Java Usage Example. To convert a Scala collection to a Java 8 Stream from within Java, it usually suffices to call ScalaStreamSupport.stream(xs) on your collection xs.If xs is a map, you may wish to get the keys or values alone by using fromKeys or fromValues.If the collection has an underlying representation that is not efficiently parallelized (e.g. scala.collection.immutable.List), then.
The following examples show how to use javax.xml.transform.stream.StreamSource.These examples are extracted from open source projects. You can vote up the examples you like and your votes will be used in our system to produce more good examples.
Stream is nice, but, in Scala, its main benefit is writing infinite sequences (particularly sequences recursively defined). One can avoid keeping all of the Stream in memory, though, by making sure you don’t keep a reference to its head (for example, by using def instead of val to define the Stream).
Custom processing with GraphStage. The GraphStage abstraction can be used to create arbitrary operators with any number of input or output ports. It is a counterpart of the GraphDSL.create() method which creates new stream processing operators by composing others. Where GraphStage differs is that it creates an operator that is itself not divisible into smaller ones, and allows state to be.
You can either get the public stream, or get the filtered stream based on a keywords. See the API documentation (Scala, Java) and examples (TwitterPopularTags and TwitterAlgebirdCMS). Flume: Spark Streaming 1.2.0 can received data from Flume 1.4.0. See the Flume Integration Guide for more details.
The function used for transformation in flatMap() is a stateless function and returns only a stream of new values. Each mapped stream is closed after its contents have been placed into new stream. flatMap() operation flattens the stream; opposite to map() operation which does not apply flattening. 3. Java Stream flatMap() example 3.1. Convert.
Kafka Stream Processing. Till now, we learned about topics, partitions, sending data to Kafka, and consuming data from the Kafka. This could be a lower level of abstraction. Thus, a higher level of abstraction is required. This consequently introduces the concept of Kafka streams. Kafka Streams.
The following examples show how to use java.io.BufferedOutputStream.These examples are extracted from open source projects. You can vote up the examples you like and your votes will be used in our system to produce more good examples.