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    <p>The first statement is true only when the number of concurrent requests is relatively small (rather in tens than thousands). It's all about using many threads (blocking) instead of one or few threads (non-blocking). Let's say you want to write an application which only downloads a file from remote server. If your application need to download only one file at a time you need only one thread. But if you have a crawler which runs thousands of HTTP requests then you need to have thousands of threads (or use limited number of threads + NIO instead). For so big number of threads the problem is context switching which can slow down your application dramatically (therefore for this number of concurrent requests NIO is better).</p> <p>But let's back to your question. Why NIO can be slower in terms of raw data throughput ? The reason is the amount of CPU time used by NIO driven applications. For such case in blocking model your code is doing only one thing - waiting for data (it executes recv() operation in a loop). In the NIO application the logic is much more complicated: in a loop the code is using the selector to select a set of keys (which involves epoll_wait system call on Linux, Oracle JVM), then iterate through the set, pick up a channel for every key and then read the data from the channel (read() operation in OS). In standard blocking model all you do is to execute the recv() system function. In summary: NIO driven application in such case use more CPU time and generates more mode switch operations because of higher number of system calls (by saying mode switch I mean the switch from user to kernel mode). Therefore the time needed to download the file will be higher.</p>
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    1. POnonblocking-io vs blocking-io on raw data throughput
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    1. COBut if almost all the threads are blocked waiting for I/O then there's no context switching going on, I thought ... so is context switching costs really the issue with using threads for many concurrent requests?
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