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Rsyncosx multipe streams6/22/2023 ![]() Val modelsStream = KafkaUtils.createDirectStream, Array](ssc,PreferConsistent, Val kafkaParams = KafkaSupport.getKafkaConsumerConfig(kafkaConfig.brokers) trigger(Trigger.Continuous("5 second")). option("", kafkaConfig.brokers).option("topic", kafkaConfig.heaterinputtopic) Var sensorQuery = ("update").format("kafka") With this in place, the following code provides implementation for controller in Spark object SparkStructuredController ).as.filter(_.sensorID >= 0) As a result, for our implementation we had to introduce a case class UnifiedDataModel, as a superset of used messages - SensorData and TemperatureControl The limitation of union is that only streams with identical schemas can be unioned together. ![]() The limitation of stream join is that it works on windows and rarely applicable for streams with significantly different frequency of records. Spark provides two main methods for joining streams - stream stream joins, introduced in Apache Spark 2.3 and union.Unfortunately, there are several caveats that need to be considered for such implementation: Spark Structured Streaming supports arbitrary stateful operations which can be used to implement stateful (and dynamically controlled) streams. You can also click on the technology you’re using for my other examples leveraging Akka Streams, Kafka Streams, and Apache Flink.ĭynamically Controlled Streams With Spark Streaming In this post, I demonstrate with code how dynamically controlled streams can be implemented leveraging Apache Spark, specifically Spark Structured Streaming, and the main properties of such implementation. While this can be implemented using different streaming engines and frameworks, making the right technology choice is based on many factors, including internal knowledge of frameworks/engines, enterprise architectural standards, and other considerations. In the introductory post of this short series, How To Serve Machine Learning Models With Dynamically Controlled Streams, I described how dynamically controlled streams is a very powerful pattern for implementing streaming applications. ![]() Cloud-Native Design Techniques for Serving Machine Learning Models with Apache Spark
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