Building Highly Scalable Servers with Java NIO (4 messages) Developing a fully functional router based on I/O multiplexing was not simple. : Building Highly Scalable Servers with Java NIO multiplexing is significantly harder to understand and to implement correctly. use the NIO API (ByteBu ers, non-blocking I/O) The classical I/O API is very easy Java NIO Framework was started after Ron Hitchen’s presentation How to Build a Scalable Multiplexed Server With NIO at the JavaOne Conference .
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To handle web requests, there are two competitive web architectures — thread-based one and event-driven one.
Long-living connections like Keep-Alive connections give rise to a large number of worker threads waiting in the idle state for whatever it is slow, e. Its concurrency model is based on an event loop.
Unfortunately, there is always a one-to-one relationship between connections and threads. I can run tens of thousands of threads on my desktop machine, but I’ve yet to see any problem where I could actually serve tens of thousands of connections from a single machine without everything crawling to a halt.
Think about switching electric current vs. A pool of threads poll the queue for incoming requests, and then process and respond. Voo, I doubt you do have tens of thousands Runnable not just idling threads. That’s the usual argument, but: So that seems a weak argument to me.
Understanding Reactor Pattern for Highly Scalable I/O Bound Web Server
How to implement an echo web server with reactor pattern in Java? Also, scheduling thousands of threads is inefficient. It reads and parses the content in the request from the socket CPU bound. It is also the best MPM for isolating each request, so that a problem with a single request will not affect any other. Also NIO allows for ‘fair’ traffic delivery which is very important and very often overlooked as it ensures stable latency for the clients.
Building Highly Scalable Servers with Java NIO (O’Reilly) 
In addition, hundreds or even thousands of concurrent threads can waste zerver great deal of stack space in the memory. That said, the point of Channel is to make this less tricky.
As to C xcalable programing with async and await keywords, that is another story. The threads are doing some rather heavyweight work, so we reach the capacity of a single server before context switching overheads get a problem. Some connections may be idle for tens of minutes at a time, but still open.
Generally, non-blocking solutions are trickier, but they avoid resource contention, which makes it much easier to scale mmultiplexed.
You’re right I only tested bandwidth more important for my problems and I don’t think I’ve seen anything about latency so far. The answer may be as simple as just one single word — scalablw. Email Required, but never shown. Reactor Pattern The reactor pattern is one implementation technique of the event-driven architecture.