Using spark inside flatMap or any transformation that occures on executors is not allowed (spark session is available on driver only). It is also not possible to create RDD of RDDs (see: Is it possible to create nested RDDs in Apache Spark?) But you can achieve this transformation in another way – read all content … Read more
Under the current implementation, flatmap is eager; like any other stateful intermediate operation (like sorted and distinct). And it’s very easy to prove : int result = Stream.of(1) .flatMap(x -> Stream.generate(() -> ThreadLocalRandom.current().nextInt())) .findFirst() .get(); System.out.println(result); This never finishes as flatMap is computed eagerly. For your example: urls.stream() .flatMap(url -> fetchDataFromInternet(url).stream()) .filter(…) .findFirst() .get(); It … Read more
As you wrote, the two functions are very similar and the subtle difference is how the output is created ( after the mapping function is applied). Flat map uses merge operator while concatMap uses concat operator. As you see the concatMap output sequence is ordered – all of the items emitted by the first Observable … Read more
map transform one event to another. flatMap transform one event to zero or more event. (this is taken from IntroToRx) As you want to transform your json to an object, using map should be enough. Dealing with the FileNotFoundException is another problem (using map or flatmap wouldn’t solve this issue). To solve your Exception problem, … Read more
It doesn’t make sense to flatMap a Stream that’s already flat, like the Stream<Integer> you’ve shown in your question. However, if you had a Stream<List<Integer>> then it would make sense and you could do this: Stream<List<Integer>> integerListStream = Stream.of( Arrays.asList(1, 2), Arrays.asList(3, 4), Arrays.asList(5) ); Stream<Integer> integerStream = integerListStream .flatMap(Collection::stream); integerStream.forEach(System.out::println); Which would print: 1 … Read more