Hi, I have Kafka streaming feeds where a row looks like below where fields are separated by "," I can split them easily with split function
scala> val oneline = "05521df6-4ccf-4b2f-b874-eb27d461b305,IBM,2018-07-30T19:51:50,190.48" oneline: String = 05521df6-4ccf-4b2f-b874-eb27d461b305,IBM,2018-07-30T19:51:50,190.48 scala> oneline.split(",") res26: Array[String] = Array(05521df6-4ccf-4b2f-b874-eb27d461b305, IBM, 2018-07-30T19:51:50, 190.48) I can get the individual columns as below scala> val key = oneline.split(",").map(_.trim).view(0).toString key: String = 05521df6-4ccf-4b2f-b874-eb27d461b305 scala> val key = oneline.split(",").map(_.trim).view(1).toString key: String = IBM scala> val key = oneline.split(",").map(_.trim).view(2).toString key: String = 2018-07-30T19:51:50 scala> val key = oneline.split(",").map(_.trim).view(3).toFloat key: Float = 190.48 Now when I apply the same to dataStream in flink it fails val dataStream = streamExecEnv .addSource(new FlinkKafkaConsumer011[String](topicsValue, new SimpleStringSchema(), properties)) *dataStream.split(",")* [error] /home/hduser/dba/bin/flink/md_streaming/src/main/scala/myPackage/md_streaming.scala:154: type mismatch; [error] found : String(",") [error] required: org.apache.flink.streaming.api.collector.selector.OutputSelector[String] [error] dataStream.split(",") [error] ^ [error] one error found [error] (compile:compileIncremental) Compilation failed What operation do I need to do on dataStream to make this split work? 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.