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  1. Spark
  2. SPARK-25603 Generalize Nested Column Pruning
  3. SPARK-30870

catalyst inception of lateral view explode with struct raise a Catalyst error

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Details

    • Sub-task
    • Status: Resolved
    • Major
    • Resolution: Fixed
    • 3.0.0
    • 3.0.0
    • Spark Core, SQL
    • None

    Description

      On spark 3.0.0.preview2 version I found a bug who are not in 3.0.0.preview version.

      With the table 

      spark.sql("select * from tmp").printSchema
root
 |-- value: struct (nullable = true)
 | |-- array: array (nullable = true)
 | | |-- element: struct (containsNull = true)
 | | | |-- subarray: array (nullable = true)
 | | | | |-- element: struct (containsNull = true)
 | | | | | |-- key1: string (nullable = true)
 | | | | | |-- key2: string (nullable = true)

      when you try a double lateral view explode 

      spark.sql("select subexplod.* from tmp lateral view explode(tmp.value.array) explod as array_explod  lateral view explode(explod.array_explod.subarray) subexplod").show()

      It's raising an error :

      org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: _gen_alias_127#127
  at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
  at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:75)
  at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:74)
  at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$1(TreeNode.scala:309)
  at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:309)
  at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$3(TreeNode.scala:314)
  at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$mapChildren$1(TreeNode.scala:399)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:237)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:397)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:350)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:314)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:298)
  at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:74)
  at org.apache.spark.sql.catalyst.expressions.BindReferences$.$anonfun$bindReferences$1(BoundAttribute.scala:96)
  at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238)
  at scala.collection.immutable.List.foreach(List.scala:392)
  at scala.collection.TraversableLike.map(TraversableLike.scala:238)
  at scala.collection.TraversableLike.map$(TraversableLike.scala:231)
  at scala.collection.immutable.List.map(List.scala:298)
  at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReferences(BoundAttribute.scala:96)
  at org.apache.spark.sql.execution.ProjectExec.doConsume(basicPhysicalOperators.scala:65)
  at org.apache.spark.sql.execution.CodegenSupport.consume(WholeStageCodegenExec.scala:194)
  at org.apache.spark.sql.execution.CodegenSupport.consume$(WholeStageCodegenExec.scala:149)
  at org.apache.spark.sql.execution.InputAdapter.consume(WholeStageCodegenExec.scala:496)
  at org.apache.spark.sql.execution.InputRDDCodegen.doProduce(WholeStageCodegenExec.scala:483)
  at org.apache.spark.sql.execution.InputRDDCodegen.doProduce$(WholeStageCodegenExec.scala:456)
  at org.apache.spark.sql.execution.InputAdapter.doProduce(WholeStageCodegenExec.scala:496)
  at org.apache.spark.sql.execution.CodegenSupport.$anonfun$produce$1(WholeStageCodegenExec.scala:95)
  at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:212)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:209)
  at org.apache.spark.sql.execution.CodegenSupport.produce(WholeStageCodegenExec.scala:90)
  at org.apache.spark.sql.execution.CodegenSupport.produce$(WholeStageCodegenExec.scala:90)
  at org.apache.spark.sql.execution.InputAdapter.produce(WholeStageCodegenExec.scala:496)
  at org.apache.spark.sql.execution.ProjectExec.doProduce(basicPhysicalOperators.scala:51)
  at org.apache.spark.sql.execution.CodegenSupport.$anonfun$produce$1(WholeStageCodegenExec.scala:95)
  at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:212)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:209)
  at org.apache.spark.sql.execution.CodegenSupport.produce(WholeStageCodegenExec.scala:90)
  at org.apache.spark.sql.execution.CodegenSupport.produce$(WholeStageCodegenExec.scala:90)
  at org.apache.spark.sql.execution.ProjectExec.produce(basicPhysicalOperators.scala:41)
  at org.apache.spark.sql.execution.WholeStageCodegenExec.doCodeGen(WholeStageCodegenExec.scala:632)
  at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:692)
  at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:174)
  at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:212)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:209)
  at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:170)
  at org.apache.spark.sql.execution.GenerateExec.doExecute(GenerateExec.scala:80)
  at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:174)
  at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:212)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:209)
  at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:170)
  at org.apache.spark.sql.execution.InputAdapter.inputRDD(WholeStageCodegenExec.scala:525)
  at org.apache.spark.sql.execution.InputRDDCodegen.inputRDDs(WholeStageCodegenExec.scala:453)
  at org.apache.spark.sql.execution.InputRDDCodegen.inputRDDs$(WholeStageCodegenExec.scala:452)
  at org.apache.spark.sql.execution.InputAdapter.inputRDDs(WholeStageCodegenExec.scala:496)
  at org.apache.spark.sql.execution.ProjectExec.inputRDDs(basicPhysicalOperators.scala:47)
  at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:720)
  at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:174)
  at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:212)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:209)
  at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:170)
  at org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:315)
  at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:433)
  at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:419)
  at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:47)
  at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3537)
  at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2590)
  at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3527)
  at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
  at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
  at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
  at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:762)
  at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
  at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3525)
  at org.apache.spark.sql.Dataset.head(Dataset.scala:2590)
  at org.apache.spark.sql.Dataset.take(Dataset.scala:2797)
  at org.apache.spark.sql.Dataset.getRows(Dataset.scala:297)
  at org.apache.spark.sql.Dataset.showString(Dataset.scala:334)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:821)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:780)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:789)
  ... 47 elided
Caused by: java.lang.RuntimeException: Couldn't find _gen_alias_127#127 in [array_explod#119]
  at scala.sys.package$.error(package.scala:30)
  at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.$anonfun$applyOrElse$1(BoundAttribute.scala:81)
  at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52)
  ... 133 more

      To recreate the bug

      import org.apache.spark.sql.Row
import org.apache.spark.sql.types.{StringType, StructField, StructType, ArrayType}
val structValue = StructType(StructField(name = "value", StringType, nullable = false) :: Nil)
val struct : StructType = StructType(StructField("array", ArrayType(StructType(StructField("subarray", ArrayType(StructType(StructField("key1",StringType, true)::StructField("key2",StringType, true)::Nil)), nullable = false) :: Nil)), nullable = false) :: Nil)
val value = "{\"array\": [{\"subarray\" : [{\"key1\":\"val1\",\"key2\":\"val2\"},{\"key1\":\"val11\",\"key2\":\"val12\"}]}]}"
spark.createDataFrame(spark.sparkContext.parallelize(Seq(Row(value))),structValue).withColumn("value", from_json(col("value"),struct)).createOrReplaceTempView("tmp")
spark.sql("select subexplod.* from tmp lateral view explode(tmp.value.array) explod as array_explod  lateral view explode(explod.array_explod.subarray) subexplod").show()

      If you just avec an Array of Array of String it's working

      import org.apache.spark.sql.Row
import org.apache.spark.sql.types.{StringType, StructField, StructType, ArrayType}
val structValue = StructType(StructField(name = "value", StringType, nullable = false) :: Nil)
val struct : StructType = StructType(StructField("array", ArrayType(StructType(StructField("subarray", ArrayType(StringType, true), nullable = false) :: Nil)), nullable = false) :: Nil)
val value = "{\"array\": [{\"subarray\" : [\"val1\",\"val2\"]}]}"
spark.createDataFrame(spark.sparkContext.parallelize(Seq(Row(value))),structValue).withColumn("value", from_json(col("value"),struct)).createOrReplaceTempView("tmp")
spark.sql("select subexplod.* from tmp lateral view explode(tmp.value.array) explod as array_explod  lateral view explode(explod.array_explod.subarray) subexplod").show()

      Attachments

        Issue Links

          is caused by

          Sub-task - The sub-task of the issue SPARK-26975 Support nested-column pruning over limit/sample/repartition

          • Major - Major loss of function.
          • Resolved
          is duplicated by

          Bug - A problem which impairs or prevents the functions of the product. SPARK-30855 Issue using 'explode' function followed by a (*)star expand selection of resulting struct

          • Major - Major loss of function.
          • Resolved
          links to

          Web Link GitHub Pull Request #27675

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            People

              petertoth Peter Toth
              tprelle-ubi Thomas Prelle
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              Dates

                Created:
                Updated:
                Resolved: