. , :
import org.apache.spark.sql.types._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.Column
val schema = StructType(Seq(
StructField("v", TimestampType),
StructField("x", IntegerType),
StructField("y", StringType),
StructField("z", DecimalType(10, 2))
))
val df = spark.readStream.schema(schema).format("csv").load("/tmp/foo")
val stats = df.select(df.dtypes.flatMap {
case (c, "StringType") =>
Seq(count(c) as s"valid_${c}", count("*") - count(c) as s"invalid_${c}")
case (c, t) if Seq("TimestampType", "DateType") contains t =>
Seq(min(c), max(c))
case (c, t) if (Seq("FloatType", "DoubleType", "IntegerType") contains t) || t.startsWith("DecimalType") =>
Seq(min(c), max(c), avg(c), stddev(c))
case _ => Seq.empty[Column]
}: _*)