On 1. - is it tied specifically to the number of source operators or to the number of Datastream objects created. I mean does the answer change if I read all the data from a single Kafka topic, get a Datastream of all events, and the apply N filters to create N individual streams?
On 3. - the problem with partitions is that watermarks cannot be different per partition, and since in this use case, each stream is from a device, the latency could be different (but order will be correct almost always) and there are high chances of loosing out on events on operators like Patterns which work with windows. Any ideas for workarounds here? Thanks, Shailesh On 09-Nov-2017 8:48 PM, "Piotr Nowojski" <pi...@data-artisans.com> wrote: Hi, 1. https://ci.apache.org/projects/flink/flink-docs- release-1.3/dev/parallel.html Number of threads executing would be roughly speaking equal to of the number of input data streams multiplied by the parallelism. 2. Yes, you could dynamically create more data streams at the job startup. 3. Running 10000 independent data streams on a small cluster (couple of nodes) will definitely be an issue, since even with parallelism set to 1, there would be quite a lot of unnecessary threads. It would be much better to treat your data as a single data input stream with multiple partitions. You could assign partitions between source instances based on parallelism. For example with parallelism 6: - source 0 could get partitions 0, 6, 12, 18 - source 1, could get partitions 1, 7, … … - source 5, could get partitions 5, 11, ... Piotrek On 9 Nov 2017, at 10:18, Shailesh Jain <shailesh.j...@stellapps.com> wrote: Hi, I'm trying to understand the runtime aspect of Flink when dealing with multiple data streams and multiple operators per data stream. Use case: N data streams in a single flink job (each data stream representing 1 device - with different time latencies), and each of these data streams gets split into two streams, of which one goes into a bunch of CEP operators, and one into a process function. Questions: 1. At runtime, will the engine create one thread per data stream? Or one thread per operator? 2. Is it possible to dynamically create a data stream at runtime when the job starts? (i.e. if N is read from a file when the job starts and corresponding N streams need to be created) 3. Are there any specific performance impacts when a large number of streams (N ~ 10000) are created, as opposed to N partitions within a single stream? Are there any internal (design) documents which can help understanding the implementation details? Any references to the source will also be really helpful. Thanks in advance. Shailesh
