streaming.dsl.mmlib.algs.SQLTableToMap.scala Maven / Gradle / Ivy
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* to you under the Apache License, Version 2.0 (the
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* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
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package streaming.dsl.mmlib.algs
import org.apache.spark.sql.expressions.UserDefinedFunction
import org.apache.spark.sql.{functions => F}
import org.apache.spark.sql.types.{ArrayType, StringType}
import org.apache.spark.sql.{DataFrame, Row, SaveMode, SparkSession}
import streaming.dsl.mmlib.SQLAlg
/**
* Created by allwefantasy on 8/2/2018.
*/
class SQLTableToMap extends SQLAlg with Functions {
override def train(df: DataFrame, path: String, params: Map[String, String]): DataFrame = {
val keyField = params.getOrElse("keyField", "key")
val valueField = params.getOrElse("keyField", "value")
val mapResult = df.select(F.col(keyField).as("key"), F.col(valueField).as("value"))
mapResult.write.mode(SaveMode.Overwrite).parquet(path)
emptyDataFrame()(df)
}
override def load(sparkSession: SparkSession, path: String, params: Map[String, String]): Any = {
sparkSession.read.parquet(path).collect().map(f => (f.get(0).toString, f.get(1).toString)).toMap
}
override def predict(sparkSession: SparkSession, _model: Any, name: String, params: Map[String, String]): UserDefinedFunction = {
val model = sparkSession.sparkContext.broadcast(_model.asInstanceOf[Map[String, String]])
val f = (name: String) => {
model.value(name)
}
UserDefinedFunction(f, StringType, Some(Seq(StringType)))
}
}