无法在Scala Spark

时间：2017-08-05 00:11:29

标签： scala apache-spark merge

我一直在尝试将1个DataFrame附加到Scala中的另一个DF。在这种情况下，追加操作只是向现有列添加相同大小的新列 - 不涉及密钥匹配。两个DataFrame具有相同的形状（仅5行和1列）。 

scala> val coefficients = lrModel.coefficients.toArray.toSeq.toDF("coefficients")
coefficients: org.apache.spark.sql.DataFrame = [coefficients: double]

scala> coefficients.show()
+--------------------+
|        coefficients|
+--------------------+
|   -59525.0697785032|
|   6957.836000531959|
|   314.2998010755629|
|-0.37884289844065666|
|  -1758.154438149325|
+--------------------+
scala> val tvalues = trainingSummary.tValues.toArray.drop(1).toSeq.toDF("t-values")
tvalues: org.apache.spark.sql.DataFrame = [t-values: double]

scala> tvalues.show()
+-------------------+
|           t-values|
+-------------------+
| 1.8267249911295418|
| 100.35507390273406|
| -8.768588605222108|
|-0.4656738230173362|
|  10.48091833711012|
+-------------------+

join()函数运行，我甚至可以获得模式，但是当我想显示新DF的所有值时，我收到错误：

scala> val outputModelDF1 = coefficients.join(tvalues)
outputModelDF1: org.apache.spark.sql.DataFrame = [coefficients: double, t-values: double]
scala> outputModelDF1.printSchema()
root
 |-- coefficients: double (nullable = false)
 |-- t-values: double (nullable = false)

scala> outputModelDF1.show()
org.apache.spark.sql.AnalysisException: Detected cartesian product for INNER join between logical plans
Project [value#359 AS coefficients#361]
+- LocalRelation [value#359]
and
Project [value#368 AS t-values#370]
+- LocalRelation [value#368]
Join condition is missing or trivial.
Use the CROSS JOIN syntax to allow cartesian products between these relations.;
  at org.apache.spark.sql.catalyst.optimizer.CheckCartesianProducts$$anonfun$apply$20.applyOrElse(Optimizer.scala:1080)
  at org.apache.spark.sql.catalyst.optimizer.CheckCartesianProducts$$anonfun$apply$20.applyOrElse(Optimizer.scala:1077)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
  at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
  at org.apache.spark.sql.catalyst.optimizer.CheckCartesianProducts.apply(Optimizer.scala:1077)
  at org.apache.spark.sql.catalyst.optimizer.CheckCartesianProducts.apply(Optimizer.scala:1062)
  at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85)
  at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82)
  at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:57)
  at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:66)
  at scala.collection.mutable.WrappedArray.foldLeft(WrappedArray.scala:35)
  at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82)
  at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74)
  at scala.collection.immutable.List.foreach(List.scala:381)
  at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)
  at org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:78)
  at org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:78)
  at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:84)
  at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:80)
  at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:89)
  at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:89)
  at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2832)
  at org.apache.spark.sql.Dataset.head(Dataset.scala:2153)
  at org.apache.spark.sql.Dataset.take(Dataset.scala:2366)
  at org.apache.spark.sql.Dataset.showString(Dataset.scala:245)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:644)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:603)
  at org.apache.spark.sql.Dataset.show(Dataset.scala:612)
  ... 52 elided

知道如何处理它以及如何简单地将这两个DF合并在一起吗？

更新1

我应该已经说明了我想要实现的输出所需的格式。请参阅以下内容：

    +--------------------+--------------------+
    |        coefficients|            t-values|
    +--------------------+--------------------+
    |   -59525.0697785032|  1.8267249911295418|
    |   6957.836000531959|  100.35507390273406|
    |   314.2998010755629|  -8.768588605222108|
    |-0.37884289844065666| -0.4656738230173362|
    |  -1758.154438149325|  -1758.154438149325|
    +--------------------+--------------------+

更新2

不幸的是，使用withColumn()的以下方法不起作用。 

scala> val outputModelDF1 = coefficients.withColumn("t-values", tvalues("t-values"))
org.apache.spark.sql.AnalysisException: resolved attribute(s) t-values#119 missing from coefficients#113 in operator !Project [coefficients#113, t-values#119 AS t-values#130];;
!Project [coefficients#113, t-values#119 AS t-values#130]
+- Project [value#111 AS coefficients#113]
   +- LocalRelation [value#111]

  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:39)
  at org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:91)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:347)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:78)
  at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
  at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:78)
  at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:91)
  at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:52)
  at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:66)
  at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2872)
  at org.apache.spark.sql.Dataset.select(Dataset.scala:1153)
  at org.apache.spark.sql.Dataset.withColumn(Dataset.scala:1908)
  ... 52 elided

1 个答案:

答案 0 :(得分：2)

一种方法是使用join在monotonicallyIncreasingId的数据框中创建关键列：

val df1 = Seq(
  (-59525.0697785032), (6957.836000531959), (314.2998010755629), (-0.37884289844065666), (-1758.154438149325)
).toDF("coefficients")

val df2 = Seq(
  (1.8267249911295418), (100.35507390273406), (-8.768588605222108), (-0.4656738230173362), (10.48091833711012)
).toDF("t-values")

val df1R = df1.withColumn("rowid", monotonicallyIncreasingId)
val df2R = df2.withColumn("rowid", monotonicallyIncreasingId)

val dfJoined = df1R.join(df2R, Seq("rowid"))

dfJoined.show
+-----+--------------------+-------------------+
|rowid|        coefficients|           t-values|
+-----+--------------------+-------------------+
|    0|   -59525.0697785032| 1.8267249911295418|
|    1|   6957.836000531959| 100.35507390273406|
|    2|   314.2998010755629| -8.768588605222108|
|    3|-0.37884289844065666|-0.4656738230173362|
|    4|  -1758.154438149325|  10.48091833711012|
+-----+--------------------+-------------------+
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