Spark 2.0隐式编码器，在类型为Option [Seq [String]]（scala）时处理缺失列(Spark 2.0 implicit encoder, deal with missing

Spark 2.0隐式编码器，在类型为Option [Seq [String]]（scala）时处理缺失列(Spark 2.0 implicit encoder, deal with missing column when type is Option[Seq[String]] (scala))

当我们的数据源中缺少某些类型为选项[Seq [String]]的列时，我遇到了一些编码数据的麻烦。 理想情况下，我希望缺少的列数据填充None 。

场景：

我们正在阅读的一些镶木地板文件有column1但不是column2 。

我们将来自这些parquet文件的数据加载到数据集中，并将其转换为MyType 。

case class MyType(column1: Option[String], column2: Option[Seq[String]]) sqlContext.read.parquet("dataSource.parquet").as[MyType]

org.apache.spark.sql.AnalysisException：无法在给定输入列的情况下解析' column2 '：[column1];

有没有办法用column2数据创建数据集为None ？

I'm having some trouble encoding data when some columns that are of type Option[Seq[String]] are missing from our data source. Ideally I would like the missing column data to be filled with None.

Scenario:

We have some parquet files that we are reading in that have column1 but not column2.

We load the data in from these parquet files into a Dataset, and cast it as MyType.

case class MyType(column1: Option[String], column2: Option[Seq[String]]) sqlContext.read.parquet("dataSource.parquet").as[MyType]

org.apache.spark.sql.AnalysisException: cannot resolve 'column2' given input columns: [column1];

Is there a way to create the Dataset with column2 data as None?

最满意答案

在简单情况下，您可以提供一个初始模式，它是预期模式的超集。 例如在你的情况下：

val schema = Seq[MyType]().toDF.schema

Seq("a", "b", "c").map(Option(_))
  .toDF("column1")
  .write.parquet("/tmp/column1only")

val df = spark.read.schema(schema).parquet("/tmp/column1only").as[MyType]
df.show
 
+-------+-------+
|column1|column2|
+-------+-------+
|      a|   null|
|      b|   null|
|      c|   null|
+-------+-------+
 
df.first
 
MyType = MyType(Some(a),None)
 
 这种方法可能有点脆弱，因此一般而言，您应该使用SQL文字来填补空白： 
 
spark.read.parquet("/tmp/column1only")
  // or ArrayType(StringType)
  .withColumn("column2", lit(null).cast("array<string>"))
  .as[MyType]
  .first
 
MyType = MyType(Some(a),None)
In simple cases you can provide an initial schema which is a superset of expected schemas. For example in your case:
 
val schema = Seq[MyType]().toDF.schema

Seq("a", "b", "c").map(Option(_))
  .toDF("column1")
  .write.parquet("/tmp/column1only")

val df = spark.read.schema(schema).parquet("/tmp/column1only").as[MyType]
df.show
 
+-------+-------+
|column1|column2|
+-------+-------+
|      a|   null|
|      b|   null|
|      c|   null|
+-------+-------+
 
df.first
 
MyType = MyType(Some(a),None)
 
This approach can be a little bit fragile so in general you should rather use SQL literals to fill the blanks:
 
spark.read.parquet("/tmp/column1only")
  // or ArrayType(StringType)
  .withColumn("column2", lit(null).cast("array<string>"))
  .as[MyType]
  .first
 
MyType = MyType(Some(a),None)