Core Java. Vol. II: Advanced Features by Cay S. Horstmann

By Cay S. Horstmann

Center Java® has lengthy been well-known because the top, no-nonsense educational and reference for knowledgeable programmers who are looking to write strong Java code for real-world functions. Now, middle Java®, quantity I—Fundamentals, 10th variation, has been largely up-to-date to mirror the main eagerly awaited and cutting edge model of Java in years: Java SE eight. Rewritten and reorganized to light up new Java SE eight beneficial properties, idioms, and top practices, it comprises countless numbers of instance programs—all rigorously crafted for simple figuring out and functional applicability.

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Collect( groupingBy(City::getState, summingInt(City::getPopulation))); computes the sum of populations per state in a stream of cities. comparing(City::getPopulation)))); produces the largest city per state. Within each state, we produce the names of the cities and reduce by maximum length. The mapping method also yields a nicer solution to a problem from the preceding section— gathering a set of all languages in a country. In this form, you don’t need to worry about combining the individual sets.

Static Optional empty() yields an empty Optional. Otherwise, an empty Optional is returned. You can then build a pipeline of steps, simply by chaining calls to flatMap, that will succeed only when all steps do. empty(), the result is empty. flatMap method works in the same way if you interpret an optional value as a stream of size zero or one. 3 demonstrates the Optional API. super T,Optional> mapper) yields the result of applying mapper to the value of this Optional, or an empty Optional if this Optional is empty.

If it is acceptable to retain any of the unique elements, all segments can be processed concurrently (using a shared set to track duplicates). To benefit from parallelism, the order of the map values will not be the same as the stream order. If you were to modify that collection, the outcome of the stream operations would be undefined. It applies both to sequential and parallel streams. It would be inefficient to have to wait for the data to arrive. iterate does not. If the total work load is not large, it does not make sense to pay for the cost of setting up the parallel computation.

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