streaming.core.compositor.spark.transformation.AlgorithmCompositor.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.core.compositor.spark.transformation
import java.util
import org.apache.log4j.Logger
import org.apache.spark.ml.BaseAlgorithmTransformer
import org.apache.spark.sql.DataFrame
import serviceframework.dispatcher.{Processor, Strategy}
import scala.collection.JavaConversions._
/**
* 7/27/16 WilliamZhu([email protected])
*/
class AlgorithmCompositor[T] extends BaseAlgorithmCompositor[T] {
val logger = Logger.getLogger(classOf[SQLCompositor[T]].getName)
override def mapping = Map(
"als" -> "org.apache.spark.ml.algs.ALSTransformer",
"lr" -> "org.apache.spark.ml.algs.LinearRegressionTransformer",
"lr2" -> "org.apache.spark.ml.algs.LogicRegressionTransformer"
)
override def result(processors: util.List[Processor[T]], ref: util.List[Strategy[T]], middleResult: util.List[T], params: util.Map[Any, Any]): util.List[T] = {
if (!params.containsKey(FUNC)) {
val _inputTableName = inputTableName.get
val input = sparkSession(params).table(_inputTableName)
val output = algorithm(path).asInstanceOf[BaseAlgorithmTransformer].transform(input)
outputTableName match {
case Some(name) if name != null && name != "-" && !name.isEmpty =>
output.createOrReplaceTempView(name)
case None =>
}
return if (middleResult == null) List() else middleResult
} else {
val func = params.get(FUNC).asInstanceOf[(DataFrame) => DataFrame]
params.put(FUNC, (df: DataFrame) => {
val newDF = algorithm(path).asInstanceOf[BaseAlgorithmTransformer].transform(func(df))
outputTableName match {
case Some(tableName) =>
newDF.createOrReplaceTempView(tableName)
case None =>
}
newDF
})
params.remove(TABLE)
return middleResult
}
}
}