)
> at
> org.apache.spark.sql.execution.CodegenSupport.constructDoConsumeFunction(WholeStageCodegenExec.scala:223)
> at
> org.apache.spark.sql.execution.CodegenSupport.consume(WholeStageCodegenExec.scala:194)
> at
> org.apache.spark.sql.execution.CodegenSupport.consume$(WholeStageCodegenExec.scala:151)
> at
> org.apache.spark.sql.execution.ProjectExec.consume(basicPhysicalOperators.scala:42)
> at
> org.apache.spark.sql.execution.ProjectExec.doConsume(basicPhysicalOperators.scala:89)
> at
> org.apache.spark.sql.execution.CodegenSupport.consume(WholeStageCodegenExec.scala:196)
> at
> org.apache.spark.sql.execution.CodegenSupport.consume$(WholeStageCodegenExec.scala:151)
> at
> org.apache.spark.sql.execution.GenerateExec.consume(GenerateExec.scala:58)
> at
> org.apache.spark.sql.execution.GenerateExec.codeGenCollection(GenerateExec.scala:232)
> at
> org.apache.spark.sql.execution.GenerateExec.doConsume(GenerateExec.scala:145)
> at
> org.apache.spark.sql.execution.CodegenSupport.constructDoConsumeFunction(WholeStageCodegenExec.scala:223)
> at
> org.apache.spark.sql.execution.CodegenSupport.consume(WholeStageCodegenExec.scala:194)
> at
> org.apache.spark.sql.execution.CodegenSupport.consume$(WholeStageCodegenExec.scala:151)
> at
> org.apache.spark.sql.execution.ProjectExec.consume(basicPhysicalOperators.scala:42)
> at
> org.apache.spark.sql.execution.ProjectExec.doConsume(basicPhysicalOperators.scala:89)
> at
> org.apache.spark.sql.execution.CodegenSupport.consume(WholeStageCodegenExec.scala:196)
> at
> org.apache.spark.sql.execution.CodegenSupport.consume$(WholeStageCodegenExec.scala:151)
> at
> org.apache.spark.sql.execution.RDDScanExec.consume(ExistingRDD.scala:132)
> at
> org.apache.spark.sql.execution.InputRDDCodegen.doProduce(WholeStageCodegenExec.scala:485)
> at
> org.apache.spark.sql.execution.InputRDDCodegen.doProduce$(WholeStageCodegenExec.scala:458)
> at
> org.apache.spark.sql.execution.RDDScanExec.doProduce(ExistingRDD.scala:132)
> at
> org.apache.spark.sql.execution.CodegenSupport.$anonfun$produce$1(WholeStageCodegenExec.scala:97)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:232)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:229)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce$(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.RDDScanExec.produce(ExistingRDD.scala:132)
> at
> org.apache.spark.sql.execution.ProjectExec.doProduce(basicPhysicalOperators.scala:55)
> at
> org.apache.spark.sql.execution.CodegenSupport.$anonfun$produce$1(WholeStageCodegenExec.scala:97)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:232)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:229)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce$(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.ProjectExec.produce(basicPhysicalOperators.scala:42)
> at
> org.apache.spark.sql.execution.GenerateExec.doProduce(GenerateExec.scala:134)
> at
> org.apache.spark.sql.execution.CodegenSupport.$anonfun$produce$1(WholeStageCodegenExec.scala:97)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:232)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:229)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce$(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.GenerateExec.produce(GenerateExec.scala:58)
> at
> org.apache.spark.sql.execution.ProjectExec.doProduce(basicPhysicalOperators.scala:55)
> at
> org.apache.spark.sql.execution.CodegenSupport.$anonfun$produce$1(WholeStageCodegenExec.scala:97)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:232)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:229)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce$(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.ProjectExec.produce(basicPhysicalOperators.scala:42)
> at
> org.apache.spark.sql.execution.GenerateExec.doProduce(GenerateExec.scala:134)
> at
> org.apache.spark.sql.execution.CodegenSupport.$anonfun$produce$1(WholeStageCodegenExec.scala:97)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:232)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:229)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce$(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.GenerateExec.produce(GenerateExec.scala:58)
> at
> org.apache.spark.sql.execution.GenerateExec.doProduce(GenerateExec.scala:134)
> at
> org.apache.spark.sql.execution.CodegenSupport.$anonfun$produce$1(WholeStageCodegenExec.scala:97)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:232)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:229)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce$(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.GenerateExec.produce(GenerateExec.scala:58)
> at
> org.apache.spark.sql.execution.ProjectExec.doProduce(basicPhysicalOperators.scala:55)
> at
> org.apache.spark.sql.execution.CodegenSupport.$anonfun$produce$1(WholeStageCodegenExec.scala:97)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:232)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:229)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce$(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.ProjectExec.produce(basicPhysicalOperators.scala:42)
> at
> org.apache.spark.sql.execution.GenerateExec.doProduce(GenerateExec.scala:134)
> at
> org.apache.spark.sql.execution.CodegenSupport.$anonfun$produce$1(WholeStageCodegenExec.scala:97)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:232)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:229)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce$(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.GenerateExec.produce(GenerateExec.scala:58)
> at
> org.apache.spark.sql.execution.ProjectExec.doProduce(basicPhysicalOperators.scala:55)
> at
> org.apache.spark.sql.execution.CodegenSupport.$anonfun$produce$1(WholeStageCodegenExec.scala:97)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:232)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:229)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce$(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.ProjectExec.produce(basicPhysicalOperators.scala:42)
> at
> org.apache.spark.sql.execution.GenerateExec.doProduce(GenerateExec.scala:134)
> at
> org.apache.spark.sql.execution.CodegenSupport.$anonfun$produce$1(WholeStageCodegenExec.scala:97)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:232)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:229)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce$(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.GenerateExec.produce(GenerateExec.scala:58)
> at
> org.apache.spark.sql.execution.ProjectExec.doProduce(basicPhysicalOperators.scala:55)
> at
> org.apache.spark.sql.execution.CodegenSupport.$anonfun$produce$1(WholeStageCodegenExec.scala:97)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:232)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:229)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.CodegenSupport.produce$(WholeStageCodegenExec.scala:92)
> at
> org.apache.spark.sql.execution.ProjectExec.produce(basicPhysicalOperators.scala:42)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec.doCodeGen(WholeStageCodegenExec.scala:660)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:723)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:194)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:232)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:229)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:190)
> at
> org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:340)
> at
> org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:473)
> at
> org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:459)
> at
> org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:48)
> at
> org.apache.spark.sql.Dataset.$anonfun$collectToPython$1(Dataset.scala:3688)
> at org.apache.spark.sql.Dataset.$anonfun$withAction$2(Dataset.scala:3858)
> at
> org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:510)
> at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3856)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:109)
> at
> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:169)
> at
> org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:95)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3856)
> at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:3685)
> 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.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
> at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
> at java.lang.Thread.run(Thread.java:748){code}
>
>
>
>
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