在简单的情况下，您可以提供一个初始模式，该模式是预期模式的超集.例如，在您的情况下:

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)

这种方法可能有点脆弱，因此通常您应该使用SQL文字来填补空白:

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)