Threads are relatively costly system resources. For example, each thread needs memory for the call stack. How much this is depends on the operating system, but typically it's something like 1 or 2 MB. This means it's not a good idea to start thousands of threads - you'd waste 1 or 2 GB memory just on the call stacks of 1000 threads.
So, to do things more efficiently you want to limit the number of threads, for example using a thread pool to handle work. The thread pool makes it possible to manage the number of threads that are being used.
However, imagine that you'd have a thread pool with 10 threads, and then 10 requests come in. Each of your threads will be reserved to handle a request. While they are busy, you can't handle request #11 because there is no thread free. When you are using blocking I/O, then, even though all your 10 threads are doing nothing (waiting for I/O to complete), request #11 cannot be handled...
When you use non-blocking I/O, threads will never need to wait for I/O - so when the handling request #3 is suspended because it needs the result of an I/O operation, the thread that was handling it can temporarily switch to handling other requests.
So, with non-blocking I/O, you never have waiting threads and you are using system resources more efficiently.
This will only work if you are using non-blocking I/O from the front to the back of your system. If at the back-end you are using JDBC, which is a blocking API, then you'll loose the full benefit of non-blocking I/O.
Therefore, if you have a database at the back-end, this works best if you have a DB which supports non-blocking I/O. Some NoSQL databases like MongoDB support this, and for some relational databases there are special drivers / APIs available that support this. You won't be using JDBC in that case, because JDBC is an inherently blocking API.
Oracle is working on a new API for relational databases tentatively called
ADBA which will allow you to do non-blocking / async I/O with relational databases but it's not ready yet.