Hi, Mich:
You can add write a sink function for that.
On Mon, Jul 30, 2018 at 2:58 PM Mich Talebzadeh <mich.talebza...@gmail.com>
wrote:
>
> Hi,
>
> I have a Kafka topic that transmits 100 security prices ever 2 seconds.
>
> In Spark streaming I go through the RDD and walk through rows one by one
> and check prices
> In prices are valuable I store them into an Hbase table
>
> val dstream = KafkaUtils.createDirectStream[String, String,
> StringDecoder, StringDecoder](streamingContext, kafkaParams, topics)
> dstream.cache()
> dstream.foreachRDD
> { pricesRDD =>
> // Work on individual messages
> * 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)
> {
> //save to Hbase table
> }
> }
> }
>
> That works fine.
>
> In Flink I define my source as below
>
> val streamExecEnv = StreamExecutionEnvironment.getExecutionEnvironment
> streamExecEnv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
> val stream = streamExecEnv
> .addSource(new FlinkKafkaConsumer011[String](topicsValue, new
> SimpleStringSchema(), properties))
>
> Is there anyway I can perform similar operation in Flink? I need to go
> through every topic load sent and look at the individual rows/ For example
> what is the equivalent of
>
> *for(line <- pricesRDD.collect.toArray)*
> In flink?
>
> Thanks
>
> Dr Mich Talebzadeh
>
>
>
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--
Liu, Renjie
Software Engineer, MVAD
