Hi, The way I have used streams processing in past; use case to process streams is when you have a continuous stream of data which needs to be processed and used by certain applications. Since in kafka streams can be a simple java application, this application can run in its own JVM which is different from say actual client application. It can be on same physical or virtual machine, but some degree of separation is best.
Regarding streams the way I look at it that, it is some continuous process whose data downstream is used by micro services. The downstream data can be stored using stream's state stores or can be some external data store (say mongodb, cassandra, etc). Hope it answers some of your questions. Thanks Sachin On Mon, Jan 13, 2020 at 1:32 AM M. Manna <manme...@gmail.com> wrote: > Hello, > > Even though I have been using Kafka for a while, it's primarily for > publish/subscribe event messaging ( and I understand them reasonably well). > But I would like to do more regarding streams. > > For my initiative, I have been going through the code written in "examples" > folder. I would like to apologise for such newbie questions in advance. > > With reference to WordCountDemo.java - I wanted to understand something > related to Stream Processor integration with business applications (i.e. > clients). Is it a good practice to always keep the stream processor > topology separate from actual client application who uses the processed > data? > > My understanding (from what I can see at first glace) multiple > streams.start() needs careful observation for scaling up/out in long term. > To separate problems, I would expected this to be deployed separately (may > be microservices?) But again, I am simply entering this world of streams, > so I could really use some insight into how some of us has tackled this > over the years. > > Kindest Regards, >
