scala如何参数化案例类，并将案例类变量传递给[T <：Product：TypeTag]

Question

// class definition of RsGoods schema
case class RsGoods(add_time: Int)

// my operation
originRDD.toDF[Schemas.RsGoods]()

// and the function definition
def toDF[T <: Product: TypeTag](): DataFrame = mongoSpark.toDF[T]()

now i defined too many schemas(RsGoods1,RsGoods2,RsGoods3), and more will be added in the future.

so the question is how to pass a case class as a variable to structure the code

Attach sbt dependency

  "org.apache.spark" % "spark-core_2.11" % "2.3.0",
  "org.apache.spark" %% "spark-sql" % "2.3.0",
  "org.mongodb.spark" %% "mongo-spark-connector" % "2.3.1",

Attach the key code snippet

  var originRDD = MongoSpark.load(sc, readConfig)
  val df = table match {
    case "rs_goods_multi" => originRDD.toDF[Schemas.RsGoodsMulti]()
    case "rs_goods" => originRDD.toDF[Schemas.RsGoods]()
    case "ma_item_price" => originRDD.toDF[Schemas.MaItemPrice]()
    case "ma_siteuid" => originRDD.toDF[Schemas.MaSiteuid]()
    case "pi_attribute" => originRDD.toDF[Schemas.PiAttribute]()
    case "pi_attribute_name" => originRDD.toDF[Schemas.PiAttributeName]()
    case "pi_attribute_value" => originRDD.toDF[Schemas.PiAttributeValue]()
    case "pi_attribute_value_name" => originRDD.toDF[Schemas.PiAttributeValueName]()

Answer 1

From what I have understood about your requirement, i think following should be a decent starting point.

def readDataset[A: Encoder](
  spark: SparkSession,
  mongoUrl: String,
  collectionName: String,
  clazz: Class[A]
): Dataset[A] = {
  val config = ReadConfig(
    Map("uri" -> s"$mongoUrl.$collectionName")
  )

  val df = MongoSpark.load(spark, config)

  val fieldNames = clazz.getDeclaredFields.map(f => f.getName).dropRight(1).toList

  val dfWithMatchingFieldNames = df.toDf(fieldNames: _*)

  dfWithMatchingFieldNames.as[A]
}

You can use it like this,

case class RsGoods(add_time: Int)

val spark: SparkSession = ...

import spark.implicts._

val rdGoodsDS = readDataset[RsGoods](
  spark,
  "mongodb://example.com/database",
  "rs_goods",
  classOf[RsGoods]
)

Also, the following two lines,

val fieldNames = clazz.getDeclaredFields.map(f => f.getName).dropRight(1).toList

val dfWithMatchingFieldNames = df.toDf(fieldNames: _*)

are only required because normally Spark reads DataFrames with column names like value1, value2, ... . So we want to change the column names to match what we have in our case class .

I am not sure what these "defalut" column names will be because MongoSpark is involved.

You should first check the column names in the df created as following,

val config = ReadConfig(
  Map("uri" -> s"$mongoUrl.$collectionName")
)

val df = MongoSpark.load(spark, config)

If, MongoSpark fixes the problem of these "default" column names and picks the coulmn names from your collection then those 2 lines will not be required and your method will become just this,

def readDataset[A: Encoder](
  spark: SparkSession,
  mongoUrl: String,
  collectionName: String,
): Dataset[A] = {
  val config = ReadConfig(
    Map("uri" -> s"$mongoUrl.$collectionName")
  )

  val df = MongoSpark.load(spark, config)

  df.as[A]
}

And,

val rsGoodsDS = readDataset[RsGoods](
  spark,
  "mongodb://example.com/database",
  "rs_goods"
)