You may need to import implicits from your spark session like below: (Below code is borrowed from https://spark.apache.org/docs/latest/sql-programming-guide.html)
import org.apache.spark.sql.SparkSession val spark = SparkSession .builder() .appName("Spark SQL basic example") .config("spark.some.config.option", "some-value") .getOrCreate() // For implicit conversions like converting RDDs to DataFramesimport spark.implicits._ 2018년 9월 5일 (수) 오후 5:11, Mich Talebzadeh <mich.talebza...@gmail.com>님이 작성: > Hi, > > I have spark streaming that send data and I need to put that data into > MongoDB for test purposes. The easiest way is to create a DF from the > individual list of columns as below > > I loop over individual rows in RDD and perform the following > > case class columns(KEY: String, TICKER: String, TIMEISSUED: String, > PRICE: Float) > > for(line <- pricesRDD.collect.toArray) > { > var key = line._2.split(',').view(0).toString > var ticker = line._2.split(',').view(1).toString > var timeissued = line._2.split(',').view(2).toString > var price = line._2.split(',').view(3).toFloat > val priceToString = line._2.split(',').view(3) > if (price > 90.0) > { > println ("price > 90.0, saving to MongoDB collection!") > // Save prices to mongoDB collection > * var df = Seq(columns(key, ticker, timeissued, price)).toDF* > > but it fails with message > > value toDF is not a member of Seq[columns]. > > What would be the easiest way of resolving this please? > > thanks > > Dr Mich Talebzadeh > > > > LinkedIn * > https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw > <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* > > > > http://talebzadehmich.wordpress.com > > > *Disclaimer:* Use it at your own risk. Any and all responsibility for any > loss, damage or destruction of data or any other property which may arise > from relying on this email's technical content is explicitly disclaimed. > The author will in no case be liable for any monetary damages arising from > such loss, damage or destruction. > > >