Re: Subscribe Multiple Topics Structured Streaming
I would like to know how to create stream and sink operations outside "main" method - just like another class which I can invoke from main. So that I can have different implementations for each topic which I subscribed in a specific class file. Is it a good practice or always the whole implementations should go inside "main" method? On Mon, Sep 17, 2018 at 11:35 PM naresh Goud wrote: > You can have below statement for multiple topics > > val dfStatus = spark.readStream. > format("kafka"). > option("subscribe", "utility-status, utility-critical"). > option("kafka.bootstrap.servers", "localhost:9092"). > option("startingOffsets", "earliest") > .load() > > > > > > On Mon, Sep 17, 2018 at 3:28 AM sivaprakash < > sivaprakashshanmu...@gmail.com> wrote: > >> Hi >> >> I have integrated Spark Streaming with Kafka in which Im listening 2 >> topics >> >> def main(args: Array[String]): Unit = { >> >> val schema = StructType( >> List( >> StructField("gatewayId", StringType, true), >> StructField("userId", StringType, true) >> ) >> ) >> >> val spark = SparkSession >> .builder >> .master("local[4]") >> .appName("DeviceAutomation") >> .getOrCreate() >> >> val dfStatus = spark.readStream. >> format("kafka"). >> option("subscribe", "utility-status, utility-critical"). >> option("kafka.bootstrap.servers", "localhost:9092"). >> option("startingOffsets", "earliest") >> .load() >> >> >> } >> >> Since I have few more topics to be listed and perform different >> operations I >> would like to move each topics into separate case class for better >> clarity. >> Is it possible? >> >> >> >> -- >> Sent from: http://apache-spark-user-list.1001560.n3.nabble.com/ >> >> - >> To unsubscribe e-mail: user-unsubscr...@spark.apache.org >> >> -- > Thanks, > Naresh > www.linkedin.com/in/naresh-dulam > http://hadoopandspark.blogspot.com/ > > -- - Prakash.
Re: Subscribe Multiple Topics Structured Streaming
You can have below statement for multiple topics val dfStatus = spark.readStream. format("kafka"). option("subscribe", "utility-status, utility-critical"). option("kafka.bootstrap.servers", "localhost:9092"). option("startingOffsets", "earliest") .load() On Mon, Sep 17, 2018 at 3:28 AM sivaprakash wrote: > Hi > > I have integrated Spark Streaming with Kafka in which Im listening 2 topics > > def main(args: Array[String]): Unit = { > > val schema = StructType( > List( > StructField("gatewayId", StringType, true), > StructField("userId", StringType, true) > ) > ) > > val spark = SparkSession > .builder > .master("local[4]") > .appName("DeviceAutomation") > .getOrCreate() > > val dfStatus = spark.readStream. > format("kafka"). > option("subscribe", "utility-status, utility-critical"). > option("kafka.bootstrap.servers", "localhost:9092"). > option("startingOffsets", "earliest") > .load() > > > } > > Since I have few more topics to be listed and perform different operations > I > would like to move each topics into separate case class for better clarity. > Is it possible? > > > > -- > Sent from: http://apache-spark-user-list.1001560.n3.nabble.com/ > > - > To unsubscribe e-mail: user-unsubscr...@spark.apache.org > > -- Thanks, Naresh www.linkedin.com/in/naresh-dulam http://hadoopandspark.blogspot.com/
Subscribe Multiple Topics Structured Streaming
Hi I have integrated Spark Streaming with Kafka in which Im listening 2 topics def main(args: Array[String]): Unit = { val schema = StructType( List( StructField("gatewayId", StringType, true), StructField("userId", StringType, true) ) ) val spark = SparkSession .builder .master("local[4]") .appName("DeviceAutomation") .getOrCreate() val dfStatus = spark.readStream. format("kafka"). option("subscribe", "utility-status, utility-critical"). option("kafka.bootstrap.servers", "localhost:9092"). option("startingOffsets", "earliest") .load() } Since I have few more topics to be listed and perform different operations I would like to move each topics into separate case class for better clarity. Is it possible? -- Sent from: http://apache-spark-user-list.1001560.n3.nabble.com/ - To unsubscribe e-mail: user-unsubscr...@spark.apache.org