Question

我有两个实木复合地板文件，一个包含一个整数字段myField，另一个包含一个双字段myField。尝试一次读取两个文件时

val basePath = "/path/to/file/"
val fileWithInt = basePath + "intFile.snappy.parquet"
val fileWithDouble = basePath + "doubleFile.snappy.parquet"
val result = spark.sqlContext.read.option("mergeSchema", true).option("basePath", basePath).parquet(Seq(fileWithInt, fileWithDouble): _*).select("myField")

我收到以下错误

Caused by: org.apache.spark.SparkException: Failed to merge fields 'myField' and 'myField'. Failed to merge incompatible data types IntegerType and DoubleType

传递显式架构时

val schema = StructType(Seq(new StructField("myField", IntegerType)))
val result = spark.sqlContext.read.schema(schema).option("mergeSchema", true).option("basePath", basePath).parquet(Seq(fileWithInt, fileWithDouble): _*).select("myField")

失败，并显示以下

java.lang.UnsupportedOperationException: org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainDoubleDictionary
    at org.apache.parquet.column.Dictionary.decodeToInt(Dictionary.java:48)

投射成两倍时

val schema = StructType(Seq(new StructField("myField", DoubleType)))

我明白了

java.lang.UnsupportedOperationException: org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainIntegerDictionary
    at org.apache.parquet.column.Dictionary.decodeToDouble(Dictionary.java:60)

除了重新处理源数据以外，是否有人知道解决此问题的方法。

Answer 1

根据要读取的文件数，可以使用以下两种方法之一：

这对于较小数量的实木复合地板文件是最好的

def merge(spark: SparkSession, paths: Seq[String]): DataFrame = {
    import spark.implicits._

    paths.par.map {
      path =>
        spark.read.parquet(path).withColumn("myField", $"myField".cast(DoubleType))
    }.reduce(_.union(_))
  }

这种方法最好处理大量文件，因为它将使谱系简短

def merge2(spark: SparkSession, paths: Seq[String]): DataFrame = {
    import spark.implicits._

    spark.sparkContext.union(paths.par.map {
      path =>
        spark.read.parquet(path).withColumn("myField", $"myField".cast(DoubleType)).as[Double].rdd
    }.toList).toDF
  }

读取镶木地板文件时无法解析具有int和double的合并架构

1 个答案: