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And this occurs only because the function application is strictly evaluated. The resulting Stream is not evaluated, and this does not depend upon the fact that the initial stream was built with evaluated or non evaluated data.
To understand what is happening, we can imagine that the functions to bind are stored somewhere and they become part of the data producer for the new non evaluated resulting stream.
It may not look like a big trouble since it is so easy to define a method for doing this. For example, given the following function:.
Converting this stream of streams of integers to a stream of integers is very straightforward using the functional paradigm: one just need to flatMap the identity function to it:.
Streams are evaluated when we apply to them some specific operations called terminal operation. This may be done only once.
Once a terminal operation is applied to a stream, is is no longer usable. Terminal operations are:. Some of these methods are short circuiting.
For example, findFirst will return as soon as the first element will be found. Non terminal operations are called intermediate and can be stateful if evaluation of an element depends upon the evaluation of the previous or stateless.
Intermediate operations are:. Several intermediate operations may be applied to a stream, but only one terminal operation may be use.
One most advertised functionality of streams is that they allow automatic parallelization of processing. And one can find the amazing demonstrations on the web, mainly based of the same example of a program contacting a server to get the values corresponding to a list of stocks and finding the highest one not exceeding a given limit value.
But this example as little to do with parallel processing. It is an example of concurrent processing, which means that the increase of speed will be observed also on a single processor computer.
Parallel processing is about running at the same time tasks that do no wait, such as intensive calculations. Automatic parallelization will generally not give the expected result for at least two reasons:.
Whatever the kind of tasks to parallelize, the strategy applied by parallel streams will be the same, unless you devise this strategy yourself, which will remove much of the interest of parallel streams.
Parallelization requires:. Without entering the details, all this implies some overhead. It will show amazing results when:.
If all subtasks imply intense calculation, the potential gain is limited by the number of available processors. Of course, if each subtask is essentially waiting, the gain may appear to be huge.
The worst case is if the application runs in a server or a container alongside other applications, and subtasks do not imply waiting.
In such a case, for example running in a J2EE server , parallel streams will often be slower that serial ones. Imagine a server serving hundreds of requests each second.
There are great chances that several streams might be evaluated at the same time, so the work is already parallelized. A new layer of parallelization at the business level will most probably make things slower.
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