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import akka.NotUsed
import akka.http.scaladsl.model.HttpResponse
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import{Sink, Source}
import com.typesafe.scalalogging.LazyLogging
import de.codingchallenge.models.{Article, ProductExport}
import de.codingchallenge.repositories.{ArticleRepository, ProductExportRepository}
import scala.concurrent.Future
class ArticleExportService(
articleRepository: ArticleRepository,
productExportRepository: ProductExportRepository,
mat: Materializer
) extends LazyLogging {
implicit val m: Materializer = mat
* Streams articles to generate a product export.
* Filters unavailable articles
* accumulates articles until id change (groupBy does not close the substream and waits for more articles which match
* the predicate)
* Accumulated articles are combined (we might use a semigroup here) to a tuple of the article with the lowest price
* and the sum of stock values)
* Result is transformed to a 'ProductExport' which later on will be processed by the productExportRepository
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* FIXME: This solution actually conflicts with the way I understood this part of the task:
* Pro Produkt soll jeweils der günstigste Artikel mit Bestand > 0 im Ergebnis enthalten
* sein (mit seinen jeweiligen Eigenschaften).
* But its considered valid by the server
* @return
def exportArticles(productsSize: Int): Future[HttpResponse] = productExportRepository.add(Source.fromGraph[ProductExport, NotUsed](
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// .filter(_.stock > 0)
.map(a => (a, a.stock))
.via(new AccumulateWhileUnchanged[(Article, Int), String](_._1.productId))
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.map { case (article, stockSum) =>"Reduced to article: $article and stockSum: $stockSum")
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ProductExport(article, stockSum) }
case p: ProductExport if p.stockSum > 0 => p
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), productsSize)
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def reduceTuples(t: Seq[(Article, Int)]): (Article, Int) = {"reducing group $t")
t.reduceLeft[(Article, Int)] {
case ((a1, c1), (a2, c2)) if a1.price > a2.price => (a2, c1 + c2)
case ((a1, c1), (_, c2)) => (a1, c1 + c2)
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