1. It’s a little bit more complicated then that. Each operator chain/task will be executed in separate thread (parallelism Multiplies that). You can check in web ui how was your job split into tasks.
3. Yes that’s true, this is an issue. To preserve the individual watermarks/latencies (assuming that you have some way to calculate them individually per each device), you could either: a) have separate jobs per each device with parallelism 1. Pros: independent failures/checkpoints, Cons: resource usage (number of threads increases with number of devices, there are also other resources consumed by each job), efficiency, b) have one job with multiple data streams. Cons: resource usage (threads) c) ignore Flink’s watermarks, and implement your own code in place of it. You could read all of your data in single data stream, keyBy partition/device and manually handle watermarks logic. You could either try to wrap CEP/Window operators or copy/paste and modify them to suite your needs. I would start and try out from a). If it work for your cluster/scale then that’s fine. If not try b) (would share most of the code with a), and as a last resort try c). Kostas, would you like to add something? Piotrek > On 9 Nov 2017, at 19:16, Shailesh Jain <shailesh.j...@stellapps.com> wrote: > > 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 > <mailto:pi...@data-artisans.com>> wrote: > Hi, > > 1. > https://ci.apache.org/projects/flink/flink-docs-release-1.3/dev/parallel.html > <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 >> <mailto: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 >> >> > >
