在使用 Spark Scala 进行透视后，按名称选择具有多个聚合列的列

Question

我正在尝试在 Scala Spark 2.0.1 中的透视后聚合多个列：

scala> val df = List((1, 2, 3, None), (1, 3, 4, Some(1))).toDF("a", "b", "c", "d")
df: org.apache.spark.sql.DataFrame = [a: int, b: int ... 2 more fields]
scala> df.show
+---+---+---+----+
|  a|  b|  c|   d|
+---+---+---+----+
|  1|  2|  3|null|
|  1|  3|  4|   1|
+---+---+---+----+
scala> val pivoted = df.groupBy("a").pivot("b").agg(max("c"), max("d"))
pivoted: org.apache.spark.sql.DataFrame = [a: int, 2_max(`c`): int ... 3 more fields]
scala> pivoted.show
+---+----------+----------+----------+----------+
|  a|2_max(`c`)|2_max(`d`)|3_max(`c`)|3_max(`d`)|
+---+----------+----------+----------+----------+
|  1|         3|      null|         4|         1|
+---+----------+----------+----------+----------+

到目前为止，我无法选择或重命名这些列：

scala> pivoted.select("3_max(`d`)")
org.apache.spark.sql.AnalysisException: syntax error in attribute name: 3_max(`d`);
scala> pivoted.select("`3_max(`d`)`")
org.apache.spark.sql.AnalysisException: syntax error in attribute name: `3_max(`d`)`;
scala> pivoted.select("`3_max(d)`")
org.apache.spark.sql.AnalysisException: cannot resolve '`3_max(d)`' given input columns: [2_max(`c`), 3_max(`d`), a, 2_max(`d`), 3_max(`c`)];

这里一定有一个简单的技巧，有什么想法吗？谢谢。

Answer 1

似乎是一个错误，背面刻度导致了问题。这里的一个解决方法是从列名称中删除反引号：

val pivotedNewName = pivoted.columns.foldLeft(pivoted)((df, col) => 
                             df.withColumnRenamed(col, col.replace("`", "")))

现在，您可以像往常一样按列名进行选择：

pivotedNewName.select("2_max(c)").show
+--------+
|2_max(c)|
+--------+
|       3|
+--------+